Reviewerly/exHarmony huggingface dataset:
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule

Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation. However, more recent works find that existing differentiable NAS techniques struggle to outperform naive baselines, yielding deteriorative architectures as the search proceeds. Rather than directly optimizing the architecture parameters, this paper formulates the neural architecture search as a distribution learning problem through relaxing the architecture weights into Gaussian distributions. By leveraging the natural-gradient variational inference (NGVI), the architecture distribution can be easily optimized based on existing codebases without incurring more memory and computational consumption. We demonstrate how the differentiable NAS benefits from Bayesian principles, enhancing exploration and improving stability. The experimental results on NAS benchmark datasets confirm the significant improvements the proposed framework can make. In addition, instead of simply applying the argmax on the learned parameters, we further leverage the recently-proposed training-free proxies in NAS to select the optimal architecture from a group architectures drawn from the optimized distribution, where we achieve state-of-the-art results on the NAS-Bench-201 and NAS-Bench-1shot1 benchmarks. Our best architecture in the DARTS search space also obtains competitive test errors with 2.37%, 15.72%, and 24.2% on CIFAR-10, CIFAR-100, and ImageNet, respectively.
TSSB-3M

Single statement bugs are one of the most important ingredients in the evaluation of modern bug detection and automatic program repair methods. By affecting only a single statement, single statement bugs represent a type of bug often overlooked by developers, while still being small enough to be detected and fixed by automatic methods. With the rise of data-driven automatic repair the availability of single statement bugs at the scale of millionth of examples is more important than ever; not only for testing these methods but also for providing sufficient real world examples for training. To provide access to bug fix datasets of this scale, we are releasing two datasets called SSB-9M and TSSB-3M. While SSB-9M provides access to a collection of over 9M general single statement bug fixes from over 500K open source Python projects, TSSB-3M focuses on over 3M single statement bugs which can be fixed solely by a single statement change. To facilitate future research and empirical investigations, we annotated each bug fix with one of 20 single statement bug (SStuB) patterns typical for Python together with a characterization of the code change as a sequence of AST modifications. Our initial investigation shows that at least 40% of all single statement bug fixes mined fit at least one SStuB pattern, and that the majority of 72% of all bugs can be fixed with the same syntactic modifications as needed for fixing SStuBs.
On the Versatile Uses of Partial Distance Correlation in Deep Learning

Comparing the functional behavior of neural network models, whether it is a single network over time or two (or more networks) during or post-training, is an essential step in understanding what they are learning (and what they are not), and for identifying strategies for regularization or efficiency improvements. Despite recent progress, e.g., comparing vision transformers to CNNs, systematic comparison of function, especially across different networks, remains difficult and is often carried out layer by layer. Approaches such as canonical correlation analysis (CCA) are applicable in principle, but have been sparingly used so far. In this paper, we revisit a (less widely known) from statistics, called distance correlation (and its partial variant), designed to evaluate correlation between feature spaces of different dimensions. We describe the steps necessary to carry out its deployment for large scale models - this opens the door to a surprising array of applications ranging from conditioning one deep model w.r.t. another, learning disentangled representations as well as optimizing diverse models that would directly be more robust to adversarial attacks. Our experiments suggest a versatile regularizer (or constraint) with many advantages, which avoids some of the common difficulties one faces in such analyses .
Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval

Unsupervised video hashing usually optimizes binary codes by learning to reconstruct input videos. Such reconstruction constraint spends much effort on frame-level temporal context changes without focusing on video-level global semantics that are more useful for retrieval. Hence, we address this problem by decomposing video information into reconstruction-dependent and semantic-dependent information, which disentangles the semantic extraction from reconstruction constraint. Specifically, we first design a simple dual-stream structure, including a temporal layer and a hash layer. Then, with the help of semantic similarity knowledge obtained from self-supervision, the hash layer learns to capture information for semantic retrieval, while the temporal layer learns to capture the information for reconstruction. In this way, the model naturally preserves the disentangled semantics into binary codes. Validated by comprehensive experiments, our method consistently outperforms the state-of-the-arts on three video benchmarks.
Effects of Rendering Styles of a Virtual Character on Avoidance Movement Behavior

