Reviewerly/exHarmony huggingface dataset:
The Key Success Factors for Attracting Foreign Investment in the Post-Epidemic Era

The global economy has been hit by the unexpected COVID-19 outbreak, and foreign investment has been seen as one of the most important tools to boost the economy. However, in the highly uncertain post-epidemic era, determining how to attract foreign investment is the key to revitalizing the economy. What are the important factors for governments to attract investment, and how to improve them? This will be an important decision in the post-epidemic era. Therefore, this study develops a novel decision-making model to explore the key factors in attracting foreign investment. The model first uses fuzzy Delphi to explore the key factors of attracting foreign investment in the post-epidemic era, and then uses DEMATEL to construct the causal relationships among these factors. To overcome the uncertainty of various information sources and inconsistent messages from decision-makers, this study combined neutrosophic set theory to conduct quantitative analysis. The results of the study show that the model is suitable for analyzing the key factors of investment attraction in the post-epidemic period. Based on the results of the study, we also propose strategies that will help the relevant policy-making departments to understand the root causes of the problem and to formulate appropriate investment strategies in advance. In addition, the model is also used for comparative analysis, which reveals that this novel approach can integrate more incomplete information and present expert opinions in a more objective way.
k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

At present, the explosive growth of data and the mass storage state have brought many problems such as computational complexity and insufficient computational power to clustering research. The distributed computing platform through load balancing dynamically configures a large number of virtual computing resources, effectively breaking through the bottleneck of time and energy consumption, and embodies its unique advantages in massive data mining. This paper studies the parallel k-means extensively. This article first initializes random sampling and second parallelizes the distance calculation process that provides independence between the data objects to perform cluster analysis in parallel. After the parallel processing of the MapReduce, we use many nodes to calculate distance, which speeds up the efficiency of the algorithm. Finally, the clustering of data objects is parallelized. Results show that our method can provide services efficiently and stably and have good convergence.
Weak-Light Image Enhancement Method Based on Adaptive Local Gamma Transform and Color Compensation

In weak-light environments, images suffer from low contrast and the loss of details. Traditional image enhancement models are usually failure to avoid the issue of overenhancement. In this paper, a simple and novel correction method is proposed based on an adaptive local gamma transformation and color compensation, which is inspired by the illumination reflection model. Our proposed method converts the source image into YUV color space, and the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mi>Y</mi></math> component is estimated with a fast guided filter. The local gamma transform function is used to improve the brightness of the image by adaptively adjusting the parameters. Finally, the dynamic range of the image is optimized by a color compensation mechanism and a linear stretching strategy. By comparing with the state-of-the-art algorithms, it is demonstrated that the proposed method adaptively reduces the influence of uneven illumination to avoid overenhancement and improve the visual effect of low-light images.
Improved Image Matting via Real-time User Clicks and Uncertainty Estimation

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous, thus restricting the application of these methods. Recently, some trimap-free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, without the trimap guidance in some cases, the target network is ambiguous about which is the foreground target. In fact, choosing the foreground is a subjective procedure and depends on the user’s intention. To this end, this paper proposes an improved deep image matting framework which is trimap-free and only needs several user click interactions to eliminate the ambiguity. Moreover, we introduce a new uncertainty estimation module that can predict which parts need polishing and a following local refinement module. Based on the computation budget, users can choose how many local parts to improve with the uncertainty guidance. Quantitative and qualitative results show that our method performs better than existing trimap-free methods and comparably to state-of-the-art trimap-based methods with minimal user effort.
An effective compression algorithm for real-time transmission data using predictive coding with mixed models of LSTM and XGBoost

