Reviewerly/exHarmony huggingface dataset:
Depth Separable-CNN for Improved Spectral Super-Resolution

Hyperspectral Image reconstruction from RGB images is a low-cost and convenient alternative to acquiring hyperspectral images directly.The challenge in estimating the spectral response function and using it for generating the hyperspectral image data is addressed effectively by the use of Deep convolutional networks for the task.Deep networks, however, involve expensive convolutions.To address this problem, we have adapted deep convolution networks into Deep Separable convolution networks.We propose an isotropic network which consists of a cascade of blocks implementing depth separable convolution constructed by a series combination of a spatial convolution followed by convolution along the spectral domain.The two sub-blocks are termed as intra-channel sub-block and intra-pixel sub-block respectively.Intra-channel sub-block helps in capturing spatial correlation and intra-pixel subblock captures spectral correlations.The explicit use of spectral-domain convolution results in a more direct approach to interpolating information across bands and hence helps in obtaining more accurate Hyperspectral data from RGB images.In this study we have conducted experiments on three benchmark datasets to show that deep separable-CNN achieves better accuracy.These result and the ablation study demonstrate the superiority of our technique.
Integrating Cognition Cost With Reliability QoS for Dynamic Workflow Scheduling Using Reinforcement Learning

The rapid rise of microservice architecture poses severe challenges to workflow scheduling, resource allocation, and goal optimization. However, faults and failures usually happen during workflow running. To ensure the workflow's successful execution during scheduling microservices, this article proposes a dynamic workflow scheduling algorithm by integrating cognition cost and reliability QoS for using reinforcement learning (WS-CCR). First, we explore the ‘restart policy’ of containers in the Kubernetes architecture, which lays the foundation for our work that adopts the redundancy strategy to ensure workflow operation. Then we consider the cognitive cost based on the fact that users have a cognitive process for different microservices in selecting microservices. Additionally, another optimization goal is the reliability of workflows. On this basis, we design a reasonable reward function in reinforcement learning to generate dynamic strategies. Furthermore, following some generated strategies, our engine will schedule candidate microservices for tasks to execute step by step. A series of experiments on Alibaba and business areas workflows have proven the superior performance of our algorithm. Our WS-CCR can generate better Pareto solution sets than other baselines in terms of improving the reliability of running workflows. Finally, we give a case study to prove the practicability of our method.
Structure-Texture Consistent Painting Completion for Artworks

Image completion techniques have made rapid and impressive progress due to advancements in deep learning and traditional patch-based approaches. The surrounding regions of a hole played a crucial role in repairing missing areas during the restoration process. However, large holes could result in suboptimal restoration outcomes due to complex textures causing significant changes in color gradations. As a result, they led to errors such as color discrepancies, blurriness, artifacts, and unnatural colors. Additionally, recent image completion approaches focused mainly on scenery and face images with fewer textures. Given these observations, we present a structure-texture consistent completion approach for filling large holes with detailed textures. Our method focuses on improving image completion in the context of artworks, which are expressions of creativity and often have more diverse structures and textures from applying paint to a surface using brush strokes. To handle the unique challenges posed by artwork, we segment non-homogeneous areas and then use Cohesive Laplacian Fusion to complete the texture of the remaining missing segmented area. This technique involves detecting changes in base structures and textures using multiple matched patches to achieve more consistent results. The experimental results show that our proposed method is competitive and outperforms state-of-the-art methods in missing regions and color gradations of art paintings.
Embeddings for Automatic Short Answer Grading: A Scoping Review

Automatic grading of short answers is an important task in computer-assisted assessment (CAA). Recently, embeddings, as semantic-rich textual representations, have been increasingly used to represent short answers and predict the grade. Despite the recent trend of applying embeddings in automatic short answer grading (ASAG), there are no systematic reviews of literature reporting on their usage. Therefore, following the PRISMA-ScR guidelines, this scoping review summarizes relevant literature on the use of embeddings in ASAG, and reports on the current state of the art in that research area and on the identified knowledge gaps. We searched seven research databases for the relevant journal, conference, and workshop papers published from 2016 to 2021. The inclusion criteria were based on the type of publication, its venue ranking, study focus, and evaluation methods. Upon the full-text screening, 17 articles were included in the scoping review. Among these, most of the articles used word embeddings, mainly to estimate the similarity of student and model answers using the cosine similarity measure or to initialize a neural network-based classification model. The contribution of embeddings to the performance of ASAG models compared to nonembedding features is inconclusive. Models employing embeddings were mostly evaluated on four public ASAG datasets using earlier ASAG methods as baselines. We summarize the reported evaluation results and draw conclusions on the performance of the state-of-the-art ASAG models.
Active and Passive Physical Attacks on Neural Network Accelerators

