Reviewerly/exHarmony huggingface dataset:
Smart Solid Waste Management System Using Blockchain and IoT for Smart Cities

Because of urbanization and industrialization, non-biodegradable garbage is growing at an exponential rate. Industries have their own waste management and treatment divisions to take care of their waste products. However, civilian entities are facing many issues in waste management due to the lack of proper systems for segregating waste materials. This article proposed a unique smart waste management system using Blockchain and Internet of Things (IoT) to simplify the waste segregation with the help of smart bins. The proposed system distributes rewards to users for proper disposal of waste into smart bins using smart contracts. We deployed a prototype model on different test networks to compare its real-time performance. From the experimental analysis, we can conclude that the proposed model performs better on the Matic test network than the Binance Smart Chain (BSC) and Ropsten test networks. Finally, the proposed solution ensures system transparency, traceability, and scalability, as well as eliminating single points of failure (SPoF).
SwiniPASSR: Swin Transformer based Parallax Attention Network for Stereo Image Super-Resolution

With binocular cameras being widely accepted, the study of stereo image super resolution (Stereo SR) has received increasing attention. Different from single image super resolution (SISR) setting, it is more challenging for utilizing both intra-view and cross-view information. Although prior convolution-based works have achieved admirable progress, few attempts have explored the possibility of the Transformer-based architecture for stereo image SR, which has demonstrated promising performance in several visual tasks. In this paper, we propose a novel approach namely SwiniPASSR, which adopts Swin Transformer as the backbone, meanwhile incorporating it with the Bi-directional Parallax Attention Module (biPAM) to maximize auxiliary information given by the binocular mechanism. Even Transformer and parallax attention mechanism (PAM) have been separately proved usefulness by prior studies, we find that simply integrating convolution-based PAM with Transformer or directly optimizing for stereo SR problem was may not achieve desirable result. We therefore introduced a conversion layer to resolve integration and adopted progressive training strategy to learn disparity correspondence through progressively enlarged receptive fields. Both extensive experiments and ablation studies demonstrate the effectiveness of our proposed SwiniPASSR. In particular, in the NTIRE 2022: Stereo Image Super-Resolution Challenge, we report 23.71dB PSNR and 0.7295 SSIM performance which ranked 2nd place on the leaderboard. Source code is available at https://gj.thub.com/SMI-Lab/SwinlPASSR.
GenSynth: a generative synthesis approach to learning generative machines for generate efficient neural networks

The tremendous potential exhibited by deep learning is often offset by architectural and computational complexity, making widespread deployment a challenge for edge scenarios such as mobile and other consumer devices. To tackle this challenge, we explore the following idea: Can we learn generative machines to automatically generate deep neural networks with efficient network architectures? In this study, we introduce the idea of generative synthesis, which is premised on the intricate interplay between a generator-inquisitor pair that work in tandem to garner insights and learn to generate highly efficient deep neural networks that best satisfies operational requirements. Experimental results for image classification, semantic segmentation, and object detection tasks illustrate the efficacy of generative synthesis (GenSynth) in producing generators that automatically generate highly efficient deep neural networks (which we nickname FermiNets with higher model efficiency and lower computational costs (reaching more efficient and fewer multiply-accumulate operations than several tested state-of-the-art networks), as well as higher energy efficiency (reaching improvements in image inferences per joule consumed on a Nvidia Tegra X2 mobile processor). As such, GenSynth can be a powerful, generalised approach for accelerating and improving the building of deep neural networks for on-device edge scenarios.
Dense Inception Attention Neural Network for In-Loop Filter

Recently, deep learning technology has made significant progresses in high efficiency video coding (HEVC), especially in in-loop filter. In this paper, we propose a dense inception attention network (DIA_Net) to delve into image information and model capacity. The DIA_Net contains multiple inception blocks which have different size kernels so as to dig out various scales information. Meanwhile, attention mechanism including spatial attention and channel attention is utilized to fully exploit feature information. Further we adopt a dense residual structure to deepen the network. We attach DIA_Net to the end of in-loop filter part in HEVC as a post-processor and apply it to luma components. The experimental results demonstrate the proposed DIA_Net has remarkable improvement over the standard HEVC. With all-intra(AI) and random access(RA) configurations, It achieves 8.2% bd-rate reduction in AI configuration and 5.6% bd-rate reduction in RA configuration.
Joint-teaching: Learning to Refine Knowledge for Resource-constrained Unsupervised Cross-modal Retrieval