Avoidance movements between humans performing locomotive tasks and virtual characters represent typical interactions in immer-sive virtual environments. When performing these avoidance maneu-vers, decisions are based on several factors, including self-presence and the perception of other virtual individuals. However, the impact of visual information on collision-free interactions is not yet fully understood. In this study, we aim to manipulate how the appearance of virtual characters is represented using five different rendering styles (realistic, toon, creepy, scary, and robot) to understand how alternate appearances could potentially affect the avoidance movement behavior of participants. To gather avoidance behavior data, 24 healthy participants performed a two-trial locomotive task toward a target in a virtual environment. Our results indicated that eerie ren-dering styles affected trajectory length, duration, speed, minimum distance, and side-by-side distance measurements. Additionally, most participants maintained a minimum distance behind the virtual characters and avoided them by keeping to their left side in all ex-amined conditions. We posit that the presented findings contribute toward a fuller understanding of the effects of the appearance of virtual characters on avoidance movement behavior in room-scale virtual environments.
Analysis of the Impact of White Box Adversarial Attacks in ResNet While Classifying Retinal Fundus Images

Medical image analysis with deep learning techniques has been widely recognized to provide support in medical diagnosis. Among the several attacks on the deep learning (DL) models that aim to decrease the reliability of the models, this paper deals with the adversarial attacks. Adversarial attacks and the ways to defend the attacks or make the DL models robust towards these attacks have been an increasingly important research topic with a surge of work carried out on both sides. The adversarial attacks of the white box category, namely Fast Gradient Sign Method (FGSM), the Box-constrained Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS-B) attack and a variant of the L-BFGS-B attack are studied in this paper. In this work, we have used two defense mechanisms, namely, Adversarial Training and Defensive distillation-Gradient masking. The reliability of these defense mechanisms against the attacks are studied. The effect of noise in FGSM is studied in detail. Retinal fundus images for the diabetic retinopathy disease are used in the experimentation. The effect of the attack reveals the vulnerability of the Resnet model for these attacks.
Irrigation Prediction using Machine Learning in Precision Agriculture

Agriculture has contributed to the economy in every country. Currently, agriculture has coped with several challenges, such as irrigation in water management. Agriculture used traditional techniques to manage irrigation. So, it requires much cost and labour. Besides, machine learning has evolved in various sectors to increase quality within. In a previous study, Fuzzy was proposed to model the data in statics based on rules and had difficulty computing the data automatically. This paper proposed irrigation prediction by using a machine learning algorithm. Classification algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naive Bayes, Random Forest, and Decision Tree, are investigated to forecast accurate irrigation. This paper calculates accuracy, precision, recall, and Fl-score to evaluate the algorithm's performance. Experimental results show that Decision Tree outperforms other algorithms using its performance using the same agricultural data according to accuracy, recall, precision, and fl-measure score. In addition, the results of this experiment will be implemented in predicting automatic paddy irrigation using loT -based sensor data.
Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning

Semi-supervised learning (SSL) has been studied for a long time to solve vision tasks in data-efficient application scenarios. SSL aims to learn a good classification model using a few labeled data together with large-scale unlabeled data. Recent advances achieve the goal by combining multiple SSL techniques, e.g., self-training and consistency regularization. From unlabeled samples, they usually adopt a confidence filter (CF) to select reliable ones with high prediction confidence. In this work, we study whether the moderately confident samples are useless and how to select the useful ones to improve model optimization. To answer these problems, we propose a novel Taylor expansion inspired filtration (TEIF) framework, which admits the samples of moderate confidence with similar feature or gradient to the respective one averaged over the labeled and highly confident unlabeled data. It can produce a stable and new information induced network update, leading to better generalization. Two novel filters are derived from this framework and can be naturally explained in two perspectives. One is gradient synchronization filter (GSF), which strengthens the optimization dynamic of fully-supervised learning; it selects the samples whose gradients are similar to classwise majority gradients. The other is prototype proximity filter (PPF), which involves more prototypical samples in training to learn better semantic representations; it selects the samples near classwise prototypes. They can be integrated into SSL methods with CF. We use the state-of-the-art Fix-Match as the baseline. Experiments on popular SSL benchmarks show that we achieve the new state of the art.
Teaching Quantum Computing without prerequisites: a case study

We present the results of two semesters of a new undergraduate course on Quantum Computing for Fundamental Sciences and Engineering students. The course was taught employing a blended learning approach, with a combination of synchronous classes, asynchronous video lessons and projects using IBM’s Qiskit framework. As the students did not need any prerequisites in order to enroll, we treated the whole course as applied linear algebra, and it turns out that quantum computing can become the gateway to quantum physics instead of the other way around.
Improved LBP and Discriminative LBP: Two novel local descriptors for Face Recognition

