Reviewerly/exHarmony huggingface dataset:
Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a commuter bus

We report on the results of a measurement study carried out on a commuter bus in Dublin, Ireland using the Google/Apple Exposure Notification (GAEN) API. This API is likely to be widely used by Covid-19 contact tracing apps. Measurements were collected between 60 pairs of Android handset locations and are publicly available. We find that the attenuation level reported by the GAEN API need not increase with distance between handsets, consistent with there being a complex radio environment inside a bus caused by the metal-rich environment. Changing the people sitting in a pair of seats can cause variations of ±10dB in the attenuation level reported by the GAEN API. Applying the rule used by the Swiss Covid-19 contact tracing app to trigger an exposure notification to our bus measurements we find that no exposure notifications would have been triggered despite the fact that all pairs of handsets were within 2m of one another for at least 15 mins. Applying an alternative threshold-based exposure notification rule can somewhat improve performance to a detection rate of 5% when an exposure duration threshold of 15 minutes is used, increasing to 8% when the exposure duration threshold is reduced to 10 mins. Stratifying the data by distance between pairs of handsets indicates that there is only a weak dependence of detection rate on distance.
Handling missing data through deep convolutional neural network

The presence of missing data is a challenging issue in processing real-world datasets. It is necessary to improve the data quality by imputing the missing values so that effective learning from data can be achieved. Recently, deep learning has become the most powerful type of machine learning techniques, which can be used for discovering the hidden knowledge that exists in a large dataset to make accurate predictions. In this paper, we propose an imputation method that involves using a convolutional neural network to impute the missing values. The missing value of each instance is imputed essentially by using a trained kernel. The weights of the kernel are determined by learning from the given data that are arranged spatially in the data matrix. The kernel carries out a weighted sum of neighboring elements in an array for imputing the missing values. In addition, in the absence of the true values with which the missing values are expected to be replaced, a loss function is designed without the need to know the true value. Our method is evaluated on UCI datasets in comparison with state-of-the-art methods. The experimental results show that the proposed approach performs closely to or better than other methods.
Learning to Stylize Novel Views

We tackle a 3D scene stylization problem — generating stylized images of a scene from arbitrary novel views given a set of images of the same scene and a reference image of the desired style as inputs. Direct solution of combining novel view synthesis and stylization approaches lead to results that are blurry or not consistent across different views. We propose a point cloud-based method for consistent 3D scene stylization. First, we construct the point cloud by back-projecting the image features to the 3D space. Second, we develop point cloud aggregation modules to gather the style information of the 3D scene, and then modulate the features in the point cloud with a linear transformation matrix. Finally, we project the transformed features to 2D space to obtain the novel views. Experimental results on two diverse datasets of real-world scenes validate that our method generates consistent stylized novel view synthesis results against other alternative approaches.
Parsing Table Structures in the Wild

This paper tackles the problem of table structure parsing (TSP) from images in the wild. In contrast to existing studies that mainly focus on parsing well-aligned tabular images with simple layouts from scanned PDF documents, we aim to establish a practical table structure parsing system for real-world scenarios where tabular input images are taken or scanned with severe deformation, bending or occlusions. For designing such a system, we propose an approach named Cycle-CenterNet on the top of CenterNet with a novel cycle-pairing module to simultaneously detect and group tabular cells into structured tables. In the cycle-pairing module, a new pairing loss function is proposed for the network training. Alongside with our Cycle-CenterNet, we also present a large-scale dataset, named Wired Table in the Wild (WTW), which includes well-annotated structure parsing of multiple style tables in several scenes like photo, scanning files, web pages, etc.. In experiments, we demonstrate that our Cycle-CenterNet consistently achieves the best accuracy of table structure parsing on the new WTW dataset by 24.6% absolute improvement evaluated by the TEDS metric. A more comprehensive experimental analysis also validates the advantages of our proposed methods for the TSP task.
A Miniature Video Stabilization System for Flapping-Wing Aerial Vehicles

In this paper, a miniature video stabilization system is designed to deal with the image jitter and motion blur problem for flapping-wing aerial vehicles (FWAVs). First, a light and two-axis pan–tilt (about 13[Formula: see text]g) is built for the FWAV to counteract most of the jitter effect. Then, an electronic image stabilization method combined with a Micro-Electro Mechanical Systems (MEMSs) gyroscope is proposed to further stabilize the images. Finally, flight experiment results show that the designed video stabilization system effectively improves the quality of aerial videos.
Influence Selection for Active Learning

