Reviewerly/exHarmony huggingface dataset:
Blockchain for Waste Management in Smart Cities: A Survey

Smart cities have the potential to overcome environmental problems caused by improper waste disposal by improving human health, protecting the aquatic ecosystem, and reducing air pollution. However, today’s systems, approaches, and technologies leveraged for waste management are manual and centralized. This fact makes them vulnerable to manipulation and the single point of failure problem. Also, a large portion of the existing waste management systems within smart cities fall short in providing operational transparency, traceability, audit, security, and trusted data provenance features. In this paper, we explore the key role of blockchain technology in managing waste within smart cities as it can offer traceability, immutability, transparency, and audit features in a decentralized, trusted, and secure manner. We discuss the opportunities brought about by blockchain technology in various waste management use cases and application scenarios, including real-time tracing and tracking of waste, reliable channelization and compliance with waste treatment laws, efficient waste resources management, protection of waste management documentation, and fleet management. We introduce a framework that leverages blockchain-based smart contracts to automate the key services in terms of waste management of smart cities. We compare the existing blockchain-based waste management solutions based on important parameters. Furthermore, we present insightful discussions on several ongoing blockchain-based research projects and case studies to highlight the practicability of blockchain in waste management. Finally, we present open challenges that act as future research directions.
InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics

This work explores the biases in learning processes based on deep neural network architectures. We analyze how bias affects deep learning processes through a toy example using the MNIST database and a case study in gender detection from face images. We employ two gender detection models based on popular deep neural networks. We present a comprehensive analysis of bias effects when using an unbalanced training dataset on the features learned by the models. We show how bias impacts in the activations of gender detection models based on face images. We finally propose InsideBias, a novel method to detect biased models. InsideBias is based on how the models represent the information instead of how they perform, which is the normal practice in other existing methods for bias detection. Our strategy with InsideBias allows to detect biased models with very few samples (only 15 images in our case study). Our experiments include 72K face images from 24K identities and 3 ethnic groups.
A Systematic IoU-Related Method: Beyond Simplified Regression for Better Localization

Four-variable-independent-regression localization losses, such as Smooth-$\ell_1$ Loss, are used by default in modern detectors. Nevertheless, this kind of loss is oversimplified so that it is inconsistent with the final evaluation metric, intersection over union (IoU). Directly employing the standard IoU is also not infeasible, since the constant-zero plateau in the case of non-overlapping boxes and the non-zero gradient at the minimum may make it not trainable. Accordingly, we propose a systematic method to address these problems. Firstly, we propose a new metric, the extended IoU (EIoU), which is well-defined when two boxes are not overlapping and reduced to the standard IoU when overlapping. Secondly, we present the convexification technique (CT) to construct a loss on the basis of EIoU, which can guarantee the gradient at the minimum to be zero. Thirdly, we propose a steady optimization technique (SOT) to make the fractional EIoU loss approaching the minimum more steadily and smoothly. Fourthly, to fully exploit the capability of the EIoU based loss, we introduce an interrelated IoU-predicting head to further boost localization accuracy. With the proposed contributions, the new method incorporated into Faster R-CNN with ResNet50+FPN as the backbone yields \textbf{4.2 mAP} gain on VOC2007 and \textbf{2.3 mAP} gain on COCO2017 over the baseline Smooth-$\ell_1$ Loss, at almost \textbf{no training and inferencing computational cost}. Specifically, the stricter the metric is, the more notable the gain is, improving \textbf{8.2 mAP} on VOC2007 and \textbf{5.4 mAP} on COCO2017 at metric $AP_{90}$.
Deep Multiple Auto-Encoder-Based Multi-view Clustering

Abstract Multi-view clustering (MVC), which aims to explore the underlying structure of data by leveraging heterogeneous information of different views, has brought along a growth of attention. Multi-view clustering algorithms based on different theories have been proposed and extended in various applications. However, most existing MVC algorithms are shallow models, which learn structure information of multi-view data by mapping multi-view data to low-dimensional representation space directly, ignoring the nonlinear structure information hidden in each view, and thus, the performance of multi-view clustering is weakened to a certain extent. In this paper, we propose a deep multi-view clustering algorithm based on multiple auto-encoder, termed MVC-MAE, to cluster multi-view data. MVC-MAE adopts auto-encoder to capture the nonlinear structure information of each view in a layer-wise manner and incorporate the local invariance within each view and consistent as well as complementary information between any two views together. Besides, we integrate the representation learning and clustering into a unified framework, such that two tasks can be jointly optimized. Extensive experiments on six real-world datasets demonstrate the promising performance of our algorithm compared with 15 baseline algorithms in terms of two evaluation metrics.
Operationalizing Human-Centered Perspectives in Explainable AI

