Reviewerly/exHarmony huggingface dataset:
Histopathological Image Classification of Breast Cancer using EfficientNet

Deep learning algorithms help achieve promising results in diagnosis of cancer patient. However, obtaining patient confidential data and medical images of cancer patient from medical hospitals and diagnosis centre is challenging thus preventing the deep learning usage in analysis of medical data of patient. Among woman, Breast cancer is prevailing cancer and it has become common health problem. The classification of Breast cancer focuses on identifying breast tumours sub-classes. Deep learning is among the current trends in computer science research and it enhances neural networks. The layers in neural networks are more, deep which allows higher abstraction levels and help to get accurate analysis of data. Google’s recently proposed architecture-EfficientNet, has less parameters which results in faster convergence for classification using ImageNet which showed that EfficientNet outperformed prevailing architectures such as DenseNet and ResNet. This research work include comparing the performance of EfficientNetV2-B0 architecture with DenseNet and ResNet architectures and few others for multiple class tumour classification on BreakHis dataset, which contains around 8000 histopathology images of breast that vary in magnification. This research work offers a methods that is based on transfer learning that improves existing architecture in multi-class classification by relying on pretrained DCNN trained on ImageNet dataset. Pretrained weights of EfficientNetV2-B0 are transferred as initial weights and the model is fine tuned in order to identify different benign and malignant tissue samples in tumor cells and classify them into multiclass. The model proposed has been tested with optimised hyperparameters irrespective of magnification of the images and was able to achieve accuracy upto 98%. The results surpass earlier studies in terms of accuracy across the performance metrics defined for CAD systems of breast cancer based on histological images.
Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to compress the data effectively during simulation, but they also need to perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor for scientific data, and cuSZ is a version of SZ designed to take advantage of the GPU's power. At present, cuSZ's compression performance has been optimized significantly while its decompression still suffers considerably lower performance because of its sophisticated loss-less compression step-a customized Huffman decoding. In this work, we aim to significantly improve the Huffman decoding performance for cuSZ, thus improving the overall decompression performance in turn. To this end, we first investigate two state-of-the-art GPU Huffman decoders in depth. Then, we propose a deep architectural optimization for both algorithms. Specifically, we take full advantage of CUDA GPU architectures by using shared memory on decoding/writing phases, online tuning the amount of shared memory to use, improving memory access patterns, and reducing warp divergence. Finally, we evaluate our optimized decoders on an Nvidia V100 GPU using eight representative scientific datasets. Our new decoding solution obtains an average speedup of 3.64× over cuSZ's Huffman decoder and improves its overall decompression performance by 2.43× on average.
Integrated Models-Driven Framework to Generate Various Online and Print Tests

The ongoing digital transformation influence all fields from education to economics. That is why many efforts are taken to develop different business models to reflect the new reality. The e-tests are the core of contemporary e-learning and are suitable to conduct proper checks about the level of competency for different occupations. In this regard the current article describes an integrated framework based on different optimization models to select a set of questions to compose e-tests or print tests. The basic workflow algorithm of this framework is described. The framework' distinguish feature is the possibility to generate different levels of tests' complexity thanks to the specifics of formulated single criterion mixed-integer and multi-criteria models. The applicability of the integrated models-driven framework is demonstrated by using a test bank with predefined questions with different difficulties. The obtained results prove the possibility to generate tests with different degrees of complexity accordingly to the formulated models.
TPE: Lightweight Transformer Photo Enhancement Based on Curve Adjustment

In recent years, learning-based methods have made great progress in the field of photo enhancement. However, the enhancement methods rely on complex network structures and consume excessive amounts of computing resources, which greatly increases the difficulty of their deployment on lightweight devices. Additionally, the methods have poor real-time performance when processing very large resolution images. In contrast to previous works on designing structurally diverse CNN networks, photo enhancement can be achieved through a lightweight self-attentive mechanism for global-local tuning. In this paper, we design a lightweight photo enhancement tool based on Transformer; we dub the tool, TPE. TPE captures long-range dependencies among image patches and can efficiently extract the structural relationships within an image. A multistage curve adjustment strategy overcomes the problem of the limited adjustment capabilities of the global adjustment function, allowing the method to combine both global modifications and local fine-tuning. Experiments on various benchmarks demonstrate the qualitative and quantitative advantages of TPE over state-of-the-art methods in photo retouching and low-light image enhancement tasks.
Joint Self-Attention for Remote Sensing Image Matching