Cross-modal retrieval has received considerable attention owing to its applicability to enable users to search desired information with diversified forms. Existing retrieval methods retain good performance mainly relying on complex deep neural networks and high-quality supervision signals, which deters them from real-world resource-constrained development and deployment. In this paper, we propose an effective unsupervised learning framework named JOint-teachinG (JOG) to pursue a high-performance yet light-weight cross-modal retrieval model. The key idea is to utilize the knowledge of a pre-trained model (a.k.a. the "teacher") to endow the to-be-learned model (a.k.a. the "student") with strong feature learning ability and predictive power. Considering that a teacher model serving the same task as the student is not always available, we resort to a cross-task teacher to leverage transferrable knowledge to guide student learning. To eliminate the inevitable noises in the distilled knowledge resulting from the task discrepancy, an online knowledge-refinement strategy is designed to progressively improve the quality of the cross-task knowledge in a joint-teaching manner, where a peer student is engaged. In addition, the proposed JOG learns to represent the original high-dimensional data with compact binary codes to accelerate the query processing, further facilitating resource-limited retrieval. Through extensive experiments, we demonstrate that in various network structures, the proposed method can yield promising learning results on widely-used benchmarks. The proposed research is a pioneering work for resource-constrained cross-modal retrieval, which has strong potential to be applied to on-device deployment and is hoped to pave the way for further study.
An Efficient Hindi Text Classification Model Using SVM

In today’s world, several digitized Hindi text documents are generated daily at the Government sites, news portals, and public and private sectors, which are required to be classified effectively into various mutually exclusive pre-defined categories. As such, many Hindi text-based processing systems exist in application domains of information retrieval, machine translation, text summarization, simplification, keyword extraction, and other related parsing and linguistic perspectives, but still, there is a wide scope to classify the extracted text of Hindi documents into pre-defined categories using a classifier. In this paper, a Hindi Text Classification model is proposed, which accepts a set of known Hindi documents, preprocesses them at document, sentence and word levels, extracts features, and trains SVM classifier, which further classifies a set of Hindi unknown documents. Such text classification becomes challenging in Hindi due to its large set of available conjuncts and letter combinations, its sentence structure, and multisense words. The experiments have been performed on a set of four Hindi documents of two categories, which have been classified by SVM with 100% accuracy.
BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named-Entity Recognition

Building on the achievements of the BiLSTM-CRF in named-entity recognition (NER), this paper introduces the BiLSTM-SSVM, an equivalent neural model where training is performed using a structured hinge loss.The typical loss functions used for evaluating NER are entity-level variants of the F 1 score such as the CoNLL and MUC losses.Unfortunately, the common loss function used for training NER -the cross entropy -is only loosely related to the evaluation losses.For this reason, in this paper we propose a training approach for the BiLSTM-CRF that leverages a hinge loss bounding the CoNLL loss from above.In addition, we present a mixed hinge loss that bounds either the CoNLL loss or the Hamming loss based on the density of entity tokens in each sentence.The experimental results over four benchmark languages (English, German, Spanish and Dutch) show that training with the mixed hinge loss has led to small but consistent improvements over the cross entropy across all languages and four different evaluation measures.
Header Based Email Spam Detection Framework Using Support Vector Machine (SVM) Technique

Email spam is continuously a major problem, especially on the Internet. Spam consists of malicious malwares which attack user’s machine to steal information, destroy the user’s machine and trick the user into buying their products. Although spam detection or email spam filtering was developed, there is still a rising number of emails that contain spam. The study of this research is to identify the potentially useful email header features for email spam detection by analyzing two (2) email datasets, the Anomaly Detection Challenges and Cyber Security Data Mining from website. By analyzing the datasets, the main objective of this research is to extract the suitable features of the email header and examine the features to classify the features using Support Vector Machine (SVM) using RapidMiner Studio and Weka 3.9.2. There are five (5) phases in the methodology which are Data Collection, data Pre-Processing, Features Selection, Classification and Detection. Classification of the email header using Support Vector Machine (SVM) for CSDM2010 is higher than the Anomaly Detection Challenges datasets at 88.80% and 87.20% respectively. Thus, SVM proves as a good classifier which produced above 80% accuracy rate for both datasets.
Short-term solar irradiance prediction based on spatiotemporal graph convolutional recurrent neural network