The realm of Artificial Intelligence (AI)’s impact on our lives is far reaching – with AI systems proliferating high-stakes domains such as healthcare, finance, mobility, law, etc., these systems must be able to explain their decision to diverse end-users comprehensibly. Yet the discourse of Explainable AI (XAI) has been predominantly focused on algorithm-centered approaches, suffering from gaps in meeting user needs and exacerbating issues of algorithmic opacity. To address these issues, researchers have called for human-centered approaches to XAI. There is a need to chart the domain and shape the discourse of XAI with reflective discussions from diverse stakeholders. The goal of this workshop is to examine how human-centered perspectives in XAI can be operationalized at the conceptual, methodological, and technical levels. Encouraging holistic (historical, sociological, and technical) approaches, we put an emphasis on “operationalizing”, aiming to produce actionable frameworks, transferable evaluation methods, concrete design guidelines, and articulate a coordinated research agenda for XAI.
Acute Lymphoblastic Leukemia Detection Based on Adaptive Unsharpening and Deep Learning

Computer Aided Diagnosis (CAD) systems are increasingly utilizing image analysis and Deep Learning (DL) techniques, due to their high accuracy in several medical imaging fields, including the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) from peripheral blood samples. However, no method in the literature has specifically analyzed the focus quality of ALL images or proposed a technique for sharpening the samples in an adaptive way for the purpose of classification. To address this issue, in this paper we propose the first machine learning-based approach able to enhance blood sample images by an adaptive unsharpening method. The method uses image processing techniques and DL to normalize the radius of the cell, estimate the focus quality, adaptively improve the sharpness of the images, and then perform the classification. We evaluated the methodology on a public database of ALL images, considering several state-of-the-art CNNs to perform the classification, with results showing the validity of the proposed approach. For a complete reproducibility of the work, the source code is available at: http://iebil.di.unimi.it/cnnALL/index.htm.
Evaluating genetic algorithms through the approximability hierarchy

Optimization problems frequently appear in any scientific domain. Most of the times, the corresponding decision problem turns out to be NP-hard, and in these cases genetic algorithms are often used to obtain approximated solutions. However, the difficulty to approximate different NP-hard problems can vary a lot. In this paper, we analyze the usefulness of using genetic algorithms depending on the approximation class the problem belongs to. In particular, we use the standard approximability hierarchy, showing that genetic algorithms are especially useful for the most pessimistic classes of the hierarchy
Learning Video Moment Retrieval Without a Single Annotated Video

Video moment retrieval has progressed significantly over the past few years, aiming to search the moment that is most relevant to a given natural language query. Most existing methods are trained in a fully-supervised or a weakly-supervised manner, which requires a time-consuming and expensive manually labeling process. In this work, we propose an alternative approach to achieving video moment retrieval that requires no textual annotations of videos and instead leverages the existing visual concept detectors and a pre-trained image-sentence embedding space. Specifically, we design a video-conditioned sentence generator to produce a suitable sentence representation by utilizing the mined visual concepts in videos. We then design a GNN-based relation-aware moment localizer to reasonably select a portion of video clips under the guidance of the generated sentence. Finally, the pre-trained image-sentence embedding space is adopted to evaluate the matching scores between the generated sentence and moment representations with the knowledge transferred from the image domain. By maximizing these scores, the sentence generator and moment localizer can enhance and complement each other to achieve the moment retrieval task. Experimental results on the Charades-STA and ActivityNet Captions datasets demonstrate the effectiveness of our proposed method.
An efficient classification framework for breast cancer using hyper parameter tuned Random Decision Forest Classifier and Bayesian Optimization

Decision tree algorithm is one of the algorithm which is easily understandable and interpretable algorithm used in both training and application purpose during breast cancer prognosis. To address this problem, Random Decision Forests are proposed. In this manuscript, the breast cancer classification can be determined by combining the advantages of Feature Weight and Hyper Parameter Tuned Random Decision Forest classifier. Here the Kernel Neutrosophic C-Means Clustering is used as Feature Weight, which allocates greater weights to applicable features and smaller weights to less applicable features. Then Random Decision Forest classifier model are optimized with the help of the Bayesian Optimization algorithm to obtain optimal hyper tuning parameters. By this, the accurate classification of breast cancer is successfully achieved. Then the efficiency of the proposed system is executed in python. The performance analysis are executed in Wisconsin prognostic Breast Cancer (WPBC) dataset, 70 % training and remaining 30 % testing is compared with the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the accuracy analysis of proposed feature weight and Random Decision Forest Classifier with Bayesian Optimization (FW + BOA-RDF) in Breast Cancer Wisconsin (Prognosis) Data Set is 6.66 %, 12.659 % and 37.618 % higher than existing method like FW + ALO-BPNN, FW + SSA-SVM, FW + GA-SVM respectively. The performance analysis in Wisconsin prognostic Breast Cancer (WPBC) dataset, 75 % training and the remaining 25 % testing is compared at Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the accuracy analysis of FW + BOA-RDF in Breast Cancer Wisconsin (Prognosis) Data Set is 3.7146 %, 5.27398 % and 4.4413 % higher than existing method like FW + ALO-BPNN, FW + SSA-SVM, FW + GA-SVM respectively.
Algorithmic method of security of the Internet of Things based on steganographic coding

