Reviewerly/exHarmony huggingface dataset:
MORStreaming: A Multioutput Regression System for Streaming Data

With the continuous generation of huge volumes of streaming data, streaming data regression has become more complicated. A regressor that predicts two or more outputs, i.e., multioutput regression, is commonly used in many applications. However, current multioutput regressors use a batch method to handle data, which presents compatibility issues for streaming data as they need to be analyzed online. To address this issue, we present a multioutput regression system, called MORStreaming, for streaming data. MORStreaming uses an instance-based model to make predictions because this model can quickly adapt to change by only storing new instances or by throwing away old instances. However, learning instances in our regression system are constrained by online demand and need to consider the relationship between outputs. Therefore, MORStreaming consists of two algorithms: 1) an online algorithm based on topology networks which is designed to learn the instances and 2) an online algorithm based on adaptive rules which is designed to learn the correlation between outputs automatically. Experiments involving both artificial and real-world datasets indicate MORStreaming can achieve superior performance compared with other multioutput methods.
Hyper-Convergence Storage Framework for EcoCloud Correlates

Cloud computing is an emerging domain that is capturing global users from all walks of life—the corporate sector, government sector, and social arena as well. Various cloud providers have offered multiple services and facilities to this audience and the number of providers is increasing very swiftly. This enormous pace is generating the requirement of a comprehensive ecosystem that shall provide a seamless and customized user environment not only to enhance the user experience but also to improve security, availability, accessibility, and latency. Emerging technology is providing robust solutions to many of our problems, the cloud platform is one of them. It is worth mentioning that these solutions are also amplifying the complexity and need of sustenance of these rapid solutions. As with cloud computing, new entrants as cloud service providers, resellers, tech-support, hardware manufacturers, and software developers appear on a daily basis. These actors playing their role in the growth and sustenance of the cloud ecosystem. Our objective is to use convergence for cloud services, software-defined networks, network function virtualization for infrastructure, cognition for pattern development, and knowledge repository. In order to gear up these processes, machine learning to induce intelligence to maintain ecosystem growth, to monitor performance, and to become able to make decisions for the sustenance of the ecosystem. Workloads may be programmed to “superficially” imitate most business applications and create large numbers using lightweight workload generators that merely stress the storage. In today's current IT environment, when many enterprises use the cloud to service some of their application demands, a different performance testing technique that assesses more than the storage is necessary. Compute and storage are merged into a single building block with HCI(Hyper-converged infrastructure), resulting in a huge pool of compute and storage resources when clustered with other building blocks. The novelty of this work to design and test cloud storage using the measurement of availability, downtime, and outage parameters. Results showed that the storage reliability in a hyper-converged system is above 92%.
An overview of Ethereum and Solidity vulnerabilities

The security of the Ethereum Smart contracts constitutes the most important step toward securing the whole blockchain since it is opensource and anyone can write and deploy malicious contracts and make profits from doing so. The immutable aspect of the blockchain makes it hard to roll back malicious codes to restore the state of the EVM. In this paper, we propose a taxonomy for the vulnerabilities of smart contracts written in solidity language. We also proceed with some simple simulations of some attacks and errors on smart contracts and we demonstrate how these vulnerabilities can tamper with the state of the Ethereum blockchain.
Identification of the relevant input variables for predicting the parabolic trough solar collector's outlet temperature using an artificial neural network and a multiple linear regression model

The main objective of this study is to present the most influencing input variables for a parabolic trough solar collector (PTSC) outlet temperature through prediction and optimization. Six artificial neural network (ANN) and four multiple linear regression (MLR) models were proposed, validated, and compared in detail. Temperature, wind speed, rim angle, flow rate, and solar radiation were used as input variables. The simulation showed that ANN-1 and MLR with Second-Order Equation (SOE) are the models that yielded the best results with R2 = 0.9984 and R2 = 0.9958 and with an RMSE = 0.7708 and 1.6031, respectively. The sensitivity analysis results of the ANN-1 model trained, with and without biases, showed that the inlet temperature was the most significant parameter influencing the PTSC outlet temperature. Both models yielding the best results were inverted to estimate the optimal input parameter using the trust-region reflective algorithm optimization method. The optimization results showed that ANNi and MLR-SOEi estimated the input temperature with an error &amp;lt; 4.008% and had a very short-elapsed prediction time &amp;lt;0.2277 s. Due to high accuracy and short computing time, ANN-1 and ANNi are more suitable than MLR-SOE for simulating and optimizing the PTSC outlet temperature. Likewise, the MLR-SOE method proved to be a simpler and cheaper alternative than the ANN method.
Multi-scale exploration of giant Qulong porphyry deposit in a collisional setting