We propose a semantic mapping-based remote sensing image matching method, which aims to obtain the matching positions of candidate patches containing keypoints directly on the reference image, avoiding the use of cost-volume search pixel by pixel. First, a global context-fusing attention structure is created to fuse global semantic information for candidate patches with the entire sensed image. Then, a self-attention layer with semantic dependencies is proposed to extract the semantic dependencies on the reference image for cross-modal representation. The global receptive field provided by self-attention enables the proposed method to obtain the semantic mapping of candidate patches on the reference image. The experimental results show that the proposed method is insensitive to image distortion and achieves cross-modal matching of SAR-optical images with high accuracy, while still running several orders of magnitude faster. This ensures increased speed in remote sensing image analysis and pipeline processing while promoting new directions in learning-based registration.
Blockchain-Enabled Social Security Services Using Smart Contracts

With the development of the times, the existing social security system can no longer meet people's needs in terms of providing transparent, distributed sharing, tamper-proof, traceable, consensus trust and trustworthy services.Furthermore, they are centralized and subject to a single point of control and failure.In this context, we propose a consortium blockchain-based solution to establish and improve social security informatization to meet the above challenges.In this article, we present specific business situations for three important social security services.Namely, apply for social insurance, apply for social assistance and social benefits online.Our proposed solution also provides a multi-party trust and data sharing mechanism, and also demonstrates the integration of blockchain and Interplanetary File System (IPFS) storage systems to facilitate the security of approval documents, photos and videos related to the processing of social security services Accessibility and traceability.It also introduces the implementation and testing details of the algorithm in the smart contract, and expounds how to apply it in the automatic approval of social security business to reduce the workload of existing manual review.Finally, by comparing with the existing system, it is discussed that our solution has great advantages in promoting the online processing of social security business, and the safe access and traceability of approval documents, photos and videos.
GQNA: Generic Quantized DNN Accelerator With Weight-Repetition-Aware Activation Aggregating

Quantization is a prominent approach to compress model sizes of deep neural networks (DNNs), which clusters high-precision weights into a smaller set of quantization levels and represents high-precision weights by low-precision indexes. To achieve the same accuracy, nonuniform quantized DNNs (NUQ-DNNs) with unequal quantization intervals need lower index precision than uniform quantized DNNs (UQ-DNNs) with equal intervals, achieving smaller model sizes. Hence, deploying NUQ-DNNs on accelerators costs less on- and off-chip memory accesses than UQ-DNNs, which are more valuable for edge devices. However, accelerating NUQ-DNNs is nontrivial, since weight indexes cannot be directly used for computations. Previous NUQ-DNN accelerators adopt standard convolutions by decoding weight indexes into actual-weights multiplied with activations, causing abundant look-up overhead and redundant computations. In this work, we propose a weight-repetition-aware activation aggregating (WPAA) convolution approach to accelerate inference of variable-precision NUQ- and UQ-DNNs. By merging convolutions of multiple kernels, WPAA requires no look-up operation and removes redundant computations. Based on WPAA, we design a generic quantized DNN accelerator (GQNA). Furthermore, we propose a layer-adaptive kernel-reordering merging scheme to off-line adjust merging order of kernels for minimizing energy consumption of GQNA. Implemented under TSMC 28-nm technology, GQNA achieves 31.9 and 32.6 TOPS/W energy efficiency for 1-b UQ- and NUQ-VGG-16, respectively.
Orthogonal-Nets: A Large Ensemble of 2D Neural Networks for 3D Brain Tumor Segmentation

We propose Orthogonal-Nets consisting of a large number of ensembles of 2D encoder-decoder convolutional neural networks. The Orthogonal-Nets takes 2D slices of the image from axial, sagittal, and coronal views of the 3D brain volume and predicts the probability for the tumor segmentation region. The predicted probability distributions from all three views are averaged to generate a 3D probability distribution map that is subsequently used to predict the tumor regions for the 3D images. In this work, we propose a two-stage Orthogonal-Nets. Stage-I predicts the brain tumor labels for the whole 3D image using the axial, sagittal, and coronal views. The labels from the first stage are then used to crop only the tumor region. Multiple Orthogonal-Nets were then trained in stage-II, which takes only the cropped region as input. The two-stage strategy substantially reduces the computational burden on the stage-II networks and thus many Orthogonal-Nets can be used in stage-II. We used one Orthogonal-Net for stage-I and 28 Orthogonal-Nets for stage-II. The mean dice score on the testing datasets was 0.8660, 0.8776, 0.9118 for enhancing tumor, core tumor, and whole tumor respectively.
Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information

