Reviewerly/exHarmony huggingface dataset:
Tabular Data Generation to Improve Classification of Liver Disease Diagnosis

Liver diseases are among the most common diseases worldwide. Because of the high incidence and high mortality rate, these diseases diagnoses are vital. Several elements harm the liver. For instance, obesity, undiagnosed hepatitis infection, and alcohol abuse. This causes abnormal nerve function, bloody coughing or vomiting, insufficient kidney function, hepatic failure, jaundice, and liver encephalopathy.. The diagnosis of this disease is very expensive and complex. Therefore, this work aims to assess the performance of various machine learning algorithms at decreasing the cost of predictive diagnoses of chronic liver disease. In this study, five machine learning algorithms were employed: Logistic Regression, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Artificial Neural Network (ANN) algorithm. In this work, we examined the effects of the increased prediction accuracy of Generative Adversarial Networks (GANs) and the synthetic minority oversampling technique (SMOTE). Generative opponents’ networks (GANs) are a mechanism to produce artificial data with a distribution close to real data distribution. This is achieved by training two different networks: the generator, which seeks to produce new and real samples, and the discriminator, which classifies the augmented samples using supervised classifications. Statistics show that the use of increased data slightly improves the performance of the classifier.
Dead Users’ Profiles on Facebook: Limited Interaction Beyond Human Existence

Abstract Death has become increasingly visible on social networks, especially after COVID-19, and Facebook addresses that with double standards: while some profiles remain active, others turn into memorials. This article investigates how Facebook’s system deals with dead users’ profiles either to support or restrict interactions concerning users’ deaths. Our qualitative analysis of data from 54 public profiles of people who died between June 2020 and March 2021 showed that (i) Facebook fails to communicate the criteria for transforming profiles into memorials; (ii) no information about their contacts of deceased users’ profiles is given; (iii) the frequency of interaction with memorials and with active profiles is different; (iv) profiles’ privacy settings shape interaction. Our results exemplify how a sociotechnical system influences people’s interactions with dead users’ profiles. We herein highlight implications for interaction design and evaluation, besides the need to consider interaction as existence, which raises big challenges to the Human Computer Interaction (HCI) community.
Examining Finance-Growth Nexus: Empirical Evidence From the Sub-Regional Economies of Africa

This paper examined finance-growth nexus in the finance industry and the influencing factors of economic indicators which deliberate on the performance of the Solow Growth model to prove the actuality of financial development (FD) inside the economic growth (EG) model, based on the regional data from 1980 to 2017 in Africa. We applied the econometric method of GMM style panel vector autoregressive (PVAR) and panel quantile regression (PQR). With the optimal finance-growth outcome, the review shows that the economic indicators influence the finance-growth nexus. The quantile results show that high economic indicators help to strengthen the finance-growth nexus, whereas low economic indicators hinder it. The GMM style PVAR results present a mixed effect in terms of the connection and marginal significance, indicating that FD has a varied impact on economic growth. Last, the granger causality results show a two-way causal association amid finance-growth in Western, Central, Eastern, and Southern African economies and a unidirectional causal link of finance-growth in Northern Africa. The policy conclusion is that to gain the long-term economic benefits of FD, African countries should strive for low and steady economic stability. To attain the required economic stability, it may be important to use suitable fiscal and monetary policies.
Anomaly detection for construction vibration signals using unsupervised deep learning and cloud computing

In-operation construction vibration monitoring records inevitably contain various anomalies caused by sensor faults, system errors, or environmental influence. An accurate and efficient anomaly detection technique is essential for vibration impact assessment. Identifying anomalies using visualization tools is computationally expensive, time-consuming, and labor-intensive. In this study, an unsupervised approach for detecting anomalies in construction vibration monitoring data was proposed based on a temporal convolutional network and autoencoder. The anomalies were autonomously detected on the basis of the reconstruction errors between the original and reconstructed signals. Considering the false and missed detections caused by great variability in vibration signals, an adaptive threshold method was applied to achieve the best identification performance. This method used the log-likelihood of the reconstruction errors to search for an optimal coefficient for anomalies. A distributed training strategy was implemented on a cloud platform to speed up training and perform anomaly detection without significant time delay. Construction-induced accelerations measured by a real vibration monitoring system were used to evaluate the proposed method. Experimental results show that the proposed approach can successfully detect anomalies with high accuracy; and the distributed training can remarkably save training time, thereby realizing anomaly detection for online monitoring systems with accumulated massive data.
Multi-Scale Fast Fourier Transform Based Attention Network for Remote-Sensing Image Super-Resolution