Most of LBP based descriptors develop their feature size by considering the uniform coordination among neighbors and center pixel. Additionally, most of them possesses noisy thresholding function. This limits the discriminativity of these descriptors to large extent. To eliminate all above defined conditions two novel descriptors are introduced called as Improved LBP (ILBP) and Discriminative LBP (DLBP). In ILBP, initially maximum value is attained from the 3x3 patch. Then product is taken between the maximum value and one of best possible values within range (.1-.9) to develop the threshold value. For this work it has been observed that.9 gives the best accuracy therefore.9 is used for obtaining the threshold value. The best range value will be chosen for obtaining the threshold value. Then all neighbors are compared against threshold for forming ILBP code. The ILBP histogram is achieved by computing the ILBP code for each pixel position. In DLBP, the histograms of ILBP and LBP are merged to form the more robust descriptor. For feature compression PCA is used and then classification was done by RBF technique, the SVMs method. Experiments conducted on 2 benchmark datasets i.e. ORL and GT confirms ability of both the descriptors against various methods. Among all it is DLBP which achieves best accuracy.
SCAM-YOLOv5: Improved YOLOv5 Based on Spatial and Channel Attention Module

The object detection technology of deep learning has been applied in more and more scenarios, however the existing object detection network has high computational cost. Neural networks for object detection have difficulty balancing accuracy and computational cost. The practical application of lightweight object detection networks is a difficult problem. In this paper, we propose SCAM-YOLOv5, which uses a modified attention mechanism and Ghost convolution to improve the YOLOv5s network, achieving gratifying results. Compared with the vanilla network, the mAP is increased by 2.6% on the VOC dataset, while the model file is only increased by 0.75M. Experiments show that the improved network proposed in this paper can improve the detection accuracy of the neural network while little increase in model parameters.
Mechanisms of image recovery optimization in the system for recognition and classification of micro-objects

The scientific and methodological foundations for the construction of methods, models, and algorithms of the software complex for visualization, recognition, classification of micro-objects with mechanisms for optimizing image restoration based on the use of structural and statistical redundancy of information have been developed.. Mechanisms for optimizing image recovery based on the principles of using structural-statistical redundancy caused by the repetition of identical frames during stream transmission, interframe difference, and fractal characteristics of images are proposed. The image recovery mechanisms are improved on the basis of multilayer segmentation of the contour, object texture, binary alpha map, creation of metadata arrays, image scaling by bicubic interpolation. Mechanisms for image identification based on a dynamic model of neural networks with component schemes of axon branching, neuronal activity, and revealing their influence on interneuronal connections have been developed. Optimization of NN image restoration is based on the use of process features, axon movement, axon branching, choice of the direction of its growth depending on the concentration of surrounding neurons, emulation of changes in global and additional characteristics of images. A software package has been implemented that includes mechanisms for optimizing image recovery, which has been tested with 538 training sets of medical objects (unicellular microorganisms in the blood).
MODE: Multi-view Omnidirectional Depth Estimation with 360$$^\circ $$ Cameras

In this paper, we propose a two-stage omnidirectional depth estimation framework with multi-view 360 $$^\circ $$ cameras. The framework first estimates the depth maps from different camera pairs via omnidirectional stereo matching and then fuses the depth maps to achieve robustness against mud spots, water drops on camera lenses, and glare caused by intense light. We adopt spherical feature learning to address the distortion of panoramas. In addition, a synthetic 360 $$^\circ $$ dataset consisting of 12K road scene panoramas and 3K ground truth depth maps is presented to train and evaluate 360 $$^\circ $$ depth estimation algorithms. Our dataset takes soiled camera lenses and glare into consideration, which is more consistent with the real-world environment. Experimental results show that the proposed framework generates reliable results in both synthetic and real-world environments, and it achieves state-of-the-art performance on different datasets. The code and data are available at https://github.com/nju-ee/MODE-2022 .
Neural 3D Gaze: 3D Pupil Localization and Gaze Tracking based on Anatomical Eye Model and Neural Refraction Correction

