Reviewerly/exHarmony huggingface dataset:
Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning Method

Curvy roads are a particular type of urban road scenario, wherein the curvature of the road centerline changes drastically. This paper is focused on the trajectory planning task for autonomous driving on a curvy road. The prevalent on-road trajectory planners in the Frenet frame cannot impose accurate restrictions on the trajectory curvature, thus easily making the resultant trajectories beyond the ego vehicle’s kinematic capability. Regarding planning in the Cartesian frame, selection-based methods suffer from the curse of dimensionality. By contrast, optimization-based methods in the Cartesian frame are more flexible to find optima in the continuous solution space, but the new challenges are how to tackle the intractable collision-avoidance constraints and nonconvex kinematic constraints. An iterative computation framework is proposed to accumulatively handle the complex constraints. Concretely, an intermediate problem is solved in each iteration, which contains linear and tractably scaled collision-avoidance constraints and softened kinematic constraints. Compared with the existing optimization-based planners, our proposal is less sensitive to the initial guess especially when it is not kinematically feasible. The efficiency of the proposed planner is validated by both simulations and real-world experiments. Source codes of this work are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/libai1943/CartesianPlanner</uri> .
Bitcoin’s limited adoption problem

We demonstrate theoretically that Bitcoin’s limited adoption arises as an equilibrium outcome rather than as a short-lived property. Our results are driven by negative network effects which arise due to Bitcoin’s need for consensus and the existence of network delay. As the Bitcoin network expands, network delay grows thereby prolonging the time needed for generating consensus. In turn, transaction settlement becomes prolonged, and users abandon the system, yielding limited adoption. Increasing transaction rates fails to solve this problem because increasing transaction rates increases fork probabilities which prolongs the consensus process and generates limited adoption.
Learning to Resize Images for Computer Vision Tasks

For all the ways convolutional neural nets have revolutionized computer vision in recent years, one important aspect has received surprisingly little attention: the effect of image size on the accuracy of tasks being trained for. Typically, to be efficient, the input images are resized to a relatively small spatial resolution (e.g. 224 × 224), and both training and inference are carried out at this resolution. The actual mechanism for this re-scaling has been an afterthought: Namely, off-the-shelf image re sizers such as bilinear and bicubic are commonly used in most machine learning software frameworks. But do these re sizers limit the on-task performance of the trained networks? The answer is yes. Indeed, we show that the typical linear re sizer can be replaced with learned resizers that can substantially improve performance. Importantly, while the classical re-sizers typically result in better perceptual quality of the downscaled images, our proposed learned resizers do not necessarily give better visual quality, but instead improve task performance.Our learned image resizer is jointly trained with a base-line vision model. This learned CNN-based resizer creates machine friendly visual manipulations that lead to a consistent improvement of the end task metric over the baseline model. Specifically, here we focus on the classification task with the ImageNet dataset [26], and experiment with four different models to learn resizers adapted to each model. Moreover, we show that the proposed resizer can also be useful for fine-tuning the classification baselines for other vision tasks. To this end, we experiment with three different baselines to develop image quality assessment (IQA) models on the AVA dataset [24].
Application of Deep Learning in Breast Cancer Imaging

This review gives an overview of the current state of deep learning research in breast cancer imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as well as monitoring and evaluating breast cancer during treatment. The most commonly used modalities for breast imaging are digital mammography, digital breast tomosynthesis, ultrasound and magnetic resonance imaging. Nuclear medicine imaging techniques are used for detection and classification of axillary lymph nodes and distant staging in breast cancer imaging. All of these techniques are currently digitized, enabling the possibility to implement deep learning (DL), a subset of Artificial intelligence, in breast imaging. DL is nowadays embedded in a plethora of different tasks, such as lesion classification and segmentation, image reconstruction and generation, cancer risk prediction, and prediction and assessment of therapy response. Studies show similar and even better performances of DL algorithms compared to radiologists, although it is clear that large trials are needed, especially for ultrasound and magnetic resonance imaging, to exactly determine the added value of DL in breast cancer imaging. Studies on DL in nuclear medicine techniques are only sparsely available and further research is mandatory. Legal and ethical issues need to be considered before the role of DL can expand to its full potential in clinical breast care practice.
Cognitive and emotional engagement while learning with VR: The perspective of multimodal methodology

