Reviewerly/exHarmony huggingface dataset:
Language-Guided Global Image Editing via Cross-Modal Cyclic Mechanism

Editing an image automatically via a linguistic request can significantly save laborious manual work and is friendly to photography novice. In this paper, we focus on the task of language-guided global image editing. Existing works suffer from imbalanced and insufficient data distribution of real-world datasets and thus fail to understand language requests well. To handle this issue, we propose to create a cycle with our image generator by creating a novel model called Editing Description Network (EDNet) which predicts an editing embedding given a pair of images. Given the cycle, we propose several free augmentation strategies to help our model understand various editing requests given the imbalanced dataset. In addition, two other novel ideas are proposed: an Image-Request Attention (IRA) module which allows our method to edit an image spatial-adaptively when the image requires different editing degree at different regions, as well as a new evaluation metric for this task which is more semantic and reasonable than conventional pixel losses (e.g. L1). Extensive experiments on two benchmark datasets demonstrate the effectiveness of our method over existing approaches.
DLBT: Deep Learning-Based Transformer to Generate Pseudo-Code from Source Code

Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language. Pseudo-code explains and describes the content of the code without using syntax or programming language technologies. However, writing Pseudo-code to each code instruction is laborious. Recently, neural machine translation is used to generate textual descriptions for the source code. In this paper, a novel deep learning-based transformer (DLBT) model is proposed for automatic Pseudo-code generation from the source code. The proposed model uses deep learning which is based on Neural Machine Translation (NMT) to work as a language translator. The DLBT is based on the transformer which is an encoder-decoder structure. There are three major components: tokenizer and embeddings, transformer, and post-processing. Each code line is tokenized to dense vector. Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network (RNN). At the post-processing step, the generated Pseudo-code is optimized. The proposed model is assessed using a real Python dataset, which contains more than 18,800 lines of a source code written in Python. The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network (RNN). The proposed DLBT records 47.32, 68. 49 accuracy and BLEU performance measures, respectively.
Pho(SC)Net: An Approach Towards Zero-Shot Word Image Recognition in Historical Documents

Annotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers). In a real-life scenario, obtaining sample images for all possible words is also not feasible. However, Zero-shot learning methods could aptly be used to recognize unseen/out-of-lexicon words in such historical document images. Based on previous state-of-the-art methods for word spotting and recognition, we propose a hybrid representation that considers the character's shape appearance to differentiate between two different words and has shown to be more effective in recognizing unseen words. This representation has been termed as Pyramidal Histogram of Shapes (PHOS), derived from PHOC, which embeds information about the occurrence and position of characters in the word. Later, the two representations are combined and experiments were conducted to examine the effectiveness of an embedding that has properties of both PHOS and PHOC. Encouraging results were obtained on two publicly available historical document datasets and one synthetic handwritten dataset, which justifies the efficacy of "Phos" and the combined "Pho(SC)" representation.
Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation

Scene understanding is a pivotal task for autonomous vehicles to safely navigate in the environment. Recent advances in deep learning enable accurate semantic reconstruction of the surroundings from LiDAR data. However, these models encounter a large domain gap while deploying them on vehicles equipped with different LiDAR setups which drastically decreases their performance. Fine-tuning the model for every new setup is infeasible due to the expensive and cumbersome process of recording and manually labeling new data. Unsupervised Domain Adaptation (UDA) techniques are thus essential to fill this domain gap and retain the performance of models on new sensor setups without the need for additional data labeling. In this paper, we propose AdaptLPS, a novel UDA approach for LiDAR panoptic segmentation that leverages task-specific knowledge and accounts for variation in the number of scan lines, mounting position, intensity distribution, and environmental conditions. We tackle the UDA task by employing two complementary domain adaptation strategies, data-based and model-based. While data-based adaptations reduce the domain gap by processing the raw LiDAR scans to resemble the scans in the target domain, model-based techniques guide the network in extracting features that are representative for both domains. Extensive evaluations on three pairs of real-world autonomous driving datasets demonstrate that AdaptLPS outperforms existing UDA approaches by up to 6.41 pp in terms of the PQ score.
Contrastive Coding for Active Learning under Class Distribution Mismatch

