Reviewerly/exHarmony huggingface dataset:
NeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History

Systematic techniques to improve quality of deep neural networks (DNNs) are critical given the increasing demand for practical applications including safety-critical ones. The key challenge comes from the little controllability in updating DNNs. Retraining to fix some behavior often has a destructive impact on other behavior, causing regressions, i.e., the updated DNN fails with inputs correctly handled by the original one. This problem is crucial when engineers are required to investigate failures in intensive assurance activities for safety or trust. Search-based repair techniques for DNNs have potentials to tackle this challenge by enabling localized updates only on &#x201C;responsible parameters&#x201D; inside the DNN. However, the potentials have not been explored to realize sufficient controllability to suppress regressions in DNN repair tasks. In this paper, we propose a novel DNN repair method that makes use of the training history for judging which DNN parameters should be changed or not to suppress regressions. We implemented the method into a tool called Neurecover and evaluated it with three datasets. Our method outperformed the existing method by achieving often less than a quarter, even a tenth in some cases, number of regressions. Our method is especially effective when the repair requirements are tight to fix specific failure types. In such cases, our method showed stably low rates (&#x003C;2 &#x0025;) of regressions, which were in many cases a tenth of regressions caused by retraining.
An Improved YOLOv3 for Foreign Objects Detection of Transmission Lines

The surrounding of transmission lines is complex, large-scale construction machinery, and smoke and fire pose threats to the circuit facilities and wires.When anomaly occurs, much time is required for the State Grid Corporation to fix it manually.To reduce the inspection burden, we propose a lightweight model running on embedded device to detect foreign objects of transmission lines.Based on the You Only Look Once (YOLO) v3, we use Mobilenetv2 instead of Darknet-53 as the backbone, and use depthwise separable convolution to replace 3 × 3 convolutional kernels in detection head, which greatly reduces the parameter size of the network.And the Fully Convolutional One-Stage Object Detection (FCOS)-like encoding and decode scheme is adopted to reduce network complexity.Meanwhile, in order to compensate for the degradation of accuracy, we have improved data augmentation, learning rate, and loss function.The experiments show that compared with other existed models, the improved YOLOv3 model has a smaller model size and higher detection speed without notably reducing detection accuracy, which has achieved the balance between detection speed and accuracy.
Weakly Supervised Liver Tumor Segmentation Using Couinaud Segment Annotation

Automatic liver tumor segmentation is of great importance for assisting doctors in liver cancer diagnosis and treatment planning. Recently, deep learning approaches trained with pixel-level annotations have contributed many breakthroughs in image segmentation. However, acquiring such accurate dense annotations is time-consuming and labor-intensive, which limits the performance of deep neural networks for medical image segmentation. We note that Couinaud segment is widely used by radiologists when recording liver cancer-related findings in the reports, since it is well-suited for describing the localization of tumors. In this paper, we propose a novel approach to train convolutional networks for liver tumor segmentation using Couinaud segment annotations. Couinaud segment annotations are image-level labels with values ranging from 1 to 8, indicating a specific region of the liver. Our proposed model, namely CouinaudNet, can estimate pseudo tumor masks from the Couinaud segment annotations as pixel-wise supervision for training a fully supervised tumor segmentation model, and it is composed of two components: 1) an inpainting network with Couinaud segment masks which can effectively remove tumors for pathological images by filling the tumor regions with plausible healthy-looking intensities; 2) a difference spotting network for segmenting the tumors, which is trained with healthy-pathological pairs generated by an effective tumor synthesis strategy. The proposed method is extensively evaluated on two liver tumor segmentation datasets. The experimental results demonstrate that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort.
Improving recommendation quality and performance of genetic-based recommender system

The recommender system came to help the user in finding the required item in a short time by filtering the available choices. This paper addresses the problem of recommending items to users by presenting new three genetic-based recommender system (GARS+, GARS++ and HGARS). HGARS is a combination of GARS+ with GARS++. It is an enhanced version of the genetic-based recommender system that works without the being a hybrid model. In the proposed algorithms, the genetic algorithm is used to find the optimal similarity function. This function depends on a liner combination of values and weights. We experimentally prove that HGARS improves the accuracy by 16.1%, the recommendation quality by 17.2% and the performance by 40%.
BUVOTS: A Blockchain Based Unmanipulated Voting Scheme

