Reviewerly/exHarmony huggingface dataset:
Multisource Domain Adaptation for Remote Sensing Using Deep Neural Networks

In applying machine learning to remote sensing problems, it is often the case that multiple training data sources, known as domains, are available for the same task. It is sample-inefficient to train separate models per domain, which motivates learning a single model from multiple sources. For example, the local climate zone (LCZ) classification problem that aims to produce per-pixel classifications of surface structure from remotely sensed images of urban and rural environments. These classification maps need to be generated for different cities at different times. To do this efficiently, available training data from different sources (i.e., cities) must be adapted for the task at hand. However, multisource domain adaptation (MDA) is a challenging problem and is particularly apparent when there are significant changes in the data distribution among these sources. In this article, we propose a scalable yet simple adaptive MDA (AMDA) framework to address this problem. AMDA is also capable of dealing with imbalanced data distributions among the sources more effectively than existing baselines. We also extend two techniques originally proposed for domain expansion (DE) to the task of DA. AMDA and the extended DE techniques are implemented and evaluated on the LCZ classification problem. Despite its simplicity, AMDA is able to achieve more than 12% improvement over the baseline.
Hyperspectral outcrop models for palaeoseismic studies

Abstract The traditional study of palaeoseismic trenches, involving logging, stratigraphic and structural interpretation, can be time consuming and affected by biases and inaccuracies. To overcome these limitations, a new workflow is presented that integrates infrared hyperspectral and photogrammetric data to support field‐based palaeoseismic observations. As a case study, this method is applied on two palaeoseismic trenches excavated across a post‐glacial fault scarp in northern Finnish Lapland. The hyperspectral imagery (HSI) is geometrically and radiometrically corrected, processed using established image processing algorithms and machine learning approaches, and co‐registered to a structure‐from‐motion point cloud. HSI‐enhanced virtual outcrop models are a useful complement to palaeoseismic field studies as they not only provide an intuitive visualisation of the outcrop and a versatile data archive, but also enable an unbiased assessment of the mineralogical composition of lithologic units and a semi‐automatic delineation of contacts and deformational structures in a 3D virtual environment.
Bitcoin Mining with Transaction Fees: A Game on the Block Size

In a Bitcoin market, miners participate in blockchain mining with an aim to make profits. Until reaching consensus, PoW-valid blocks (including validated transactions and proper PoW solutions) can be viewed as being successfully mined and are rewarded. There are two types of rewards for miners: fixed block subsidies and time-varying transaction fees. Block subsidies, predetermined by design, are the current major revenue source. Transaction fees, offered by Bitcoin transaction senders to accelerate their transactions, heavily depend on the corresponding transaction size. Thus, a larger-size block tends to contain higher transaction fees and hence more rewards. However, the probability of a miner to successfully mine a block diminishes as the block size increases, since a larger-size block takes a longer time to reach consensus. Thus, the reward included in the block is vitally affected by its size, which is independently decided by a miner. In this paper, we use a game-theoretic approach to study how a miner's payoff, i.e., expected profits, is determined by his block size. More specifically, we derive an expression to characterize the relation between the miner's payoff and block sizes. Besides, we use game theory to analyze how profit-driven miners will manipulate their block sizes to optimize payoff instead of adopting the default block size. We conduct numerical experiments on real-world data collected from Bitcoin to find peaceful equilibrium where miners have no incentive to misbehave. The achieved block sizes thereby give guidelines on the default block size, in order to deter miners from misbehaving. Our analysis suggests a block size of 4 MB.
mVideo: Edge Computing Based Mobile Video Processing Systems

Computer vision is widely used to detect anomalies in video processing systems for public safety.Applying Deep Neural Networks (i.e., DNNs) in computer vision can achieve a high detection accuracy but it requires a huge amount of computing power, storage space, and video data.Thus, DNNs-based video analytics is mostly deployed in the cloud with video data steaming from a set of stationary cameras.There are mainly three issues in this setting.First, steaming a huge amount of video data from cameras to cloud leads to high bandwidth consumption and latency.Second, when DNNs are deployed on resource-limited devices like edge nodes to reduce communication costs, it is hard to achieve a high detection accuracy.Third, stationary cameras can only collect a limited amount of video data that covers a small area, so it barely satisfies the needs of the real-time analytics in applications like public safety.We propose a mobile edge computing-based video stream processing platform, mVideo, which conducts video analytics making full use of resources at the collaborative edge and cloud nodes.On the mVideo, a mechanism is designed to partition a video analysis task based on available resources on the mobile edge node.Then, the edge nodes pre-process video data using a lightweight DNN model and upload the results to cloud nodes for further analysis.Thus mVideo not only collects video data that covers a large area, but also reduces the communication costs.To validate the proposed platform, a face recognition application is deployed on the mVideo prototype.Experimental results reveal that compared with the existing cloud computing model, mVideo reduces video data volume transmitted to the cloud nodes and power consumption by up to 99.5% and 96.2%, respectively.mVideo also improves the execution time by 90.0% to optimize mobile video analytics performance.
Principal Component Analysis in the local differential privacy model

