Reviewerly/exHarmony huggingface dataset:
Co-Yudh: A Convolutional Neural Network (CNN)-Inspired Platform for COVID Handling and Awareness

The COVID-19 pandemic has been a menace to the World. According to WHO, a mortality rate of 1.99% is reported as of 28th November 2021. The need of the hour is to implement certain safety measures that may not eradicate but at least put a restriction on the rising number of COVID-19 cases all over the World. To ensure that the COVID-19 protocols are being abided by, a Convolutional Neural Network (CNN)-based framework "Co-Yudh" is being developed that comprises features like detecting face masks and social distancing, tracking the number of COVID-19 cases, and providing an online medical consultancy. The paper proposes two algorithms based on CNN for implementing the above features such as real-time face mask detection using the Transfer Learning approach in which the MobileNetV2 model is used which is trained on the Simulated Masked Face Dataset (SMFD). Further, the trained model is evaluated on the novel dataset-Mask Evaluation Dataset (MED). Additionally, the YOLOv4 model is used for detecting social distancing. It also uses web scraping for tracking the number of COVID-19 cases which updates on a daily basis. This is an easy-to-use framework that can be installed in various workplaces and can serve all the purposes to keep a check on the COVID-19 protocols in the area. Our preliminary results are quite satisfactory when tested against different environmental variables and show promising avenues for further exploration of the technique. The proposed framework is a more improved version of the existing works done so far.
Graph Neural Network for Higher-Order Dependency Networks

Graph neural network (GNN) has become a popular tool to analyze the graph data. Existing GNNs only focus on networks with first-order dependency, that is, conventional networks following the Markov property. However, many networks in real life own the higher-order dependency, such as click-stream data where the choice of the next page depends not only on the current page but also on previous pages. This kind of sequential data from complex systems (including natural dependencies) are often ignored by existing GNNs which makes them ineffective. To address this problem, we propose for the first time new GNN approaches for higher-order networks in this paper. First, we form sequence fragments by the current node and its predecessor nodes of different orders as candidate higher-order dependencies. When the fragment significantly affects the probability distribution of different successor nodes of the current node, we include it in the higher-order dependency set. We formulize the network with higher-order dependency as an augmented conventional first-order network, and then feed it into GNNs to derive network embeddings. Moreover, we further propose a new end-to-end GNN framework for dealing with higher-order networks directly in the model. Specifically, the higher-order dependency is used as the neighbor aggregation controller when the node is embedded and updated. In the graph convolutional layer, in addition to the first-order neighbor information, we also aggregate the middle node information from the higher-order dependency segment. We finally test the new approaches on three real networks with higher-order dependency, and compare with some state-of-the-art methods. The results show significant improvements of the new approaches which consider higher-order dependency.
Fine-Grained Ship Recognition from the Horizontal View Based on Domain Adaptation

Ship recognition is a fundamental and essential step in maritime activities, and it can be widely used in maritime rescue, vessel management, and other applications. However, most studies conducted in this area use synthetic aperture radar (SAR) images and space-borne optical images, and those studies utilizing visible images are limited to the coarse-grained level. In this study, we constructed a fine-grained ship dataset with real images and simulation images that consisted of five categories of ships. To solve the problem of low accuracy in fine-grained ship classification with different angles in visible images, a network based on domain adaptation and a transformer was proposed. Concretely, style transfer was first used to reduce the gap between the simulation images and real images. Then, with the goal of utilizing the simulation images to execute classification tasks on the real images, a domain adaptation network based on local maximum mean discrepancy (LMMD) was used to align the different domain distributions. Furthermore, considering the innate attention mechanism of the transformer, a vision transformer (ViT) was chosen as the feature extraction module to extract the fine-grained features, and a fully connected layer was used as the classifier. Finally, the experimental results showed that our network had good performance on the fine-grained ship dataset with an overall accuracy rate of 96.0%, and the mean average precision (mAP) of detecting first and then classifying with our network was 87.5%, which also verified the feasibility of using images generated by computer simulation technology for auxiliary training.
Feature selection by integrating document frequency with genetic algorithm for Amharic news document classification

Text classification is the process of categorizing documents based on their content into a predefined set of categories. Text classification algorithms typically represent documents as collections of words and it deals with a large number of features. The selection of appropriate features becomes important when the initial feature set is quite large. In this paper, we present a hybrid of document frequency (DF) and genetic algorithm (GA)-based feature selection method for Amharic text classification. We evaluate this feature selection method on Amharic news documents obtained from the Ethiopian News Agency (ENA). The number of categories used in this study is 13. Our experimental results showed that the proposed feature selection method outperformed other feature selection methods utilized for Amharic news document classification. Combining the proposed feature selection method with Extra Tree Classifier (ETC) improves classification accuracy. It improves classification accuracy up to 1% higher than the hybrid of DF, information gain (IG), chi-square (CHI), and principal component analysis (PCA), 2.47% greater than GA and 3.86% greater than a hybrid of DF, IG, and CHI.
Learning Probabilistic Box Embeddings for Effective and Efficient Ranking

