Reviewerly/exHarmony huggingface dataset:
SmartRecepies

Recommender systems are now part of our daily life more than ever and most users are confronted with some form of recommendation on a daily basis. As users of such systems, we don't need to actively seek for new content, but let it be comfortably recommended to us instead. One of the important parts of our lives that is yet to be covered in this way is the domain of cooking. A traditional dilemma of a person, who is currently in the process of shopping for food is "What else should I buy, so that I can cook something new?" In another words, the person either has to look for novel recipes upfront (which does not have to correspond with available ingredients in the shop), or buy ingredients intuitively (which does not have to correspond with recipes).
Comparative Study for Deep Reinforcement Learning with CNN, RNN, and LSTM in Autonomous Navigation

Reinforcement learning algorithms are one of the popular machine learning methods in recent years. Unlike deep learning (DL) algorithms, it does not require a data set during the training phase, increasing its popularity. Today, it offers successful results especially in the navigation of autonomous robots and in solving complex problems such as video games. The feedback process is also known as a reward or called a penalty. Given Agents and environment, it is determined which action to take. In this article, the performance of three different DL algorithms has been compared using the PyGame simulator. In the simulator created using CNN, RNN and LSTM deep learning algorithms, it is aimed that the representative will learn to move without hitting four different fixed obstacles. While creating the training environment, the movement of an autonomous robot in the field without getting stuck in obstacles was simulated. Separate results of each algorithm were reported in the training results. As a result of these reports, it has been observed that the LSTM algorithm is more successful than the others.
Stake Centralization in Decentralized Proof-of-Stake Blockchain Network

Proof-of-Stake consensus algorithm solves major drawbacks of PoW blockchain network including high energy consumption and low transaction capacity. However, PoS still faces centralization problems like PoW. In this paper, we propose an economic framework for PoS blockchain network based on staking pool optimization problem and evaluate stake concentration in the network. We prove that stake concentration is encouraged in a finite staking pool PoS blockchain network, but continued consolidation among staking pools is limited and a stable equilibrium always exists where no staking pool has incentive to deviate. Further, we prove that lower pool fixed cost and higher transaction fee raise stable equilibrium pool number and lead to higher network decentralization level. As an extension, we show that wealth discrepancy exacerbates stake centralization while tilted stakeholder selection function mitigates stake centralization.
The efficient consensus algorithm for land record management system

Abstract This research presents a scalable property/land registration framework. It is based on blockchain technology, where all registry offices come together on a single framework based on blockchain to improve the registration process. This paper mainly focuses on the problems in the land record and revenue sectors. In this paper, we explore the use of blockchain technology in current business scenarios. We discuss issues such as data security, privacy, and, more significantly, the absence of a shared forum amongst the involved organizations. This research proposes an efficient consensus mechanism to evaluate the performance of a blockchain-based implementation of a land revenue &amp; recording automation system. The proposed Modified Round Robin Consensus Algorithm (MRRCA) reduced the 30% - 40% message overhead in committing a transaction to the network and thus adding the block to the blockchain.
Decoupled gradient harmonized detector for partial annotation: Application to signet ring cell detection

Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients. Due to lack of public dataset and expert-level annotations, automatic detection on signet ring cell (SRC) has not been thoroughly investigated. In MICCAI DigestPath2019 challenge, apart from foreground (SRC region)-background (normal tissue area) class imbalance, SRCs are partially annotated due to costly medical image annotation, which introduces extra label noise. To address the issues simultaneously, we propose Decoupled Gradient Harmonizing Mechanism (DGHM) and embed it into classification loss, denoted as DGHM-C loss. Specifically, besides positive (SRCs) and negative (normal tissues) examples, we further decouple noisy examples from clean examples and harmonize the corresponding gradient distributions in classification respectively. Without whistles and bells, we achieved the 2nd place in the challenge. Ablation studies and controlled label missing rate experiments demonstrate that DGHM-C loss can bring substantial improvement in partially annotated object detection.
Efficient Revocable Attribute-Based Encryption with Hidden Policies

We present a novel Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, which bridges the gap between highly dynamic (industrial) environments and resource-constrained devices. Our construction combines outsourced-decryption, hidden policies and revocation to cope with the requirements posed by such environments. In contrast to existing schemes, which typically rely on composite order bilinear groups, we present a scheme in prime order groups. The resulting scheme is more efficient as it relies on smaller group orders. We prove our scheme is secure under the Symmetric External Diffie-Hellman (SXDH) assumption. Lastly, we compare our scheme against existing schemes and provide timing results of our software implementation. Our evaluation shows that the proposed scheme is flexible enough for the targeted environment while improving performance by an order of magnitude.
Generative adversarial network for low‐light image enhancement