The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities. Traditional video forensics approaches can localize forgery traces using spatial-temporal analysis on relatively long video clips, while falling short in real-time forgery detection. The recent work correlates time-series camera and wireless signals to detect looped videos but cannot realize fine-grained forgery localization. To overcome these limitations, we propose Secure-Pose, which exploits the pervasive coexistence of surveillance and Wi-Fi infrastructures to defend against video forgery attacks in a real-time and fine-grained manner. We observe that coexisting camera and Wi-Fi signals convey common human semantic information and forgery attacks on video streams will decouple such information correspondence. Particularly, retrievable human pose features are first extracted from concurrent video and Wi-Fi channel state information (CSI) streams. Then, a lightweight detection network is developed to accurately discover forgery attacks and an efficient localization algorithm is devised to seamlessly track forgery traces in video streams. We implement Secure-Pose using one Logitech camera and two Intel 5300 NICs and evaluate it in different environments. Secure-Pose achieves a high detection accuracy of 98.7% and localizes abnormal objects under playback and tampering attacks.
3D Hand Pose Estimation via Regularized Graph Representation Learning

This paper addresses the problem of 3D hand pose estimation from a monocular RGB image. While previous methods have shown great success, the structure of hands has not been fully exploited, which is critical in pose estimation. To this end, we propose a regularized graph representation learning under a conditional adversarial learning framework for 3D hand pose estimation, aiming to capture structural inter-dependencies of hand joints. In particular, we estimate an initial hand pose from a parametric hand model as a prior of hand structure, which regularizes the inference of the structural deformation in the prior pose for accurate graph representation learning via residual graph convolution. To optimize the hand structure further, we propose two bone-constrained loss functions, which characterize the morphable structure of hand poses explicitly. Also, we introduce an adversarial learning framework conditioned on the input image with a multi-source discriminator, which imposes the structural constraints onto the distribution of generated 3D hand poses for anthropomorphically valid hand poses. Extensive experiments demonstrate that our model sets the new state-of-the-art in 3D hand pose estimation from a monocular image on five standard benchmarks.
Nonlinear Autoregression With Convergent Dynamics on Novel Computational Platforms

Nonlinear stochastic modeling is useful for describing complex engineering systems. Meanwhile, neuromorphic (brain-inspired) computing paradigms are being developed to tackle tasks that are challenging and resource-intensive on digital computers. An emerging scheme is reservoir computing, which exploits nonlinear dynamical systems for temporal information processing. This brief introduces reservoir computers with output feedback as stationary and ergodic infinite-order nonlinear autoregressive models. We highlight the versatility of this approach by employing classical and quantum reservoir computers to model synthetic and real datasets, further exploring their potential for control applications.
Network-Level Safety Metrics for Overall Traffic Safety Assessment: A Case Study

Driving safety analysis has recently witnessed unprecedented results due to advances in computation frameworks, connected vehicle technology, new generation sensors, and artificial intelligence (AI). Particularly, the recent advances performance of deep learning (DL) methods realized higher levels of safety for autonomous vehicles and empowered volume imagery processing for driving safety analysis. An important application of DL methods is extracting driving safety metrics from traffic imagery. However, the majority of current methods use safety metrics for micro-scale analysis of individual crash incidents or near-crash events, which does not provide insightful guidelines for the overall network-level traffic management. On the other hand, large-scale safety assessment efforts mainly emphasize spatial and temporal distributions of crashes, while not always revealing the safety violations that cause crashes. To bridge these two perspectives, we define a new set of network-level safety metrics for the overall safety assessment of traffic flow by processing imagery taken by roadside infrastructure sensors. An integrative analysis of the safety metrics and crash data reveals the insightful temporal and spatial correlation between the representative network-level safety metrics and the crash frequency. The analysis is performed using two video cameras in the state of Arizona along with a 5-year crash report obtained from the Arizona Department of Transportation. The results confirm that network-level safety metrics can be used by the traffic management teams to equip traffic monitoring systems with advanced AI-based risk analysis, and timely traffic flow control decisions.
Enabling Programmatic Data Mining as Musicking: The Fluid Corpus Manipulation Toolkit