With the increasing demand for copper resources, exploration methods based on the discovered porphyry Cu deposits (PCDs) to target concealed orebodies are of greater importance. Single parameter is no longer sufficiently effective against concealed deposit exploration, and different combinations of survey techniques are required. The Qulong porphyry Cu-Mo deposit is the largest collisional PCD found in China. Here we evaluated the relationship between variations in multiple parameters and alteration to define exploration footprints around the porphyry Cu centers. In order to improve the exploration efficiency and accuracy, we combined geophysical (magnetic susceptibility and resistivity) and popular geochemical exploration methods (i.e., short wavelength infrared spectroscopy- SWIR, portable X-ray fluorescence- pXRF, and indicator minerals) to the drill hole ZK005 of Qulong PCD in southern Tibet seeking for a firm interpretation. Four orebody indicators were established in study of Qulong deposit, which are: (i) the fluctuations of Ca/K, Mg/Fe, Sr/Ti, and Si/Al ratios, (ii) long-wavelength white mica (2207–2214 nm) and short-wavelength chlorite (2247–2253 nm), (iii) sub-high magnetic susceptibility and (iv) low resistivity. Multi-scale prospecting methods can improve prospecting efficiency and narrow down the target area. Based on the local environment, selecting an appropriate combination of methods can improve exploration efficiency in the brownfields at a lower cost.
QuestionComb: A Gamification Approach for the Visual Explanation of Linguistic Phenomena through Interactive Labeling

Linguistic insight in the form of high-level relationships and rules in text builds the basis of our understanding of language. However, the data-driven generation of such structures often lacks labeled resources that can be used as training data for supervised machine learning. The creation of such ground-truth data is a time-consuming process that often requires domain expertise to resolve text ambiguities and characterize linguistic phenomena. Furthermore, the creation and refinement of machine learning models is often challenging for linguists as the models are often complex, in-transparent, and difficult to understand. To tackle these challenges, we present a visual analytics technique for interactive data labeling that applies concepts from gamification and explainable Artificial Intelligence (XAI) to support complex classification tasks. The visual-interactive labeling interface promotes the creation of effective training data. Visual explanations of learned rules unveil the decisions of the machine learning model and support iterative and interactive optimization. The gamification-inspired design guides the user through the labeling process and provides feedback on the model performance. As an instance of the proposed technique, we present QuestionComb , a workspace tailored to the task of question classification (i.e., in information-seeking vs. non-information-seeking questions). Our evaluation studies confirm that gamification concepts are beneficial to engage users through continuous feedback, offering an effective visual analytics technique when combined with active learning and XAI.
Critical Analysis of Big Data Privacy Preservation Techniques and Challenges

In the present age, diversified data—both structured and unstructured, is being produced in huge amounts from sources like database transactions, audios, images, videos, social online platforms, etc., and that too within a time period of a millionth of a second. While this data is consumed and stored in large volumes, it is quite complex in nature and is growing within the constraints of self-regulatory sources. As a result, conventional procedures of data analysis and management fail in successfully managing the large data sets, collectively called as big data. Henceforth, by using a variety of techniques in the domain of data mining on large data sets, useful information is obtained, but this process is as meticulous as it is challenging. One of the biggest challenges, among others, is maintaining data privacy and to restrict unauthorized access to sensitive data generated, exchanged or recorded in banking transactions, health sector procedures, user interactions on social media as online presence and dependency of users has exponentially increased along with proliferation of sensitive information. This paper presents research insights into challenges in big data mining and the privacy concerns in big data besides presenting gaps in the research that can be used to plan future research.
A novel solution with rapid Voronoi-based coverage path planning in irregular environment for robotic mowing systems

Abstract This paper presents a novel solution of coverage path planning for robotic mowing applications. The planning algorithm is based on the Boustrophedon motions and the rapid Voronoi diagram. The coordinate conversion and the sweeping vector is defined by minimum bounding box and the Voronoi travel paths are designed to reduce the computational cost and execution time compared to conventional heuristic methods. The tracked path is controlled via dynamic feedback in standard lawn mowing robots under robot operating system (ROS). Within ROS, the information exchanging among different tasks of both extensive simulation cases and experimental field tests can be conducted easily. When meeting unknown obstacles, the proposed algorithm can re-plan its paths dynamically. The performance of the proposed algorithm is compared to several conventional coverage algorithms in terms of time efficiency, coverage, repetition, and robustness with respect to both concave and convex shapes. The field tests are conducted to demonstrate that the applicability of the sensor fusion and robustness of the proposed algorithm for the complete coverage tasks by robotic mowers.
Next-Generation Neural Networks: Capsule Networks With Routing-by-Agreement for Text Classification

