Reviewerly/exHarmony huggingface dataset:
Classification of IQ-Modulated Signals Based on Reservoir Computing With Narrowband Optoelectronic Oscillators

We numerically perform the classification of IQ-modulated radiofrequency signals using reservoir computing based on narrowband optoelectronic oscillators (OEOs) driven by a continuous-wave semiconductor laser. In general, the OEOs used for reservoir computing are wideband and are processing analog signals in the baseband. However, their hardware architecture is inherently inadequate to directly process radiotelecom or radar signals, which are modulated carriers. On the other hand, the high- Q OEOs that have been developed for ultra-low phase noise microwave generation have the adequate hardware architecture to process such multi-GHz modulated signals, but they have never been investigated as possible reservoir computing platforms. In this article, we show that these high- Q OEOs are indeed suitable for reservoir computing with modulated carriers. Our dataset (DeepSig RadioML) is composed with 11 analog and digital formats of IQ-modulated radio signals (BPSK, QAM64, WBFM, etc.), and the task of the high- Q OEO reservoir computer is to recognize and classify them. Our numerical simulations show that with a simpler architecture, a smaller training set, fewer nodes and fewer layers than their neural network counterparts, high- Q OEO-based reservoir computers perform this classification task with an accuracy better than the state-of-the-art, for a wide range of parameters. We also investigate in detail the effects of reducing the size of the training sets on the classification performance.
Image denoising by nonlinear nonlocal diffusion equations

In this paper, we propose and study a nonlinear nonlocal diffusion equation of the Perona–Malik type for the removal of Gaussian noise in images. Based on the fixed-point theorem, we prove the unique solvability of the equation. We also show that solutions of the nonlocal equation converge to the solution of the local spatially regularized Perona–Malik equation if the kernel is rescaled appropriately. The new equation inherits the merit of the nonlocal method that restores details and textures of noisy images but avoids speckles and artifacts in homogeneous regions. Comparisons with other nonlocal methods for image denoising are presented.
AS-YOLO: An Improved YOLOv4 based on Attention Mechanism and SqueezeNet for Person Detection

With the development of deep learning in the field of object detection, a series of detection algorithms and methods are proposed to improve their performance. YOLOv4 is superior to the early algorithms in detection accuracy and speed. In order to get excellent performance in person detection, the AS-YOLO is proposed based on the YOLOv4 network. An attention mechanism is introduced in YOLOv4 to enhance the detection capability. Residual structure and SqueezeNet structure are also used as reference to deepen the network and reduce parameters. In addition, the activation function of backbone network is replaced by LeakyReLu to accelerate the detection speed. Compared with YOLOv4, the proposed network can decrease the parameters by 20.49% and increase the average precision by 2.02% on INRIA person data set, and the detection speed is also improved by 16.63%. This proves the validity of the proposed AS-YOLO.
Towards Entity Alignment in the Open World: An Unsupervised Approach

Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs). It is a pivotal step for integrating KGs to increase knowledge coverage and quality. Recent years have witnessed a rapid increase of EA frameworks. However, state-of-the-art solutions tend to rely on labeled data for model training. Additionally, they work under the closed-domain setting and cannot deal with entities that are unmatchable. To address these deficiencies, we offer an unsupervised framework that performs entity alignment in the open world. Specifically, we first mine useful features from the side information of KGs. Then, we devise an unmatchable entity prediction module to filter out unmatchable entities and produce preliminary alignment results. These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment. Finally, the progressive learning framework gradually improves the quality of structural embeddings and enhances the alignment performance by enriching the pseudo-labeled data with alignment results from the previous round. Our solution does not require labeled data and can effectively filter out unmatchable entities. Comprehensive experimental evaluations validate its superiority.
Forensic soil provenancing in an urban/suburban setting: A sequential multivariate approach

Abstract Compositional data from a soil survey over North Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier transform infrared spectroscopy (FTIR) and magnetic susceptibility (MS), and geochemical data from X‐ray fluorescence (XRF; for total major oxides) and inductively coupled plasma‐mass spectrometry (ICP‐MS; for both total and aqua regia ‐soluble trace elements) are performed on the survey's 268 topsoil samples (0–5 cm depth; 1 sample per km 2 ). Principal components (PCs) are calculated after imputation of censored data and centered log‐ratio transformation. The sequential provenancing approach is underpinned by (i) the preparation of interpolated raster grids of the soil properties (including PCs); (ii) the explicit quantification and propagation of uncertainty; (iii) the intersection of the soil property rasters with the values of the evidentiary sample (± uncertainty); and (iv) the computation of cumulative provenance rasters (“heat maps”) for the various analytical techniques. The sequential provenancing method is tested on the North Canberra soil survey with three “blind” samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR and MS (mineralogy), as well as XRF and total ICP‐MS (geochemistry) analytical methods, offer the most precise and accurate provenance predictions. Inclusion of PCs in provenancing adds marginally to the performance. Maximizing the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations and gaps, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications.
SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews

The amount of textual data generation has increased enormously due to the effortless access of the Internet and the evolution of various web 2.0 applications. These textual data productions resulted because of, the people express their opinions, emotions or sentiment regarding any service or product through tweets, posts, and reviews. Sentiment analysis concerns the computational technique of determining and classifying views stated in a piece of text, primarily to identify whether the author’s attitude toward a specific issue is positive, negative, or neutral. The impact of customer review is significant to perceive the customer attitude towards a restaurant. Thus, the automatic detection of sentiment from reviews is advantageous for the restaurant owners, service providers as well as customers to make their decisions or services more satisfactory. This paper proposes, a deep learning-based technique (i.e., BiLSTM) to classify the restaurant reviews expressed in Bengali into positive and negative polarities. A corpus consists of 8435 reviews is constructed to evaluate the proposed technique. The results of the evaluation on test dataset shows that BiLSTM technique provides the highest accuracy of 91.35% compared to other existing techniques.
Using Virtual Reality to Enhance Food Technology Education