In the Internet of Things, it is more important than ever to effectively address the problem of secure transmission based on steganographic substitution by synthesizing digital sensor data. In this case, the degree to which the grayscale message is obscured is a necessary issue. To ensure information security in IoT systems, various methods are used and information security problems are solved to one degree or another. The article proposes a method and algorithm for a computer image in grayscale, in which the value of each pixel is one sample, representing the amount of light, carrying only information about the intensity. The proposed method in grayscale using steganographic coding provides a secure implementation of data transmission in the IoT system. Study results were analyzed using PSNR (Peak Signal to Noise Ratio).
A model and query language for temporal graph databases

Graph databases are becoming increasingly popular for modeling different kinds of networks for data analysis. They are built over the property graph data model, where nodes and edges are annotated with property-value pairs. Most existing work in the field is based on graphs were the temporal dimension is not considered. However, time is present in most real-world problems. Many different kinds of changes may occur in a graph as the world it represents evolves across time. For instance, edges, nodes, and properties can be added and/or deleted, and property values can be updated. This paper addresses the problem of modeling, storing, and querying temporal property graphs, allowing keeping the history of a graph database. This paper introduces a temporal graph data model, where nodes and relationships contain attributes (properties) timestamped with a validity interval. Graphs in this model can be heterogeneous, that is, relationships may be of different kinds. Associated with the model, a high-level graph query language, denoted T-GQL, is presented, together with a collection of algorithms for computing different kinds of temporal paths in a graph, capturing different temporal path semantics. T-GQL can express queries like “Give me the friends of the friends of Mary, who lived in Brussels at the same time than her, and also give me the periods when this happened”. As a proof-of-concept, a Neo4j-based implementation of the above is also presented, and a client-side interface allows submitting queries in T-GQL to a Neo4j server. Finally, experiments were carried out over synthetic and real-world data sets, with a twofold goal: on the one hand, to show the plausibility of the approach; on the other hand, to analyze the factors that affect performance, like the length of the paths mentioned in the query, and the size of the graph.
Alive2: bounded translation validation for LLVM

We designed, implemented, and deployed Alive2: a bounded translation validation tool for the LLVM compiler’s intermediate representation (IR). It limits resource consumption by, for example, unrolling loops up to some bound, which means there are circumstances in which it misses bugs. Alive2 is designed to avoid false alarms, is fully automatic through the use of an SMT solver, and requires no changes to LLVM. By running Alive2 over LLVM’s unit test suite, we discovered and reported 47 new bugs, 28 of which have been fixed already. Moreover, our work has led to eight patches to the LLVM Language Reference—the definitive description of the semantics of its IR—and we have participated in numerous discussions with the goal of clarifying ambiguities and fixing errors in these semantics. Alive2 is open source and we also made it available on the web, where it has active users from the LLVM community.
Attitude towards Drone Food Delivery Services—Role of Innovativeness, Perceived Risk, and Green Image

The possibility of drone usage for food delivery is met with enthusiasm by businesses as it promises instantaneous benefits such as reduced costs, improved customer satisfaction, and reduced environmental imprint. The objective of this paper is to explore consumer attitude and intention towards adopting a disruptive technology such as drone food delivery in the Indian context through motivated consumer innovativeness (MCI), green image, and perceived risk. We analyzed the questionnaire survey data collected from 310 respondents using structural equation modeling—partial least squares method. Functionally motivated consumer innovativeness and cognitively motivated consumer innovativeness were found to be significant positive predictors of consumer attitude and intention. Perceived privacy risk was found to have a significant negative influence on attitude. Green image had a significant positive effect on attitude towards drone usage. Other components of MCI namely, hedonic and social as well as performance and delivery risk did not show a significance influence. This study, to our knowledge, is first of its kind in India, a populous country with an established and booming economy, where the enabling and impeding antecedents of drone food delivery usage intention is simultaneously studied. The findings of this research will mainly benefit food delivery companies in framing successful drone food delivery strategies.
A taxonomical review on recent artificial intelligence applications to PV integration into power grids