Recently, with the rise and progress of convolutional neural networks (CNNs), CNN-based remote sensing image super-resolution (RSSR) methods have gained considerable advancement and showed great power for image reconstruction tasks. However, most of these methods cannot handle well the enormous number of objects with different scales contained in remote sensing images and thus limits super-resolution performance. To address these issues, we propose a Multi-Scale Fast Fourier Transform (FFT) based Attention Network (MSFFTAN) which employs a multi-input U-shape structure as backbone for accurate remote sensing image super-resolution. Specifically, we carefully design an FFT-based residual block consisting of an image domain branch and a Fourier domain branch to extract local details and global structures simultaneously. In addition, a Local-Global Channel Attention Block (LGCAB) is developed to further enhance the reconstruction ability of small targets. Finally, we present a Branch Gated Selective Block (BGSB) to adaptively explore and aggregate features from multiple scales and depths. Extensive experiments on two publicly datasets have demonstrated the superiority of MSFFTAN over the state-of-the-art (SOAT) approaches in aspects of both quantitative metrics and visual quality. The peak signal-to-noise ratio of our network is 1.5 dB higher than the SOAT method on the UCMerced LandUse with downscaling factor 2.
How Digital Twin Technology Promotes the Development of Smart Cities

This chapter aims to study whether and how current practices based on digital twin technology can help the development of smart cities in China. This chapter will explore the roles of the government in promoting the application of digital twin technology in the development agenda of industrial infrastructure and smart cities through policies (i.e., institutions). How do these institutions promote the development of digital twin technology in various industries and how will these developments in turn affect stakeholders in various sectors? This chapter will investigate the impact of China's digital twin technology research and institutional changes on the sustainable development of smart cities. This chapter will use bibliometric analysis to perform statistical and correlation analysis on the digital twin/blockchain/AI/IoTs literature included in the Web of Science (WOS) database. Cases from China will be used for comparison. This chapter will provide suggestions for the development of digital twin technology-based ecosystems in emerging economies.
Analysis and Research on YOLOv5s Vehicle Detection with CA and BiFPN Fusion

An algorithm based on improved YOLOv5s is proposed to solve the problems of false and missing vehicle detections. Firstly, a coordinate-attention (CA) module is added to the backbone feature of an extraction network to obtain more important information during feature extraction and improve object detection accuracy. Then, the weighted bi-directional feature pyramid network (BiFPN) is adopted to replace the original PANet structure in the YOLOv5s network. This method enhances the multi-scale feature fusion of the model and improves the fusion efficiency. Experiment results present that the mean average precision (mAP) of the improved YOLOv5s algorithm on the BIT-Vehicle Dataset reaches 94.S%, which is 2.S% higher than that of the original YOLOv5s network, and the processing frame rate reaches 136.9, which allows real-time detection by satisfying its requirements.
Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM

The use of renewable energy, notably solar and wind energy, has grown exponentially in Brazil. Consumers can generate their energy using renewable sources, whether interconnected to the distribution system (on-grid) or not (off-grid). In this paper, a fuzzy method is developed for the recommendation of solar and wind sources, for any location in the Brazilian territory. In many aspects, it can be viewed as a representation of human decision-making using sets and inference rules and also can be with vagueness and uncertainty, being very useful to idealize recommendation systems. Georeferenced and historical data were obtained from 2003 to 2019 on solar irradiation and wind speed, and electricity consumption until 2021. With the energy generation data from photovoltaic panels and wind turbines, this method allows us to propose installed areas by each technology and obtain the membership of fuzzy recommendation between solar, wind, both solar and wind, unfeasible or hybrid. In addition, a long short-term memory neural network and the seasonal autoregressive integrated moving average model were used to predict consumption for more than 30 months ahead, allowing the recalculation of fuzzy memberships and updating the installation area by respective technologies. As a result, the recommendation is given as the installed area (m 2 ) of each technology per km 2 of consumer units, as a function of the regional consumption density (MWh/km 2 ). It can be concluded that it is possible to plan the viability of the type of renewable energy used, according to regional characteristics for smaller consumer units (farms, cooperatives, industries, consortiums), given the diversity of these factors in the huge Brazilian territory. This methodology is in line with the Brazilian Normative Resolution that authorizes the generation of energy by landowners.
Feature-Based Graph Backdoor Attack in the Node Classification Task