Solar irradiance data include temporal information and geospatial information, so solar irradiance prediction can be regarded as a spatiotemporal sequence prediction problem. However, at present, most of the research is based on time series prediction models, and the research studies on spatial-temporal series prediction models are relatively few. Therefore, it is necessary to integrate spatial-temporal information to construct a spatial-temporal sequence prediction model for research. In this paper, the spatial-temporal prediction model based on graph convolutional network (GCN) and long short-term memory network (LSTM) was established for short-term solar irradiance prediction. In this model, solar radiation observatories were modeled as undirected graphs, where each node corresponds to an observatory, and a GCN was used to capture spatial correlations between sites. For each node, temporal features were extracted by using a LSTM. In order to evaluate the prediction performance of this model, six solar radiation observatories located in the Xinjiang region of China were selected; together with widely used persistence model seasonal autoregressive integrated moving average and data-driven prediction models such as convolutional neural network, recurrent neural network, and LSTM, comparisons were made under different seasons and weather conditions. The experimental results show that the average root mean square error of the GCN-LSTM model at the six sites is 62.058 W/m2, which is reduced by 9.8%, 14.3%, 6.9%, and 3.3%, respectively, compared with other models; the average MAE is 25.376 W/m2, which is reduced by 27.7%, 26.5%, 20.1%, and 11%, respectively, compared with other models; the average R2 is 0.943, which is improved by 1.4%, 2.2%, 0.8%, and 0.4%, respectively, compared with other models.
Implementation of Neural Network Regression Model for Faster Redshift Analysis on Cloud-Based Spark Platform

Since observational astronomy has turned into data-driven astronomy recently, analyzing this huge data effectively to extract useful information is becoming an important and essential task day by day. In this paper, we developed a neural network model to analyze redshift data of million of extragalactic objects. In order to do that, two different approaches for faster training of neural networks have been proposed. The first approach deals with the training model using Lipschitz-based adaptive learning rate in a single node/machine whereas the second approach discusses processing astronomy data in a multinode clustered environment. This approach can scale up to accommodate multiple nodes when necessary to handle bulk data using Apache spark and Elephas. Additionally, this paper also addresses the scalability and storage issue by implementing the model on the cloud. We used the distributed processing capability of the spark that reads data directly from HDFS (Hadoop Distributed File System) of multiple machines and our experimental results show that using these approaches we can reduce training time and CPU time tremendously which is a crucial requirement while dealing with the extensive dataset. Although we have tested our experiment on a subset of huge data it can be scaled to process data of any size as well without much hurdle.
Performance Analysis of Breast Cancer: A Machine Learning Approach

Nowadays, breast cancer is the most emerging diseaseamong women both in developed as well as developing countries. Due to increased life prospects, increased urbanization, and therelinquishment of western societies, the rareness of breast cancer is supersizing in the developing world. Even it became a second popular cause of cancer that has already been announced. It's very hard to identify the early symptom of this type of cancer for reducing numerous death. Different methods of machine learning and data mining techniques are using for medical diagnosis. In this study, four machine-learning algorithms are applying to analyze breast cancer in the inflammation stage and dig up the most cabbalistic and non-cabbalistic risk factors. To analyze breast cancer data from the Coimbra dataset from the UCI machine learning repository to create accurate prediction models for breast cancer. For getting better performance and to get higher accuracy Naïve Bayes (NB), Random Forest (RF), Multilayer Perceptron (MLP), Simple Logistic Regression (SLR) are using to find out some higher accuracy sequentially 70%, 68%, 85%, and 75%. Among all the above algorithms a better accuracy was achieved using Multi-layer Perceptron. Linear Regression (LiR) models are applying to dig up the most cabbalistic and non-cabbalistic risk factors of breast cancer. These results will help the doctor to detect breast cancer easily in the early stage and take the necessary steps.
Style Curriculum Learning for Robust Medical Image Segmentation

The performance of deep segmentation models often degrades due to distribution shifts in image intensities between the training and test data sets. This is particularly pronounced in multi-centre studies involving data acquired using multi-vendor scanners, with variations in acquisition protocols. It is challenging to address this degradation because the shift is often not known a priori and hence difficult to model. We propose a novel framework to ensure robust segmentation in the presence of such distribution shifts. Our contribution is three-fold. First, inspired by the spirit of curriculum learning, we design a novel style curriculum to train the segmentation models using an easy-to-hard mode. A style transfer model with style fusion is employed to generate the curriculum samples. Gradually focusing on complex and adversarial style samples can significantly boost the robustness of the models. Second, instead of subjectively defining the curriculum complexity, we adopt an automated gradient manipulation method to control the hard and adversarial sample generation process. Third, we propose the Local Gradient Sign strategy to aggregate the gradient locally and stabilise training during gradient manipulation. The proposed framework can generalise to unknown distribution without using any target data. Extensive experiments on the public M&Ms Challenge dataset demonstrate that our proposed framework can generalise deep models well to unknown distributions and achieve significant improvements in segmentation accuracy.
Consistent online Gaussian process regression without the sample complexity bottleneck