Ranking has been one of the most important tasks in information retrieval. With the development of deep representation learning, many researchers propose to encode both the query and items into embedding vectors and rank the items according to the inner product or distance measures in the embedding space. However, the ranking models based on vector embeddings may have shortages in effectiveness and efficiency. For effectiveness, they lack the intrinsic ability to model the diversity and uncertainty of queries and items in ranking. For efficiency, nearest neighbor search in a large collection of item vectors can be costly. In this work, we propose to use the recently proposed probabilistic box embeddings for effective and efficient ranking, in which queries and items are parameterized as high-dimensional axis-aligned hyper-rectangles. For effectiveness, we utilize probabilistic box embeddings to model the diversity and uncertainty with the overlapping relations of the hyper-rectangles, and prove that such overlapping measure is a kernel function which can be adopted in other kernel-based methods. For efficiency, we propose a box embedding-based indexing method, which can safely filter irrelevant items and reduce the retrieval latency. We further design a training strategy to increase the proportion of irrelevant items that can be filtered by the index. Experiments on public datasets show that the box embeddings and the box embedding-based indexing approaches are effective and efficient in two ranking tasks: ad hoc retrieval and product recommendation.
Sequential Recommendation with Decomposed Item Feature Routing

Sequential recommendation basically aims to capture user evolving preference. Intuitively, a user interacts with an item usually because of some specific feature, and user evolving preference is essentially determined by a series of important features along the time line. However, existing sequential models usually represent each item by a unified embedding, which fails to distinguish item features, let along modeling the feature sequences. To bridge this gap, in this paper, we propose a novel sequential recommender model by learning the key item feature sequences underlying user behaviors, which facilitates more focused model optimization and better recommendation performance. To achieve this goal, we firstly represent each item by explicit or latent features, and then build both soft and hard models to route optimal feature sequences. More specifically, in the soft model, we design a 2D attention mechanism, which simultaneously distinguishes the importances of the items in a sequence and the features for the same item. For the hard model, we regard the feature routing problem as a Markov decision process, and propose a reinforcement learning method to generate feature sequences, which can lead to the lowered negative log-likelihood. In the experiments, we compare our model with the state-of-the-art methods based on real-world datasets, where we can empirically demonstrate 8.2 and 16.1 improvements of our model on NDCG and MRR, respectively.
Efficient Progressive High Dynamic Range Image Restoration via Attention and Alignment Network

HDR is an important part of computational photography technology. In this paper, we propose a lightweight neural network called Efficient Attention-and-alignment-guided Progressive Network (EAPNet) for the challenge NTIRE 2022 HDR Track 1 and Track 2. We introduce a multi-scale lightweight encoding module to extract features. Besides, we propose Progressive Dilated U-shape Block (PDUB) which is a progressive plug-and-play module for dynamically tuning MAccs and PSNR. Finally, we use fast and low-power feature-alignment module to deal with misalignment problem in place of the time-consuming Deformable Convolutional Network (DCN). The experiments show that our method achieves about 20× compression on MAccs with better PSNR-µ and PSNR compared to the state-of-the-art method. We got the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> place of both two tracks during the testing phase. Fig. 1 shows the visualized result of NTIRE 2022 HDR challenge.
An Intelligent Supervision for Supply Chain Finance and Logistics Based on Internet of Things

Supply chain finance and logistics activities are developing rapidly. In the economic activities of the tripartite cooperation between financial institutions, logistics enterprises, and loan enterprises, the goods will enter the logistics and supervision links of pledged goods immediately after they are postponed. It is proposed to integrate computer network communication technologies such as the Internet of Things with supply chain finance and logistics supervision to strengthen the information interaction between suppliers, which is widely used in supply chain activities to help realize the Internet finance. An intelligent supply chain supervision can implement monitoring and early warning of the time of the pledged goods in the warehouse, in transit, and during processing. Therefore, this paper proposes an intelligent supply chain supervision model by integrating supply chain finance, logistics, and pledge finance models into an operation management platform to better promote the smooth progress of supply chain finance and logistics supervision activities, which can effectively reduce various external risks, improve operational efficiency, and provide reference for supply chain finance and logistics activities.
Hybrid fuzzy-based Deep Remora Reinforcement Learning Based Task Scheduling in Heterogeneous Multicore-processor