This article emphasizes the growing importance of studying defenses for physical attacks on neural network accelerators, and describes approaches based on side-channel attacks and fault-injection attacks.
Locally Adaptive Channel Attention-Based Spatial–Spectral Neural Network for Image Deblurring

Recently, due to the rapid development of deep neural networks in the field of computer vision, many studies have been conducted in the field of image deblurring. However, previous methods cannot attain satisfactory deblurring performance in object boundaries and local regions containing image details in restored results. This paper proposes a new method that uses a locally adaptive channel attention module for a spectral–spatial network to resolve the problem of single-image deblurring. Unlike existing methods, our proposed method consists of a spectral restorer and spatial restorer that adopt a locally adaptive attention mechanism in the spectral–spatial domains. In addition, unlike a conventional spectral–spatial network that only considers the magnitude of the frequency coefficients, the proposed method uses both the magnitude and phase of the frequency coefficients in the training stage. Our locally adaptive channel attention spectral–spatial network can focus on informative channels that are closely related to blur artifacts. Specifically, the spatial restorer, which guides the intensity of the blur image to fit the ground truth by exploring the interdependencies of feature channels, can efficiently restore scene characteristics while the spectral restorer, which guides the magnitude and phase of the blur image’s frequency coefficients to fit those of the ground truth by exploring the interdependencies of feature channels, fine-tunes the details of structures. The experimental results show that the proposed method outperformed the deblurring results compared to benchmark methods in terms of both qualitative evaluation and quantitative metrics.
Comparison of Real-Time and Batch Job Recommendations

Collaborative filtering recommendation systems are traditionally trained in a batch manner and are designed to produce personalized recommendations for a large number of users at the same time. However, in many industrial use cases, it is reasonable to produce recommendations in real time, taking account of very recent user interactions. In this work, we present the implementation of batch and real-time recommendation systems using the example of the RP3Beta model, a simple scalable graph-based model that outperforms multiple more advanced models. Our approach can be utilized by any recommendation system if user-to-item recommendations can be obtained based on item-to-item recommendations. We show that it covers multiple common recommendation models, especially collaborative filtering approaches where user features are not available. We also provide the results of A/B tests comparing these two approaches in a real-world scenario of a job recommendation task, conducted with almost 200,000 OLX users. We report at least 10% more users applying for recommended job ads when using a real-time instead of a batch approach. We believe that our results can help other organizations to take informed decisions about whether to make the effort of moving from a batch to a real-time recommendation setting.
iLEDGER: A Lightweight Blockchain Framework with New Consensus Method for IoT Applications

Considering the growth of the IoT network, there is a demand for a decentralized solution. Incorporating the blockchain technology will eliminate the challenges faced in centralized solutions, such as i) high infrastructure, ii) maintenance cost, iii) lack of transparency, iv) privacy, and v) data tampering. Blockchain-based IoT network allows businesses to access and share the IoT data within their organization without a central authority. Data in the blockchain are stored as blocks, which should be validated and added to the chain, for this consensus mechanism plays a significant role. However, existing methods are not designed for IoT applications and lack features like i) decentralization, ii) scalability, iii) throughput, iv) faster convergence, and v) network overhead. Moreover, current blockchain frameworks failed to support resource-constrained IoT applications. In this paper, we proposed a new consensus method (WoG) and a lightweight blockchain framework (iLEDGER), mainly for resource-constrained IoT applications in a permissioned environment. The proposed work is tested in an application that tracks the assets using IoT devices (Raspberry Pi 4 and RFID). Furthermore, the proposed consensus method is analyzed against benign failures, and performance parameters such as CPU usage, memory usage, throughput, transaction execution time, and block generation time are compared with state-of-the-art methods.
Predictive Ability Tests with Possibly Overlapping Models

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Multi-strategy self-learning particle swarm optimization algorithm based on reinforcement learning

The trade-off between exploitation and exploration is a dilemma inherent to particle swarm optimization (PSO) algorithms. Therefore, a growing body of PSO variants is devoted to solving the balance between the two. Among them, the method of self-adaptive multi-strategy selection plays a crucial role in improving the performance of PSO algorithms but has yet to be well exploited. In this research, with the aid of the reinforcement learning technique to guide the generation of offspring, a novel self-adaptive multi-strategy selection mechanism is designed, and then a multi-strategy self-learning PSO algorithm based on reinforcement learning (MPSORL) is proposed. First, the fitness value of particles is regarded as a set of states that are divided into several state subsets non-uniformly. Second, the ε-greedy strategy is employed to select the optimal strategy for each particle. The personal best particle and the global best particle are then updated after executing the strategy. Subsequently, the next state is determined. Thus, the value of the Q-table, as a scheme adopted in self-learning, is reshaped by the reward value, the action and the state in a non-stationary environment. Finally, the proposed algorithm is compared with other state-of-the-art algorithms on two well-known benchmark suites and a real-world problem. Extensive experiments indicate that MPSORL has better performance in terms of accuracy, convergence speed and non-parametric tests in most cases. The multi-strategy selection mechanism presented in the manuscript is effective.
How Much Do Swarm Intelligence and Evolutionary Algorithms Improve Over a Classical Heuristic From 1960?