Gaussian processes provide a framework for nonlinear nonparametric Bayesian inference widely applicable across science and engineering. Unfortunately, their computational burden scales cubically with the training sample size, which in the case that samples arrive in perpetuity, approaches infinity. This issue necessitates approximations for use with streaming data, which to date mostly lack convergence guarantees. Thus, we develop the first online Gaussian process approximation that preserves convergence to the population posterior, i.e., asymptotic posterior consistency, while ameliorating its intractable complexity growth with the sample size. We propose an online compression scheme that, following each a posteriori update, fixes an error neighborhood with respect to the Hellinger metric centered at the current posterior, and greedily tosses out past kernel dictionary elements until its boundary is hit. We call the resulting method Parsimonious Online Gaussian Processes (POG). For diminishing error radius, asymptotic statistical stationarity is achieved (Theorem 1ii) at the cost of unbounded memory in the limit. On the other hand, for constant error radius, POG converges to a neighborhood of stationarity (Theorem 1ii) but with finite memory at-worst determined by the metric entropy of the feature space (Theorem 2). Here stationarity refers to the distributional distance between sequential marginal posteriors approaching null with the time index. Experimental results are presented on several nonlinear regression problems which illuminates the merits of this approach as compared with alternatives that fix the subspace dimension defining the history of past points.
Behaviors of quantum correlation for atoms coupled with fluctuating electromagnetic field with a perfectly reflecting boundary

We study the behaviors of quantum correlation for two atoms interacted with fluctuating electromagnetic field near a perfectly reflecting boundary. We firstly derive the master equation that governs the system evolution. Then, we discuss the generation, revival and degradation of quantum correlation, which are closely related to boundary effect and atomic polarization direction. Compared with the entanglement behaviors, it is shown that quantum correlation presents better robustness than entanglement, which may be helpful to quantum information processing. Furthermore, the presence of boundary offers us a means for preserving quantum correlation under decoherence and gives us more freedom to adjust the behaviors of quantum correlation.
Cloud Computing Trends: A Literature Review

This study is a literature review on cloud computing cloud computing trends as one the fastest growing technologies in the computer industry and their benefits and opportunities for all types of organizations. In addition, it addresses the challenges and problems that contribute to increasing the number of customers willing to adopt and use the technology. A mixed research study approach was adopted for the study, that is, by collecting and analyzing both quantitative and qualitative information within the same literature review and summarizing the findings of previous (related) studies. Results highlights the current and future trends of cloud computing and exposes readers to the challenges and problems associated with cloud computing. The reviewed literature showed that the technology is promising and is expected to grow in the future. Researchers have proposed many techniques to address the problems and challenges of cloud computing, such as security and privacy risks, through mobile cloud computing and cloud-computing governance.
Semantic Segmentation Based on Deeplabv3+ and Attention Mechanism

In this paper, we propose vertical attention and spatial attention network (VSANet), which is a semantic segmentation method based on Deeplabv3+ and attention module, for improving semantic segmentation effect for autonomous driving road scene images. The improvement of this paper is primarily on the following two aspects. One is that this paper introduces the spatial attention module (SAM) after the atrous convolution, which effectively obtains more spatial context information. Second, by studying the road scene image, it is found that there are considerable differences in the pixel-level distribution of the horizontal segmentation area in the image. For this reason, this paper introduces the vertical attention module (VAM), which can better segment the road scene image. A large number of experimental results indicate that the segmentation accuracy of the proposed model is improved by 1.94% compared with the Deeplabv3+ network model on the test dataset of Cityscapes dataset.
[Paper] Deep Reinforcement Learning-based Music Recommendation with Knowledge Graph Using Acoustic Features

In this study, we propose a new deep reinforcement learning-based music recommendation method with knowledge graphs. With the rapid development of Web services, music-related content posted on platforms, such as YouTube, is increasing dramatically. Conventional recommendation methods based on knowledge graphs have struggled with the cold-start problem caused by a lack of user preference information. The proposed method can solve this problem by introducing acoustic feature edges in the constructed knowledge graph. Furthermore, we realize efficient search using a deep reinforcement learning algorithm on a dense knowledge graph introducing acoustic feature-based edges. The proposed method can make appropriate recommendations even with a small amount of user preference information by learning the optimal action of the agent. We confirm the effectiveness of the proposed method by comparing our method with several conventional and state-of-the-art recommendation methods.
Socially acceptable approach to humans by a mobile robot

In this paper we propose a solution to endow a mobile robot with the ability to approach humans in a safe and socially acceptable way. Our proposal focuses on real world indoor environments where the usual presence of multiple humans and obstacles notably rise the complexity of the approach action. We first deal with the problem of accurately estimating the 3D poses of all humans in the work space (positions and orientations), to then focus on the estimation of the most appropriate navigation goal from which the robot should start the interaction with the user. For the latter, we define a cost function that, accounting for multiple proxemic parameters and complying with the restrictions inherent of a robot navigating in a human environment, enables ascertaining an optimal solution. Different experiments are presented to demonstrate the feasibility of our proposal to work under real world conditions.