Reviewerly/exHarmony huggingface dataset:
Fractional Calculus-Based Slime Mould Algorithm for Feature Selection Using Rough Set

Features Selection (FS) techniques have been applied to several real-world applications which contain high dimension data. These FS techniques have main objectives that aim to achieve them, such as removing irrelevant features and increasing classification accuracy. This is considered a bi-objectives optimization problem that requires a suitable technique that can balance between the objectives. So, different sets of FS techniques have been developed, and those techniques that depend on meta-heuristic (MH) established their performance overall traditional FS techniques. However, these MH approaches still require more enhancement to neutralize their exploration and exploitation abilities during the searching process. Enhancing the meta-heuristic optimization algorithm using the perspective of fractional calculus (FC) is an attractive and novel approach. In this paper, the slime mould algorithm (SMA) is modified using the FC for handling the optimizer drawback of the inefficient diversification phase. As a result, a fractional-order SMA is proposed to avoid the local solutions and discover the search landscape efficiently via considering a historic memorize of agents’ positions. The proposed FOSMA is applied to extract features from a set of real-world data and increase classification accuracy. For boosting the optimizer performance while processing with these datasets, the rough set (RS) is used as the fitness function to handle the uncertainty inside the real-world data. Finally, the proposed FOSMA’s results are compared with a set of well-known FS techniques to investigate its performance. The comparison illustrates the superiority of FOSMA in providing high accuracy.
Fast CRDNN: Towards on Site Training of Mobile Construction Machines

The CRDNN is a combined neural network that can increase the holistic efficiency of torque based mobile working machines by about 9% by means of accurately detecting the truck loading cycles. On the one hand, it is a robust but offline learning algorithm so that it is more accurate and much quicker than the previous methods. However, on the other hand, its accuracy can not always be guaranteed because of the diversity of the mobile machines industry and the nature of the offline method. To address the problem, we utilize the transfer learning algorithm and the Internet of Things (IoT) technology. Concretely, the CRDNN is first trained by computer and then saved in the on-board ECU. In case that the pre-trained CRDNN is not suitable for the new machine, the operator can label some new data by our App connected to the on-board ECU of that machine through Bluetooth. With the newly labeled data, we can directly further train the pre-trained CRDNN on the ECU without overloading since transfer learning requires less computation effort than training the networks from scratch. In our paper, we prove this idea and show that CRDNN is always competent, with the help of transfer learning and IoT technology by field experiment, even the new machine may have a different distribution. Also, we compared the performance of other SOTA multivariate time series algorithms on predicting the working state of the mobile machines, which denotes that the CRDNNs are still the most suitable solution. As a by-product, we build up a human-machine communication system to label the dataset, which can be operated by engineers without knowledge about Artificial Intelligence (AI).
YouRefIt: Embodied Reference Understanding with Language and Gesture

We study the machine’s understanding of embodied reference: One agent uses both language and gesture to refer to an object to another agent in a shared physical environment. Of note, this new visual task requires understanding multimodal cues with perspective-taking to identify which object is being referred to. To tackle this problem, we introduce YouRefIt, a new crowd-sourced dataset of embodied reference collected in various physical scenes; the dataset contains 4,195 unique reference clips in 432 indoor scenes. To the best of our knowledge, this is the first embodied reference dataset that allows us to study referring expressions in daily physical scenes to understand referential behavior, human communication, and human-robot interaction. We further devise two benchmarks for image-based and video-based embodied reference understanding. Comprehensive baselines and extensive experiments provide the very first result of machine perception on how the referring expressions and gestures affect the embodied reference understanding. Our results provide essential evidence that gestural cues are as critical as language cues in understanding the embodied reference.
A Microcontroller is All You Need: Enabling Transformer Execution on Low-Power IoT Endnodes

Transformer networks have become state-of-the-art for many tasks such as NLP and are closing the gap on other tasks like image recognition. Similarly, Transformers and Attention methods are starting to attract attention on smaller-scale tasks, which fit the typical memory envelope of MCUs. In this work, we propose a new set of execution kernels tuned for efficient execution on MCU-class RISC-V and ARM Cortex-M cores. We focus on minimizing memory movements while maximizing data reuse in the Attention layers. With our library, we obtain 3.4×, 1.8×, and 2.1× lower latency and energy on 8-bit Attention layers, compared to previous state-of-the-art (SoA) linear and matrix multiplication kernels in the CMSIS-NN and PULP-NN libraries on the STM32H7 (Cortex M7), STM32L4 (Cortex M4), and GAP8 (RISC-V IMC-Xpulp) platforms, respectively. As a use case for our TinyTransformer library, we also demonstrate that we can fit a 263 kB Transformer on the GAP8 platform, outperforming the previous SoA convolutional architecture on the TinyRadarNN dataset, with a latency of 9.24 ms and 0.47 mJ energy consumption and an accuracy improvement of 3.5%.
High-Performance Software Load Balancer for Cloud-Native Architecture