Active learning (AL) is successful based on the assumption that labeled and unlabeled data are obtained from the same class distribution. However, its performance deteriorates under class distribution mismatch, wherein the un-labeled data contain many samples out of the class distribution of labeled data. To effectively handle the problems under class distribution mismatch, we propose a contrastive coding based AL framework named CCAL. Unlike the existing AL methods that focus on selecting the most informative samples for annotating, CCAL extracts both semantic and distinctive features by contrastive learning and combines them in a query strategy to choose the most informative un-labeled samples with matched categories. Theoretically, we prove that the AL error of CCAL has a tight upper bound. Experimentally, we evaluate its performance on CIFAR10, CIFAR100, and an artificial cross-dataset that consists of five datasets; consequently, CCAL achieves state-of-the-art performance by a large margin with remarkably lower annotation cost. To the best of our knowledge, CCAL is the first work related to AL for class distribution mismatch.
Projection Filtering With Observed State Increments With Applications in Continuous-Time Circular Filtering

Angular path integration is the ability of a system to estimate its own heading direction from potentially noisy angular velocity (or increment) observations. Non-probabilistic algorithms for angular path integration, which rely on a summation of these noisy increments, do not appropriately take into account the reliability of such observations, which is essential for appropriately weighing one's current heading direction estimate against incoming information. In a probabilistic setting, angular path integration can be formulated as a continuous-time nonlinear filtering problem (circular filtering) with observed state increments. The circular symmetry of heading direction makes this inference task inherently nonlinear, thereby precluding the use of popular inference algorithms such as Kalman filters, rendering the problem analytically inaccessible. Here, we derive an approximate solution to circular continuous-time filtering, which integrates state increment observations while maintaining a fixed representation through both state propagation and observational updates. Specifically, we extend the established projection-filtering method to account for observed state increments and apply this framework to the circular filtering problem. We further propose a generative model for continuous-time angular-valued direct observations of the hidden state, which we integrate seamlessly into the projection filter. Applying the resulting scheme to a model of probabilistic angular path integration, we derive an algorithm for circular filtering, which we term the circular Kalman filter. Importantly, this algorithm is analytically accessible, interpretable, and outperforms an alternative filter based on a Gaussian approximation.
Hippie: A Data-Paralleled Pipeline Approach to Improve Memory-Efficiency and Scalability for Large DNN Training

With the increase of both data and parameter volume, it has become a big challenge to efficiently train large-scale DNN models on distributed platforms. Ordinary parallelism modes, i.e., data parallelism, model parallelism and pipeline parallelism, can no longer satisfy the efficient scaling of large DNN model training on multiple nodes. Meanwhile, the problem of too much memory consumption seriously restricts GPU computing efficiency and training throughput. In this paper, we propose Hippie, a hybrid parallel training framework that integrates pipeline parallelism and data parallelism to improve the memory efficiency and scalability of large DNN training. Hippie adopts a hybrid parallel method based on hiding gradient communication, which improves the throughput and scalability of training. Meanwhile, Hippie introduces the last-stage pipeline scheduling and recomputation for specific layers to effectively reduce the memory overhead and ease the difficulties of training large DNN models on memory-constrained devices. To achieve a more reasonable evaluation of the optimization effect, we propose an index of memory efficiency (ME) to represent the tradeoff between throughput and memory overhead. We implement Hippie based on PyTorch and NCCL. Experiments on various models show that Hippie achieves above 90% scaling efficiency on a 16-GPU platform. Moreover, Hippie increases throughput by up to 80% while saving 57% of memory overhead, achieving 4.18 × memory efficiency.
The Teaching Design Methods Under Educational Psychology Based on Deep Learning and Artificial Intelligence

This study aims to evaluate the practical application value of the teaching method under the guidance of educational psychology and artificial intelligence (AI) design, taking the deep learning theory as the basis of teaching design. The research objects of this study involve all the teachers, students, and students' parents of Ningbo Middle School. The questionnaires are developed to survey the changes in the performance of students before and after the implementation of the teaching design and the satisfaction of all teachers, students, and parents to different teaching methods by comparing the two results and the satisfaction ratings. All objects in this study volunteer to participate in the questionnaire survey. The results suggest the following: (1) the effective return rates of the questionnaires to teachers, students, and parents are 97, 99, and 95%, respectively, before implementation; whereas those after implementation are 98, 99, and 99%, respectively. Comparison of the two return results suggests that there was no significant difference statistically (P > 0.05). (2) Proportion of scoring results before and after implementation is given as follows: the proportions of levels A, B, C, and D are 35, 40, 15, and 10% before implementation, respectively; while those after implementation are 47, 36, 12, and 5%, respectively. After the implementation, the proportion of level A is obviously higher than that before the implementation, and the proportions of other levels decreased in contrast to those before the implementation, showing statistically obvious differences (P < 0.05). (3) The change in the performance of each subject after 1 year implementation is significantly higher than that before the implementation, and the change in the average performance of each subject shows an upward trend. In summary, (1) the comparison on the effective return rate of the satisfaction survey questionnaire proves the feasibility of its scoring results. (2) The comparison of the survey scoring results shows that people are more satisfied with the new educational design teaching method. (3) The comparison of the change in the performance of each subject before and after the implementation indirectly reflects the drawbacks of partial subject education, indicating that the school should pay the same equal attention to every subject. (4) Due to various objective and subjective factors, the results of this study may be different from the actual situation slightly, and its accuracy has to be further explored in the future.
An Improved Mobilenet-SSD Approach For Face Detection