The existing active learning methods select the samples by evaluating the sample’s uncertainty or its effect on the diversity of labeled datasets based on different task-specific or model-specific criteria. In this paper, we propose the Influence Selection for Active Learning(ISAL) which selects the unlabeled samples that can provide the most positive influence on model performance. To obtain the influence of the unlabeled sample in the active learning scenario, we design the Untrained Unlabeled sample Influence Calculation(UUIC) to estimate the unlabeled sample’s expected gradient with which we calculate its influence. To prove the effectiveness of UUIC, we provide both theoretical and experimental analyses. Since the UUIC just depends on the model gradients, which can be obtained easily from any neural network, our active learning algorithm is task-agnostic and model-agnostic. ISAL achieves state-of-the-art performance in different active learning settings for different tasks with different datasets. Compared with previous methods, our method decreases the annotation cost at least by 12%, 13% and 16% on CIFAR10, VOC2012 and COCO, respectively.
How to Train Neural Networks for Flare Removal

When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts. Flares appear in a wide variety of patterns (halos, streaks, color bleeding, haze, etc.) and this diversity in appearance makes flare removal challenging. Existing analytical solutions make strong assumptions about the artifact’s geometry or brightness, and therefore only work well on a small subset of flares. Machine learning techniques have shown success in removing other types of artifacts, like reflections, but have not been widely applied to flare removal due to the lack of training data. To solve this problem, we explicitly model the optical causes of flare either empirically or using wave optics, and generate semi-synthetic pairs of flare-corrupted and clean images. This enables us to train neural networks to remove lens flare for the first time. Experiments show our data synthesis approach is critical for accurate flare removal, and that models trained with our technique generalize well to real lens flares across different scenes, lighting conditions, and cameras.
Distillation-guided Image Inpainting

Image inpainting methods have shown significant improvements by using deep neural networks recently. However, many of these techniques often create distorted structures or blurry inconsistent textures. The problem is rooted in the encoder layers’ ineffectiveness in building a complete and faithful embedding of the missing regions from scratch. Existing solutions like course-to-fine, progressive refinement, structural guidance, etc. suffer from huge computational overheads owing to multiple generator networks, limited ability of handcrafted features, and sub-optimal utilization of the information present in the ground truth. We propose a distillation-based approach for inpainting, where we provide direct feature level supervision while training. We deploy cross and self-distillation techniques and design a dedicated completion-block in encoder to produce more accurate encoding of the holes. Next, we demonstrate how an inpainting network’s attention module can improve by leveraging a distillation-based attention transfer technique and further enhance coherence by using a pixeladaptive global-local feature fusion. We conduct extensive evaluations on multiple datasets to validate our method. Along with achieving significant improvements over previous SOTA methods, the proposed approach’s effectiveness is also demonstrated through its ability to improve existing inpainting works.
Rethinking Deep Image Prior for Denoising

Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems. Among them, denoising is known to be particularly challenging for the DIP due to noise fitting with the requirement of an early stopping. To address the issue, we first analyze the DIP by the notion of effective degrees of freedom (DF) to monitor the optimization progress and propose a principled stopping criterion before fitting to noise without access of a paired ground truth image for Gaussian noise. We also propose the ‘stochastic temporal ensemble (STE)’ method for incorporating techniques to further improve DIP’s performance for denoising. We additionally extend our method to Poisson noise. Our empirical validations show that given a single noisy image, our method denoises the image while preserving rich textual details. Further, our approach outperforms prior arts in LPIPS by large margins with comparable PSNR and SSIM on seven different datasets.
FaaSFlow: enable efficient workflow execution for function-as-a-service

Serverless computing (Function-as-a-Service) provides fine-grain resource sharing by running functions (or Lambdas) in containers. Data-dependent functions are required to be invoked following a pre-defined logic, which is known as serverless workflows. However, our investigation shows that the traditional master-worker based workflow execution architecture performs poorly in serverless context. One significant overhead results from the master-side workflow schedule pattern, with which the functions are triggered in the master node and assigned to worker nodes for execution. Besides, the data movement between workers also reduces the throughput.
Saliency and Granularity: Discovering Temporal Coherence for Video-Based Person Re-Identification

