Reviewerly/exHarmony huggingface dataset:
A Novel Segmentation Error Minimization-Based Method for Multilevel Optimal Threshold Selection Using Opposition Equilibrium Optimizer

Image segmentation is imperative for image processing applications. Thresholding technique is the easiest way of partitioning an image into different regions. Mostly, entropy-based threshold selection methods are used for multilevel thresholding. However, these methods suffer from their dependencies on spatial distribution of gray values. To solve this issue, a novel segmentation error minimization (SEM)-based method for multilevel optimal threshold selection using opposition equilibrium optimizer (OEO) is suggested. In this contribution, a new segmentation score (SS) (objective function) is derived while minimizing the segmentation error function. Our proposal is explicitly free from gray level spatial distribution of an image. Optimal threshold values are achieved by maximizing the SS (fitness value) using OEO. The key to success is the maximization of score among classes, ensuring the sharpening of the shred boundary between classes, leading to an improved threshold selection method. It is empirically demonstrated how the optimal threshold selection is made. Experimental results are presented using standard test images. Standard measures like PSNR, SSIM and FSIM are used for validation The results are compared with state-of-the-art entropy-based technique. Our method performs well both qualitatively and quantitatively. The suggested technique would be useful for biomedical image segmentation.
Blind Adaptive Structure-Preserving Imaging Enhancement for Low-Light Condition

In this letter, a novel and effective algorithm based on Retinex model is proposed for low-light image enhancement, named Blind Adaptive Structure-Preserving Image Enhancement (BASSY). The low-light image enhancement is still a challenging task because the decomposition of images into light components and reflection components is an ill-posed problem. BASSY adopts a content-adaptive guided filtering based on local variances to estimate the proper illumination map. The salient features of the proposed approach are: (1) For the illumination component, the overall structure in the low-light image is preserved and the texture details are smoothed. (2) The reflectance is estimated without logarithmic transformation to reduce the computational burden and to avoid over-smoothing the reflectance component. (3) The adaptive gamma correction for the illumination map is used to reconstruct the enhanced image. (4) BASSY can be implemented efficiently due to the low computation complexity Ο(N). Experimental results on six public datasets show that the enhanced images by the BASSY exhibit higher naturalness and better visual quality than six state-of-the-art methods.
An information theoretic parameter tuning for MEMS-based reservoir computing

With respect to the next frontier of neuromorphic sensing, we propose a parameter tuning method based on mutual information criteria for MEMS-based reservoir computing. It is required for MEMS reservoirs to tune the balance of the linear and nonlinear characteristics and to control their dynamical behaviors depending on driving forces, such as chaos and hysteresis. We focus on a pre-training method for machine learning called the intrinsic plasticity (IP) learning, and apply it to controlling the dynamical behaviors of MEMS reservoirs. First, we demonstrate simulation results for chaos suppression. Next, we applied our IP learning to parameter tuning of the MEMS-based reservoir Finally, we show that our approach can improve prediction accuracy in nonlinear transformation tasks.
Frameworks Enabling Ubiquitous Mixed Reality Applications Across Dynamically Adaptable Device Configurations

If Mixed Reality applications are supposed to become truly ubiquitous, they face the challenge of an ever evolving set of hardware and software systems - each with their own standards and APIs–that need to work together and become part of the same shared environment (the application). A unified standard is unlikely so we can not rely on a single software development stack to incorporate all necessary parts. Instead we need frameworks that are modular and flexible enough to be adapted to the needs of the application at hand and are able to incorporate a wide range of setups for devices, services, etc. We identified a set of common questions that can be used to characterize and analyze Mixed Reality applications and use these same questions to identify challenges as well as present solutions in the form of three frameworks tackling the fields of tracking and inference (UbiTrack), interaction (Ubi-Interact) and visualization (UbiVis). Tracking and inference has been addressed for quite some time now while interaction is a current topic with existing solutions. Visualization will be focused more in the future. We present several applications in development together with their future vision and explain how the frameworks help realize these and other potential apps.
Mixed reality towards environmentally sustainable manufacturing – overview, barriers and design recommendations