Driven by increasing in the demand for cloud computing, cloud providers are constantly seeking configuration mechanisms designed to simply install a reliable and easy-to-manage cloud architecture-similar to installing an operating system on a computer using a thumb drive.Accordingly, cloud software components can be packaged into lightweight and portable containers, and then be easily deployed and managed through orchestration tools such as Kubernetes.Similarly, load balancers can also be deployed in containerized cloud environments and managed as a container, simplifying the process of scaling in or out according to the network status or amounts of incoming traffic.In this study, we implemented a containerized high-performance load balancer that distributes traffic using eBPF/XDP within the Linux kernel, which can easily be managed via Kubernetes.We compared the performance of the proposed load balancer with iptables DNAT and loopback based on the RFC2544 performance standard, and also performed tests simulating realworld traffic patterns by using IMIX traffic streams.Our experimental results indicate that the throughput performance of the proposed load balancer is considerably better than that of iptables DNAT; the difference in performance increased with decreasing packet size.The difference in performance between the loopback (representing the theoretical maximum performance limit) and the proposed load balancer was minimal.
MRI super-resolution via realistic downsampling with adversarial learning

Many deep learning (DL) frameworks have demonstrated state-of-the-art performance in the super-resolution (SR) task of magnetic resonance imaging, but most performances have been achieved with simulated low-resolution (LR) images rather than LR images from real acquisition. Due to the limited generalizability of the SR network, enhancement is not guaranteed for real LR images because of the unreality of the training LR images. In this study, we proposed a DL-based SR framework with an emphasis on data construction to achieve better performance on real LR MR images. The framework comprised two steps: (a) downsampling training using a generative adversarial network (GAN) to construct more realistic and perfectly matched LR/high-resolution (HR) pairs. The downsampling GAN input was real LR and HR images. The generator translated the HR images to LR images and the discriminator distinguished the patch-level difference between the synthetic and real LR images. (b) SR training was performed using an enhance4d deep super-resolution network (EDSR). In the controlled experiments, three EDSRs were trained using our proposed method, Gaussian blur, and k-space zero-filling. As for the data, liver MR images were obtained from 24 patients using breath-hold serial LR and HR scans (only HR images were used in the conventional methods). The k-space zero-filling group delivered almost zero enhancement on the real LR images and the Gaussian group produced a considerable number of artifacts. The proposed method exhibited significantly better resolution enhancement and fewer artifacts compared with the other two networks. Our method outperformed the Gaussian method by an improvement of 0.111 ± 0.016 in the structural similarity index and 2.76 ± 0.98 dB in the peak signal-to-noise ratio. The blind/reference-less image spatial quality evaluator metric of the conventional Gaussian method and proposed method were 46.6 ± 4.2 and 34.1 ± 2.4, respectively.
Image super-resolution based on adaptive cascading attention network

Single image super-resolution (SISR) refers to the task restoring a high-resolution (HR) image from its low-resolution (LR) counterpart. Deep convolutional neural networks (CNNs) have significantly improved SISR performance. However, the improvements usually come at the cost of the increased network size, which is not practical for the resource constrained mobile devices. In this paper, we propose a lightweight adaptive cascading attention network (ACAN) for SISR. Our contributions are threefold. First, ACAN can assign different weights to each pixel on each channel, so as to select the important information to reconstruct a high quality HR image. Second, ACAN can adaptively combine hierarchical features and effectively reuse the features. Third, ACAN can adaptively reconstruct an HR image using multi-scale global features. Compared with the other state of the art methods, ACAN achieves a better balance between performance and computational cost.
Toward a research framework to conceptualize data as a factor of production: The data marketplace perspective

