Reviewerly/exHarmony huggingface dataset:
I<sup>3</sup>Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors

Recent works on two-stage cross-domain detection have widely explored the local feature patterns to achieve more accurate adaptation results. These methods heavily rely on the region proposal mechanisms and ROI-based instance-level features to design fine-grained feature alignment modules with respect to the foreground objects. However, for one-stage detectors, it is hard or even impossible to obtain explicit instance-level features in the detection pipelines. Motivated by this, we propose an Implicit Instance-Invariant Network (I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net), which is tailored for adapting one-stage detectors and implicitly learns instance-invariant features via exploiting the natural characteristics of deep features in different layers. Specifically, we facilitate the adaptation from three aspects: (1) Dynamic and Class-Balanced Reweighting (DCBR) strategy, which considers the coexistence of intra-domain and intra-class variations to assign larger weights to those sample-scarce categories and easy-to-adapt samples; (2) Category-aware Object Pattern Matching (COPM) module, which boosts the cross-domain foreground objects matching guided by the categorical information and suppresses the uninformative background features; (3) Regularized Joint Category Alignment (RJCA) module, which jointly enforces the category alignment at different domain-specific layers with a consistency regularization. Experiments reveal that I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net exceeds the state-of-the-art performance on benchmark datasets.
DNNFusion: accelerating deep neural networks execution with advanced operator fusion

Deep Neural Networks (DNNs) have emerged as the core enabler of many major applications on mobile devices. To achieve high accuracy, DNN models have become increasingly deep with hundreds or even thousands of operator layers, leading to high memory and computational requirements for inference. Operator fusion (or kernel/layer fusion) is key optimization in many state-of-the-art DNN execution frameworks, such as TensorFlow, TVM, and MNN, that aim to improve the efficiency of the DNN inference. However, these frameworks usually adopt fusion approaches based on certain patterns that are too restrictive to cover the diversity of operators and layer connections, especially those seen in many extremely deep models. Polyhedral-based loop fusion techniques, on the other hand, work on a low-level view of the computation without operator-level information, and can also miss potential fusion opportunities. To address this challenge, this paper proposes a novel and extensive loop fusion framework called DNNFusion. The basic idea of this work is to work at an operator view of DNNs, but expand fusion opportunities by developing a classification of both individual operators and their combinations. In addition, DNNFusion includes 1) a novel mathematical-property-based graph rewriting framework to reduce evaluation costs and facilitate subsequent operator fusion, 2) an integrated fusion plan generation that leverages the high-level analysis and accurate light-weight profiling, and 3) additional optimizations during fusion code generation. DNNFusion is extensively evaluated on 15 DNN models with varied types of tasks, model sizes, and layer counts. The evaluation results demonstrate that DNNFusion finds up to 8.8 × higher fusion opportunities, outperforms four state-of-the-art DNN execution frameworks with 9.3× speedup. The memory requirement reduction and speedups can enable the execution of many of the target models on mobile devices and even make them part of a real-time application.
SliceNet: deep dense depth estimation from a single indoor panorama using a slice-based representation

We introduce a novel deep neural network to estimate a depth map from a single monocular indoor panorama. The network directly works on the equirectangular projection, exploiting the properties of indoor 360° images. Starting from the fact that gravity plays an important role in the design and construction of man-made indoor scenes, we propose a compact representation of the scene into vertical slices of the sphere, and we exploit long- and short-term relationships among slices to recover the equirectangular depth map. Our design makes it possible to maintain high-resolution information in the extracted features even with a deep network. The experimental results demonstrate that our method outperforms current state-of-the-art solutions in prediction accuracy, particularly for real-world data.
Essentials for Class Incremental Learning

Contemporary neural networks are limited in their ability to learn from evolving streams of training data. When trained sequentially on new or evolving tasks, their accuracy drops sharply, making them unsuitable for many real-world applications. In this work, we shed light on the causes of this well known yet unsolved phenomenon - often referred to as catastrophic forgetting - in a class-incremental setup. We show that a combination of simple components and a loss that balances intra-task and intertask learning can already resolve forgetting to the same extent as more complex measures proposed in literature. Moreover, we identify poor quality of the learned representation as another reason for catastrophic forgetting in class-IL. We show that performance is correlated with secondary class information (dark knowledge) learned by the model and it can be improved by an appropriate regularizer. With these lessons learned, class-incremental learning results on CIFAR-100 and ImageNet improve over the state-of-the-art by a large margin, while keeping the approach simple.
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding

The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing, segmenting and reasoning of visual affordance. Relevant studies in 2D and 2.5D image domains have been made previously, however, a truly functional understanding of object affordance requires learning and prediction in the 3D physical domain, which is still absent in the community. In this work, we present a 3D AffordanceNet dataset, a bench-mark of 23k shapes from 23 semantic object categories, annotated with 18 visual affordance categories. Based on this dataset, we provide three benchmarking tasks for evaluating visual affordance understanding, including full-shape, partial-view and rotation-invariant affordance estimations. Three state-of-the-art point cloud deep learning networks are evaluated on all tasks. In addition we also investigate a semi-supervised learning setup to explore the possibility to benefit from unlabeled data. Comprehensive results on our contributed dataset show the promise of visual affordance understanding as a valuable yet challenging benchmark.
Opposition-based moth swarm algorithm

Nowadays, resource-optimizing techniques are required in many engineering areas to obtain the most appropriate solutions for complex problems. For this reason, there is a trend among researchers to improve existing swarm-based algorithms through different evolutionary techniques and to create new population-based methods that can accurately explore the feature space. The recently proposed Moth swarm algorithm (MSA) inspired by the orientation of moths towards moonlight is an associative learning mechanism with immediate memory that uses Lévy mutation to cross-population diversity and spiral movement. The MSA is a population-based method used for tackling complex optimization problems. It presents an adequate capacity for exploration and exploitation trends; however, due to its nature of operators, this type of method is prone to get stuck in sub-optimal locations, which affects the speed of convergence and the computational effort to reach better solutions. To mitigate these shortcomings, this paper proposes an improved MSA that combines opposition-based learning (OBL) as a mechanism to enhance the exploration drifts of the basic version and increase the speed of convergence to obtain more accurate solutions. The proposed approach is called OBMSA. It has been tested for solving three classic engineering design problems (welded beam, tension/compression spring, and pressure vessel designs) with constraints, 19 benchmark functions comprising 7 unimodal, 6 multimodal, and 6 composite functions. Experimental results and comparisons provide evidence that the performance and accuracy of the proposed method are superior to the original MSA. We hope the community utilizes the proposed MSA-based approach for solving other complex problems.
Accessible Visualization: Design Space, Opportunities, and Challenges

Abstract Visualizations are now widely used across disciplines to understand and communicate data. The benefit of visualizations lies in leveraging our natural visual perception. However, the sole dependency on vision can produce unintended discrimination against people with visual impairments. While the visualization field has seen enormous growth in recent years, supporting people with disabilities is much less explored. In this work, we examine approaches to support this marginalized user group, focusing on visual disabilities. We collected and analyzed papers published for the last 20 years on visualization accessibility. We mapped a design space for accessible visualization that includes seven dimensions: user group, literacy task, chart type, interaction, information granularity, sensory modality, assistive technology. We described the current knowledge gap in light of the latest advances in visualization and presented a preliminary accessibility model by synthesizing findings from existing research. Finally, we reflected on the dimensions and discussed opportunities and challenges for future research.
Cooperative Driving of Automated Vehicles Using B-Splines for Trajectory Planning

Vehicle automation is used to increase traffic throughput and road safety. Automated vehicles should be highly versatile to navigate on both highways and urban roads. To do so, fully automated autonomous vehicles often adopt motion planners to ensure collision-free and comfortable trajectories. In contrast, in cooperative driving, feedback control is often used to achieve string-stable vehicle-following at very short inter-vehicle distances. In this work, the gap between cooperative vehicle following and trajectory planning is bridged, by using a trajectory planner to generate cooperative trajectories, such that the versatility of an autonomous vehicle is retained, but cooperation is included. The presented method uses B-splines to generate cooperative trajectories. Simulations are used to demonstrate that even under large communication delays, string stable behaviour can be achieved. The use of parametrized trajectories in the form of B-splines ensures only a small number of parameters needs to be communicated between vehicles.
Implementing a Magnonic Reservoir Computer Model Based on Time-Delay Multiplexing