Mixed reality (MR) stands for the seamless combination of the physical world and virtual objects, e.g. in form of augmented reality (AR) on head mounted displays or on smartphones. The underlying technologies are more and more mature and various use cases in and beyond manufacturing can be found in industry and research. Typical applications deal with e.g. support of maintenance, quality management, assembly operations or training. However, the question arises how promising MR potentials can also be tapped towards environmental sustainability in manufacturing. Against this background, this paper identifies and analyses existing MR approaches with direct or indirect contributions, but also analyses related efforts and barriers. Based on this, feasible design strategies are suggested to support the development of future environmentally sustainability oriented MR solutions for manufacturing.
Fully Convolutional Neural Network Structure and Its Loss Function for Image Classification

The overall structure of a convolutional neural network classifier includes multiple convolutional layers and one or more linear layers.Due to the fully connected characteristics of linear layer networks, there are usually many parameters, which may easily lead to local optimization and over-fitting of the model.Therefore, its indispensability in Convolutional Neural Networks (CNNs) classifier is questionable.At the same time, the excessive number of output features of the convolutional layer will also lead to the curse of dimensionality.After verifying the redundancy characteristics of the final activation function, linear layer, and the number of channels by eigen-values analysis of latent features and experiments, we propose a Fully Convolutional Neural Network (FCNN) classifier architecture, which removes the linear layers and the corresponding activation functions from the conventional CNN classifiers.By modifying the number of output channels of the last convolutional layer, the network can be trained directly through Softmax Loss.Furthermore, a softmax-free loss (POD Loss) based on Predefined Optimal-Distribution of latent features is adopted instead of Softmax Loss to obtain better recognition performance.Experiments on multiple commonly used datasets and typical networks have proved that the network structure not only reduces the amount of parameters and calculation, but also improves the recognition rate.In the meanwhile, the adoption of POD Loss further improves the classification accuracy and robustness of the model, making them play a better synergy.Code is available in https://github.com/TianYuZu/Fully-Convolutional-Network.
A Multisize no Migration Island-Based Differential Evolution Algorithm With Removal of Ineffective Islands

The paper is a continuation of our previous research where a novel concept of multisize island model was proposed.Such multisize approach facilitates the design of island-based algorithms and brings such benefits as: improved fitness dynamics throughout the entire time of operation even without the migration of solutions between the islands.The absence of migration eliminates the need to establish the topology and the policy of migration.It also makes the efficiency of multisize island-based algorithms independent of the particular islands' size and eliminates the need of tuning the size of islands which is usually done in the case of the canonical island model.All these features indicate the superiority of the multisize island model over the canonical one.In this paper we improved earlier proposed multisize islandbased DE algorithm by adding to it the ability to automatically optimize the number of islands in operation.This feature enables the release of most computational units before the algorithm completes its operation in the case of concurrent execution of the algorithm on multiple computational units, or reduction of the algorithm running time in the case of its execution on a single computational unit.The proposed algorithm was tested by solving computationally difficult scheduling problem, which is the discrete-continuous scheduling with continuous resource discretization.
Heterogeneous graph embedding with single-level aggregation and infomax encoding

Abstract There has been an increasing interest in developing embedding methods for heterogeneous graph-structured data. The state-of-the-art approaches often adopt a bi-level aggregation scheme, where the first level aggregates information of neighbors belonging to the same type or group, and the second level employs the averaging or attention mechanism to aggregate the outputs of the first level. We find that bi-level aggregation may suffer from a down-weighting issue and overlook individual node information, especially when there is an imbalance in the number of different typed relations. We develop a new simple yet effective single-level aggregation scheme with infomax encoding, named HIME, for unsupervised heterogeneous graph embedding. Our single-level aggregation scheme performs relation-specific transformation to obtain homogeneous embeddings before aggregating information from multiple typed neighbors. Thus, it emphasizes each neighbor’s equal contribution and does not suffer from the down-weighting issue. Extensive experiments demonstrate that HIME consistently outperforms the state-of-the-art approaches in link prediction, node classification, and node clustering tasks.
A New Non-Linear Hyperbolic-Parabolic Coupled PDE Model for Image Despeckling

In this work, we propose a non-linear hyperbolic-parabolic coupled Partial Differential Equation (PDE) based model for image despeckling. Here, a separate equation is used to calculate the edge variable, which improves the quality of edge information in the despeckled images. The existence of the weak solution of the present system is achieved via Schauder fixed point theorem. We used a generalized weighted average finite-difference scheme and the Gauss-Seidel iterative technique to solve the coupled system. Numerical studies are reported to show the effectiveness of the proposed approach with respect to standard PDE-based and nonlocal methods available in the literature. Numerical experiments are performed over gray-level images degraded by artificial speckle noise. Additionally, we investigate the noise removal efficiency of the proposed algorithm when applied to real synthetic aperture radar (SAR) and Ultrasound images. Overall, our study confirms that in most cases, the present model performs better than the other PDE-based models and shows competitive performance with the nonlocal technique. To the best of our knowledge, the proposed despeckling approach is the first work that utilizes the advantage of the non-linear coupled hyperbolic-parabolic PDEs for image despeckling.
Artificial Intelligence