In this paper, we study the Principal Component Analysis (PCA) problem under the (distributed) non-interactive local differential privacy model. For the low dimensional case (i.e., p≪n), we show the optimal rate of Θ(kpnϵ2) (omitting the eigenvalue terms) for the private minimax risk of the k-dimensional PCA using the squared subspace distance as the measurement, where n is the sample size and ϵ is the privacy parameter. For the high dimensional (i.e., p≫n) row sparse case, we first give a lower bound of Ω(kslog⁡pnϵ2) on the private minimax risk, where s is the underlying sparsity parameter. Then we provide an efficient algorithm to achieve the upper bound of O(s2log⁡pnϵ2). Experiments on both synthetic and real world datasets confirm our theoretical guarantees.
Facial Synthesis From Visual Attributes via Sketch Using Multiscale Generators

Automatic synthesis of faces from visual attributes is an important problem in computer vision and has wide applications in law enforcement and entertainment. With the advent of deep generative convolutional neural networks (CNNs), attempts have been made to synthesize face images from attributes and text descriptions. In this paper, we take a different approach, where we formulate the original problem as a stage-wise learning problem. We first synthesize the facial sketch corresponding to the visual attributes and then we generate the face image based on the synthesized sketch. The proposed framework, is based on a combination of two different Generative Adversarial Networks (GANs) - (1) a sketch generator network which synthesizes realistic sketch from the input attributes, and (2) a face generator network which synthesizes facial images from the synthesized sketch images with the help of facial attributes. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed attribute-based two-stage face synthesis method.
Symmetric Key Encryption Based on Rotation-Translation Equation

In this paper, an improved encryption algorithm based on numerical methods and rotation–translation equation is proposed. We develop the new encryption-decryption algorithm by using the concept of symmetric key instead of public key. Symmetric key algorithms use the same key for both encryption and decryption. Most symmetric key encryption algorithms use either block ciphers or stream ciphers. Our goal in this work is to improve an existing encryption algorithm by using a faster convergent iterative method, providing secure convergence of the corresponding numerical scheme, and improved security by a using rotation–translation formula.
Blockchain and Industry 4.0: Overview, Convergence, and Analysis

As a consequence of the upcoming fourth Industrial Revolution, also known as Industry 4.0 (Industry 4.0), new disruptive technologies are being considered to be incorporated in the factory environment. One of these solutions are the Blockchain, which aims to integrate heterogeneous systems, manage commercial transactions and foster the assets’ traceability. Thus, this technology contributes to create a whole optimized supply chain that can impact in the global market. This chapter exposes the convergence of Blockchain and Industry 4.0. In this way, academical, commercial, and governmental initiatives are explored to present examples of what is currently being proposed, fostered and developed. In addition, a critical analysis covers the aspects of how these technologies can be synergically and efficiently integrated in a future society and industrial environment.
Scalable Person Re-Identification by Harmonious Attention

Abstract Existing person re-identification (re-id) deep learning methods rely heavily on the utilisation of large and computationally expensive convolutional neural networks. They are therefore not scalable to large scale re-id deployment scenarios with the need of processing a large amount of surveillance video data, due to the lengthy inference process with high computing costs. In this work, we address this limitation via jointly learning re-id attention selection. Specifically, we formulate a novel harmonious attention network (HAN) framework to jointly learn soft pixel attention and hard region attention alongside simultaneous deep feature representation learning, particularly enabling more discriminative re-id matching by efficient networks with more scalable model inference and feature matching. Extensive evaluations validate the cost-effectiveness superiority of the proposed HAN approach for person re-id against a wide variety of state-of-the-art methods on four large benchmark datasets: CUHK03, Market-1501, DukeMTMC, and MSMT17.
A Survey of Hidden Convex Optimization

Motivated by the fact that not all nonconvex optimization problems are difficult to solve, we survey in this paper three widely used ways to reveal the hidden convex structure for different classes of nonconvex optimization problems. Finally, ten open problems are raised.
Batch Uniformization for Minimizing Maximum Anomaly Score of Dnn-Based Anomaly Detection in Sounds