Mobilenet-SSD is a lightweight network with high efficiency, which is widely used in the field of real-time face detection. Whereas, it fails to achieve similar high performances compared to region-based CNN methods. In this paper, we propose an improved Mobilenet-SSD approach by optimizing the feature map and the number of prior boxes of the original Mobilenet-SSD. These changes permit the proposed approach to get a high precision and recall in face detection. To assist further with face detection, the method of non-maximum suppression is employed to remove redundant candidate boxes. To evaluate the proposed method, we conduct experiments on the well-known FDDB benchmark dataset. For 300×300 input, the proposed method achieves 91.92% average precision (AP) at 39.0 frames per second (FPS) on GEFORCE GTX 1650. Throughout experimental results, we demonstrate that our approach achieves a considerable improvement on the AP with a slightly degraded speed compared with Mobilenet-SSD. In addition, our approach also outperforms Single Shot MultiBox Detector(SSD) in terms of speed and model size.
Blockumulus: A Scalable Framework for Smart Contracts on the Cloud

Public blockchains have spurred the growing popularity of decentralized transactions and smart contracts, especially on the financial market. However, public blockchains exhibit their limitations on the transaction throughput, storage availability, and compute capacity. To avoid transaction gridlock, public blockchains impose large fees and per-block resource limits, making it difficult to accommodate the ever-growing high transaction demand. Previous research endeavors to improve the scalability and performance of blockchain through various technologies, such as side-chaining, sharding, secured off-chain computation, communication network optimizations, and efficient consensus protocols. However, these approaches have not attained a widespread adoption due to their inability in delivering a cloud-like performance, in terms of the scalability in transaction throughput, storage, and compute capacity. In this work, we determine that the major obstacle to public blockchain scalability is their underlying unstructured P2P networks. We further show that a centralized network can support the deployment of decentralized smart contracts. We propose a novel approach for achieving scalable decentralization: instead of trying to make blockchain scalable, we deliver decentralization to already scalable cloud by using an Ethereum smart contract. We introduce Blockumulus, a framework that can deploy decentralized cloud smart contract environments using a novel technique called overlay consensus. Through experiments, we demonstrate that Blockumulus is scalable in all three dimensions: computation, data storage, and transaction throughput. Besides eliminating the current code execution and storage restrictions, Blockumulus delivers a transaction latency between 2 and 5 seconds under normal load. Moreover, the stress test of our prototype reveals the ability to execute 20,000 simultaneous transactions under 26 seconds, which is on par with the average throughput of worldwide credit card transactions.
Boundary Information Progressive Guidance Network for Salient Object Detection

In recent years, the use of boundary information in saliency detection has been receiving increasing attention. In some cases, existing methods can output saliency maps with clear object boundaries by learning boundary information. However, their boundary prediction structures are generally separated from the prediction branches of the salient regions, and the resulting boundary features may not match the salient objects. We propose a simple saliency detection unit (SDU) to learn more accurate boundary features, and apply multiple such units to construct a boundary information progressive guidance network (BIPGNet). The SDU cascades the salient region and boundary detections, where the boundary features are directly extracted from the salient regions. In the BIPGNet, semantic and boundary features are progressively merged to produce complementary features. We use the complementary features of each stage in one SDU for detecting the salient objects. In addition, a novel boundary information guidance (BIG) module is designed that focuses on the boundary information in a feature layer. We apply multiple BIG modules to the complementary features at different stages. The quality of output saliency map is improved by modifying the complementary features. Experimental results demonstrate that our method can achieve better performance on five benchmark datasets, consistently surpassing 15 state-of-the-art methods. Our source code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CKYiu/BIPG</uri> .
Hierarchical Convolution Fusion-Based Adaptive Siamese Network for Infrared Target Tracking