We currently live in the “big data” era in which we have the ability to collect massive amounts of data that are too onerous for a single individual to process. Artificial intelligence has already proven to be beneficial in a variety of industries, including technology, banking, marketing, and entertainment. For example, transportation, including the transition to self-driving, or autonomous cars, has been one of the most transformative transformations. Another example is that of Amazon. It suggests other clothes or products you might like based on some of your searches. Even Netflix predicts which movies you may like to watch next. AI is also being used in a variety of sectors that we aren't aware of, such as credit card fraud detection and mortgage loan approval, with more uses on the way. Artificial intelligence is beginning to have an impact on our lives in a variety of ways. Thus, this chapter will analyse the important issues and challenges in the area of AI.
Towards Assisted Autonomy for Supply Chain Compliance Management

In an agricultural supply chain, farmers, food processors, transportation agencies, importers, and exporters must comply with different regulations imposed by one or more jurisdictions depending on the nature of their business operations.Supply chain stakeholders conventionally transport their goods, along with the corresponding documentation via regulators for compliance checks.This is generally followed by a tedious and manual process to ensure the goods meet regulatory requirements.However, supply chain systems are changing through digitization.In digitized supply chains, data is shared with the relevant stakeholders through digital supply chain platforms, including blockchain technology.In such data-driven digital supply chains, the regulators may be able to leverage digital technologies, such as artificial intelligence and machine learning, to automate the compliance verification process.However, a barrier to progress is the risk that information will not be credible, thus reversing the gains that automation could achieve.Automating compliance based on inaccurate data may compromise the safety and credibility of the agricultural supply chain, which discourages regulators and other stakeholders from adopting and relying on automation.Within this article we consider the challenges of digital supply chains when we describe parts of the compliance management process and how it can be automated to improve the operational efficiency of agricultural supply chains.We introduce assisted autonomy as a means to pragmatically automate the compliance verification process by combining the power of digital systems while keeping the human in-the-loop.We argue that autonomous compliance is possible, but that the need for human led inspection processes will never be replaced by machines, however it can be minimised through "assisted autonomy".
A Legal Question Answering System Based on BERT 

With the development of artificial intelligence technology, intelligent question-answering systems in general fields have been widely accepted by people. However, the development of intelligent question-answering systems in limited areas is not very satisfactory. Moreover, due to the diversification of Chinese expressions, matching user input problems with prior problems is very important. This paper proposes a scheme to obtain the problem vector representation based on the BERT model. In addition, the Milvus vector search engine is used in this paper, which can not only provide store vector representation information but also calculate vector similarity. Finally, we return the answer through the database. When the threshold value of our proposed scheme is 0.2, the recall rate reaches 86%, and the mismatch rate reaches 84%. The results verify that the system has relatively good performance.
End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents

Chatbots are intelligent software built to be used as a replacement for human interaction. Existing studies typically do not provide enough support for low-resource languages like Bangla. Due to the increasing popularity of social media, we can also see the rise of interactions in Bangla transliteration (mostly in English) among the native Bangla speakers. In this paper, we propose a novel approach to build a Bangla chatbot aimed to be used as a business assistant which can communicate in low-resource langauage like Bangla and Bangla Transliteration in English with high confidence consistently. Since annotated data was not available for this purpose, we had to work on the whole machine learning life cycle (data preparation, machine learning modeling, and model deployment) using Rasa Open Source Framework, fastText embeddings, Polyglot embeddings, Flask, and other systems as building blocks. While working with the skewed annotated dataset, we try out different components and pipelines to evaluate which works best and provide possible reasoning behind the observed results. Finally, we present a pipeline for intent classification and entity extraction which achieves reasonable performance (accuracy: 83.02%, precision: 80.82%, recall: 83.02%, F1-score: 80%).
Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