This study uses a multimodal data analysis approach to provide a more continuous and objective insight into how students' engagement unfolds and impacts learning achievements. In this study, 61 nursing students' learning processes with a virtual reality (VR)-based simulation were captured by psycho-physiological data streams of facial expression, eye-tracking, and electrodermal activity (EDA) sensors, as well as by subjective self-reports. Students’ learning achievements were evaluated by a pre- and post-test content knowledge test. Overall, while both facial expression and self-report modalities revealed that students experienced significantly higher levels of positive than negative emotions, only the facial expression data channel was able to detect fluctuations in engagement during the different learning session phases. Findings point towards the VR procedural learning phase as a reengaging learning activity, which induces more facial expressions of joy and triggers a higher mental effort as measured by eye tracking and EDA metrics. Most importantly, a regression analysis demonstrated that the combination of modalities explained 51% of post-test knowledge achievements. Specifically, higher levels of prior knowledge and self-reported enthusiasm, and lower levels of angry facial expressions, blink rate, and devotion of visual fixations to irrelevant information, were associated with higher achievements. This study demonstrates that the methodology of using multimodal data channels encompassing different types of objective and subjective measures, can provide insights into a more holistic understanding of engagement in learning and learning achievements.
A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools

Delivering high-quality software products is a challenging task. It needs proper coordination from various teams in planning, execution, and testing. Many software products have high numbers of defects revealed in a production environment. Software failures are costly regarding money, time, and reputation for a business and even life-threatening if utilized in critical applications. Identifying and fixing software defects in the production system is costly, which could be a trivial task if detected before shipping the product. Binary classification is commonly used in existing software defect prediction studies. With the advancements in Artificial Intelligence techniques, there is a great potential to provide meaningful information to software development teams for producing quality software products. An extensive survey for Software Defect Prediction is necessary for exploring datasets, data validation methods, defect detection, and prediction approaches and tools. The survey infers standard datasets utilized in early studies lack adequate features and data validation techniques. According to the finding of the literature survey, the standard datasets has few labels, resulting in insufficient details regarding defects. Systematic Literature Reviews (SLR) on Software Defect Prediction are limited. Hence this SLR presents a comprehensive analysis of defect datasets, dataset validation, detection, prediction approaches, and tools for Software Defect Prediction. The survey exhibits the futuristic recommendations that will allow researchers to develop a tool for Software Defect Prediction. The survey introduces the architecture for developing a software prediction dataset with adequate features and statistical data validation techniques for multi-label classification for software defects.
Survey on image encryption techniques using chaotic maps in spatial, transform and spatiotemporal domains

Abstract Chaos-based cryptosystems have been an active area of research in recent years. Although these algorithms are not standardized like AES, DES, RSA, etc., chaos-based cryptosystems like Chebyshev polynomials can provide additional security when used with standard public key cryptosystems like RSA and El-gamal. Standard encryption algorithms such as AES have always been the primary choice, but when it comes to image or video encryption, many researchers recommend chaos-based encryption techniques due to their computational efficiency. This paper presents a survey on the most up-to-date chaos-based image encryption techniques and classifies them into spatial, temporal and spatiotemporal domains for better understanding. The significant improvements in the field of image encryption are discussed. In addition, comparative analysis is performed to validate the evaluation matrices for quantifying the encryption algorithms’ security and performance in recent papers.
Photonic Spin Lattices: Symmetry Constraints for Skyrmion and Meron Topologies

Symmetry and topology govern many electronic, magnetic, and photonic phenomena in condensed matter physics and optics, resulting in counterintuitive skyrmion, meron, and other phenomena important for modern technologies. Here we demonstrate photonic spin lattices as a new topological construct governed by the spin-orbit coupling in an optical field. The symmetry of the electromagnetic field in the presence of the spin-orbit interaction may result in only two types of photonic spin lattices: either hexagonal spin-skyrmion or square spin-meron lattices. We show that these spin structures correspond to the lowest energy of the electromagnetic field configuration, therefore, energetically stable. We further show that in the absence of spin-orbit coupling these spin topologies are degenerated in dynamic field skyrmions, unifying the description of electromagnetic field topologies. The results provide a new understanding of electromagnetic field topology and its transformations as well as new opportunities for applications in quantum technologies, spin optics, and topological photonics.
Simulation of Quantum Circuits Using the Big-Batch Tensor Network Method