Recently we demonstrated experimentally that microwave oscillators based on the time delay feedback provided by traveling spin waves could operate as reservoir computers. In the present paper, we extend this concept by adding the feature of time multiplexing made available by the large propagation times/distances of traveling spin waves. The system utilizes the nonlinear behavior of propagating magnetostatic surface spin waves in a yttrium-iron garnet thin film and the time delay inherent in the active ring configuration to process time dependent data streams. Higher reservoir dimensionality is obtained through the time-multiplexing method, emulating "virtual" neurons as temporally separated spin-wave pulses circulating in the active ring below the auto-oscillation threshold. To demonstrate the efficacy of the concept, the active ring reservoir computer is evaluated on the short-term memory and parity check benchmark tasks, and the physical system parameters are tuned to optimize performance. By incorporating a reference line to mix the input signal directly onto the active ring output, both the amplitude and phase nonlinearity of the spin waves can be exploited, resulting in significant improvement on the nonlinear parity check task. We also find that the fading memory capacity of the system can be easily tuned by controlling the active ring gain. Finally, we show that the addition of a second spin-wave delay line configured to transmit backward volume spin waves can partly compensate dispersive pulse broadening and enhance the fading memory capacity of the active ring.
On Robustness and Transferability of Convolutional Neural Networks

Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. However, several recent breakthroughs in transfer learning suggest that these networks can cope with severe distribution shifts and successfully adapt to new tasks from a few training examples. In this work we study the interplay between out-of-distribution and transfer performance of modern image classification CNNs for the first time and investigate the impact of the pre-training data size, the model scale, and the data preprocessing pipeline. We find that increasing both the training set and model sizes significantly improve the distributional shift robustness. Furthermore, we show that, perhaps surprisingly, simple changes in the preprocessing such as modifying the image resolution can significantly mitigate robustness issues in some cases. Finally, we outline the shortcomings of existing robustness evaluation datasets and introduce a synthetic dataset SI-SCORE we use for a systematic analysis across factors of variation common in visual data such as object size and position.
Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks

Integrating photovoltaics (PV) into electricity grids is challenged by potentially large fluctuations in power generation. In recent years, sky image-based PV output prediction using convolutional neural networks (CNNs) has emerged as a promising approach to forecasting fluctuations. A key challenge is imbalanced sky image datasets: because of the geography of solar PV system installations, sky image datasets are often rich in sunny condition data but deficient in cloudy condition data. This imbalance contrasts with the fact that model errors are dominated by cloudy condition performance. In this study, we attempt to remedy this by exploring the enrichment and augmentation of an imbalanced sky images dataset for two PV output prediction tasks: nowcasting (predicting concurrent PV output) and forecasting (predicting 15-minute-ahead future PV output). We empirically examine the efficacy of using different resampling and data augmentation approaches to create a rebalanced dataset for model development. A three-stage greedy search is used to determine the optimal resampling approach, data augmentation techniques and over-sampling rate. The results show that for the nowcast problem, resampling and data augmentation can effectively enhance the model performance, reducing overall root mean squared error (RMSE) by an average of 4%, or a 15 std. (standard deviation) of improvement compared to the variability of the baseline model. In contrast, the treatment RMSE for the forecast problem nearly always overlaps the baseline performance at the ± 2 std. level. The optimal resampling approach expands on the original dataset by over-sampling the minority cloudy data, with the best results from large over-sampling rate (e.g., 4 ~ 6 times over-sampling of cloudy images).
FairLens: Auditing black-box clinical decision support systems

The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The detection and mitigation of model bias is a very delicate task that should be tackled with care and involving domain experts in the loop. In this paper we introduce FairLens, a methodology for discovering and explaining biases. We show how this tool can audit a fictional commercial black-box model acting as a clinical decision support system (DSS). In this scenario, the healthcare facility experts can use FairLens on their historical data to discover the biases of the model before incorporating it into the clinical decision flow. FairLens first stratifies the available patient data according to demographic attributes such as age, ethnicity, gender and healthcare insurance; it then assesses the model performance on such groups highlighting the most common misclassifications. Finally, FairLens allows the expert to examine one misclassification of interest by explaining which elements of the affected patients’ clinical history drive the model error in the problematic group. We validate FairLens’ ability to highlight bias in multilabel clinical DSSs introducing a multilabel-appropriate metric of disparity and proving its efficacy against other standard metrics.
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices

Single-Image Super Resolution (SISR) is a classical computer vision problem and it has been studied for over decades. With the recent success of deep learning methods, recent work on SISR focuses solutions with deep learning methodologies and achieves state-of-the-art results. However most of the state-of-the-art SISR methods contain millions of parameters and layers, which limits their practical applications. In this paper, we propose a hardware (Synaptics Dolphin NPU) limitation aware, extremely lightweight quantization robust real-time super resolution network (XLSR). The proposed model’s building block is inspired from root modules introduced in [15] for Image classification. We successfully applied root modules to SISR problem, further more to make the model uint8 quantization robust we used Clipped ReLU at the last layer of the network and achieved great balance between reconstruction quality and runtime. Furthermore, although the proposed network contains 30x fewer parameters than VDSR [16] its performance surpasses it on Div2K validation set. The network proved itself by winning Mobile AI 2021 Real-Time Single Image Super Resolution Challenge.
A real-time one-time pad DNA-chaos image encryption algorithm based on multiple keys

Image encryption is an important necessity when sharing data over public networks, and many image encryption algorithms have been proposed for the task. However, most of these algorithms are complex and slow. We propose a real-time one-time pad DNA-chaos image encryption algorithm using multiple keys. The proposed encryption algorithm uses a greatly simplified mechanism and a large, unpredictable sequence to encrypt the image. Our algorithm has three main advantages. First, its reliance on the original image for four of the keys makes it sensitive to changes in the original image. Small changes in the image produce large changes in the keys, making the algorithm resistant to differential attacks. Second, we integrate the natural DNA sequence into the process of generating the key, and the algorithm uses the massive DNA sequences stored in the DNA gene bank to solve the problem that it is difficult to store one-time pad. The keys have the property of one-time pad, and the key space is considerablely large. Finally, our algorithm encrypts quickly enough to make it suitable for real-time applications. On the premise of ensuring that the results of this paper are not worse than those of the comparison literature in other performance indicators, in terms of encryption time, even if we use a computing environment that is worse than all the comparison literature computer configurations, the algorithm of this paper can still save at least 14%–86%. As the picture size becomes larger, the advantages of the algorithm in this paper are more obvious.
Enhanced Harris hawks optimization with multi-strategy for global optimization tasks

Harris Hawks Optimization (HHO) algorithm is a newly proposed meta-heuristic optimization algorithm that simulates the hunting process of the Harris hawks. It has the characteristics of fewer adjustment parameters and a strong optimization effect, resulting in strong competitiveness in similar optimization algorithms. However, HHO is prone to premature convergence and low convergence accuracy when dealing with specific complex optimization problems. Therefore, our work integrates two novel strategies into the standard HHO to gain enhanced exploration and exploitation capabilities. Specifically, our work firstly proposed an exploration strategy based on logarithmic spiral and opposition-based learning to improve the exploration ability of HHO. Secondly, the local search technique for Rosenbrock Method (RM) is modified to dynamically fuse into the standard HHO to enhance the algorithm’s local search capability and improve the convergence accuracy. The novel meta-heuristic algorithm proposed in this paper is called RLHHO. Finally, to validate the algorithm’s effectiveness, the proposed RLHHO algorithm is fully performance tested with eight other traditional meta-heuristic optimization algorithms on 23 benchmark functions and 30 IEEE CEC2014 test functions. Besides, another six advanced meta-heuristics algorithms are also compared in the 30 CEC’2014 test functions. The experimental results show that RLHHO performs significantly better than HHO as well as other traditional and advanced meta-heuristic algorithms in most of the test functions. To test the scalability of RLHHO in complex real-world problems, it was used to optimize the solution of three constrained real-world engineering problems, and the experimental results show that RLHHO’s powerful performance can be used as an effective tool for solving constrained engineering problems. Also, an effective hybrid model of kernel extreme learning machine is developed on the basis of RLHHO to cope with bankruptcy prediction problem. The experimental results show that this hybrid model is highly competitive with other mainstream classifiers regarding stability and prediction accuracy. The supplementary info and answers to possiblequeries will be publicly available at https://www.researchgate.net/profile/Chenyang_Li39/research.