In this paper, a compression and decompression algorithm is proposed for real-time transmission data by combining discrete wavelet decomposition and reconstruction, hybrid model prediction of long short-term memory (LSTM) and extreme gradient boosting (XGBoost), and the development of quantization sequences. The forward compression and reverse decompression are achieved by continuously acquiring the prediction error and data series with quantization error through the iterative shifting approach. To accurately forecast data series with quantization errors, three classes of data with specific distribution characteristics collected from the measurement-while-drilling (MWD) operation field are decomposed into approximate data and detailed data, which are then predicted by autoregressive integrated moving average (ARIMA), support vector regression (SVR), XGBoost, backpropagation neural network (BPNN), radial basis function neural network (RBFNN), and LSTM models. Particularly, the LSTM model and the XGBoost model are proven to be suitable strategies for working with approximate data and detailed data separately by comparing with conventional approaches in terms of the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) predictive indexes. Besides, experiment studies show that almost 50% compression performance can be reached under one in ten thousand distortions. It is verified that the proposed algorithm is appropriate and efficient for real-time data transmission applications with high performance.
SLA-Based Scheduling of Spark Jobs in Hybrid Cloud Computing Environments

Big data frameworks such as Apache Spark is becoming prominent to perform large-scale data analytics jobs in various domains. However, due to limited resource availability, the local or on-premise computing resources are often not sufficient to run these jobs. Therefore, public cloud resources can be hired on a pay-per-use basis from the cloud service providers to deploy a Spark cluster entirely on the cloud. Nevertheless, using only cloud resources can be costly. Hence, both local and cloud resources nowadays are used together to deploy a hybrid cloud computing cluster. However, scheduling jobs in a cluster deployed on hybrid clouds is challenging in the presence of various Service-Level Agreement (SLA) demands such as cost minimization and job deadline guarantee. Most of the existing works either consider a public or a locally deployed cluster and mainly focus on improving job performance in the cluster. In this article, we propose efficient scheduling algorithms that leverage from different VM instance pricing in a hybrid cloud deployed cluster to optimize the Virtual Machine (VM) usage cost for both local and cloud resources and maximize the job deadline met percentage. We have conducted extensive simulation-based experiments to compare our proposed algorithms with the baseline approaches. In addition, we have developed a prototype system on top of Apache Mesos cluster manager and performed real experiments to evaluate the applicability of our proposed approaches in a real platform with benchmark applications. The results show that our proposed algorithms are highly scalable and reduce the cost of VM usage of a hybrid cluster for up to 20 percent.
Discrete Multiple Kernel k-means

The multiple kernel k-means (MKKM) and its variants utilize complementary information from different kernels, achieving better performance than kernel k-means (KKM). However, the optimization procedures of previous works all comprise two stages, learning the continuous relaxed label matrix and obtaining the discrete one by extra discretization procedures. Such a two-stage strategy gives rise to a mismatched problem and severe information loss. To address this problem, we elaborate a novel Discrete Multiple Kernel k-means (DMKKM) model solved by an optimization algorithm that directly obtains the cluster indicator matrix without subsequent discretization procedures. Moreover, DMKKM can strictly measure the correlations among kernels, which is capable of enhancing kernel fusion by reducing redundancy and improving diversity. What’s more, DMKKM is parameter-free avoiding intractable hyperparameter tuning, which makes it feasible in practical applications. Extensive experiments illustrated the effectiveness and superiority of the proposed model.
Super-robust nonadiabatic geometric quantum control

Nonadiabatic geometric quantum computation (NGQC) and nonadiabatic holonomic quantum computation (NHQC) have been proposed to reduce the run time of geometric quantum gates. However, in terms of robustness against experimental control errors, the existing NGQC and NHQC scenarios have no advantage over standard dynamical gates in most cases. Here, we give the reasons why nonadiabatic geometric gates are sensitive to the control errors and, further, we propose a scheme of super-robust nonadiabatic geometric quantum control, in which the super-robust condition can guarantee both high speed and robustness of the geometric gate. To illustrate the working mechanism of super-robust geometric quantum gates, we give two simple examples of SR-NGQC and SR-NHQC for two- and three-level quantum systems, respectively. Theoretical and numerical results with the experimental parameters indicate that our scheme can significantly improve the gate performance compared to the previous NGQC, NHQC, and standard dynamical schemes. Super-robust geometric quantum computation can be applied to various physical platforms such as superconducting qubits, quantum dots, and trapped ions. All of these sufficiently show that our scheme provides a promising way towards robust geometric quantum computation.Received 18 March 2021Accepted 7 September 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.L032066Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasGeometric & topological phasesQuantum controlQuantum gatesQuantum information with solid state qubitsTopological quantum computingQuantum Information
Tailoring data source distributions for fairness-aware data integration

