Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Ensemble Architecture of Residual Attention-Based Deep Metric Learning for Remote Sensing Image Retrieval

Recently, deep metric learning (DML) has received widespread attention in the field of remote sensing image retrieval (RSIR), owing to its ability to extract discriminative features to represent images and then to measure the similarity between images via learning a distance function among feature vectors. However, the distinguishability of features extracted by the most current DML-based methods for RSIR is still not sufficient, and the retrieval efficiency needs to be further improved. To this end, we propose a novel ensemble architecture of residual attention-based deep metric learning (EARA) for RSIR. In our proposed architecture, residual attention is introduced and ameliorated to increase feature discriminability, maintain global features, and concatenate feature vectors of different weights. Then, descriptor ensemble rather than embedding ensemble is chosen to further boost the performance of RSIR with reduced time cost and memory consumption. Furthermore, our proposed architecture can be flexibly extended with different types of deep neural networks, loss functions, and feature descriptors. To evaluate the performance and efficiency of our architecture, we conduct exhaustive experiments on three benchmark remote sensing datasets, including UCMD, SIRI-WHU, and AID. The experimental results demonstrate that the proposed architecture outperforms the four state-of-the-art methods, including BIER, A-BIER, DCES, and ABE, by 15.45%, 13.04%, 10.31%, and 6.62% in the mean Average Precision (mAP), respectively. As for the retrieval execution complexity, the retrieval time and floating point of operations (FLOPs), needed by the proposed architecture on AID, reduce by 92% and 80% compared to those needed by ABE, albeit with the same Recall@1 between the two methods.
Machine Learning-Based Stress Level Detection from EEG Signals

Recent statistical studies indicate an increase in mental stress in human beings around the world. Due to the recent pandemic and the subsequent lockdowns, people are suffering from different types of stress for being jobless, financially damaged, loss of business, deterioration of personal/family relationships, etc. Stress could be a severe factor for many common disorders if experienced for a long time. Stress is associated with the brain activities of human beings that can be scanned by electroencephalogram (EEG) signals which is very complex and often challenging to understand the signal’s pattern. This paper presented a system to detect the stress level from the EEG signals using machine learning algorithms. The proposed method, at first, removed physiological noises from the EEG signal applying a band-pass FIR filter. A discrete wavelet transform (DWT) method was used for features extraction from the filtered EEG signal. The features were classified using a set of classifiers those are k-nearest neighbors (kNN), support vector machine (SVM), Naïve Bayes, and linear discriminant analysis (LDA). Two levels of stressed EEG data were considered and found the classification accuracy of 86.3%, 91.0%, 81.7%, and 90.0%. The highest classification accuracy, the SVM classifier, outperforms the current state of the art by 15.8%.
A Transfer Learning Approach for Securing Resource-Constrained IoT Devices

In recent years, Internet of Things (IoT) security has attracted significant interest by researchers due to new characteristics of IoT such as heterogeneity of devices, resource constraints, and new types of attacks targeting IoT. Intrusion detection, which is an indispensable part of a security system, is also included in these studies. In order to explore the complex characteristics of IoT, machine learning methods, which rely on long training time to generate intrusion detection models, are proposed in the literature. Furthermore, these systems need to learn a new/fresh model from scratch when the environment changes. This study explores the use of transfer learning in order to generate intrusion detection algorithms for such dynamically changing IoT. Transfer learning is an approach that stores knowledge learned from a problem domain/task and applies that knowledge to another problem domain/task. Here, it is employed in the following two settings: transferring knowledge for generating suitable intrusion algorithms for new devices, transferring knowledge for detecting new types of attacks. In this study, Routing Protocol for Low-Power and Lossy Network (RPL), a routing protocol for resource-constrained wireless networks, is used as an exemplar protocol and specific attacks against RPL are targeted. The experimental results show that the transfer learning approach gives better performance than the traditional approach. Moreover, the proposed approach significantly reduces learning time, which is an important factor for putting devices/networks in operation in a timely manner. Even though transfer learning has been considered a potential candidate for improving IoT security, to the best of our knowledge, this is the first application of transfer learning under these two settings in RPL-based IoT networks.
Latency-Bounded Off-Site Virtual Node Protection in NFV