Eye tracking has already made its way to current commercial wearable display devices, and is becoming increasingly important for virtual and augmented reality applications. However, the existing model-based eye tracking solutions are not capable of conducting very accurate gaze angle measurements, and may not be sufficient to solve challenging display problems such as pupil steering or eyebox expansion. In this paper, we argue that accurate detection and localization of pupil in 3D space is a necessary intermediate step in model-based eye tracking. Existing methods and datasets either ignore evaluating the accuracy of 3D pupil localization or evaluate it only on synthetic data. To this end, we capture the first 3D pupilgaze-measurement dataset using a high precision setup with head stabilization and release it as the first benchmark dataset to evaluate both 3D pupil localization and gaze tracking methods. Furthermore, we utilize an advanced eye model to replace the commonly used oversimplified eye model. Leveraging the eye model, we propose a novel 3D pupil localization method with a deep learning-based corneal refraction correction. We demonstrate that our method outperforms the state-of-the-art works by reducing the 3D pupil localization error by 47.5% and the gaze estimation error by 18.7%. Our dataset and codes can be found here: link.
How to Solve Combinatorial Optimization Problems Using Real Quantum Machines: A Recent Survey

A combinatorial optimization problem (COP) is the problem of finding the optimal solution in a finite set.When the size of the feasible solution set is large, the complexity of the problem increases, and it is not easy to solve in a reasonable time with the current classical computer technology.Quantum annealing (QA) is a method that replaces classical simulated annealing (SA) methods that do not solve these cases.Therefore, several attempts have been made to solve this problem using a special-purpose quantum annealer to which the QA method is applied.In this survey, we analyze recent studies that solve real-scale COPs using quantum annealers.Through this, we discuss how to reduce the size of the COP to be input to overcome the hardware limitations of the existing quantum annealer.Additionally, we demonstrated the applicability of quantum annealer to COP on a practical scale by comparing and analyzing the results of the classical simulated annealing (SA) and QA method from each study.
Balancing Stability and Plasticity Through Advanced Null Space in Continual Learning

Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i.e., it is uneasy to simultaneously have the stability to prevent catastrophic forgetting of old tasks and the plasticity to learn new tasks well. In this paper, we propose a new continual learning approach, Advanced Null Space (AdNS), to balance the stability and plasticity without storing any old data of previous tasks. Specifically, to obtain better stability, AdNS makes use of low-rank approximation to obtain a novel null space and projects the gradient onto the null space to prevent the interference on the past tasks. To control the generation of the null space, we introduce a non-uniform constraint strength to further reduce forgetting. Furthermore, we present a simple but effective method, intra-task distillation, to improve the performance of the current task. Finally, we theoretically find that null space plays a key role in plasticity and stability, respectively. Experimental results show that the proposed method can achieve better performance compared to state-of-the-art continual learning approaches.
Toward Novel Time Representations for RFF Identification Using Imperfect Data Sets

As an inherent attribute of hardware circuit, radio-frequency fingerprint (RFF) is hardly forged and unique. Recently, the connection with deep learning has made it one of the most powerful guarantees of physical-layer security. Most existing RFF-based methods are designed under ideal data sets, thus tend to be less versatile in real-world scenarios. To address this problem, we propose a novel RFF identification scheme toward imperfect data sets of small sample size and fragmentary signal. Two novel time representations are proposed to visualize the RFFs by digging the embedded temporal information, which are named 1-D Gramian angular fields (1DGAF) and 2-D Gramian angular fields (2DGAF), respectively. Specifically, extremely short segments of the received preamble are transformed into images using 1DGAF and 2DGAF. Then, the images are fed into a channel-selectable convolutional neural network (CNN) for further identification. Theoretical analysis indicates that the proposed methods can augment the separability of the original data, leading to a better identification performance. Experimental results show that the accuracy reach 94.81% with only three half-sine waves and 99.26% with a quarter of the preamble at the signal-to-noise ratio level of 30 dB. The robustness of the proposed methods using unseen symbols has also been verified.
Fine-Grained Continual Learning for SAR Target Recognition

Synthetic Aperture Radar (SAR) systems work in the open and dynamic environment, and capture new data or new targets continually over time. It requires that SAR target recognition algorithms should have the capability to learn new targets incrementally without forgetting the previously learned targets. Besides, SAR targets of different classes usually have subtle differences which makes the recognition more challenging. In this paper, we propose a fine-grained continual learning algorithm for SAR incremental target recognition. Since data imbalance between old and new classes results in the forgetting of old classes, class-balanced loss is introduced to alleviate this phenomenon. In addition, covariance pooling network is utilized to explore the higher-order statistical information to improve the discrimination of features. Experimental results on real SAR datasets validate the effectiveness of the proposed method.