Video-based person re-identification (ReID) matches the same people across the video sequences with rich spatial and temporal information in complex scenes. It is highly challenging to capture discriminative information when occlusions and pose variations exist between frames. A key solution to this problem rests on extracting the temporal invariant features of video sequences. In this paper, we propose a novel method for discovering temporal coherence by designing a region-level saliency and granularity mining network (SGMN). Firstly, to address the varying noisy frame problem, we design a temporal spatial-relation module (TSRM) to locate frame-level salient regions, adaptively modeling the temporal relations on spatial dimension through a probe-buffer mechanism. It avoids the information redundancy between frames and captures the informative cues of each frame. Secondly, a temporal channel-relation module (TCRM) is proposed to further mine the small granularity information of each frame, which is complementary to TSRM by concentrating on discriminative small-scale regions. TCRM exploits a one-and-rest difference relation on channel dimension to enhance the granularity features, leading to stronger robustness against misalignments. Finally, we evaluate our SGMN with four representative video-based datasets, including iLIDS-VID, MARS, DukeMTMC-VideoReID, and LS-VID, and the results indicate the effectiveness of the proposed method.
Max-Index Based Local Self-Similarity Descriptor for Robust Multi-Modal Image Registration

In order to address problems, such as radiation and intensity differences in multi-modal images, this letter proposes a novel idea that integrates maximal indices into the construction of a local self-similarity (LSS) descriptor. The LSS vectors at the same angles but different radial intervals are added to construct the max-index similarity map (MISM) and form the proposed descriptor. This novel descriptor is named max-index-based local self-similarity (MLSS). The MLSS descriptor not only captures the shape similarity between images but is also robust to radiation distortions. Furthermore, a fast and robust algorithm is introduced based on the MLSS descriptor. Comprehensive analysis of accuracy, precision, and computational efficiency shows that the proposed method outperforms five other state-of-the-art methods with stable and better performance on nine pairs of multi-modal test images.
WB-DETR: Transformer-Based Detector without Backbone

Transformer-based detector is a new paradigm in object detection, which aims to achieve pretty-well performance while eliminates the priori knowledge driven components, e.g., anchors, proposals and the NMS. DETR, the state-of-the-art model among them, is composed of three sub-modules, i.e., a CNN-based backbone and paired transformer encoder-decoder. The CNN is applied to extract local features and the transformer is used to capture global contexts. This pipeline, however, is not concise enough. In this paper, we propose WB-DETR (DETR-based detector Without Backbone) to prove that the reliance on CNN features extraction for a transformer-based detector is not necessary. Unlike the original DETR, WB-DETR is composed of only an encoder and a decoder without CNN backbone. For an input image, WB-DETR serializes it directly to encode the local features into each individual token. To make up the deficiency of transformer in modeling local information, we design an LIE-T2T (local information enhancement tokens to token) module to enhance the internal information of tokens after unfolding. Experimental results demonstrate that WB-DETR, the first pure-transformer detector without CNN to our knowledge, yields on par accuracy and faster inference speed with only half number of parameters compared with DETR baseline.
Zero-Shot Day-Night Domain Adaptation with a Physics Prior

We explore the zero-shot setting for day-night domain adaptation. The traditional domain adaptation setting is to train on one domain and adapt to the target domain by exploiting unlabeled data samples from the test set. As gathering relevant test data is expensive and sometimes even impossible, we remove any reliance on test data imagery and instead exploit a visual inductive prior derived from physics-based reflection models for domain adaptation. We cast a number of color invariant edge detectors as trainable layers in a convolutional neural network and evaluate their robustness to illumination changes. We show that the color invariant layer reduces the day-night distribution shift in feature map activations throughout the network. We demonstrate improved performance for zero-shot day to night domain adaptation on both synthetic as well as natural datasets in various tasks, including classification, segmentation and place recognition.
Support-Set Based Cross-Supervision for Video Grounding