These days, neural networks constantly prove their high capacity for nearly every application case and are considered as key technology for learning systems.However, neural networks need to continuously evolve for managing new arising challenges like increasing task complexity, explainability of decision making processes, expanded problem domains, providing resilient and robust systems etc.One possible enhancement of traditional neural networks constitutes the innovative Capsule Network (CapsNet) technology, which combines the expressiveness of distributed entity representations with an intelligent and interpretable signal propagation, named as routing-by-agreement. Since CapsNets represent a relatively young acquirement, further research is essential for gaining profound knowledge about CapsNet theory and best practices for diverse application areas.This paper wants to contribute to the progress of CapsNets for the task of text classification.For this purpose, various research questions about this technology get formulated and experimentally answered with the aid of six selected datasets.In addition, this paper serves as a possible starting point for researchers as well as for practitioners to deal with CapsNets in the text domain, by supplying a survey about its theory, text classification basics and the combination of both areas.The analysis results empirically prove the robustness of CapsNets with routing-by-agreement for a wide spectrum of net architectures, datasets and text classification tasks.Hence, CapsNets can be viewed as a next-generation neural network technology, which offers high potential as text classification method and should be topic of future research.
High-Order-Interaction for weakly supervised Fine-Grained Visual Categorization

Fine-Grained Visual Categorization (FGVC) is a challenging task due to the large intra-subcategory and small inter-subcategory variances. Recent studies tackle this task through a weakly supervised manner without using the part annotation from the experts. Of those, methods based on bilinear pooling are one of the main categories for computing the interaction between deep features and have shown high effectiveness. However, these methods mainly focus on the correlation within one specific layer but largely ignore the high interactions between multiple layers. In this study, we argue that considering the high interaction between the features from multiple layers can help to learn more distinguishing fine-grained features. To this end, we propose a High-Order-Interaction (HOI) method for FGVC. In our HOI, an efficient cross-layer trilinear pooling is introduced to calculate the third-order interaction between three different layers. Third-order interactions of different combinations are then fused to form the final representation. HOI can produce more discriminative representations and be readily integrated with the two popular techniques, attention mechanism and triplet loss, to obtain superposed improvement. Extensive experiments conducted on four FGVC datasets show the great superiority of our method over bilinear-based methods and demonstrate that the proposed method achieves the state of the art.
Settling the Sharp Reconstruction Thresholds of Random Graph Matching

This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs. We focus on the Gaussian model where the two graphs are complete graphs with correlated Gaussian weights and the Erdős-Rényi model where the two graphs are subsampled from a common parent Erdős-Rényi graph <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{G}(n, p)$</tex> . For dense graphs with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p=n^{-o(1)}$</tex> , we prove that there exists a sharp threshold, above which one can correctly match all but a vanishing fraction of the vertices and below which correctly matching any positive fraction is impossible, a phenomenon known as the “all-or-nothing” phase transition. Even more strikingly, in the Gaussian setting, above the threshold all vertices can be exactly matched with high probability. In contrast, for sparse Erdős-Rényi graphs with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p=n^{-\Theta(1)}$</tex> , we show that the all-or-nothing phenomenon no longer holds and we determine the thresholds up to a constant factor. Along the way, we also derive the sharp threshold for exact recovery, sharpening the existing results in Erdős-Rényi graphs [1], [2]. The proof of the negative results builds upon a tight characterization of the mutual information based on the truncated second-moment computation in [3] and an “area theorem” that relates the mutual information to the integral of the reconstruction error. The positive results follows from a tight analysis of the maximum likelihood estimator that takes into account the cycle structure of the induced permutation on the edges.
Differentially Private Federated Learning: An Information-Theoretic Perspective

We propose a new technique for deriving the differential privacy parameters in federated learning (FL). We consider the setting where a machine learning model is iteratively trained using stochastic gradient descent (SGD) and only the last update is publicly released. In this approach, we interpret each training iteration as a Markov kernel. We then quantify the impact of the kernel on privacy parameters via the contraction coefficient of the <tex>$E_{\gamma}$</tex>-divergence that underlies differential privacy. To do so, we generalize the well-known Dobrushin&#x0027;s ergodicity coefficient, originally defined in terms of total variation distance, to a family of <tex>$f$</tex>-divergences. We then analyze the convergence rate of SGD under the proposed private FL framework.
Temporal Ordering of Events via Deep Neural Networks