The widespread use of machine learning techniques and artificial intelligence algorithms has highlighted the strategic role of data. To acquire data for training algorithms and eventually empowering the digital transformation, data marketplaces are often required to support and coordinate cross-organizational data transactions. However, the prior industry practices have suggested that the transaction costs in the data marketplaces are severely high, and the supporting infrastructure is far from mature. This paper proposes a data attributes-affected data exchange (DADE) conceptual model to understand the challenges and directions for developing data marketplaces. Specifically, our model framework is built upon two dimensions, data lifecycle maturity and data asset specificity. Based on the DADE model, we propose four approaches for developing data marketplaces and discuss future research directions with an overview of computational methods as potential technical solutions.
RAFNet: RGB-D attention feature fusion network for indoor semantic segmentation

Semantic segmentation based on the complementary information from RGB and depth images has recently gained great popularity, but due to the difference between RGB and depth maps, how to effectively use RGB-D information is still a problem. In this paper, we propose a novel RGB-D semantic segmentation network named RAFNet, which can selectively gather features from the RGB and depth information. Specifically, we construct an architecture with three parallel branches and propose several complementary attention modules. This structure enables a fusion branch and we add the Bi-directional Multi-step Propagation (BMP) strategy to it, which can not only retain the feature streams of the original RGB and depth branches but also fully utilize the feature flow of the fusion branch. There are three kinds of complementary attention modules that we have constructed. The RGB-D fusion module can effectively extract important features from the RGB and depth branch streams. The refinement module can reduce the loss of semantic information and the context aggregation module can help propagate and integrate information better. We train and evaluate our model on NYUDv2 and SUN-RGBD datasets, and prove that our model achieves state-of-the-art performances.
GNHK: A Dataset for English Handwriting in the Wild

In this paper, we present the GoodNotes Handwriting Kollection (GNHK) dataset. The GNHK dataset includes unconstrained camera-captured images of English handwritten text sourced from different regions around the world. The dataset is modeled after scene text datasets allowing researchers to investigate new localisation and text recognition techniques. We presented benchmark text localisation and recognition results with well-studied frameworks. The dataset and benchmark results are available at https://github.com/GoodNotes/GNHK-dataset.
Secure and Efficient Blockchain-Based Knowledge Sharing for Intelligent Connected Vehicles

The emergence of Intelligent Connected Vehicles (ICVs) shows great potential for future intelligent traffic systems, enhancing both traffic safety and road efficiency. However, the ICVs relying on data driven perception and driving models face many challenges, such as the lack of comprehensive knowledge to deal with complicated driving context. In this paper, we investigate cooperative knowledge sharing for ICVs. We propose a secure and efficient blockchain based knowledge sharing framework, wherein a distributed learning based scheme is utilized to enhance the efficiency of knowledge sharing and a directed acyclic graph (DAG) system is designed to guarantee the security of shared learning models. To cater for the time-intense demand of highly dynamic vehicular networks, a lightweight DAG is designed to reduce the operation latency in terms of fast consensus and authentication. Moreover, to further enhance model accuracy as well as minimizing bandwidth consumption, an adaptive asynchronous distributed learning (ADL) based scheme is proposed for model uploading and downloading. Experiment results show that the DAG based framework is lightweight and secure, which reduces both chosen and confirmation delay as well as resisting malicious attacks. In addition, the proposed adaptive ADL scheme enhances driving safety related performance compared to several existing algorithms.
Room temperature demonstration of in-materio reservoir computing for optimizing Boolean function with single-walled carbon nanotube/porphyrin-polyoxometalate composite

A method for room temperature demonstration of in-materio reservoir computing (RC) with a single-walled carbon nanotube/porphyrin-polyoxometalate network (SWNT/Por-POM) is proposed. Boolean functions of OR, AND, NOR, NAND, XOR, and XNOR, all were reconstructed with an accuracy >90% via supervised training of linear voltage readouts. The RC pre-requisite of echo-state property and recurrent connection allowed for consistent performances over multiple test datasets and time-shifted target sequences. Moreover, a non-zero machine intelligence index confirmed the presence of negative differential resistance dynamics, incorporating in SWNT/Por-POM the mathematical equivalence of additive and subtractive functions, thereby aiding the construction of such complex Boolean functions.
Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma

Although deep learning networks applied to digital images have shown impressive results for many pathology-related tasks, their black-box approach and limitation in terms of interpretability are significant obstacles for their widespread clinical utility. This study investigates the visualization of deep features (DFs) to characterize two lung cancer subtypes, adenocarcinoma and squamous cell carcinoma. It demonstrates that a subset of DFs, called prominent DFs, can accurately distinguish these two cancer subtypes. Visualization of such individual DFs allows for a better understanding of histopathologic patterns at both the whole-slide and patch levels, and discrimination of these cancer types. These DFs were visualized at the whole slide image level through DF-specific heatmaps and at tissue patch level through the generation of activation maps. In addition, these prominent DFs can distinguish carcinomas of organs other than the lung. This framework may serve as a platform for evaluating the interpretability of any deep network for diagnostic decision making.
IICNet: A Generic Framework for Reversible Image Conversion