Data scientists often develop data sets for analysis by drawing upon sources of data available to them. A major challenge is to ensure that the data set used for analysis has an appropriate representation of relevant (demographic) groups: it meets desired distribution requirements. Whether data is collected through some experiment or obtained from some data provider, the data from any single source may not meet the desired distribution requirements. Therefore, a union of data from multiple sources is often required. In this paper, we study how to acquire such data in the most cost effective manner, for typical cost functions observed in practice. We present an optimal solution for binary groups when the underlying distributions of data sources are known and all data sources have equal costs. For the generic case with unequal costs, we design an approximation algorithm that performs well in practice. When the underlying distributions are unknown, we develop an exploration-exploitation based strategy with a reward function that captures the cost and approximations of group distributions in each data source. Besides theoretical analysis, we conduct comprehensive experiments that confirm the effectiveness of our algorithms.
Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network

Adaption of end-to-end speech recognition systems to new tasks is known to be challenging. A number of solutions have been proposed which apply external language models with various fusion methods, possibly with a combination of two-pass decoding. Also TTS systems have been used to generate adaptation data for the end-to-end models. In this paper we show that RNN-transducer models can be effectively adapted to new domains using only small amounts of textual data. By taking advantage of model's inherent structure, where the prediction network is interpreted as a language model, we can apply fast adaptation to the model. Adapting the model avoids the need for complicated decoding time fusions and external language models. Using appropriate regularization, the prediction network can be adapted to new domains while still retaining good generalization capabilities. We show with multiple ASR evaluation tasks how this method can provide relative gains of 10-45% in target task WER. We also share insights how RNN-transducer prediction network performs as a language model.
Towards Lifelong Learning of End-to-End ASR

Automatic speech recognition (ASR) technologies today are primarily optimized for given datasets; thus, any changes in the application environment (e.g., acoustic conditions or topic domains) may inevitably degrade the performance. We can collect new data describing the new environment and fine-tune the system, but this naturally leads to higher error rates for the earlier datasets, referred to as catastrophic forgetting. The concept of lifelong learning (LLL) aiming to enable a machine to sequentially learn new tasks from new datasets describing the changing real world without forgetting the previously learned knowledge is thus brought to attention. This paper reports, to our knowledge, the first effort to extensively consider and analyze the use of various approaches of LLL in end-to-end (E2E) ASR, including proposing novel methods in saving data for past domains to mitigate the catastrophic forgetting problem. An overall relative reduction of 28.7% in WER was achieved compared to the fine-tuning baseline when sequentially learning on three very different benchmark corpora. This can be the first step toward the highly desired ASR technologies capable of synchronizing with the continuously changing real world.
EVCsiNet: Eigenvector-Based CSI Feedback Under 3GPP Link-Level Channels

Recently, deep learning based methods have been widely used for wireless communications. In this letter, different from current researches considering full channel state information (F-CSI) feedback, the eigenvector based CSI feedback with deep learning approach is proposed, referred to as EVCsiNet, where the joint eigenvector concatenated from multiple subbands is compressed and recovered at the encoder and decoder, respectively. Simulation results verify the superiority of our schemes over CSI recovery accuracy and feedback overhead compared with conventional methods using codebooks on link-level channel models in different scenarios.
Utilization of Radial Basis Function Neural Network model for Water production forecasting in Seawater Greenhouse units

The solar seawater greenhouse desalination is a plant that simulates the natural water cycle through seawater evaporation and condensation into freshwater. In the present work, the radial basis function neural network integrated with different optimization algorithms is presented to predict the water production capacity considering the effect of various parameters on the performance seawater greenhouse system. Different statistical metrics are employed to examine the performance of the proposed models. Also, in order to obtain a more satisfactory performance of water production forecasting in a seawater greenhouse, the effect of neurons’ numbers in the hidden layer of the proposed neural network is studied. According to the obtained results, the forecasting accuracy of the proposed radial basis function neural network optimized with the hybrid particle swarm optimization–gray wolf optimizer algorithm with nine neurons in the hidden layer with the correlation coefficient and coefficient of determination of 0.998 and 0.996 in training phase is much better than those of the other models. Also, the best values for the front greenhouse dimension are obtained as Width = 125 m, Length = 200 m, and Height = 4 m. Also, the roof transparency is obtained by 0.6.
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance

The detection of contextual anomalies is a challenging task for surveillance since an observation can be considered anomalous or normal in a specific environmental context. An unmanned aerial vehicle (UAV) can utilize its aerial monitoring capability and employ multiple sensors to gather contextual information about the environment and perform contextual anomaly detection. In this work, we introduce a deep neural network-based method (CADNet) to find point anomalies (i.e., single instance anomalous data) and contextual anomalies (i.e., context-specific abnormality) in an environment using a UAV. The method is based on a variational autoencoder (VAE) with a context sub-network. The context sub-network extracts contextual information regarding the environment using GPS and time data, then feeds it to the VAE to predict anomalies conditioned on the context. To the best of our knowledge, our method is the first contextual anomaly detection method for UAV-assisted aerial surveillance. We evaluate our method on the AU-AIR dataset in a traffic surveillance scenario. Quantitative comparisons against several baselines demonstrate the superiority of our approach in the anomaly detection tasks. The codes and data will be available at https://bozcani.github.io/cadnet.
FedAffect: Few-shot federated learning for facial expression recognition

Annotation of large-scale facial expression datasets in the real world is a major challenge because of privacy concerns of the individuals due to which traditional supervised learning approaches won’t scale. Moreover, training models on large curated datasets often leads to dataset bias which reduces generalizability for real world use. Federated learning is a recent paradigm for training models collaboratively with decentralized private data on user devices. In this paper, we propose a few-shot federated learning framework which utilizes few samples of labeled private facial expression data to train local models in each training round and aggregates all the local model weights in the central server to get a globally optimal model. In addition, as the user devices are a large source of unlabeled data, we design a federated learning based self-supervised method to disjointly update the feature extractor network on unlabeled private facial data in order to learn robust and diverse face representations. Experimental results by testing the globally trained model on benchmark datasets (FER-2013 and FERG) show comparable performance with state of the art centralized approaches. To the best of author’s knowledge, this is the first work on few-shot federated learning for facial expression recognition.
Internet of Things-based Smart Healthcare Systems and Wireless Biomedical Sensing Devices in Monitoring, Detection, and Prevention of COVID-19

(Usak et al., 2020) Cloud and wireless sensor networks (Lazaroiu et al., 2021) harnessed in data processing and storage (Andronie et al., 2021a, b) can ensure monitoring rehabilitation and recovery processes by analyzing health status and behavioral changes. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. (Khan and Algarni, 2020) The advancement of smart and computerized molecular diagnostic tools harnessing biomedical big data analysis, cloud computing, and machine learning-based real-time data sensing and processing (Kovacova and Lazaroiu, 2021) can assist in COVID-19 detection, monitoring, and treatment, and cloud data storage for supportive decisions. Conclusions, Implications, Limitations, and Further Research Directions Internet of Medical Things assists smart healthcare systems in analyzing gathered data, integrating wearable health monitoring sensors, diagnostics tools, and telemedicine equipment during the COVID-19 pandemic by use of wireless biomedical sensing devices.
On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models

With an increase in low-cost machine learning APIs, advanced machine learning models may be trained on private datasets and monetized by providing them as a service. However, privacy researchers have demonstrated that these models may leak information about records in the training dataset via membership inference attacks. In this paper, we take a closer look at another inference attack reported in literature, called attribute inference, whereby an attacker tries to infer missing attributes of a partially known record used in the training dataset by accessing the machine learning model as an API. We show that even if a classification model succumbs to membership inference attacks, it is unlikely to be susceptible to attribute inference attacks. We demonstrate that this is because membership inference attacks fail to distinguish a member from a nearby non-member. We call the ability of an attacker to distinguish the two (similar) vectors as strong membership inference. We show that membership inference attacks cannot infer membership in this strong setting, and hence inferring attributes is infeasible. However, under a relaxed notion of attribute inference, called approximate attribute inference, we show that it is possible to infer attributes close to the true attributes. We verify our results on three publicly available datasets, five membership, and three attribute inference attacks reported in literature.