Infrared target tracking is a key technology in many applications of computer vision, such as traffic control, pedestrian surveillance, intelligent detection and so on. However, due to the lack of color and texture information, tracking drift is easy to happen when the tracker encounters objects that are similar to the target in terms of grayscale distribution or contour shape. To address this issue, we propose a hierarchical convolution fusion-based adaptive Siamese network (HCFA-Siam) for infrared target tracking. Firstly, in order to improve the discriminative capability of the network, a new hierarchical convolution fusion network is proposed to fuse the shallow spatial information and deep semantic information. Then, a channel attention module is followed to selectively enhance the feature channels of infrared target, making the feature more discriminative. Finally, to further distinguish the target from the above-mentioned interference of appearance similarity, we add an adaptive update mechanism-based negative template pool at the front-end of the network as the second input of the template branch. The experimental results on infrared sequences show that our method outperforms other state-of-the-art trackers in both success rate and precision.
BQE-CVP: Blind Quality Evaluator for Colored Point Cloud Based on Visual Perception

The current researches on 3D point cloud quality evaluation focus on the full reference methods, but the blind quality evaluation has more advantages in actual applications. Therefore, a Blind Quality Evaluator for Colored Point Cloud (CPC) based on Visual Perception (BQE-CVP) is proposed. Firstly, considering the visual masking effect of CPC's geometric information, CPC is segmented into different regions and distributed with different weights to describe the influence of visual masking effect in CPC quality assessment. At the same time, a geometric combination feature vector is defined. Secondly, considering the visual masking effect of CPC's color information, the CPC is projected to the plane and the corresponding visual perception features are extracted. Then, the CPC's color combination feature vector is extracted in the 3D domain to better reflect the color distortion. Thirdly, the joint feature of CPC is extracted as the supplement of the geometric and color features. Finally, all the extracted features are constituted as a feature vector to predict the quality of CPC. Experimental results on three databases (CPCD2.0, IRPC and SJTU-PCQA) show that the proposed metric is better than relevant existing methods.
WDCCNet: Weighted Double-Classifier Constraint Neural Network for Mammographic Image Classification

The early detection and timely treatment of breast cancer can save lives. Mammography is one of the most efficient approaches to screening early breast cancer. An automatic mammographic image classification method could improve the work efficiency of radiologists. Current deep learning-based methods typically use the traditional softmax loss to optimize the feature extraction part, which aims to learn the features of mammographic images. However, previous studies have shown that the feature extraction part cannot learn discriminative features from complex data using the standard softmax loss. In this paper, we design a new architecture and propose respective loss functions. Specifically, we develop a double-classifier network architecture that constrains the extracted features' distribution by changing the classifiers' decision boundaries. Then, we propose the double-classifier constraint loss function to constrain the decision boundaries so that the feature extraction part can learn discriminative features. Furthermore, by taking advantage of the architecture of two classifiers, the neural network can detect the difficult-to-classify samples. We propose a weighted double-classifier constraint method to make the feature extract part pay more attention to learning difficult-to-classify samples' features. Our proposed method can be easily applied to an existing convolutional neural network to improve mammographic image classification performance. We conducted extensive experiments to evaluate our methods on three public benchmark mammographic image datasets. The results showed that our methods outperformed many other similar methods and state-of-the-art methods on the three public medical benchmarks. Our code and weights can be found on GitHub.
Coupling-modulation–mediated generation of stable entanglement of superconducting qubits via dissipation

Abstract We propose an experimentally feasible scheme for the dissipative generation of stable entanglement of superconducting qubits via coupling modulation without applying any drives on qubits and resonator. Firstly, we study the circuit of one superconducting transmission line resonator coupled to two separated qubits via two superconducting quantum interference devices (SUQIDs). By modulating the inductance of the SQUIDs via external fluxes, we can tailor an appropriate qubit-resonator coupling with both red- and blue-sideband interactions. Combined with the photon loss of the resonator, the two qubits can be autonomously steered into a long-lived entangled state with high fidelity. Moreover, we extend the model to one resonator coupled to two separated qubit chains, each of which contains N linearly coupled superconducting qubits. We show that the lossy resonator can drive the whole system into a unique dark state, i.e. , a series of N entangled pairs of qubits across the chains can be stabilized at the stationary state. So, the present work enables the preparation of a stable long-range entangled state between the two qubits in the end sites of the chains, which plays an important role for implementing scalable quantum computation and quantum communication.