Hundreds of variants of Swarm Intelligence or Evolutionary Algorithms are proposed each year and numerous competitions and comparisons between algorithms may suggest rapid improvement in the field. However, such comparisons are often done between a limited number of methods and are based on averaged ranks of algorithms. This way they measure whether one method is on average ranked better than the others, without giving any information on how much improvement is in fact obtained. In this study we show a general comparison between 69 algorithms, starting from methods proposed in the 1960’s up to variants developed in the early 2020’s, on single-objective static numerical problems. Algorithms are compared on searching for a minimum of 30 different 50-dimensional mathematical functions, and on 22 real-world problems. We focus on the relative improvement achieved by various algorithms over a single-solution based method proposed in 1960 by Howard Rosenbrock. We find that the general improvement of Evolutionary Algorithms over Rosenbrock’s algorithm is relatively limited. It is high for the artificial benchmarks, for which many Evolutionary Algorithms find solutions 10 times closer to the global optimum in terms of fitness than Rosenbrock’s algorithm, but much lower for real-world problems. Improvement is also higher when performance averaged over many runs is compared, but lower when the best results from multiple runs are analyzed. In the last case, only the best Evolutionary Algorithms are able to find solutions of a “typical” real-world problem that are 2–3 times better in terms of fitness than those found by Rosenbrock’s algorithm. The relative improvement of recently proposed algorithms is not much better than the improvement achieved by algorithms proposed over a decade ago.
Attention Mechanism, Linked Networks, and Pyramid Pooling Enabled 3D Biomedical Image Segmentation

We present an approach to detect and segment tumorous regions of the brain by establishing three varied segmentation architectures for multiclass semantic segmentation along with data-specific customizations like residual blocks, soft attention mechanism, pyramid pooling, linked architecture, and 3D compatibility to work with 3D brain MRI images. The proposed segmentation architectures namely, Attention Residual U-Net 3D (ARU-Net 3D), LinkNet 3D and PSPNet 3D, segment the MRI images and successfully isolate three classes of tumors. By assigning pixel probabilities, each of these models differentiates between pixels belonging to tumorous and non-tumorous regions of the brain. By experimenting and observing the performance of each of the three architectures using metrics like Dice Loss and Dice Score, on the BraTS2020 dataset, we successfully establish the following validation scores: 0.6488, 0.6485, and 0.6501 for the ARU-Net, LinkNet, and PSPNet 3D architectures respectively. Code has been made available at: https://github.com/indiradutta/BrainTumorSeg-III-D.
Research on Precision Motion Control of Micro-motion Platform Based on Uncalibrated Visual Servo

A visual servo control algorithm based on back propagation (BP) neural network and genetic algorithm is proposed for the problem of slow recognition speed and low accuracy of visual servo control. The algorithm models the robot and image complex Jacobi matrix to get the initial BP neural network visual servo controller, and then uses genetic algorithm to train the initial weights and thresholds of the controller to finally obtain the hybrid optimized visual control model, which can effectively combine the good global search ability of genetic algorithm with the accurate local search function of BP neural network. The experimental results show that the convergence speed is accelerated while the error is reduced to 4.6% of the original one, which provides a simple and effective method for robot control.
Detecting Design Patterns in UML Class Diagram Images using Deep Learning

Detecting software design pattern is an important part of software reverse engineering because design patterns can provide the most intuitive design idea of software products, which can be useful for maintenance engineers. In past studies, a lot of approaches have been proposed to detect design patterns, and the machine learning-based approach is a new trend in recent years. In this paper, we propose a preliminary idea of a deep learning-based approach to detect design patterns from UML class diagrams of software products, which can be used in some cases that traditional approaches may not work. We propose an overall process, which is divided into preparation phase and application phase. In preparation phase, we train a deep learning-based classifier to do the image classification task. In application phase, users may input the UML class diagram of a micro-architecture into the model and get the pattern it belongs to. We conduct a preliminary experiment to show the effectiveness of our approach, we train a Convolutional Neural Network (CNN) as the classifier and test it on our image dataset, which is constructed with UML images we collected from the Internet. We also use Gradient-weighted Class Activation Mapping (Grad-CAM) to do the visualization and use it to explain why our approach works. Lastly, we analyze the potential advantages and disadvantages of our approach.
Homography-Based Visual Servoing for Eye-in-Hand Robots with Unknown Feature Positions