The electronic voting system is introduced in most countries to prevent vote tampering. The infrastructure of the current voting system is not transparent and it can easily be manipulated by political power and corruption. To avoid this kind of circumstance, this paper is proposing an algorithmic model-based scheme for the electronic voting system using blockchain technology to prevent the votes from tampering. The number of blocks will define the security strength of the scheme. This scheme will also reduce the complexity and hassle which the voters face during the election. Every single vote is precious and must need to be secured. The security of every single vote can be made sure by implementing this scheme. For the betterment of future voting aspects, this model-based approach will create a better mutual relationship between people, government, and nation.
Virtual Reality

We conducted a survey to inform the expansion of a Virtual Reality (VR) service in our library. The survey assessed user experience, demographics, academic interests in VR, and methods of discovery. Currently our institution offers one HTC Vive VR system that can be reserved and used by patrons within the library, but we would like to expand the service to meet the interests and needs of our patrons. We found use among all measured demographics and sufficient patron interest for us to justify expansion of our current services. The data resulting from this survey and the subsequent focus groups can be used to inform other academic libraries exploring or developing similar VR services.&#x0D;
Meta multi-task nuclei segmentation with fewer training samples

Cells/nuclei deliver massive information of microenvironment. An automatic nuclei segmentation approach can reduce pathologists' workload and allow precise of the microenvironment for biological and clinical researches. Existing deep learning models have achieved outstanding performance under the supervision of a large amount of labeled data. However, when data from the unseen domain comes, we still have to prepare a certain degree of manual annotations for training for each domain. Unfortunately, obtaining histopathological annotations is extremely difficult. It is high expertise-dependent and time-consuming. In this paper, we attempt to build a generalized nuclei segmentation model with less data dependency and more generalizability. To this end, we propose a meta multi-task learning (Meta-MTL) model for nuclei segmentation which requires fewer training samples. A model-agnostic meta-learning is applied as the outer optimization algorithm for the segmentation model. We introduce a contour-aware multi-task learning model as the inner model. A feature fusion and interaction block (FFIB) is proposed to allow feature communication across both tasks. Extensive experiments prove that our proposed Meta-MTL model can improve the model generalization and obtain a comparable performance with state-of-the-art models with fewer training samples. Our model can also perform fast adaptation on the unseen domain with only a few manual annotations. Code is available at https://github.com/ChuHan89/Meta-MTL4NucleiSegmentation.
Attention-Aligned Transformer for Image Captioning

Recently, attention-based image captioning models, which are expected to ground correct image regions for proper word generations, have achieved remarkable performance. However, some researchers have argued “deviated focus” problem of existing attention mechanisms in determining the effective and influential image features. In this paper, we present A2 - an attention-aligned Transformer for image captioning, which guides attention learning in a perturbation-based self-supervised manner, without any annotation overhead. Specifically, we add mask operation on image regions through a learnable network to estimate the true function in ultimate description generation. We hypothesize that the necessary image region features, where small disturbance causes an obvious performance degradation, deserve more attention weight. Then, we propose four aligned strategies to use this information to refine attention weight distribution. Under such a pattern, image regions are attended correctly with the output words. Extensive experiments conducted on the MS COCO dataset demonstrate that the proposed A2 Transformer consistently outperforms baselines in both automatic metrics and human evaluation. Trained models and code for reproducing the experiments are publicly available.
Adaptive aggregation-distillation autoencoder for unsupervised anomaly detection

Anomaly detection (AD) has been receiving great attention as it plays a crucial role in many areas of basic research and industrial applications. However, most existing AD methods not only rely on training on normal data, but also ignore the multi-cluster nature of normal and abnormal patterns. To overcome these limitations, this paper proposes a novel method called Adaptive Aggregation-Distillation AutoEncoder (AADAE) for unsupervised anomaly detection. AADAE is built upon the density-based landmark selection in respect to representing diverse normal patterns. During training, AADAE adaptively updates the location and quantity of landmarks. Then, an aggregation-distillation mechanism is constructed: Firstly, it aggregates the latent representations of normal and anomalous to different landmark-guided regions within the convex polygon with landmarks as vertices, which minimizes the intra-class variation and promotes the separability of normal and abnormal samples. Secondly, the distillation mechanism is applied to obtain reliable detection results when there are anomalies in the training set. The aggregation process motivates AADAE to learn the distribution of multi-cluster normal samples with the help of landmarks, which in turn facilitates the distillation process to differentiate normal from anomalies for training. Extensive empirical studies on ten datasets from different application domains demonstrate the efficiency and generalization ability of the method.
Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction

In this paper, we investigate improvements to the GEC sequence tagging architecture with a focus on ensembling of recent cutting-edge Transformer-based encoders in Large configurations.We encourage ensembling models by majority votes on span-level edits because this approach is tolerant to the model architecture and vocabulary size.Our best ensemble achieves a new SOTA result with an F 0.5 score of 76.05 on BEA-2019 (test), even without pretraining on synthetic datasets.In addition, we perform knowledge distillation with a trained ensemble to generate new synthetic training datasets, "Troy-Blogs" and "Troy-1BW".Our best single sequence tagging model that is pretrained on the generated Troy-datasets in combination with the publicly available synthetic PIE dataset achieves a near-SOTA 1 result with an F 0.5 score of 73.21 on BEA-2019 (test).The code, datasets, and trained models are publicly available. 2* This research was performed during Maksym Tarnavskyi's work on Ms.Sc.thesis
latent-GLAT: Glancing at Latent Variables for Parallel Text Generation

Recently, parallel text generation has received widespread attention due to its success in generation efficiency.Although many advanced techniques are proposed to improve its generation quality, they still need the help of an autoregressive model for training to overcome the one-to-many multi-modal phenomenon in the dataset, limiting their applications.In this paper, we propose latent-GLAT, which employs the discrete latent variables to capture word categorical information and invoke an advanced curriculum learning technique, alleviating the multi-modality problem.Experiment results show that our method outperforms strong baselines without the help of an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm.
Evaluation model of multimedia-aided teaching effect of physical education course based on random forest algorithm

Abstract The multimedia technology and computer technology supported by the development of modern science and technology provide an important platform for the development of college physical education teaching activities. To better play the role of network auxiliary teaching platform in college sports teaching and improve the effectiveness of college sports teaching, the construction method of multimedia auxiliary teaching effect evaluation model based on the random number forest algorithm is proposed. Through the specification of the random forest algorithm and the optimization of the teaching quality evaluation index, the auxiliary teaching level of the college physical education network is analyzed, and the evaluation of the multimedia auxiliary teaching effect of the physical education courses is completed. The experimental results verify the effectiveness of the evaluation model designed in this article, with a user satisfaction of 72%. Teachers and students can use the evaluation model to improve the teaching quality and teaching efficiency, improve the management work, and promote the scientific, standardization, and specialization of physical education teaching management in colleges and universities.
FedFAIM: A Model Performance-based Fair Incentive Mechanism for Federated Learning

Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning paradigm. To motivate data owners to contribute towards FL, research on FL incentive mechanisms is gaining great interest. Existing monetary incentive mechanisms generally share the same FL model with all participants regardless of their contributions. Such an assumption can be unfair towards participants who contributed more and promote undesirable free-riding, especially when the final model is of great utility value to participants. In this paper, we propose a Fairness-Aware Incentive Mechanism for federated learning (FedFAIM) to address such problem. It satisfies two types of fairness notion: 1) aggregation fairness, which determines aggregation results according to data quality; 2) reward fairness, which assigns each participant a unique model with performance reflecting his contribution. Aggregation fairness is achieved through efficient gradient aggregation which examines local gradient quality and aggregates them based on data quality. Reward fairness is achieved through an efficient Shapley value-based contribution assessment method and a novel reward allocation method based on reputation and distribution of local and global gradients. We further prove reward fairness is theoretically guaranteed. Extensive experiments show that FedFAIM provides stronger incentives than similar non-monetary FL incentive mechanisms while achieving a high level of fairness.
BIM-based immersive collaborative environment for furniture, fixture and equipment design

One of the most critical issues related to the current application of virtual reality during design appraisal is the inability to have a collaborative virtual environment where a group of geographically remote stakeholders can interact and communicate effectively in real-time. This paper addresses this shortcoming by proposing a collaborative furniture, fixture and equipment virtual environment (COFFEE) that allows concurrent multi-users to interact, communicate and collaborate virtually during the design appraisal of interior furnishings of a facility The testing of the proposed system among various construction stakeholders (n = 26) to demonstrate the usability and functionality showed high degree of acceptance by stakeholders as a result of improved visualisation, multi-user communication, and collaboration in the virtual environment. In practice COFFEE is expected to assist interior design stakeholders to make informed decisions and create shared understanding before commencement of construction activity.
Tree-based data augmentation and mutual learning for offline handwritten mathematical expression recognition