Reversible image conversion (RIC) aims to build a reversible transformation between specific visual content (e.g., short videos) and an embedding image, where the original content can be restored from the embedding when necessary. This work develops Invertible Image Conversion Net (IIC-Net) as a generic solution to various RIC tasks due to its strong capacity and task-independent design. Unlike previous encoder-decoder based methods, IICNet maintains a highly invertible structure based on invertible neural networks (INNs) to better preserve the information during conversion. We use a relation module and a channel squeeze layer to improve the INN nonlinearity to extract cross-image relations and the network flexibility, respectively. Experimental results demonstrate that IICNet outperforms the specifically-designed methods on existing RIC tasks and can generalize well to various newly-explored tasks. With our generic IICNet, we no longer need to hand-engineer task-specific embedding networks for rapidly occurring visual content. Our source codes are available at: https://github.com/felixcheng97/IICNet.
Continuous reinforcement learning based ramp jump control for single-track two-wheeled robots

Single-track two-wheeled robots have become an important research topic in recent years, owing to their simple structure, energy savings and ability to run on narrow roads. However, the ramp jump remains a challenging task. In this study, we propose to realize a single-track two-wheeled robot ramp jump. We present a control method that employs continuous action reinforcement learning techniques for single-track two-wheeled robot control. We design a novel reward function for reinforcement learning, optimize the dimensions of the action space, and enable training under the deep deterministic policy gradient algorithm. Finally, we validate the control method through simulation experiments and successfully realize the single-track two-wheeled robot ramp jump task. Simulation results validate that the control method is effective and has several advantages over high-dimension action space control, reinforcement learning control of sparse reward function and discrete action reinforcement learning control.
Applicability of Blockchain Technology in Healthcare Industry: Applications, Challenges, and Solutions

Blockchain is widely used technology and is employed in various fields such as agriculture, Internet of Things (IoT), and Industry 4.0. The data are being stored in the form of block of chain in various repositories. Blockchain possesses several characteristics such as no involvement of third party for transaction purposes, no alteration in structure of data, sequential recording of data, and pure transparency in performing transactions. Health is considered as one of the most important factors in human life. In order to have secure health-related data, blockchain technology is widely used. It is the most suited method for recording, storing, and reading sensitive data. Security is one of the most important aspects in human life. In order to maintain the confidentiality in the credentials of patient, data are being saved in the form of record in the database. Different users such as doctors, patients, and pharmaceuticals can connect themselves and provide secure and reliable data delivery. In this chapter, we explore the applicability of blockchain technology in the healthcare sector. This chapter presents various applications, advantages, challenges, and future research strategies of blockchain technology in the healthcare industry. The chapter presents the conceptual background of blockchain technology and highlights its characteristics, architecture, classification, and the related consensus protocols. Further, this chapter highlights the need and applicability of blockchain technology in healthcare applications. Finally, this chapter outlines several unresolved challenges that can pave way for future research directions in this field.
Variability realization in model-based system engineering using software product line techniques

Efficiently handling system variants is rising of importance in industry and challenges the application of model-based systems engineering.
Evaluating transformer-based semantic segmentation networks for pathological image segmentation

Histopathology has played an essential role in cancer diagnosis. With the rapid advances in convolutional neural networks (CNN), various CNN-based automated pathological image segmentation approaches have been developed in computer-assisted pathological image analysis. In the past few years, Transformer neural networks (Transformer) have shown the unique merit of capturing the global long-distance dependencies across the entire image as a new deep learning paradigm. Such merit is appealing for exploring spatially heterogeneous pathological images. However, there have been very few, if any, studies that have systematically evaluated the current Transformer-based approaches in pathological image segmentation. To assess the performance of Transformer segmentation models on whole slide images (WSI), we quantitatively evaluated six prevalent transformer-based models on tumor segmentation, using the widely used PAIP liver histopathological dataset. For a more comprehensive analysis, we also compare the transformer-based models with six major traditional CNN-based models. The results show that the Transformer-based models exhibit a general superior performance over the CNN-based models. In particular, Segmenter, Swin-Transformer and TransUNet–all transformer-based–came out as the best performers among the twelve evaluated models.