Graph neural networks (GNNs) have shown significant performance in various practical applications due to their strong learning capabilities. Backdoor attacks are a type of attack that can produce hidden attacks on machine learning models. GNNs take backdoor datasets as input to produce an adversary-specified output on poisoned data but perform normally on clean data, which can have grave implications for applications. Backdoor attacks are under-researched in the graph domain, and almost existing graph backdoor attacks focus on the graph-level classification task. To close this gap, we propose a novel graph backdoor attack that uses node features as triggers and does not need knowledge of the GNNs parameters. In the experiments, we find that feature triggers can destroy the feature spaces of the original datasets, resulting in GNNs inability to identify poisoned data and clean data well. An adaptive method is proposed to improve the performance of the backdoor model by adjusting the graph structure. We conducted extensive experiments to validate the effectiveness of our model on three benchmark datasets.
TGOpt

Temporal Graph Neural Networks are gaining popularity in modeling interactions on dynamic graphs. Among them, Temporal Graph Attention Networks (TGAT) have gained adoption in predictive tasks, such as link prediction, in a range of application domains. Most optimizations and frameworks for Graph Neural Networks (GNNs) focus on GNN models that operate on static graphs. While a few of these optimizations exploit redundant computations on static graphs, they are either not applicable to the self-attention mechanism used in TGATs or do not exploit optimization opportunities that are tied to temporal execution behavior.
Adore

There has been a recent effort in applying differential privacy on memory access patterns to enhance data privacy. This is called differential obliviousness. Differential obliviousness is a promising direction because it provides a principled trade-off between performance and desired level of privacy. To date, it is still an open question whether differential obliviousness can speed up database processing with respect to full obliviousness. In this paper, we present the design and implementation of Adore: A set of D ifferentially O blivious RE lational database operators. Adore includes selection with projection, grouping with aggregation, and foreign key join. We prove that they satisfy the notion of differential obliviousness. Our differentially oblivious operators have reduced cache complexity, runtime complexity, and output size compared to their state-of-the-art fully oblivious counterparts. We also demonstrate that our implementation of these differentially oblivious operators can outperform their state-of-the-art fully oblivious counterparts by up to 7.4X.
Pantheon

Consider a cloud server that owns a key-value store and provides a private query service to its clients. Preserving client privacy in this setting is difficult because the key-value store is public , and a client cannot encrypt or modify it. Therefore, privacy in this context implies hiding the access pattern of a client. Pantheon is a system that cryptographically allows a client to retrieve the value corresponding to a key from a public key-value store without allowing the server or any adversary to know any information about the key or value accessed. Pantheon devises a single-round retrieval protocol which reduces server-side latency by refining its cryptographic machinery and massively parallelizing the query execution workload. Using these novel techniques, Pantheon achieves a 93X improvement for server-side latency over a state-of-the-art solution.
L2chain

With the rapid development of blockchain, the concept of decentralized applications (DApps), built upon smart contracts, has attracted much attention in academia and industry. However, significant issues w.r.t. system throughput, transaction confidentiality, and the security guarantee of the DApp transaction execution and order correctness hinder the border adoption of blockchain DApps. To address these issues, we propose L2chain, a novel blockchain framework aiming to scale the system through a layer-2 network where DApps process transactions in the layer-2 network and only the system state digest, acting as the state integrity proof, is maintained on-chain. To achieve high performance, we introduce the split-execute-merge (SEM) transaction processing workflow with the help of the RSA accumulator, allowing DApps to lock and update a part of the state digest in parallel. We also design a witness cache mechanism for DApp executors to reduce the transaction processing latency. To fulfill confidentiality, we leverage the trusted execution environment (TEE) for DApps to execute encrypted transactions off-chain. To ensure transaction execution and order correctness, we propose a two-step execution process for DApps to prevent attacks ( i.e. , rollback attacks) from subverting the state transition. Extensive experiments have demonstrated that L2chain can achieve 1.5X to 42.2X and 7.1X to 8.9X throughput improvements in permissioned and permissionless settings respectively.
Nanopublication-based semantic publishing and reviewing: a field study with formalization papers