Visual servoing can effectively control robots using visual feedback information to improve the intelligence and reliability. In most existing dynamics-based image-based visual servoing methods, a restricted condition that the number of the feature points is no larger than 3 is needed to achieve pixel error convergence, which makes them difficlut to achieve three-dimensional (3-D) pose control since at least 4 feature points on a plane are needed to determine the unique end-effector pose. This paper puts forward to a dynamics-based adaptive homography-based visual servoing (HBVS) controller to regulate robot manipulators with eye-in-hand monocular cameras to the desired pose under unknown but constant feature positions. The uncertain depth is represented as a linear form of its position parameters in the Cartesian space, and a composite learning technique is applied to guarantee parameter convergence under a much weaker condition of interval excitation than persistent excitation, resulting in exact depth estimation and 3-D pose regulation. Experiments on a collaborative robot with 7 degrees of freedom named Franka Emika Panda have illustrated the effectiveness of the proposed method.
A DDQN-Based Path Planning Method for Multi-UAVs in a 3D Indoor Environment

Path planning is one of the most essential for unmanned aerial vehicle (UAV) autonomous navigation. The deep Q-network (DQN) method is widely used for solving the path planning problem, but most researchers simplify the scene into the 2D environment with a single UAV and ignore the fact that there are always multi-UAVs working in 3D environments. Therefore, a double deep Q-network (DDQN) based global path planning algorithm for multi-UAVs in a 3D indoor environment is proposed in this paper. Firstly, the double deep Q-network was designed to approximate the action of multi-UAVs. The 3D space is discretized into grids while each gird is a basic unit of path planning and the whole grid map is the input for the neural network. Then, a continual reward function generated by building an artificial potential field was determined to replace the traditional sparse reward function. Moreover, the action selection strategy is used to determine the current optimal action so that multi-UAVs are able to find the path to reach target points in a simulated indoor environment and avoid crashing into each other and obstacles at the same time. Finally, the experiment verifies the effectiveness of the proposed method. The simulation result demonstrates that the agents can effectively avoid local optimal solution and correctly predict the global optimal action.
Async-Fork: Mitigating Query Latency Spikes Incurred by the Fork-based Snapshot Mechanism from the OS Level

In-memory key-value stores (IMKVSes) serve many online applications. They generally adopt the fork-based snapshot mechanism to support data backup. However, this method can result in query latency spikes because the engine is out-of-service for queries during the snapshot. In contrast to existing research optimizing snapshot algorithms, we address the problem from the operating system (OS) level, while keeping the data persistent mechanism in IMKVSes unchanged. Specifically, we first study the impact of the fork operation on query latency. Based on findings in the study, we propose Async-fork, which performs the fork operation asynchronously to reduce the out-of-service time of the engine. Async-fork is implemented in the Linux kernel and deployed into the online Redis database in public clouds. Our experiment results show that Async-fork can significantly reduce the tail latency of queries during the snapshot.
Identifying Disease of Interest With Deep Learning Using Diagnosis Code

Autoencoder (AE) is one of the deep learning techniques that uses an artificial neural network to reconstruct its input data in the output layer. We constructed a novel supervised AE model and tested its performance in the prediction of a co-existence of the disease of interest only using diagnostic codes.Diagnostic codes of one million randomly sampled patients listed in the Korean National Health Information Database in 2019 were used to train, validate, and test the prediction model. The first used AE solely for a feature engineering tool for an input of a classifier. Supervised Multi-Layer Perceptron (sMLP) was added to train a classifier to predict a binary level with latent representation as an input (AE + sMLP). The second model simultaneously updated the parameters in the AE and the connected MLP classifier during the learning process (End-to-End Supervised AE [EEsAE]). We tested the performances of these two models against baseline models, eXtreme Gradient Boosting (XGB) and naïve Bayes, in the prediction of co-existing gastric cancer diagnosis.The proposed EEsAE model yielded the highest F1-score and highest area under the curve (0.86). The EEsAE and AE + sMLP gave the highest recalls. XGB yielded the highest precision. Ablation study revealed that iron deficiency anemia, gastroesophageal reflux disease, essential hypertension, gastric ulcers, benign prostate hyperplasia, and shoulder lesion were the top 6 most influential diagnoses on performance.A novel EEsAE model showed promising performance in the prediction of a disease of interest.