Reviewerly/exHarmony huggingface dataset:
CS-DE: Cooperative Strategy based Differential Evolution with population diversity enhancement

Differential Evolution (DE) is one of the most effective approaches in Evolutionary Computation to tackling complex black-box optimization, however, the overall performance of a DE variant is heavily dependent on its mutation strategy and the associated parameter control schemes. According to the “No free Lunch theorem”, every mutation strategy has its own fatal defect, therefore, DE variant with a single mutation strategy can not tackle all optimization problems. Furthermore, DE variant with ensemble of mutation strategies usually needs more parameter control schemes, which inevitably decreases the practicality of it. Here in this paper, a novel Cooperative Strategy based DE (CS-DE) with population diversity enhancement is proposed, and the CS-DE algorithm has the following contributions: First, a cooperative strategy pool containing two similar mutation strategies is advanced in the algorithm, and the two mutation strategies share the same parameter control. Second, a novel grouping strategy is proposed, then the two mutation strategies can be chosen in an adaptive manner by different individuals in generating trial vectors during the evolution. Third, novel adaptation schemes for the three control parameters F,CR and PS as well as a stagnation based re-initialization mechanism are also proposed in our CS-DE algorithm. 58 benchmarks from the CEC2013 and CEC2014 test suites are employed in algorithm validation, and experiment results show the competitiveness of the CS-DE algorithm with several state-of-the-art DE variants.
Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems (CADs) for breast cancer directly extract latent features from input mammogram image and ignore the importance of morphological features. In this paper, we introduce a novel end-to-end deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predicts diagnosis results. Specifically, our method is constructed in a dual-path architecture that solves the mapping in a dual-problem manner, with an additional consideration of important shape and boundary knowledge. One path, called the Locality Preserving Learner (LPL), is devoted to hierarchically extracting and exploiting intrinsic features of the input. Whereas the other path, called the Conditional Graph Learner (CGL), focuses on generating geometrical features via modeling pixel-wise image to mask correlations. By integrating the two learners, both the cancer semantics and cancer representations are well learned, and the component learning paths in return complement each other, contributing an improvement to the mass segmentation and cancer classification problem at the same time. In addition, by integrating an automatic detection set-up, the DualCoreNet achieves fully automatic breast cancer diagnosis practically. Experimental results show that in benchmark DDSM dataset, DualCoreNet has outperformed other related works in both segmentation and classification tasks, achieving 92.27% DI coefficient and 0.85 AUC score. In another benchmark INbreast dataset, DualCoreNet achieves the best mammography segmentation (93.69% DI coefficient) and competitive classification performance (0.93 AUC score).
Incremental Generative Occlusion Adversarial Suppression Network for Person ReID

Person re-identification (re-id) suffers from the significant challenge of occlusion, where an image contains occlusions and less discriminative pedestrian information. However, certain work consistently attempts to design complex modules to capture implicit information (including human pose landmarks, mask maps, and spatial information). The network, consequently, focuses on discriminative features learning on human non-occluded body regions and realizes effective matching under spatial misalignment. Few studies have focused on data augmentation, given that existing single-based data augmentation methods bring limited performance improvement. To address the occlusion problem, we propose a novel Incremental Generative Occlusion Adversarial Suppression (IGOAS) network. It consists of 1) an incremental generative occlusion block, generating easy-to-hard occlusion data, that makes the network more robust to occlusion by gradually learning harder occlusion instead of hardest occlusion directly. And 2) a global-adversarial suppression (G&A) framework with a global branch and an adversarial suppression branch. The global branch extracts steady global features of the images. The adversarial suppression branch, embedded with two occlusion suppression module, minimizes the generated occlusion’s response and strengthens attentive feature representation on human non-occluded body regions. Finally, we get a more discriminative pedestrian feature descriptor by concatenating two branches’ features, which is robust to the occlusion problem. The experiments on the occluded dataset show the competitive performance of IGOAS. On Occluded-DukeMTMC, it achieves 60.1% Rank-1 accuracy and 49.4% mAP.
MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation

In recent years, how to strike a good trade-off between accuracy, inference speed, and model size has become the core issue for real-time semantic segmentation applications, which plays a vital role in real-world scenarios such as autonomous driving systems and drones. In this study, we devise a novel lightweight network using a multi-scale context fusion (MSCFNet) scheme, which explores an asymmetric encoder-decoder architecture to alleviate these problems. More specifically, the encoder adopts some developed efficient asymmetric residual (EAR) modules, which are composed of factorization depth-wise convolution and dilation convolution. Meanwhile, instead of complicated computation, simple deconvolution is applied in the decoder to further reduce the amount of parameters while still maintaining the high segmentation accuracy. Also, MSCFNet has branches with efficient attention modules from different stages of the network to well capture multi-scale contextual information. Then we combine them before the final classification to enhance the expression of the features and improve the segmentation efficiency. Comprehensive experiments on challenging datasets have demonstrated that the proposed MSCFNet, which contains only 1.15M parameters, achieves 71.9% Mean IoU on the Cityscapes testing dataset and can run at over 50 FPS on a single Titan XP GPU configuration.
Sparsity-Aware and Re-configurable NPU Architecture for Samsung Flagship Mobile SoC

Of late, deep neural networks have become ubiquitous in mobile applications. As mobile devices generally require immediate response while maintaining user privacy, the demand for on-device machine learning technology is on the increase. Nevertheless, mobile devices suffer from restricted hardware resources, whereas deep neural networks involve considerable computation and communication. Therefore, the implementation of a neural-network specialized hardware accelerator, generally called neural processing unit (NPU), has started to gain attention for the mobile application processor (AP). However, NPUs for commercial mobile AP face two challenges that are difficult to realize simultaneously: execution of a wide range of applications and efficient performance.In this paper, we propose a flexible but efficient NPU architecture for a Samsung flagship mobile system-on-chip (SoC). To implement an efficient NPU, we design an energy-efficient inner-product engine that utilizes the input feature map sparsity. We propose a re-configurable MAC array to enhance the flexibility of the proposed NPU, dynamic internal memory port assignment to maximize on-chip memory bandwidth utilization, and efficient architecture to support mixed-precision arithmetic. We implement the proposed NPU using the Samsung 5nm library. Our silicon measurement experiments demonstrate that the proposed NPU achieves 290.7 FPS and 13.6 TOPS/W, when executing an 8-bit quantized Inception-v3 model [1] with a single NPU core. In addition, we analyze the proposed zero-skipping architecture in detail. Finally, we present the findings and lessons learned when implementing the commercial mobile NPU and interesting avenues for future work.
Self-attention negative feedback network for real-time image super-resolution

In the field of real-time image enhancement, image super-resolution (SR) is an important research hotspot. As an image super-resolution method, deep learning can extract more stable and higher level features. However, image super-resolution processing is an ill posed problem. Due to the lack of self-attentional negative feedback mechanism, the existing methods can not better constrain the mapping space from low-resolution image to high-resolution image, so generated high-resolution (HR) image does not conform to human visual perception. Therefore, this paper proposes a self-attention negative feedback network (SRAFBN) for realizing the real-time image SR. The network model constrains the image mapping space and selects the key information of the image through the self-attention negative feedback model, so that higher quality images can be generated to meet human visual perception. Specifically, a Recurrent Neural Network (RNN) is firstly utilized to construct multiple negative feedback modules, and each module generates a HR image. Then, the key information of the generated image is extracted by the self-attention mechanism. Finally, the extracted information is fused to generate the final HR image. Experimental results prove the proposed SRAFBN network model not only has higher PSNR and SSIM, but also can reconstruct more realistic and clear real-time images.
Finding and removing Clever Hans: Using explanation methods to debug and improve deep models

Contemporary learning models for computer vision are typically trained on very large (benchmark) datasets with millions of samples. These may, however, contain biases, artifacts, or errors that have gone unnoticed and are exploitable by the model. In the worst case, the trained model does not learn a valid and generalizable strategy to solve the problem it was trained for, and becomes a “Clever Hans” predictor that bases its decisions on spurious correlations in the training data, potentially yielding an unrepresentative or unfair, and possibly even hazardous predictor. In this paper, we contribute by providing a comprehensive analysis framework based on a scalable statistical analysis of attributions from explanation methods for large data corpora. Based on a recent technique — Spectral Relevance Analysis — we propose the following technical contributions and resulting findings: (a) a scalable quantification of artifactual and poisoned classes where the machine learning models under study exhibit Clever Hans behavior, (b) several approaches we collectively denote as Class Artifact Compensation, which are able to effectively and significantly reduce a model’s Clever Hans behavior, i.e., we are able to un-Hans models trained on (poisoned) datasets, such as the popular ImageNet data corpus. We demonstrate that Class Artifact Compensation, defined in a simple theoretical framework, may be implemented as part of a neural network’s training or fine-tuning process, or in a post-hoc manner by injecting additional layers, preventing any further propagation of undesired Clever Hans features, into the network architecture. Using our proposed methods, we provide qualitative and quantitative analyses of the biases and artifacts in, e.g., the ImageNet dataset, the Adience benchmark dataset of unfiltered faces, and the ISIC 2019 skin lesion analysis dataset. We demonstrate that these insights can give rise to improved, more representative, and fairer models operating on implicitly cleaned data corpora.
XLINK