The exponential growth of solar power has been witnessed in the past decade and is projected by the ambitious policy targets. Nevertheless, the proliferation of solar energy poses challenges to power system operations, mostly due to its uncertainty, locational specificity, and variability. The prevalence of smart grids enables artificial intelligence (AI) techniques to mitigate solar integration problems with massive amounts of solar energy data. Different AI subfields (e.g., machine learning, deep learning, ensemble learning, and metaheuristic learning) have brought breakthroughs in solar energy, especially in its grid integration. However, AI research in solar integration is still at the preliminary stage, and is lagging behind the AI mainstream. Aiming to inspire deep AI involvement in the solar energy domain, this paper presents a taxonomical overview of AI applications in solar photovoltaic (PV) systems. Text mining techniques are first used as an assistive tool to collect, analyze, and categorize a large volume of literature in this field. Then, based on the constructed literature infrastructure, recent advancements in AI applications to solar forecasting, PV array detection, PV system fault detection, design optimization, and maximum power point tracking control problems are comprehensively reviewed. Current challenges and future trends of AI applications in solar integration are also discussed for each application theme.
Spatio-Contextual Deep Network-Based Multimodal Pedestrian Detection for Autonomous Driving

Pedestrian Detection is the most critical module of an Autonomous Driving system. Although a camera is commonly used for this purpose, its quality degrades severely in low-light night time driving scenarios. On the other hand, the quality of a thermal camera image remains unaffected in similar conditions. This paper proposes an end-to-end multimodal fusion model for pedestrian detection using RGB and thermal images. Its novel spatio-contextual deep network architecture is capable of exploiting the multimodal input efficiently. It consists of two distinct deformable ResNeXt-50 encoders for feature extraction from the two modalities. Fusion of these two encoded features takes place inside a multimodal feature embedding module (MuFEm) consisting of several groups of a pair of Graph Attention Network and a feature fusion unit. The output of the last feature fusion unit of MuFEm is subsequently passed to two CRFs for their spatial refinement. Further enhancement of the features is achieved by applying channel-wise attention and extraction of contextual information with the help of four RNNs traversing in four different directions. Finally, these feature maps are used by a single-stage decoder to generate the bounding box of each pedestrian and the score map. We have performed extensive experiments of the proposed framework on three publicly available multimodal pedestrian detection benchmark datasets, namely KAIST, CVC-14, and UTokyo. The results on each of them improved the respective state-of-the-art performance. A short video giving an overview of this work along with its qualitative results can be seen at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/FDJdSifuuCs</uri> . Our source code will be released upon publication of the paper.
A Taxonomic Review of the Use of IoT and Blockchain in Healthcare Applications

With the rapid evolution and technology advancement, the healthcare sector is evolving day by day. It is taking advantage of different technologies such as Internet of things and Blockchain. Several applications related to daily healthcare activities are adopting the use of these technologies. In this paper, we present a review in which we group different healthcare applications that integrate the Internet of things and Blockchain in their systems. A review study about the integration of IoT and Blockchain in healthcare systems was conducted. We searched the databases ScienceDirect, IEEE Xplore, Google Scholar and ACM Digital Library. This review focuses on categorizing the use cases of IoT and Blockchain in the healthcare sector. The study listed 6 applications in medical services, namely, remote patient monitoring, electronic medical records management, disease prediction, patient tracking, drug traceability and fighting infectious disease especially COVID-19. The paper also investigates the challenges associated with the adoption of the Blockchain technology in healthcare IoT-based systems and some of the existing solutions. It also introduces some future research directions. The survey of the use cases of IoT and Blockchain in the healthcare sector will serve as a state of the art for future researches. In addition, the paper gives some directions to new possible researches that could help to revolutionize the healthcare sector by using other technologies such as artificial intelligence, big data, fog and cloud computing.
Fast hierarchical clustering of local density peaks via an association degree transfer method

Density Peak clustering (DPC) as a novel algorithm can fast identify density peaks. But it comes along with two drawbacks: its allocation strategy may produce some non-adjacent associations that may lead to poor clustering results and even cause the malfunction of its cluster center selection method to mistakenly identify cluster centers; it may perform poorly with its high complex O(n2) when comes to large-scale data. Herein, a fast hierarchical clustering of local density peaks via an association degree transfer method (FHC-LDP) is proposed. To avoid DPC’s drawbacks caused by non-adjacent associations, FHC-LDP only considers the association between neighbors and design an association degree transfer method to evaluate the association between points that are not neighbors. FHC-LDP can fast identify local density peaks as sub-cluster centers to generate sub-clusters automatically and evaluate the similarity between sub-clusters. Then, by analyzing the similarity of sub-cluster centers, a hierarchical structure of sub-clusters is built. FHC-LDP replaces DPC’s cluster center selection method with a bottom-up hierarchical approach to ensure sub-clusters in each cluster are most similar. In FHC-LDP, only neighbor information of data is required, so by using a fast KNN algorithm, FHC-LDP can run about O(nlog(n)). Experimental results demonstrate FHC-LDP is remarkably superior to traditional clustering algorithms and other variants of DPC in recognizing cluster structure and running speed.