Recently, thanks to the successful application of the attention-based encoder-decoder framework, handwritten mathematical expression recognition (HMER) has achieved significant improvement. However, HMER is still a challenging task in the handwriting recognition area, which suffers from the ambiguity of handwritten symbols, the two-dimensional structure of mathematical expressions, and the lack of labeled data. In this paper, we attempt to improve the recognition performance and generalization ability of the existing state-of-the-art method from two perspectives: data augmentation and model design. We first propose a tree-based multi-level (including symbol level, sub-expression level, and image level) data augmentation strategy, which can generate many synthetic images. Then, we present a novel encoder-decoder hybrid model via tree-based mutual learning to fully utilize the complementarity between tree decoder and string decoder. Benefitting from our data augmentation strategy, we achieve 58.47%/57.82%/62.67% and 74.45% expression recognition accuracy respectively on the CROHME14/16/19 competition datasets and the OffRaSHME20 competition dataset. Moreover, tree-based data augmentation is a key technology to our champion system for the OffRaSHME20 competition. Our tree-based mutual learning method further improves the recognition accuracy to 61.63%/59.81%/64.38% and 75.68% on these datasets. Further quantitative and qualitative analyses also demonstrate the effectiveness and robustness of our proposed methods.
An Improved YOLO Algorithm for Fast and Accurate Underwater Object Detection

Due to the abundant natural resources of the underwater world, autonomous exploration using underwater robots has become an effective technological tool in recent years. Real-time object detection is critical when employing robots for independent underwater exploration. However, when a robot detects underwater, its computing power is usually limited, which makes it challenging to detect objects effectively. To solve this problem, this study presents a novel algorithm for underwater object detection based on YOLOv4-tiny to achieve better performance with less computational cost. First, a symmetrical bottleneck-type structure is introduced into the YOLOv4-tiny’s backbone network based on dilated convolution and 1 × 1 convolution. It captures contextual information in feature maps with reasonable computational cost and improves the mAP score by 8.74% compared to YOLOv4-tiny. Second, inspired by the convolutional block attention module, a symmetric FPN-Attention module is constructed by integrating the channel-attention module and the spatial-attention module. Features extracted by the backbone network can be fused more efficiently by the symmetric FPN-Attention module, achieving a performance improvement of 8.75% as measured by mAP score compared to YOLOv4-tiny. Finally, this work proposed the YOLO-UOD for underwater object detection through the fusion of the YOLOv4-tiny structure, symmetric FPN-Attention module, symmetric bottleneck-type dilated convolutional layers, and label smoothing training strategy. It can efficiently detect underwater objects in an embedded system environment with limited computing power. Experiments show that the proposed YOLO-UOD outperforms the baseline model on the Brackish underwater dataset, with a detection mAP of 87.88%, 10.5% higher than that of YOLOv4-tiny’s 77.38%, and the detection result exceeds YOLOv5s’s 83.05% and YOLOv5m’s 84.34%. YOLO-UOD is deployed on the embedded system Jetson Nano 2 GB with a detection speed of 9.24 FPS, which shows that it can detect effectively in scenarios with limited computing power.
Complete fully automatic detection, segmentation and 3D reconstruction of tumor volume for non-small cell lung cancer using YOLOv4 and region-based active contour model

• Development of an automatic system for NSCLC detection and segmentation. • YOLOv4 and region-based active contour model were used to develop. • YOLOv4 was trained on pre-trained weights to ensure good performance. • Extracted contours were rendered into to a 3D volumetric tumor. • System accuracy was verified using 3D printed images. We aim to develop a fully automatic system that will detect, segment and accurately reconstruct non-small cell lung cancer tumors into space using YOLOv4 and region-based active contour model. The system consists of two main sections which are detection and volumetric rendering. The detection section is composed of image enhancement, augmentation, labeling and localization while the volumetric rendering is mainly image filtering, tumor extraction, region-based active contour and 3D reconstruction. In this method the images are enhanced to eliminate noise before augmentation which is intended to multiply and diversify the image data. Labeling was then carried out in order to create a solid learning foundation for the localization model. Images with localized tumors were passed through smoothing filters and then clustered to extract tumor masks. Lastly contour information was obtained to render the volumetric tumor. The designed system displays a strong detection performance with a precision of 96.57 % , sensitivity and F 1 score of 97.02 % and 96.79 % respectively at a detection speed of 34 fps, prediction time per image of 21.38 ms. The system segmentation validation achieved a dice score coefficient of 92.19% on tumor extraction. A 99.74% accuracy was obtained during the verification of the method’s volumetric rendering using a 3D printed image of the rendered tumor. The rendering of the volumetric tumor was obtained at an average time of 11 seconds. This system shows a strong performance and reliability due to its ability to detect, segment and reconstruct a volumetric tumor into space with high confidence.