With the rapidly increasing amount of scientific literature, it is getting continuously more difficult for researchers in different disciplines to keep up-to-date with the recent findings in their field of study. Processing scientific articles in an automated fashion has been proposed as a solution to this problem, but the accuracy of such processing remains very poor for extraction tasks beyond the most basic ones (like locating and identifying entities and simple classification based on predefined categories). Few approaches have tried to change how we publish scientific results in the first place, such as by making articles machine-interpretable by expressing them with formal semantics from the start. In the work presented here, we propose a first step in this direction by setting out to demonstrate that we can formally publish high-level scientific claims in formal logic, and publish the results in a special issue of an existing journal. We use the concept and technology of nanopublications for this endeavor, and represent not just the submissions and final papers in this RDF-based format, but also the whole process in between, including reviews, responses, and decisions. We do this by performing a field study with what we call formalization papers, which contribute a novel formalization of a previously published claim. We received 15 submissions from 18 authors, who then went through the whole publication process leading to the publication of their contributions in the special issue. Our evaluation shows the technical and practical feasibility of our approach. The participating authors mostly showed high levels of interest and confidence, and mostly experienced the process as not very difficult, despite the technical nature of the current user interfaces. We believe that these results indicate that it is possible to publish scientific results from different fields with machine-interpretable semantics from the start, which in turn opens countless possibilities to radically improve in the future the effectiveness and efficiency of the scientific endeavor as a whole.
Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm

Parameter identification of the dynamic model of collaborative robots is the basis of the development of collaborative robot motion state control, path tracking, state monitoring, fault diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research. Aiming at the identification of dynamic parameters of the collaborative robot, this paper proposes an identification algorithm based on weighted least squares and random weighted particle swarm optimization (WLS-RWPSO). Firstly, the dynamics mathematical model of the robot is established using the Lagrangian method, the dynamic parameters of the robot to be identified are determined, and the linear form of the dynamics model of the robot is derived taking into account the joint friction characteristics. Secondly, the weighted least squares method is used to obtain the initial solution of the parameters to be identified. Based on the traditional particle swarm optimization algorithm, a random weight particle swarm optimization algorithm is proposed for the local optimal problem to identify the dynamic parameters of the robot. Thirdly, the fifth-order Fourier series is designed as the excitation trajectory, and the original data collected by the sensor are denoised and smoothed by the Kalman filter algorithm. Finally, the experimental verification on a six-degree-of-freedom collaborative robot proves that the predicted torque obtained by the identification algorithm in this paper has a high degree of matching with the measured torque, and the established model can reflect the dynamic characteristics of the robot, effectively improving the identification accuracy.
Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph

Due to the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the ranking-based group identification (RGI) task, i.e., recommending groups to users. The major challenge in this task is how to effectively and efficiently leverage the item interaction information from users' and groups' online behaviors, in addition to the information from social interaction between users and groups. Though recent developments of Graph Neural Networks (GNNs) succeed in aggregating both social and user-item interaction simultaneously, they however fail to comprehensively resolve this RGI task. In this paper, we propose a novel GNN-based framework named Contextualized Factorized Attention for Group identification (CFAG). We devise tripartite graph convolution to aggregate information from different types of neighborhoods among users, groups, and items. To cope with the data sparsity issue, we devise a novel propagation augmentation (PA) layer, which is based on our proposed factorized attention mechanism. PA layers efficiently learn the relevance degree of non-neighbor nodes to improve the information propagation to users. Experimental results on three benchmark datasets verify the superiority of CFAG. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.
Robust Training of Graph Neural Networks via Noise Governance

Graph Neural Networks (GNNs) have become widely-used models for semi-supervised learning. However, the robustness of GNNs in the presence of label noise remains a largely under-explored problem. In this paper, we consider an important yet challenging scenario where labels on nodes of graphs are not only noisy but also scarce. In this scenario, the performance of GNNs is prone to degrade due to label noise propagation and insufficient learning. To address these issues, we propose a novel RTGNN (<u>R</u>obust <u>T</u>raining of <u>G</u>raph <u>N</u>eural <u>N</u>etworks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise. More specifically, we introduce self-reinforcement and consistency regularization as supplemental supervision. The self-reinforcement supervision is inspired by the memorization effects of deep neural networks and aims to correct noisy labels. Further, the consistency regularization prevents GNNs from overfitting to noisy labels via mimicry loss in both the inter-view and intra-view perspectives. To leverage such supervisions, we divide labels into clean and noisy types, rectify inaccurate labels, and further generate pseudo-labels on unlabeled nodes. Supervision for nodes with different types of labels is then chosen adaptively. This enables sufficient learning from clean labels while limiting the impact of noisy ones. We conduct extensive experiments to evaluate the effectiveness of our RTGNN framework, and the results validate its consistent superior performance over state-of-the-art methods with two types of label noises and various noise rates.