We report XLINK, a multi-path QUIC video transport solution with experiments in Taobao short videos. XLINK is designed to meet two operational challenges at the same time: (1) Optimized user-perceived quality of experience (QoE) in terms of robustness, smoothness, responsiveness, and mobility and (2) Minimized cost overhead for service providers (typically CDNs). The core of XLINK is to take the opportunity of QUIC as a user-space protocol and directly capture user-perceived video QoE intent to control multi-path scheduling and management. We overcome major hurdles such as multi-path head-of-line blocking, network heterogeneity, and rapid link variations and balance cost and performance.
SlimChain

Blockchain technology has emerged as the cornerstone of many decentralized applications operating among otherwise untrusted peers. However, it is well known that existing blockchain systems do not scale well. Transactions are often executed and committed sequentially in order to maintain the same view of the total order. Furthermore, it is necessary to duplicate both transaction data and their executions in every node in the blockchain network for integrity assurance. Such storage and computation requirements put significant burdens on the blockchain system, not only limiting system scalability but also undermining system security and robustness by making the network more centralized. To tackle these problems, in this paper, we propose SlimChain, a novel blockchain system that scales transactions through off-chain storage and parallel processing. Advocating a stateless design, SlimChain maintains only the short commitments of ledger states on-chain while dedicating transaction executions and data storage to off-chain nodes. To realize SlimChain, we propose new schemes for off-chain smart contract execution, on-chain transaction validation, and state commitment. We also propose optimizations to reduce network transmissions and a new sharding technique to improve system scalability further. Extensive experiments are conducted to validate the performance of the proposed SlimChain system. Compared with the existing systems, SlimChain reduces the on-chain storage requirements by 97% ~ 99%, while also improving the peak throughput by 1.4× ~ 15.6×.
Innovative platform operations with the use of technologies in the blockchain era

With the development of ecommerce and digital technologies, we have witnessed a boom of platform operations. Amid their strong growth prospects, it is observed that platforms have created controversial issues and challenges, which could be potentially solved by the combination of blockchain features. More and more studies exploring the implementation of blockchain in platform operations have appeared in recent years. In this paper, motivated by the industrial practices of different types of product selling and service platforms, we first conduct a holistic literature review of the most representative papers relevant to platform operations. Examining the value of different blockchain features in operations management field, we further review the related works on platform operations with blockchain technologies. Afterwards, we analyse the innovative applications and challenges in platforms such as physical and virtual product selling platforms, rental/sharing and on-demand service platforms. A future research agenda is established which lays the solid foundation for further studies on platform operations in the blockchain era.
How Can We Know <i>When</i> Language Models Know? On the Calibration of Language Models for Question Answering

Abstract Recent works have shown that language models (LM) capture different types of knowledge regarding facts or common sense. However, because no model is perfect, they still fail to provide appropriate answers in many cases. In this paper, we ask the question, “How can we know when language models know, with confidence, the answer to a particular query?” We examine this question from the point of view of calibration, the property of a probabilistic model’s predicted probabilities actually being well correlated with the probabilities of correctness. We examine three strong generative models—T5, BART, and GPT-2—and study whether their probabilities on QA tasks are well calibrated, finding the answer is a relatively emphatic no. We then examine methods to calibrate such models to make their confidence scores correlate better with the likelihood of correctness through fine-tuning, post-hoc probability modification, or adjustment of the predicted outputs or inputs. Experiments on a diverse range of datasets demonstrate the effectiveness of our methods. We also perform analysis to study the strengths and limitations of these methods, shedding light on further improvements that may be made in methods for calibrating LMs. We have released the code at https://github.com/jzbjyb/lm-calibration.
Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results