We propose a tensor network approach to compute amplitudes and probabilities for a large number of correlated bitstrings in the final state of a quantum circuit. As an application, we study Google's Sycamore circuits, which are believed to be beyond the reach of classical supercomputers and have been used to demonstrate quantum supremacy. By employing a small computational cluster containing 60 graphical processing units (GPUs), we compute exact amplitudes and probabilities of 2×10^{6} correlated bitstrings with some entries fixed (which span a subspace of the output probability distribution) for the Sycamore circuit with 53 qubits and 20 cycles. The obtained results verify the Porter-Thomas distribution of the large and deep quantum circuits of Google, provide datasets and benchmarks for developing approximate simulation methods, and can be used for spoofing the linear cross entropy benchmark of quantum supremacy. Then we extend the proposed big-batch method to a full-amplitude simulation approach that is more efficient than the existing Schrödinger method on shallow circuits and the Schrödinger-Feynman method in general, enabling us to obtain the state vector of Google's simplifiable circuit with n=43 qubits and m=14 cycles using only one GPU. We also manage to obtain the state vector for Google's simplifiable circuits with n=50 qubits and m=14 cycles using a small GPU cluster, breaking the previous record on the number of qubits in full-amplitude simulations. Our method is general in computing bitstring probabilities for a broad class of quantum circuits and can find applications in the verification of quantum computers. We anticipate that our method will pave the way for combining tensor network-based classical computations and near-term quantum computations for solving challenging problems in the real world.
Forward Compatible Few-Shot Class-Incremental Learning

Novel classes frequently arise in our dynamically changing world, e.g., new users in the authentication system, and a machine learning model should recognize new classes without forgetting old ones. This scenario becomes more challenging when new class instances are insufficient, which is called few-shot class-incremental learning (FSCIL). Cur-rent methods handle incremental learning retrospectively by making the updated model similar to the old one. By contrast, we suggest learning prospectively to prepare for future updates, and propose ForwArd Compatible Training (FACT) for FSCIL. Forward compatibility requires future new classes to be easily incorporated into the current model based on the current stage data, and we seek to realize it by reserving embedding space for future new classes. In detail, we assign virtual prototypes to squeeze the embedding of known classes and reserve for new ones. Besides, we forecast possible new classes and prepare for the updating process. The virtual prototypes allow the model to accept possible updates in the future, which act as proxies scattered among embedding space to build a stronger classifier during inference. Fact efficiently incorporates new classes with forward compatibility and meanwhile resists for-getting of old ones. Extensive experiments validate FACT's state-of-the-art performance. Code is available at: https://github.com/zhoudw-zdw/CVPR22-Fact
Unconstrained Scene Generation with Locally Conditioned Radiance Fields

We tackle the challenge of learning a distribution over complex, realistic, indoor scenes. In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose scenes into a collection of many local radiance fields that can be rendered from a free moving camera. Our model can be used as a prior to generate new scenes, or to complete a scene given only sparse 2D observations. Recent work has shown that generative models of radiance fields can capture properties such as multi-view consistency and view-dependent lighting. However, these models are specialized for constrained viewing of single objects, such as cars or faces. Due to the size and complexity of realistic indoor environments, existing models lack the representational capacity to adequately capture them. Our decomposition scheme scales to larger and more complex scenes while preserving details and diversity, and the learned prior enables high-quality rendering from viewpoints that are significantly different from observed viewpoints. When compared to existing models, GSN produces quantitatively higher-quality scene renderings across several different scene datasets.
Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection

This paper presents a novel hybrid approach for detecting frauds in automobile insurance claims by applying Genetic Algorithm (GA) based Fuzzy C-Means (FCM) clustering and various supervised classifier models. Initially, a test set is extracted from the original insurance dataset. The remaining train set is subjected to the clustering technique for undersampling after generating some meaningful clusters. The test instances are then segregated into genuine, malicious or suspicious classes after subjecting to the clusters. The genuine and fraudulent records are discarded, while the suspicious cases are further analyzed by four classifiers – Decision Tree (DT), Support Vector Machine (SVM), Group Method of Data Handling (GMDH) and Multi-Layer Perceptron (MLP) individually. The 10-fold cross validation method is used throughout the work for training and validation of the models. The efficacy of the proposed system is illustrated by conducting several experiments on a real world automobile insurance dataset.
Differential Evolution With Underestimation-Based Multimutation Strategy

As we know, the performance of differential evolution (DE) highly depends on the mutation strategy. However, it is difficult to choose a suitable mutation strategy for a specific problem or different running stages. This paper proposes an underestimation-based multimutation strategy (UMS) for DE. In the UMS, a set of candidate offsprings are simultaneously generated for each target individual by utilizing multiple mutation strategies. Then a cheap abstract convex underestimation model is built based on some selected individuals to obtain the underestimation value of each candidate offspring. According to the quality of each candidate offspring measured by the underestimation value, the most promising candidate solution is chosen as the offspring. Compared to the existing probability-based multimutation techniques, no mutation strategies are lost during the search process as each mutation strategy has the same probability to generate a candidate solution. Moreover, no extra function evaluations are produced because the candidate solutions are filtered by the underestimation value. The UMS is integrated into some DE variants and compared with their original algorithms and several advanced DE approaches over the CEC 2013 and 2014 benchmark sets. Additionally, a well-known real-world problem is employed to evaluate the performance of the UMS. Experimental results show that the proposed UMS can improve the performance of the advanced DE variants.
A Dynamic Virtual Machine Placement and Migration Scheme for Data Centers