In network function virtualization (NFV), the client’s service requests will go through multiple service functions (SFs). The instances of the required SFs will be hosted on the geographically-distributed physical nodes in physical networks (PNs). The failure of any physical nodes or virtual nodes (i.e., SF instances) will impact the delivery of services and the client’s experience. It is essential for service providers to take the node failure and network reliability into account. Different from physical node failures that will affect the PN’s topology and connectivity, virtual node failures impact the services delivery of certain clients. As a result, applying traditional backup schemes that are designed for physical node failures may not efficiently provide protection for virtual node failures. In this work, we define and mathematically formulate a new latency-bounded off-site virtual node protection (LOVNP) problem in NFV. After proving the NP-hardness of the LOVNP problem, we introduce a novel shared protection technique called pairwise node protection to effectively facilitate the protection of node failures in NFV. Then, we propose an efficient heuristic algorithm called protection centrality based pairwise node protection (PC-PNP) to optimize the LOVNP problem and prove that PC-PNP has a logarithm-approximation boundary. Our extensive simulations and analysis show that the proposed algorithm significantly outperforms the algorithms that are extended from the existing work.
Full scale subsurface flow constructed wetlands for domestic wastewater treatment: 3 years' experience

In the present study, a horizontal subsurface flow constructed wetlands divided three equal parts was designed and constructed at a full scale to treat wastewater of Yörükçal village located in Black Sea region. Three macrophytes namely Juncus acutus, Cortaderia selloana, and Phragmites australis were used in each wetland system. The total surface area of all wetlands is 520 m2. The average removal efficiencies of J. acutus, C. selloana, and P. australis were obtained as 57, 56, and 54% for BOD; 51, 33, and 27% for TN; 55, 58, and 46% for OM; 45, 39, and 32% for orthophosphate; 22, 38, and 24% for NH4+ ‐N; 13, 20, and 24% for TSS; 33, 44 and 39% for SO42– , respectively. One‐way analysis of variance (ANOVA) between groups was applied to determine any difference in the removal of all parameters between the plant types and months depending on the mean values of pollutant removal. Due to the problems such as costs of construction, maintenance and repair, and reduced flow rate encountered especially during the collection of wastewater in rural regions and transport to wastewater treatment plants in the city, the in‐situ treatment applications may be unavoidable. According to the results, Constructed Wetlands (CW) designed correctly are economically and environmentally to treat wastewater of rural settlements. © 2018 American Institute of Chemical Engineers Environ Prog, 37: 1348–1360, 2018
Effect of method, step size and drying temperature on sorption isotherms

The determination of the sorption curve has traditionally been a very lengthy and laborious process. Therefore there is an increasing interest in developing and using time saving methods for sorption measurements. These could be using fewer measurement points and/or increasing drying temperatures. When reducing the time used for tests the risk of not reaching the equilibrium and by that the true equilibrium moisture content will increase. A sufficient compromise between the accuracy and the time/labour savings should possibly be found.
Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks

We present a Long Short Term Memory (LSTM) encoder-decoder architecture to anticipate the future positions of vehicles in a road network given several seconds of historical observations and associated map features. Unlike existing architectures, the proposed method incorporates and updates the surrounding vehicle information in both the encoder and decoder, making use of dynamically predicted new data for accurate prediction in longer time horizons. It seamlessly performs four tasks: the first task encodes a feature given the past observations, the second task estimates future maneuvers given the encoded state, the third task predicts the future motion given the estimated maneuvers and the initially encoded states, and the fourth task estimates future trajectory given the encoded state and the predicted maneuvers and motions. Experiments on a public benchmark and a new, publicly available radar dataset demonstrate that our approach can equal or surpass the state-of-the-art for long term trajectory prediction.
Evaluation of the Social Restriction and its Effect to the COVID-19 Spread in Indonesia

The government of Indonesia has implemented a Large-Scale Social Restriction (LSSR/PSBB) of various levels, in the local and national region to control the COVID-19 transmission over the country. Successful social restrictions are believed as a powerful way in controlling COVID-19 spread. This paper evaluated the social restriction that has been implemented in Indonesia and its connection to the spread of COVID-19 in the period 15 February 2020 to 10 January 2021. The social restriction is quantified by the changes in human movement, while the spread of COVID-19 is computed by a growth rate and doubling time of the COVID-19 cases. A statistic measure by linear regression is applied to investigate the dependency of the COVID-19 spread and human movement. This paper showed the social restriction reduced human mobility up to more than 50% when the restriction was started. It flattened the cases but it only lasted for approximately two months. Afterward, the growth rate and the doubling time showed an increase of the COVID-19 cases and it was getting much worse starting at the end of the year 2020. Moreover, we found that the implemented social restriction in Indonesia is less effective to reduce the COVID-19 spread in Indonesia as human mobility during a holiday, an especially long weekend is much stronger.
Channel Estimation and Hybrid Precoding for Millimeter Wave Communications: A Deep Learning-Based Approach