Abstract This article presents a new software toolbox to enable programmatic mining of sound banks for musicking and musicking-driven research. The toolbox is available for three popular creative coding environments currently used by “techno-fluent” musicians. The article describes the design rationale and functionality of the toolbox and its ecosystem, then the development methodology—several versions of the toolbox have been seeded to early adopters who have, in turn, contributed to the design. Examples of these early usages are presented, and we describe some observed musical affordances of the proposed approach to the exploration and manipulation of music corpora, as well as the main roadblocks encountered. We finally reflect on a few emerging themes for the next steps in building a community around critical programmatic mining of sound banks.
Boosting Image Super-Resolution via Fusion of Complementary Information Captured by Multi-Modal Sensors

Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution sensors for a wide range of optical applications. It is noted that the costs for capturing high-resolution images in various spectral ranges are significantly different, thus it is reasonable to utilize low-cost channel images (e.g., visible/depth images) as guidance to boost the accuracy of SR results of the expensive channel (e.g., thermal images) significantly. In this paper, we attempt to leverage complementary information from low-cost channels (visible/depth) to boost image quality of an expensive channel (thermal) using fewer parameters. To this end, we first build a multi-modal imaging system and present an effective method to generate pixel-wise aligned visible and thermal images via virtual 3D viewpoint rendering. Then, we design a feature-level multispectral fusion residual network model to perform high-accuracy SR of thermal images by adaptively integrating co-occurrence features presented in multispectral images. Experimental results demonstrate that this novel approach can effectively alleviate the ill-posed inverse problem of image SR by taking into account complementary information from an additional low-cost channel, significantly outperforming state-of-the-art SR approaches in terms of both accuracy and efficiency.
Continual Learning with Knowledge Transfer for Sentiment Classification

This paper studies continual learning (CL) for sentiment classification (SC). In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where each task builds a classifier to classify the sentiment of reviews of a particular product category or domain. Two natural questions are: Can the system transfer the knowledge learned in the past from the previous tasks to the new task to help it learn a better model for the new task? And, can old models for previous tasks be improved in the process as well? This paper proposes a novel technique called KAN to achieve these objectives. KAN can markedly improve the SC accuracy of both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of KAN is demonstrated through extensive experiments (Code and data are available at: https://github.com/ZixuanKe/LifelongSentClass).
Smart Mobility Ontology: Current Trends and Future Directions

Ontology is the explicit and formal representation of the concepts in a domain and relations among them. Transportation science is a wide domain dealing with mobility over various complex and interconnected transportation systems, such as land, aviation, and maritime transport, and can take considerable advantage from ontology development. While several studies can be found in the recent literature, there exists a large potential to improve and develop a comprehensive smart mobility ontology. The current chapter aims to present different aspects of ontology development in general, such as ontology development methods, languages, tools, and software. Subsequently, it presents the currently available mobility-related ontologies developed across different domains, such as transportation, smart cities, goods mobility, and sensors. Current gaps in the available ontologies are identified, and future directions regarding ontology development are proposed that can incorporate the forthcoming autonomous and connected vehicles, mobility as a service (MaaS), and other disruptive transportation technologies and services.
An augmented reality application for jewelry shopping

Developing an augmented reality-based application to place virtual replicas of objects instead of real jewelry in jewelry shops. A virtual object is superimposed on some target that appears to be a real one. The user must wear a marker (different for the corresponding item), and that particular item will be superimposed onto that. By utilizing ICP calculation, the recreation of 3D items is conceivable. The item will be overseen itself as indicated by the client continuously by utilizing HAAR. Payment integrations, user authentication, and order functionality have been integrated to make this app as competitive as other e-commerce applications.
Texture-Based Image Transformations for Improved Deep Learning Classification

In this paper, we examine the effect of texture-based image transformation on classification performance. A novel combination of mathematical morphology operations and contrast-limited adaptive histogram equalization is proposed to enhance image textural features. The suggested operations are applied in HSV colour space, where the intensity component is separated from the colour information. Two publicly available, texture-oriented datasets are used for evaluation in this study. The KTH-TIPS2-b dataset is utilised to illustrate the general effectiveness and applicability of the proposed solution on standardized texture images. The Virus Texture dataset is subsequently used to demonstrate a statistically significant classification improvement in a particular biomedical image recognition task.