Use of an autoencoder (AE) as a normal model is a state-of-the-art technique for unsupervised-anomaly detection in sounds (ADS). The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch. One problem with this approach is that the anomaly score of rare-normal sounds becomes higher than that of frequent-normal sounds, because the sample mean is strongly affected by frequent- normal samples, resulting in preferentially decreasing the anomaly score of frequent-normal samples. To decrease anomaly scores for both frequent- and rare-normal sounds, we propose batch uniformization, a training method for unsupervised-ADS for minimizing a weighted average of the anomaly score on each sample in a mini-batch. We used the reciprocal of the probabilistic density of each sample as the weight, more intuitively, a large weight is given for rare-normal sounds. Such a weight works to give a constant anomaly score for both frequent- and rare-normal sounds. Since the probabilistic density is unknown, we estimate it by using the kernel density estimation on each training mini-batch. Verification- and objective-experiments show that the proposed batch uniformization improves the performance of unsupervised-ADS.
Bitcoin Price Forecasting using LSTM and 10-Fold Cross validation

This research paper reports the proposed model for price prediction of the popular Bitcoin crypto currency while applying different neural network approaches namely Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) along with 10-fold cross validation. In this work, the analysis of various trends of Bitcoin market is carried out and learning of important features used for price prediction is done. Daily price change is estimated by the neural network models. New activation functions are utilized in this research paper for improving efficiency. Further, this research paper compares the proposed model with other existing models namely; RNN with LSTM, Linear Regression and Random Forest applied in the same domain. The dataset utilized in this work is taken from the website named coinmarket and live streaming data is considered for the experimental work. Keras, Tensorflow and Scikit Learn have been used for performing the experimental work of the proposed model. The performance analysis of the proposed model with the existing ones has been carried out in terms of the Mean Absolute Error (MAE). It is observed from the results retrieved as a part of this work that the MAE for the proposed model came out to be 0.0043s which was significantly less than its existing counterparts.
Finding Arbitrary-Oriented Ships From Remote Sensing Images Using Corner Detection

Ship detection in remote sensing images is a challenging task. In this letter, a novel anchor-free framework is proposed for detecting arbitrary-oriented ships in remote sensing images. First, an end-to-end fully convolutional network is designed to detect the three key points, including the bow, stern, and center of the ship, as well as its angle. Second, the key points of the bow and stern are combined to generate possible rotated bounding boxes. Third, the predicted center and angle information of the ship are used to confirm the bounding box. In the designed network, feature fusion and feature enhancement modules are introduced to improve the performance in complex scenes. The proposed method avoids complicated anchor design compared with anchor-based methods. The experimental results show that with good robustness to haze occlusion, scale variation, and adjacent ship disturbances, our method outperforms other state-of-the-art methods.
Logistic regression paradigm for training a single-hidden layer feedforward neural network. Application to gene expression datasets for cancer research

The speed of the diagnosis process is vital in pursuing the trial of curing cancer. During the last decade, precision medicine evolved by detecting different types of cancer through microarrays (MA) of deoxyribonucleic acid (DNA) processed by machine learning (ML) algorithms. Personalized diagnosis, followed by personalized treatment, should imply personalized hyperparameters of the ML. The goal of this paper is to propose a novel adaptive ML method that embeds knowledge into the architecture of the algorithm and also filters the features in order to reduce their number, increase computational speed, and decrease computational cost and time. fLogSLFN is a novel two-fold theoretically effective ML that can be used in two-class decision problems that embeds the logistic regression in such a manner that the hidden nodes of a single-hidden layer feedforward neural network (SLFN) are problem dependent. A filtering module based on the significance of each attribute is embedded in order to avoid the 'curse of dimensionality' phenomenon. The proposed model has been tested on three publicly available high-dimensional cancer datasets that contain gene expressions provided by complementary DNA (cDNA) array, and DNA microarray. The proposed novel method filtered logistic SLFN (fLogSLFN) has been also compared and statistically benchmarked to four ML algorithms: extreme learning machine (ELM), radial basis function network (RBF), single-hidden layer feedforward neural network trained by the backpropagation algorithm (BPNN), logistic regression with the LASSO penalty, and the adaptive single-hidden layer feedforward network (aSLFN). The experimental results showed that the fLogSLFN is competitive to the other state-of-the-art models, obtaining accuracies between 64.70% and 98.66% depending on the dataset it had been applied on. In contrast to other state-of-the-art ML algorithms, the fLogSLFN is capable to embed the knowledge extracted from the data into its architecture, making it problem dependent. The filtering module increases its computational speed, while decreasing computational cost and time. The statistical analysis revealed the fact that by filtering the features the performance is kept, making the algorithm more efficient.
Privacy-preserving self-serviced medical diagnosis scheme based on secure multi-party computation