Low-light image enhancement is rapidly gaining research attention due to the increasing demands of extreme visual tasks in various applications. Although numerous methods exist to enhance image qualities in low light, it is still undetermined how to trade-off between the human observation and computer vision processing. In this work, an effective generative adversarial network structure is proposed comprising both the densely residual block (DRB) and the enhancing block (EB) for low-light image enhancement. Specifically, the proposed end-to-end image enhancement method, consisting of a generator and a discriminator, is trained using the hyper loss function. The DRB adopts the residual and dense skip connections to connect and enhance the features extracted from different depths in the network while the EB receives unique multi-scale features to ensure feature diversity. Additionally, increasing the feature sizes allows the discriminator to further distinguish between fake and real images from the patch levels. The merits of the loss function are also studied to recover both contextual and local details. Extensive experimental results show that our method is capable of dealing with extremely low-light scenes and the realistic feature generator outperforms several state-of-the-art methods in a number of qualitative and quantitative evaluation tests.
Intelligent Obstacle Avoiding AGV Using Vector Field Histogram and Supervisory Control

Abstract Automated Guided Vehicles are used to help the process of carrying materials from one place to another. AGVs are widely used in the materials handling process in different industries hence, the reasoning behind their extremely wide usage in industries such as Automotive, manufacturing, Medical, etc. This research deals with the obstacle avoidance of Automated Guided Vehicles (AGV). This paper propose an algorithm that helps to build obstacle-free trajectories for AGV that leave the original track and again get back to the original track by avoiding the obstacle. The new path also have to act according to the dynamic &amp; kinematic constraints of the AGV. The proposed method will be validated using simulations.
Development path based on the equalization of public services under the management mode of the Internet of Things

The Internet of Things management model is a management process and scientific management model, which effectively connects the end of perception and the end of communication. The effective linkage between the cloud and the application side promotes technological innovation, management innovation and system innovation. This article aims to study the development of equalization of public services under the management mode of the Internet of Things. This article uses quantitative analysis and comparative research methods to analyze the imbalance of public services, and compares the level of equalization of public services in different time periods, conceives a way to develop equalization of public services under the management mode of the Internet of Things, and discusses Its construction method and its impact on equalization. The experimental results of this paper show that the development of equalization of public services under the management mode of the Internet of Things is getting better and better. The government's investment in public services has increased by 24%. The applications of various applications in the Internet of Things have slowly penetrated, but because the Internet of Things is still It is in its infancy, so the growth rate is a bit slow. Among them, the application of education in the Internet of Things has increased from 25% to 29%.
Introduction to Symbolic Plan and Goal Recognition

Plan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents. This sy
GAN based Data Augmentation for Enhanced Tumor Classification

With the incredible breakthrough of medical imaging intertwined with the computer aided diagnosis and artificial intelligence paved a way for the early detection of tumor. Though Deep Neural Networks is the new paradigm in the field of computer vision yet, they are highly reliable on large dataset to avoid overfitting. To overcome this problem, our work focuses on data augmentation, a quintessential solution to handle the inadequate medical data. This paper involves conventional data augmentation using affine transformations. The conventionally augmented data are further synthesized using General Adversarial Networks (GANs). These methods are employed on the benchmark breast tumor datasets namely MIAS, DDSM and INBreast. The classification of benchmark dataset resulted in an accuracy of 69.85%, which are increased by the conventional data augmentation techniques to 88%. The synthesized image when merged with the original benchmark dataset and the conventional augmented images outperformed the others with an increased accuracy of 94%.
Group-Development Psychology Training: The Perceived Effects on Agile Software-Development Teams

Research has shown that the maturity of small workgroups from a psychological perspective is intimately connected to team agility. We, therefore, tested if agile team members appreciated group development psychology training. Our results show that the participating teams seem to have a very positive view of group development training and state that they now have a new way of thinking about teamwork and new tools to deal with team-related problems. We, therefore, see huge potential in training agile teams in group development psychology since the positive effects might span over the entire software development organization.
Recapturing the Structure of Group of Units of Any Finite Commutative Chain Ring