In recent times, heterogeneous multicore processors have played a critical role in many applications because they provide better performance in adapting power constraints with higher flexibility. Hence, various techniques and optimal frameworks are introduced into the task scheduling of heterogeneous multicore processors, which is unique from application to application, but identifying the optimal task scheduler in a heterogeneous multicore processor is essential. A hybrid fuzzy-based deep remora reinforcement learning (HFDRRL) approach is introduced to achieve efficient multi-processor task scheduling. It will optimally perform the task scheduling process in a heterogeneous multicore processor to improve the overall system performance. Further, the solutions that fall within the local optimum are identified. The optimal weight solution for deep learning architecture is obtained using the novel bio-inspired remora optimization algorithm (ROA). The experimental analysis will be performed to analyze the performance of the improved approach and compared with existing approaches in terms of analyzing tasks execution time, energy consumption, utilization of task and task processing time. The proposed approach provides better performance in task scheduling of heterogeneous multicore processors.
Bayesian Regression for Solar Power Forecasting

The solar power forecasting is important factor that provides support to planning terms of power distribution organizations. The time based forecasting is feasible due to dependable outcome of solar power generation on weather status. The weather status itself is prediction method involving approach which is becoming considerably accurate these days. The power generation outcome is the multiple parameter regression model. This paper shows the experimental outcome of solar power generation forecasting with linear, ridge and Bayesian regression models. The best performing Bayesian model is compared with other existing methods in which Bayesian model outperforms in terms of mean square error for 15 minutes time interval data in batch processing approach.
Fusion of Two Novel Local Descriptors for Face Recognition in Distinct Challenges

This work suggests the 2 LBP variants so-called Neighborhood Difference LBP (ND-LBP) and Neighborhood Mean LBP (NM-LBP). In ND-LBP, the comparison is conducted among the neighbor pixels line up along the clockwise direction and in NM-LBP, the neighbors are compared with its mean. The launched descriptors utilizes the new concept as compared to LBP. In LBP, the surrounding neighbors are compared to center pixel. The histograms build from ND-LBP and NM-LBP are amalgamated for constructing the size of discriminant face descriptor so-called ND-LBP+NM-LBP. PCA is processed next for the size compaction and the 2 classifiers owned for matching are SVMs and NN. The datasets on which evaluation is carried out are ORL and GT. The ND-LBP+NM-LBP descriptor brings off splendid results than many other descriptors in MATLAB R2018a. On ORL and GT, the maximum rates attained are 96% and 86.33% by using the RBF (SVM) classifier.
Clustering by centroid drift and boundary shrinkage

Locating the centers before assigning clustering labels is a traditional routine of clustering methods, which also limits the development of new clustering ideas. In this paper, we achieve the clustering task by firstly identifying the boundary points in the feature space, and then we shrink the boundary points to allocate the un-clustered points. Concretely, we propose a Centroid Drift (CD) metric and a Boundary Shrinkage (BS) strategy to detect boundary points in the feature space and allocate labels for un-clustered points, respectively. Both the CD and BS are closely related to the pre-computed k-nearest neighbor matrix, contributing to the decrease of algorithm parameters. Moreover, the common problems of noise points and non-uniform density distribution of data points in clustering task can also be alleviated with our proposed large value suppression and normalization of k-nearest neighbor distance techniques. The experiments on synthetic datasets, real-world face image datasets and hyperspectral images demonstrate the superiorities of our proposed clustering framework.
Tracking moving target for 6 degree-of-freedom robot manipulator with adaptive visual servoing based on deep reinforcement learning PID controller

In this paper, an image-based visual servoing (IBVS) controller with a 6 degree-of-freedom robotic manipulator that tracks moving objects is investigated using the proposed Deep Q-Networks and proportional-integral-derivative (DQN-PID) controller. First, the classical IBVS controller and the problem of feature loss and large steady-state error for tracking moving targets are introduced. Then, a DQN-PID based IBVS method is proposed to solve the problem of feature loss and large steady-state error and improve the servo precision, as the existing methods are hard to use for solve the problems. Specifically, the IBVS method is inherited by our controller to build the tracking model, and a value-based reinforcement learning method is proposed as an adaptive law for dynamically tuning the PID parameters in the discrete space, which can track the moving target and keep the servo feature in the field of the camera. Finally, compared with the different existing methods, the DQN-PID based IBVS method has merits of higher accuracy and more stable tracking, or generalization.
Intertextuality and Sociolectal Differentiation in Old Saxon and Old English Verse: A Stylometric Analysis Using N-grams