In this work, we propose hyperparameters optimization using grid search to optimize the parameters of eight existing models and apply the best parameters to predict the outcomes of HIV tests from the Ethiopian Demographic and Health Survey (EDHS), HIV/AIDS dataset. The core challenge of this work is to find the right or optimum parameter values that generate the optimal model and uncertain training computing costs and test predictive models using various values of hyperparameters. To overcome these challenges, we explore the effects of hyperparameters optimizations by applying a proposed grid search hyperparameter optimization (GSHPO) on the considered models to robust the prediction power. An extensive number of experiments are conducted to affirm the feasibility of our proposed methods. These experiments are done in two separate phases. In the first phase, we test our method with the selected models before hyperparameter optimization is applying (using the default parameters). The second phase of the experiment is done after the hyperparameter optimization is applying (using GSHPO). During the experiment, the 10-fold cross validation technique is used to solve the bias of the models. The proposed system helped to tune the hyperparameters using the grid search approach to the prediction algorithms. Several standard metrics are used to assess the method's efficiency, like accuracy, precision, recall, f1-score, AUC-ROC, MAE, RMSE, R2 and confusion matrix to compare results of each experiments. The results obtained by after applying 10-fold cross validation techniques and the proposed GSHPO are promising. Our findings suggest that the hyper-parameters of tuning models have a statistically important positive impact on the models' prediction accuracy.
Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools

Nowadays, machine learning projects have become more and more relevant to various real-world use cases. The success of complex Neural Network models depends upon many factors, as the requirement for structured and machine learning-centric project development management arises. Due to the multitude of tools available for different operational phases, responsibilities and requirements become more and more unclear. In this work, Machine Learning Operations (MLOps) technologies and tools for every part of the overall project pipeline, as well as involved roles, are examined and clearly defined. With the focus on the inter-connectivity of specific tools and comparison by well-selected requirements of MLOps, model performance, input data, and system quality metrics are briefly discussed. By identifying aspects of machine learning, which can be reused from project to project, open-source tools which help in specific parts of the pipeline, and possible combinations, an overview of support in MLOps is given. Deep learning has revolutionized the field of Image processing, and building an automated machine learning workflow for object detection is of great interest for many organizations. For this, a simple MLOps workflow for object detection with images is portrayed.
Multiple Meta-model Quantifying for Medical Visual Question Answering

Transfer learning is an important step to extract meaningful features and overcome the data limitation in the medical Visual Question Answering (VQA) task. However, most of the existing medical VQA methods rely on external data for transfer learning, while the meta-data within the dataset is not fully utilized. In this paper, we present a new multiple meta-model quantifying method that effectively learns meta-annotation and leverages meaningful features to the medical VQA task. Our proposed method is designed to increase meta-data by auto-annotation, deal with noisy labels, and output meta-models which provide robust features for medical VQA tasks. Extensively experimental results on two public medical VQA datasets show that our approach achieves superior accuracy in comparison with other state-of-the-art methods, while does not require external data to train meta-models. Source code available at: https://github.com/aioz-ai/MICCAI21_MMQ.
Space-Time-Separable Graph Convolutional Network for Pose Forecasting

Human pose forecasting is a complex structured-data sequence-modelling task, which has received increasing attention, also due to numerous potential applications. Research has mainly addressed the temporal dimension as time series and the interaction of human body joints with a kinematic tree or by a graph. This has decoupled the two aspects and leveraged progress from the relevant fields, but it has also limited the understanding of the complex structural joint spatio-temporal dynamics of the human pose.Here we propose a novel Space-Time-Separable Graph Convolutional Network (STS-GCN) for pose forecasting. For the first time, STS-GCN models the human pose dynamics only with a graph convolutional network (GCN), including the temporal evolution and the spatial joint interaction within a single-graph framework, which allows the cross-talk of motion and spatial correlations. Concurrently, STS-GCN is the first space-time-separable GCN: the space-time graph connectivity is factored into space and time affinity matrices, which bottlenecks the space-time cross-talk, while enabling full joint-joint and time-time correlations. Both affinity matrices are learnt end-to-end, which results in connections substantially deviating from the standard kinematic tree and the linear-time time series.In experimental evaluation on three complex, recent and large-scale benchmarks, Human3.6M [24], AMASS [34] and 3DPW [48], STS-GCN outperforms the state-of-the-art, surpassing the current best technique [35] by over 32% in average at the most difficult long-term predictions, while only requiring 1.7% of its parameters. We explain the results qualitatively and illustrate the graph interactions by the factored joint-joint and time-time learnt graph connections. Our source code is available at: https://github.com/FraLuca/STSGCN