A finite ring with an identity whose lattice of ideals forms a unique chain is called a finite chain ring. Let R be a commutative chain ring with invariants p,n,r,k,m. It is known that R is an Eisenstein extension of degree k of a Galois ring S=GR(pn,r). If p−1 does not divide k, the structure of the unit group U(R) is known. The case (p−1)∣k was partially considered by M. Luis (1991) by providing counterexamples demonstrated that the results of Ayoub failed to capture the direct decomposition of U(R). In this article, we manage to determine the structure of U(R) when (p−1)∣k by fixing Ayoub’s approach. We also sharpen our results by introducing a system of generators for the unit group and enumerating the generators of the same order.
Sentiment analysis of healthcare Tweets using SVM Classifier

Web-based social media offers countless opportunities to have their valuable suggestions for experiences. Twitter can be used effectively in current circumstances and with open modern developments to collect data as opposed to social relationship data in traditional techniques. Twitter is perhaps the most loyal member's online large frequency advantage that encourages individuals to communicate and collect data. Here we discuss which country/place frequently speaks about specific diseases. We can forecast where the disease's impact will be important. This system handles the problems that occur during the Sentiment Analysis cycle spent; ongoing tweeting are known to be rich sources of data for evaluation extraction and feeling study. The basic aim of this system is to carry out a continuous sentimental review of the tweets removed by twitter.
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning

While Graph Neural Network (GNN) has shown superiority in learning node representations of homogeneous graphs, leveraging GNN on heterogeneous graphs remains a challenging problem. The dominating reason is that GNN learns node representations by aggregating neighbors' information regardless of node types. Some work is proposed to alleviate such issue by exploiting relations or meta-path to sample neighbors with distinct categories, then use attention mechanism to learn different importance for different categories. However, one limitation is that the learned representations for different types of nodes should own different feature spaces, while all the above work still project node representations into one feature space. Moreover, after exploring massive heterogeneous graphs, we identify a fact that multiple nodes with the same type always connect to a node with another type, which reveals the many-to-one schema, a.k.a. the hierarchical tree structure. But all the above work cannot preserve such tree structure, since the exact multi-hop path correlation from neighbors to the target node would be erased through aggregation. Therefore, to overcome the limitations of the literature, we propose T-GNN, a tree structure-aware graph neural network model for graph representation learning. Specifically, the proposed T-GNN consists of two modules: (1) the integrated hierarchical aggregation module and (2) the relational metric learning module. The integrated hierarchical aggregation module aims to preserve the tree structure by combining GNN with gated recurrent unit to integrate the hierarchical and sequential neighborhood information on the tree structure to node representations. The relational metric learning module aims to preserve the heterogeneity by embedding each type of nodes into a type-specific space with distinct distribution based on similarity metrics. In this way, our proposed T-GNN is capable of simultaneously preserving the heterogeneity and the tree structure inherent in heterogeneous graphs. Finally, we conduct extensive experiments to show the outstanding performance of T-GNN in tasks of node clustering and classification, inductive node clustering and classification, and link prediction.
Brain-Inspired Fast Saliency-Based Filtering Algorithm for Ship Detection in High-Resolution SAR Images

In this article, we aim to improve the performance of synthetic aperture radar (SAR) ship detection under complex conditions. The complex backgrounds are commonly encountered for high-resolution (HR) SAR ship detection data set, and they greatly influence the detection performance of ships. In recent years, deep neural networks (DNNs) have made substantial improvements on detection by adopting data augmentation. However, the improvement is limited since the models are sensitive to noise. To address this problem, a Fast Saliency-based Filtering algorithm (FSF) is proposed to filter out interference information. The FSF method is inspired by the filtering mechanisms of the human brain, which help people filter out target-irrelevant information fast to better extract target-relevant information. The FSF includes two parts of the bottom-up process and the top-down process. The bottom-up process is used to extract a saliency map of an input image, and the other one is used to filter out target-irrelevant information based on the saliency map. The FSF can be a front-end preprocessing module of DNNs to fast filter out target-irrelevant information and obtain a primary priority map of an input image. Experimental results demonstrate that our brain-inspired FSF method obtains obvious improvement of detection performance on AIR-SARShip-1.0.
Deep Semantic Network Representation