Current approaches for video grounding propose kinds of complex architectures to capture the video-text relations, and have achieved impressive improvements. However, it is hard to learn the complicated multi-modal relations by only architecture designing in fact. In this paper, we introduce a novel Support-set Based Cross-Supervision (Sscs) module which can improve existing methods during training phase without extra inference cost. The proposed Sscs module contains two main components, i.e., discriminative contrastive objective and generative caption objective. The contrastive objective aims to learn effective representations by contrastive learning, while the caption objective can train a powerful video encoder supervised by texts. Due to the co-existence of some visual entities in both ground-truth and background intervals, i.e. mutual exclusion, naively contrastive learning is unsuitable to video grounding. We address the problem by boosting the cross-supervision with the support-set concept, which collects visual information from the whole video and eliminates the mutual exclusion of entities. Combined with the original objectives, Sscs can enhance the abilities of multi-modal relation modeling for existing approaches. We extensively evaluate Sscs on three challenging datasets, and show that our method can improve current state-of-the-art methods by large margins, especially 6.35% in terms of R1@0.5 on Charades-STA.
RL based hyper-parameters optimization algorithm (ROA) for convolutional neural network

Abstract Many real-world applications necessitate optimization in dynamic situations, where the difficulty is to locate and follow the optima of a time-dependent objective function. To solve dynamic optimization problems (DOPs), many evolutionary techniques have been created. However, more efficient solutions are still required. Recently, a new intriguing trend in dealing with optimization in dynamic environments has developed, with new reinforcement learning (RL) algorithms predicted to breathe fresh life into the DOPs community. In this paper, a new Q-learning RL-based optimization algorithm (ROA) for CNN hyperparameter optimization is proposed. Two datasets were used to test the proposed RL model (MNIST dataset, and CIFAR-10 dataset). Due to the use of RL for hyperparameter optimization, very competitive results and good performance were produced. From the experimental results, it is observed that the CNN optimized by ROA has higher accuracy than CNN without optimization. When using the MNIST dataset, it is shown that the accuracy of the CNN optimized by ROA when learning 5 epoch is 98.97%, which is greater than the 97.62% of the CNN without optimization. When using the CIFAR-10 dataset, it is shown that the accuracy of the CNN optimized by ROA when learning 10 epoch is 73.40 percent, which is greater than 71.73% of the CNN without optimization.
Do Not Rug on Me: Leveraging Machine Learning Techniques for Automated Scam Detection

Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver.
Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers

Blood cancer, or leukemia, has a negative impact on the blood and/or bone marrow of children and adults. Acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML) are two sub-types of acute leukemia. The Internet of Medical Things (IoMT) and artificial intelligence have allowed for the development of advanced technologies to assist in recently introduced medical procedures. Hence, in this paper, we propose a new intelligent IoMT framework for the automated classification of acute leukemias using microscopic blood images. The workflow of our proposed framework includes three main stages, as follows. First, blood samples are collected by wireless digital microscopy and sent to a cloud server. Second, the cloud server carries out automatic identification of the blood conditions-either leukemias or healthy-utilizing our developed generative adversarial network (GAN) classifier. Finally, the classification results are sent to a hematologist for medical approval. The developed GAN classifier was successfully evaluated on two public data sets: ALL-IDB and ASH image bank. It achieved the best accuracy scores of 98.67% for binary classification (ALL or healthy) and 95.5% for multi-class classification (ALL, AML, and normal blood cells), when compared with existing state-of-the-art methods. The results of this study demonstrate the feasibility of our proposed IoMT framework for automated diagnosis of acute leukemia tests. Clinical realization of this blood diagnosis system is our future work.
Affinity and transformed class probability-based fuzzy least squares support vector machines

Inspired by the generalization efficiency of affinity and class probability-based fuzzy support vector machine (ACFSVM), a pair of class affinity and nonlinear transformed class probability-based fuzzy least squares support vector machine approaches is proposed. The proposed approaches handle the class imbalance problem by employing cost-sensitive learning, and by utilizing the samples' class probability determined using a novel nonlinear probability equation that adjusts itself with class size. Further, the sensitivity to outliers and noise is reduced with the help of each sample's affinity to its class obtained with the help of least squares one-class support vector machine. The first proposed approach incorporates fuzzy membership values, computed using transformed class probability and class affinity, into the objective function of LS-SVM type formulation, and introduces a new cost sensitive term based on the class cardinalities to normalize the effect of the class imbalance problem. The inherent noise and outlier sensitivity of the quadratic least squares loss function of the first approach is further reduced in the second proposed approach by truncating the quadratic growth of the loss function at a specified score. Thus, the concerns due to noise and outliers are further handled at the optimization level. However, the employed truncated loss function of the second approach takes a non-convex structure, which in turn, is resolved using ConCave-Convex Procedure (CCCP) for global convergence. Numerical experiments on artificial and real-world datasets of different imbalance ratio establish the effectiveness of the proposed approaches.