In order to address the problems of Coyote Optimization Algorithm in image thresholding, such as easily falling into local optimum, and slow convergence speed, a Fuzzy Hybrid Coyote Optimization Algorithm (hereinafter referred to as FHCOA) based on chaotic initialization and reverse learning strategy is proposed, and its effect on image thresholding is verified.Through chaotic initialization, the random number initialization mode in the standard coyote optimization algorithm (COA) is replaced by chaotic sequence.Such sequence is nonlinear and long-term unpredictable, these characteristics can effectively improve the diversity of the population in the optimization algorithm.Therefore, in this paper we first perform chaotic initialization, using chaotic sequence to replace random number initialization in standard COA.By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy, a hybrid reverse learning strategy is then formed.In the process of algorithm traversal, the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively, which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence.Based on the above improvements, the coyote optimization algorithm has better global convergence and computational robustness.The simulation results show that the algorithm has better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.
Multitarget Stochastic Configuration Network and Applications

Existing stochastic configuration network (SCN)-based modeling methods are underperformed in handling multitarget regression problems. An important reason is that they ignore the intertarget correlations, which have an important effect on improving the modeling accuracy. To enhance the performance of these SCN-based models, in this article, a novel multitarget SCN (MTSCN) modeling approach is presented. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> norm of a structure matrix can be utilized to explicitly reveal the correlations between multiple targets and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> term are attached to the cost function of SCN. Considering the nonsmoothness of the constructed cost function, an alternating optimization algorithm is adopted to compute the structure matrix and the output weights of MTSCN. Then, a new supervisory mechanism is proposed to ensure the convergence of MTSCN. Finally, experimental results using the synthetic data and several real-world datasets show that the developed MTSCN is more superior to other modeling methods in resolving multitarget modeling problems.
Semantic Tradeoff for Heterogeneous Graph Embedding

Recently, many data mining models based on heterogeneous graph (HG) have emerged. Among these, HG embedding is an important and indispensable process. However, the existing HG embedding models usually use graph neural network to learn embeddings separately on different meta-paths, ignoring the fact that different meta-paths contain not only their unique semantics but also related semantics. This may result in semantic overlap or irrelevance, which needs to be a feasible and effective tradeoff for high-quality HG embedding, yet studies thereof have rarely been reported. In this article, we propose semantic tradeoff HG embedding (STHGE) by first introducing the Hilbert–Schmidt independence criterion (HSIC) as restriction. The main idea of STHGE is to regard semantic tradeoff as independence tradeoff (or correlation) between different meta-path spaces. Specifically, we first transform the original features of nodes into different meta-path feature spaces with HSIC restriction between them. Then, we use graph attention network to learn the embeddings of nodes on different meta-paths with HSIC restrictions. Finally, we concatenate the embeddings on different meta-paths to perform prediction. Experimental results on three heterogeneous datasets not only demonstrate the effectiveness of STHGE but also demonstrate that STHGE can achieve a new semantic tradeoff between different meta-paths. Furthermore, we demonstrate the robustness of STHGE.
Segmentation of multiple myeloma plasma cells in microscopy images with noisy labels

A key component towards an improved cancer diagnosis is the development of computer-assisted tools. In this article, we present the solution that won the SegPC-2021 competition for the segmentation of multiple myeloma plasma cells in microscopy images. The labels in the competition dataset were generated semi-automatically and presented noise. To deal with it, new labels were generated from existing ones, heavy image augmentation was carried out and predictions were combined by a custom ensemble strategy. These techniques, along with state-of-the-art feature extractors and instance segmentation architectures, resulted in a mean Intersection-over-Union of 0.9389 on the SegPC-2021 final test set.
U19-Net: a deep learning approach for obstacle detection in self-driving cars

Development of self-driving cars aims to drive safely from one point to another in a coordinated system where an on-board system should react and possibly alert drivers about the driving environments and possible collisions that may arise between drivers and obstacles. There are many deep learning approaches available for obstacle detection especially convolutional neural networks (CNNs) with improvement accuracy, and encoder–decoder networks are CNNs with a current attraction for researchers mainly because these models provide better results than classical statistical models for image segmentation and object classification tasks. This work proposes U19-Net an encoder–decoder deep model that explores the deep layers of a VGG19 model as an encoder following a symmetrical approach with an U-Net decoder designed for pixel-wise classifications. The U19-Net has end-to-end learning successfully effectiveness for the vehicle and pedestrian detection within the open-source Udacity dataset showing an IoU score of 87.08 and 78.18%, respectively. The proposed U19-Net is compared with five recent CNN networks using the AP metric, obtaining near results (less than 5%) for the faster R-CNN, one of the most commonly used networks for object detection.