This essay examines the relationship between Old Saxon and Old English poetry using computational stylometry, specifically n-gram analysis. The shared n-grams indicate that the two verse corpora are so closely related that they can be considered expressions of the same poetic language. The Old Saxon Heliand and Genesis bear greatest resemblance to the West Saxon Meters of Boethius and Genesis B. These similarities derive from the poems’ common derivation from a Saxon verse sociolect, a poetic subtradition analogous to a language dialect. There are at least two more sociolects in Old English verse, whose members correspond to the “Beowulf plus Caedmonian” works and the “Cynewulfian” poems. This is confirmed by a network analysis that treats individual Old English and Old Saxon poems as system components and models their relationship by means of a force-directed graph. These findings have important implications for our understanding of the literary histories of both poetic traditions.
Blockchain-based IoT Device Security

Due to the quick increase of IoT devices, they lack the authentication standards and administration needed to keep user data secure. Hackers could cause significant infrastructure harm by infiltrating a wide spectrum of IoT devices. Blockchain use in IoT technology guarantees trust and authentication across all IoT elements, resulting in IoT security. Blockchain is a decentralized, distributed, and shared database that enables the creation of decentralized apps. Traceability, openness, immutability, and fault tolerance are some of the qualities of this technology that help maintain data privacy in IoT scenarios and thus create a safe environment. We look at a potential strategy for securely controlling IoT devices,i.e., devices connected to the internet using smart contracts on the blockchain in this study. This paper demonstrates how the proposed system comprising of a blockchain and smart contracts work efficiently in concurrence to avoid tampering by unauthorized parties. We have employed web3 library to control the linked devices by implementing Ethereum nodes (second most popular blockchain) on Raspberry Pi simulations and node.js.
Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention

Self-Attention is a widely used building block in neural modeling to mix long-range data elements. Most self-attention neural networks employ pairwise dot-products to specify the attention coefficients. However, these methods require O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) computing cost for sequence length N. Even though some approximation methods have been introduced to relieve the quadratic cost, the performance of the dot-product approach is still bottlenecked by the lowrank constraint in the attention matrix factorization. In this paper, we propose a novel scalable and effective mixing building block called Paramixer. Our method factorizes the interaction matrix into several sparse matrices, where we parameterize the non-zero entries by MLPs with the data elements as input. The overall computing cost of the new building block is as low as O(N log N). Moreover, all factorizing matrices in Paramixer are full-rank, so it does not suffer from the low-rank bottleneck. We have tested the new method on both synthetic and various real-world long sequential data sets and compared it with several state-of-the-art attention networks. The experimental results show that Paramixer has better performance in most learning tasks.
End-to-end learning of self-rectification and self-supervised disparity prediction for stereo vision

Stereo rectification and stereo matching are two critical components for the practical application of stereo vision systems. Previous studies treat them as two individual issues. For stereo rectification, various traditional algorithms are proposed to estimate homography transformations, but the performance and the efficiency are unsatisfactory for real-time deployment. For stereo matching, disparity accuracy has been largely improved by learning based methods. However, the input data of all previous stereo networks are assumed to be a pair of offline pre-rectified images, making them invalidate for accurate matching when the stereo vision system suffers from mechanical misalignment due to external collisions or temperature variations. In this paper, we optimize these two components jointly and propose an end-to-end learning framework to achieve online self-rectification and self-supervised disparity prediction simultaneously. The overall network contains two cascaded subnetworks which enable stereo rectification and stereo matching sequentially for a pair of unrectified images. The experimental results are evaluated on both publicly available datasets and realistic scenarios. Evaluation results demonstrate that, the proposed network produces state-of-the-art results for self-rectification in terms of computation accuracy and speed, and also produces competitive disparity results with previous self-supervised methods. Therefore, the proposed design provides a more practical and efficient solution for stereo vision systems deployed on mobile platforms.
Deep Neural Network based Medical Image Steganography

Securing patients data during communication over network is very important. A DNN based solution that includes encryption, integrity protection, and steganography is proposed to retain confidential diagnostic structures of an image. The deep neural network is used for data preprocessing, hiding images as well as revealing the stego image which is carried out by preparation network, hiding network and revealing network respectively. To evaluate the proposed method, the Least Significant Bit (LSB), Discrete Coefficient Transfer (DCT), Discrete Wavelet Transfer (DWT) and Binary Pattern Complexity were used. The evaluation of the proposed code showed that the PSNR loss of 17.54% is recorded in proposed method, which is the least among traditional and recently reported methods. Proposed method also preserves the high image quality