Hybrid analog and digital beamforming (HBF) has been regarded as a key technology for future millimeter wave (mmWave) communication systems due to its ability to obtain a good trade-off between achievable beamforming gain and hardware cost. In this paper, we investigate the channel estimation and hybrid precoding for mmWave MIMO systems with deep learning. We adopt the hierarchical codebook based algorithm for channel estimation as it requires limited number of pilot transmissions, and enhance its performance by proposing a new codebook design algorithm based on manifold optimization (MO). With the estimated channel state information (CSI) as the input, we develop a robust HBF network (HBF-Net) by applying convolutional layers and attention mechanism, which can be trained to generate a robust HBF matrix targeting at spectral efficiency maximization with imperfect CSI. To further improve the performance, we propose a joint channel estimation and HBF optimization network (CE-HBF-Net). Considering that the adaptively selected HBF vectors in the hierarchical codebook based channel estimation are different for different channel realizations, we skillfully propose an index assign-and-input method to efficiently feed such information to the CE-HBF-Net to reduce the network input dimensions and make the network trainable. Furthermore, we propose a signal self-attention mechanism to enable the CE-HBF-Net to intelligently assign larger weight coefficients to those signals that contribute more to channel estimation. Simulation results show that the well-designed HBF-Net and CE-HBF-Net outperform the conventional HBF algorithms with imperfect channel and exhibit robustness to mismatches between offline training and online deployment stages.
Deep Learning-Based Automated Lip-Reading: A Survey

A survey on automated lip-reading approaches is presented in this paper with the main focus being on deep learning related methodologies which have proven to be more fruitful for both feature extraction and classification. This survey also provides comparisons of all the different components that make up automated lip-reading systems including the audio-visual databases, feature extraction, classification networks and classification schemas. The main contributions and unique insights of this survey are: 1) A comparison of Convolutional Neural Networks with other neural network architectures for feature extraction; 2) A critical review on the advantages of Attention-Transformers and Temporal Convolutional Networks to Recurrent Neural Networks for classification; 3) A comparison of different classification schemas used for lip-reading including ASCII characters, phonemes and visemes, and 4) A review of the most up-to-date lip-reading systems up until early 2021.
Variation Characteristic of NDVI and its Response to Climate Change in the Middle and Upper Reaches of Yellow River Basin, China

Observing vegetation normalized difference vegetation index (NDVI) changes, climate change characteristics, and their response relationship have a great significance to the ecosystem's regulation and improvement the human settlements. Based on GIMMS AVHRR NDVI and MODIS NDVI datasets as well as temperature, precipitation, and sunshine duration data, this study used unitary linear trend analysis, correlation analysis, RS, and GIS data to analyze the spatiotemporal variation characteristics of vegetation NDVI in the middle and upper reaches of the Yellow River between 1989 and 2018. It also analyzed the spatiotemporal response between vegetation NDVI and climate factors (temperature, precipitation, and sunshine duration). The results showed that the vegetation NDVI in the study area had an increasing trend over the past 30 years, growing by 31.28%, and the NDVI change in 81.83% of the pixels was positive, the highest being 0.025. The temperature in the middle and upper reaches of the Yellow River showed an obvious upward trend, showing an overall distribution pattern of low temperature in the southwest and high temperature in the southeast. The precipitation showed a gentle upward trend and a spatial distribution pattern of a gradual decrease from southeast to northwest. The sunshine duration showed an obvious decreasing trend and a spatial distribution pattern of gradually increasing from southeast to northwest. In the past 30 years, the annual mean NDVI in the study area had a positive correlation with temperature and precipitation and a negative correlation with sunshine duration.
A Comparative Study of Deep Learning-based Depth Estimation Approaches: Application to Smart Mobility

In autonomous vehicle systems, the quality of scene perception is of great importance for security preoccupation in road environments. In this context, an accurate localization of potential obstacles is one of the most challenging tasks. In recent years, substantial progress has been made in the field of depth estimation for detection purposes with the spread of methods relying on deep learning with monocular or stereo-scopic camera(s). These two families of approaches did show an upstanding yet inconsistent performance in different road scenes circumstances. A deep understanding and comparison of these approaches is required to allow the community an easier assessment, which breeds to more adequate choice for their own systems. In this paper, we propose a comparative study of state-of-the-art deep learning depth estimation methods using monocular and stereoscopic cameras. The evaluation is performed on road environment over the challenging KITTI dataset.
Comparative Evaluation of Efficiency and Reliability of Single-Phase Five-Level NPC Inverters for Photovoltaic Systems