Network representation aims to learn low-dimensional vector representations of network nodes while preserving the inherent properties of the network. For all its popularity, majority of the existing methods focus on exploitation of diverse information, including network topology and semantic information on nodes of network, and ignore their implicit semantics. For example, we all know the saying that birds of a feather flock together. More concretely, semantic information of one node can be influenced by its neighbors' semantic information. Furthermore, even two nodes are not directly connected, they may have similar implicit semantic information (i.e., high-order semantic proximity). Thus, they should be close in the represented vector space. To this end, we propose a Deep Semantic Network Representation approach (DSNR) in the self-translation framework from sequence to sequence. To excavate the implicit semantic information of nodes and capture the high-order semantic proximity, three key components make our approach effective, i.e., aggregation of nodes neighbors' semantic information and enhancement to the semantic feature representations of nodes by a deep autoencoder, integration of nodes semantic information in node identity sequence to generate node semantic sequence, and translation from node semantic sequence to node identity sequence to capture the high-order semantic proximity in an attention-enhanced seq2seq framework. Extensive experiments based on three real-world datasets have verified the effectiveness of our proposed approach <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Code is available at https://github.com/DASE4/DSNR.
Automated Segmentation Method for Low Field 3D Stomach MRI Using Transferred Learning Image Enhancement Network

Accurate segmentation of abdominal organs has always been a difficult problem, especially for organs with cavities. And MRI-guided radiotherapy is particularly attractive for abdominal targets compared with low CT contrast. But in the limit of radiotherapy environment, only low field MRI segmentation can be used for stomach location, tracking, and treatment planning. In clinical applications, the existing 3D segmentation network model is trained by the low field MRI, and the segmentation result cannot be used in radiotherapy plan since the bad segmentation performance. Another way is that historical high field intensity MR images are directly used for data expansion to network learning; there will be a domain shift problem. How to use different domain images to improve the segmentation accuracy of deep neural network? A 3D low field MRI stomach segmentation method based on transfer learning image enhancement is proposed in this paper. In this method, Cycle Generative Adversarial Network (CycleGAN) is used to construct and learn the mapping relationship between high and low field intensity MRI and to overcome domain shift. Then, the image generated by the high field intensity MRI through the CycleGAN network is with transferred information as the extended data. The low field MRI combines these extended datasets to form the training data for training the 3D Res-Unet segmentation network. Furthermore, the convolution layer, batch normalization layer, and Relu layer together were replaced with a residual module to relieve the gradient disappearance of the neural network. The experimental results show that the Dice coefficient is 2.5 percent better than the baseline method. The over segmentation and under segmentation are reduced by 0.7 and 5.5 percent, respectively. And the sensitivity is improved by 6.4 percent.
Machine learning-based image analysis for accelerating the diagnosis of complicated preneoplastic and neoplastic ductal lesions in breast biopsy tissues

Diagnosis of breast preneoplastic and neoplastic lesions is difficult due to their similar morphology in breast biopsy specimens. To diagnose these lesions, pathologists perform immunohistochemical analysis and consult with expert breast pathologists. These additional examinations are time-consuming and expensive. Artificial intelligence (AI)-based image analysis has recently improved, and may help in ordinal pathological diagnosis. Here, we showed the significance of machine learning-based image analysis of breast preneoplastic and neoplastic lesions for facilitating high-throughput diagnosis. Images were obtained from normal mammary glands, hyperplastic lesions, preneoplastic lesions and neoplastic lesions, such as usual ductal hyperplasia (UDH), columnar cell lesion (CCL), ductal carcinoma in situ (DCIS), and DCIS with comedo necrosis (comedo DCIS) in breast biopsy specimens. The original enhanced convoluted neural network (CNN) system was used for analyzing the pathological images. The AI-based image analysis provided the following area under the curve values (AUC): normal lesion versus DCIS, 0.9902; DCIS versus comedo DCIS, 0.9942; normal lesion versus CCL, 0.9786; and UDH versus DCIS, 1.000. Multiple comparison analysis showed precision and recall scores similar to those of single comparison analysis. Based on the gradient-weighted class activation mapping (Grad-CAM) used to visualize the important regions reflecting the result of CNN analysis, the ratio of stromal tissue in the whole weighted area was significantly higher in UDH and CCL than that in DCIS. These analyses may provide a more accurate and rapid pathological diagnosis of patients. Moreover, Grad-CAM identifies uncharted important histological characteristics for newer pathological findings and targets of research for understanding diseases.
Robust Generative Restricted Kernel Machines Using Weighted Conjugate Feature Duality

Interest in generative models has grown tremendously in the past decade. However, their training performance can be adversely affected by contamination, where outliers are encoded in the representation of the model. This results in the generation of noisy data. In this paper, we introduce weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs). The RKM formulation allows for an easy integration of methods from classical robust statistics. This formulation is used to fine-tune the latent space of generative RKMs using a weighting function based on the Minimum Covariance Determinant, which is a highly robust estimator of multivariate location and scatter. Experiments show that the weighted RKM is capable of generating clean images when contamination is present in the training data. We further show that the robust method also preserves uncorrelated feature learning through qualitative and quantitative experiments on standard datasets.