With the development of the "Internet + Intelligent Medical", patients can online diagnose some common diseases via the Internet. However, during the diagnostic process, there exist many severe problems on privacy for medical sensitive data of patients. To solve these problems, in this paper, we present a new privacy-preserving self-serviced medical diagnosis scheme based on secure multi-party computation (SMC). In our scheme, a registered patient first encrypts his/her medical health data and sends it to the hospital server, then the hospital server calculates the similarity value between the patient's medical health data and the trait vector of hospital disease. Finally, the hospital server searches for the disease that matches the patient according to the calculated similarity value, and sends the treatment method of this disease to the patient. Specifically, based on homomorphic encryption (HE) and privacy-preserving access control, our self-serviced medical diagnosis scheme can achieve privacy preservation of patient's medical health data and confidentiality of hospital diagnosis mode. Through detailed security analysis, we show that our scheme can resist various known security threats. In addition, our scheme not only reduces the cost of treatment for the patients and relieves the hospitals' heavy pressure in the course of diagnosis, but can also predict other diseases of the patients, which can make the patients a more clear understanding of their current physical condition, and the patients can obtain the most appropriate treatment.
Graph-based Arabic text semantic representation

Semantic representation reflects the meaning of the text as it may be understood by humans. Thus, it contributes to facilitating various automated language processing applications. Although semantic representation is very useful for several applications, a few models were proposed for the Arabic language. In that context, this paper proposes a graph-based semantic representation model for Arabic text. The proposed model aims to extract the semantic relations between Arabic words. Several tools and concepts have been employed such as dependency relations, part-of-speech tags, name entities, patterns, and Arabic language predefined linguistic rules. The core idea of the proposed model is to represent the meaning of Arabic sentences as a rooted acyclic graph. Textual entailment recognition challenge is considered in order to evaluate the ability of the proposed model to enhance other Arabic NLP applications. The experiments have been conducted using a benchmark Arabic textual entailment dataset, namely, ArbTED. The results proved that the proposed graph-based model is able to enhance the performance of the textual entailment recognition task in comparison to other baseline models. On average, the proposed model achieved 8.6%, 30.2%, 5.3% and 16.2% improvement in terms of accuracy, recall, precision, and F-score results, respectively.
Lightweight group convolutional network for single image super-resolution

Recently, deep learning methods have demonstrated significant reconstruction performance on Single Image Super-Resolution (SISR). However, most of them demand a huge amount of computational and memory consumption and hard to be applied to real-world applications. To this end, we propose a fast Lightweight Group Convolution Network (LGCN) model for SISR to alleviate this problem. Specifically, we develop a cascaded memory group convolutional network for SISR, which cascades several Memory Group Convolutional Networks (MGCN). There are two main merits on MGCN. One is that it consists of several group convolutional layers and 1 × 1 convolutional layers with densely connected structure. The group convolution is utilized to reduce the parameters of LGCN, and the 1 × 1 convolution is not only used to create a linear combination of the output of group convolutional layer, but also to gather local information progressively. The other one is that it utilizes channel attention unit to model channel-wise relationships to improve performance. Experimental results on four popular datasets show that the proposed LGCN not only outperforms the state-of-the-art SISR methods, but also achieves faster speed.
Local Prediction Models for Spatiotemporal Volume Visualization

We present a machine learning-based approach for detecting and visualizing complex behavior in spatiotemporal volumes. For this, we train models to predict future data values at a given position based on the past values in its neighborhood, capturing common temporal behavior in the data. We then evaluate the model's prediction on the same data. High prediction error means that the local behavior was too complex, unique or uncertain to be accurately captured during training, indicating spatiotemporal regions with interesting behavior. By training several models of varying capacity, we are able to detect spatiotemporal regions of various complexities. We aggregate the obtained prediction errors into a time series or spatial volumes and visualize them together to highlight regions of unpredictable behavior and how they differ between the models. We demonstrate two further volumetric applications: adaptive timestep selection and analysis of ensemble dissimilarity. We apply our technique to datasets from multiple application domains and demonstrate that we are able to produce meaningful results while making minimal assumptions about the underlying data.