It is required to reduce the cost of PV energy to be competitive as an alternative energy source. The efficiency and reliability of PV inverters are important aspects to be enhanced to reduce the cost of PV energy since they contribute to the reduction of operational and maintenance costs and to the increase of annual energy production. Therefore, more comprehensive analysis on both efficiency and reliability together including the impacts of various factors is required. In this paper, single-phase five-level NPC inverter topologies for photovoltaic systems are comparatively evaluated in terms of efficiency and reliability by simulations and experiments, in which the impacts of pulse width modulation (PWM) methods and installation locations are considered. The results are expected to give an insight to determine the proper topology and PWM method depending on its installation location considering the reliability as well as the efficiency and total harmonic distortion (THD).
Insider Threat Detection using Deep Autoencoder and Variational Autoencoder Neural Networks

Internal attacks are one of the biggest cybersecurity issues to companies and businesses. Despite the implemented perimeter security systems, the risk of adversely affecting the security and privacy of the organization’s information remains very high. Actually, the detection of such a threat is known to be a very complicated problem, presenting many challenges to the research community. In this paper, we investigate the effectiveness and usefulness of using Autoencoder and Variational Autoencoder deep learning algorithms to automatically defend against insider threats, without human intervention. The performance evaluation of the proposed models is done on the public CERT dataset (CERT r4.2) that contains both benign and malicious activities generated from 1000 simulated users. The comparison results with other models show that the Variational Autoencoder neural network provides the best overall performance with a higher detection accuracy and a reasonable false positive rate.
TanhSoft—Dynamic Trainable Activation Functions for Faster Learning and Better Performance

Deep learning, at its core, contains functions that are the composition of a linear transformation with a nonlinear function known as the activation function. In the past few years, there is an increasing interest in the construction of novel activation functions resulting in better learning. In this work, we propose three novel activation functions with learnable parameters, namely TanhSoft-1, TanhSoft-2, and TanhSoft-3, which are shown to outperform several well-known activation functions. For instance, replacing ReLU with TanhSoft-1, TanhSoft-2, and Tanhsot-3 improves top-1 classification accuracy by 6.06%, 5.75%, and 5.38% respectively on VGG-16(with batch-normalization), by 3.02%, 3.25% and 2.93% respectively on PreActResNet-34 in CIFAR-100 dataset, by 1.76%, 1.93%, and 1.82% respectively on WideResNet 28-10 in Tiny ImageNet dataset. TanhSoft-1, TanhSoft-2, and Tanhsot-3 outperformed ReLU on mean average precision (mAP) by 0.7%, 0.8%, and 0.6% respectively in object detection problem on SSD 300 model in Pascal VOC dataset.
Zernike Pooling: Generalizing Average Pooling Using Zernike Moments

Most of the established neural network architectures in computer vision are essentially composed of the same building blocks (e.g., convolutional, normalization, regularization, pooling layers, etc.), with their main difference being the connectivity of these components within the architecture and not the components themselves. In this paper we propose a generalization of the traditional average pooling operator. Based on the requirements of <italic>efficiency</italic> (to provide information without repetition), <italic>equivalence</italic> (to be able to produce the same output as average pooling) and <italic>extendability</italic> (to provide a natural way of obtaining novel information), we arrive at a formulation that generalizes average pooling using the Zernike moments. Experimental results on <italic>Cifar 10</italic>, <italic>Cifar 100</italic> and <italic>Rotated MNIST</italic> data-sets showed that the proposed method was able to outperform the two baseline approaches, global average pooling and average pooling <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula>, as well as the two variants of Stochastic pooling and AlphaMEX in every case. A worst-case performance analysis on <italic>Cifar-100</italic> showed that significant gains in classification accuracy can be realised with only a modest 10% increase in training time.
Stock Price Prediction Based on LSTM Deep Learning Model

Predicting the stock market is either the easiest or the toughest task in the field of computations. There are many factors related to prediction, physical factors vs. physiological, rational and irrational , capitalist sentiment, market , etc. All these aspects combine to make stock costs volatile and are extremely tough to predict with high accuracy. The prices of a stock market depend very much on demand and supply. High demand stocks will increase in price while heavy selling stocks will decrease. Fluctuations in stock prices affect investor perception and thus there is a need to predict future share prices and to predict stock market prices to make more acquaint and precise investment decisions. We examine data analysis in this domain as a game-changer. This paper proposes that historical value bears the impact of all other market events and can be used to predict future movement. Machine Learning techniques can detect paradigms and insights that can be used to construct surprisingly correct predictions. We propose the LSTM (Long Short Term Memory) model to examine the future price of a stock. This paper is to predict stock market prices to make more acquaint and precise investment decisions.