Ordering events with temporal relations in texts remains a challenge in natural language processing. In this paper, we introduce a new combined neural network architecture that is capable of classifying temporal relations between events in an Arabic sentence. Our model consists of two branches: the first one extracts the syntactic information and identifies the orientation of the relation between the two given events based on a Shortest Dependency Path (SDP) layer with Long and Short Memory (LSTM), and the second one encourages the model to focus on the important local information when learning sentence representations based on a Bidirectional-LSTM (BiLSTM) attention layer. The experiments suggest that our proposed model outperforms several previous state-of-the-art methods, with an F1-score equal to 86.40%.
Spatial-Aware Texture Transformer for High-Fidelity Garment Transfer

Garment transfer aims to transfer the desired garment from a model image with the desired clothing to a target person, which has attracted a great deal of attention due to its wider potential applications. However, considering the model and target persons are often given at different views, body shapes and poses, realistic garment transfer is facing the following challenges that have not been well addressed: 1) deforming the garment; 2) inferring unobserved appearance; 3) preserving fine texture details. To tackle these challenges, we propose a novel SPatial-Aware Texture Transformer (SPATT) model. Different from existing models, SPATT establishes correspondence and infers unobserved clothing appearance by leveraging the spatial prior information of a UV-space. Specifically, the source image is transformed into a partial UV texture map guided by the extracted dense pose. To better infer the unseen appearance utilizing seen region, we first propose a novel coordinate-prior map that defines the spatial relationship between the coordinates in the UV texture map, and design an algorithm to compute it. Based on the proposed coordinate-prior map, we present a novel spatial-aware texture generation network to complete the partial UV texture. In the second stage, we first transform the completed UV texture to fit the target person. To polish the details and improve realism, we introduce a refinement generative network conditioned on the warped image and source input. Compared with existing frameworks as shown experimentally, the proposed framework can generate more realistic images with better-preserved texture details. Furthermore, difficult cases where two persons have large pose and view differences can also be well handled by SPATT.
What and how well you exercised? An efficient analysis framework for fitness actions

Human action analysis has been an active research area in computer vision, and has many useful applications such as human computer interaction. Most of the state-of-the-art approaches of human action analysis are data-driven and focus on general action recognition. In this paper, we aim to analyze fitness actions with skeleton sequences and propose an efficient and robust fitness action analysis framework. Firstly, fitness actions from 15 subjects are captured and built to a fitness action dataset (Fitness-28). Secondly, skeleton information is extracted and made alignment with a simplified human skeleton model. Thirdly, the aligned skeleton information is transformed to an uniform human center coordinate system with the proposed spatial–temporal skeleton encoding method. Finally, the action classifier and local–global geometrical registration strategy are constructed to analyze the fitness actions. Experimental results demonstrate that our method can effectively assess fitness action, and have a good performance on artificial intelligence fitness system.
Multiobjective Evolutionary Interpretable Type-2 Fuzzy Systems With Structure and Parameter Learning for Hexapod Robot Control

This article proposes a data-driven multiobjective evolutionary interval type-2 fuzzy-logic system (IT2FLS) learning approach that considers both system performance and rule interpretability. One characteristic of the evolutionary learning framework is that the IT2FLS structure is incrementally generated instead of searching from an initially huge grid-type rule base, which significantly reduces the parameter search space. Another one is that a new constrained objective function is proposed to improve the distinguishability of the generated interval type-2 fuzzy sets (FSs) without restricting them to be evenly distributed. Because of the tradeoff between system performance and interpretability, a new multiobjective ant colony optimization (ACO) algorithm is proposed to optimize the IT2FLS parameters and improve optimization performance. The evolutionary IT2FLS learning approach is applied to control a real wall-following hexapod robot. The approach shows the advantages of model-free design and interpretability and robustness to noise in the evolved type-2 fuzzy rules. Simulations with performance comparisons and experiments in controlling a real robot show the advantages of the learning approach.
An Expert Eye for Identifying Shoplifters in Mega Stores

Ansari, Mohd. Aquib Singh, Dushyant KumarNowadays, it has been observed that the theft of retail products by customers from the mega stores is increasing enormously. Some customers perform theft by concealing the goods in the pockets or bags or under clothes when no one observes and leaves the store without paying for them. The person involved in this kind of illegal act is called a shoplifter, and this activity is called shoplifting. Shoplifting is a major threat to retailers that causes loss in business. Therefore, minimizing the effects of theft is essential in retail safety, store design, and customer service. It is complicated to detect such persons who are involved in shoplifting in the shop/stores. Consequently, we intend to build a real-time camera-based surveillance system to detect illegal events and inform security personnel when shoplifting occurs. This article proposes a deep neural network-based shoplifting detection system that uses the Inceptionv3 model to extract the features and LSTMs networks to learn the temporal sequences. The proposed system can detect the persons involved in shoplifting kind of activity with an accuracy of up to 74.53%.