We study the problem of virtual machine (VM) placement and migration in a data center. In the current approaches, VMs are assigned to physical servers using on-demand provisioning. Such an approach is simple but it often results in a poor performance due to resource fragmentation. Additionally, sub-optimal VM placement usually generates unneeded VM migration and unnecessary cross network traffic. The efficiency of a datacenter therefore significantly depends on how VMs are provisioned and where they are placed. A good placement scheme will not only improve the quality of service but also reduce the operation cost of the data center. In this paper, we study the problem of optimal VM placement and migration to minimize resource usage and power consumption in a data center. We formulate the optimization problem as a joint multiple objective function and solve it by leveraging the framework of convex optimization. Due to the intractable nature of the combinatorial optimization, we then propose Multi-level Join VM Placement and Migration (MJPM) algorithms based on the relaxed convex optimization framework to approximate the optimal solution. The theoretical analysis demonstrates the effectiveness of our proposed algorithms that substantially increases data center efficiency. In addition, our extensive simulation results on different practical topologies show significant performance improvement over the existing approaches.
Learning a Distance Metric by Balancing KL-Divergence for Imbalanced Datasets

In many real-world domains, datasets with imbalanced class distributions occur frequently, which may confuse various machine learning tasks. Among all these tasks, learning classifiers from imbalanced datasets is an important topic. To perform this task well, it is crucial to train a distance metric which can accurately measure similarities between samples from imbalanced datasets. Unfortunately, existing distance metric methods, such as large margin nearest neighbor, information-theoretic metric learning, etc., care more about distances between samples and fail to take imbalanced class distributions into consideration. Traditional distance metrics have natural tendencies to favor the majority classes, which can more easily satisfy their objective function. Those important minority classes are always neglected during the construction process of distance metrics, which severely affects the decision system of most classifiers. Therefore, how to learn an appropriate distance metric which can deal with imbalanced datasets is of vital importance, but challenging. In order to solve this problem, this paper proposes a novel distance metric learning method named distance metric by balancing KL-divergence (DMBK). DMBK defines normalized divergences using KL-divergence to describe distinctions between different classes. Then it combines geometric mean with normalized divergences and separates samples from different classes simultaneously. This procedure separates all classes in a balanced way and avoids inaccurate similarities incurred by imbalanced class distributions. Various experiments on imbalanced datasets have verified the excellent performance of our novel method.
Unsupervised Domain Adaptation Using Robust Class-Wise Matching

Unsupervised domain adaptation (DA) enables a classifier trained on data from one domain to be applied to data from another without labels. Given that the key to transferring a classifier across domains is to mitigate the data distribution mismatch for each class, most previous works completely or partially focus on global distribution matching across domains. The global data space, however, can be complicated, which makes modeling the global distribution difficult. To mitigate this problem, we present a novel unsupervised DA framework where the DA problem is addressed by proposing a robust class-wise matching strategy. Specifically, through minimizing a maximum mean discrepancy-based class-wise fisher discriminant across domains, this framework jointly optimizes two modules: a transferable feature learning module that reduces the distribution discrepancy between the same classes as well as increasing the distribution discrepancy between different classes across domains by a linear projection, and a robust classifier that exploits both the supervised information in source domain and the unsupervised low-rank property of target domain. In experiments on three DA benchmark data sets, the proposed framework shows the state-of-the-art performance.
Admissibility Analysis for Interval Type-2 Fuzzy Descriptor Systems Based on Sliding Mode Control

This paper is concerned with the sliding mode control of nonlinear singular systems in terms of the interval type-2 (IT2) fuzzy model. To better approximate the nonlinear systems, the modeling errors are considered in the IT2 fuzzy model. The uncertainties in membership functions are expressed based on their boundedness. Admissibility conditions are obtained via a linear matrix inequalities approach. A new reaching law-based sliding controller is presented by choosing a typical sliding surface to analyze the sliding motion, such that all of the input gains are allowed to be different and the chattering of the system can be minimized with a guaranteed H∞ performance. The feasibility of the proposed control method is verified by simulation results.