The use of Virtual Reality (VR) technology combined with 360-degree images and videos provide an opportunity for teachers to bring students into the classroom even when they are located somewhere else. During the COVID-19 lockdown and pandemic, with students across the world forced into home-based learning via remote teaching, a VR classroom shows potential as a tool for adding depth to their learning. The possibility of immersing students in a virtual environment could provide an answer to motivation and engagement issues for today's students as well as a solution to some of the current constraints faced by teachers. In particular, VR has the potential to increase the time students are able to spend in (virtual) environments that are suitable for teaching and learning practical skills. With the cost of VR equipment reducing rapidly and the increasing quality of virtual experiences, it appears VR is on the tipping-point of becoming a regular part of school programmes.This article outlines the development and testing of a VR Classroom for the delivery of a food-based lesson with middle school students in a New Zealand school. Kitchens are a costly commodity for schools and the obvious health and safety issues make teaching practical cooking skills challenging. With a focus on student engagement and motivation, data is collected from observation of students using the virtual classroom and a post-test survey. Results show that students were highly motivated and perceived the VR classroom as fun to use.
What is innovative to public libraries in the United States? A perspective of library administrators for classifying innovations

Innovations are critical for public libraries but rarely does any primary research study the scope and interpretation of the term “innovation” by public libraries. Also, few of the existing innovation typologies are based on data collected from public libraries. This study fills in the gap by eliciting 80 innovations reported by the administrators of 108 award-winning public libraries in the United States, and proposes the first organic classification of innovations for public libraries, with the following four types of innovations: Program (access-oriented/use-oriented), Process (efficiency-driven/effectiveness-driven), Partnership (internal/external), and Technology (web-based technologies/assistive technologies/artificial intelligence). Findings can advance the state of innovations in libraries.
Sentiment based multi-index integrated scoring method to improve the accuracy of recommender system

To the best of our knowledge, few studies have focused on the inconsistency between user ratings and reviews as well as natural noise management in recommender systems (RSs). To address these issues, this study introduces a sentiment based multi-index integrated scoring method to provide a reliable information input that reflects comprehensive user preferences for recommendation algorithms and facilitate improved performance. Initially, Bing Liu's lexicon is expanded using a semi-supervised learning technique to obtain additional sentiment words and calculate the sentiment scores of reviews; then a normalized sentiment score method based on sigmoid function that considers the emotional tendencies of different users in reviews is designed to convert the scores into values corresponding to the rating scale of RS. Subsequently, a degree classification criteria approach is adopted to assign users and items to more fine-grained classes Further, a natural noise detection method is exploited to identify and correct noise ratings according to classification conditions. To effectively integrate normalized review and denoised rating information, two factors, user consistency and review feedback, are considered to obtain the importance of reviews and ratings; then, a weighted average method is used to generate a set of comprehensive ratings. The experimental results on two benchmark datasets indicate that the superiority of memory-based or model-based collaborative filtering methods (CFs) using comprehensive ratings over their respective methods using original ratings is determined by various accuracy metrics, which demonstrates that our scheme can enhance the reliability and accuracy of user information. Thus, the proposed scheme provides new insights for improving the accuracy of RSs from the perspective of multiple information sources. Additionally, this method exhibits good generalizability and practicality.
Design and Implementation of Three-Axis Solar Tracking System with High Efficiency

The modern eras have seen different sources of renewable resources, which are naturally finding on a human timescale, such as sunlight, wind, tides, waves, and geothermal heat sources. One such example is solar power. The challenge remains to maximize the capture of the rays from the sun for conversion into electricity. A solar panel receives the maximum sunlight when it is perpendicular to the sun's rays but, the sunlight direction changes regularly with changing time. A fixed solar panel has a fixed orientation to the sky and does not turn to follow the sun. So, the attached solar panel is not able to generate adequate power. The best efficiency of the majority of commercially available solar cells ranges between 10-20%. This percentage shows that there is a high improvement needed. This advanced system deliberates the design and construction of a prototype model for a solar tracking system that has three-axis freedom, which can follow the sunlight in different directions automatically. The proposed method presents the fabrication and installation of a solar panel mount with a multiple-axis solar tracking controller. When sunlight falls perpendicularly onto the solar panels to maximize the capture of the rays by pointing the solar panels towards the sun & following its path across the sky. This way, the maximum electricity generation is ensuring high efficiency. The efficiency can be increased by 25%.
Pair-wise ranking based preference learning for points-of-interest recommendation

Recommending point-of-interest (POI) to users accurately is a hot topic in business. In the past, many researchers proposed recommendation models based on collaborative filtering or matrix factorization from the perspectives of time, geography, and social relationship. However, only a few studies have focused on user preference which is the key factor influencing user decision. This work focuses on studying the representation and mining of user preference from check-in data for POI recommendation. Pair-wise ranking is the common solution for implementing preference learning. However, traditional ways of constructing pair-wise data cut off the connections between multiple options in the decision process, affecting the effectiveness of preference learning. In this work, we change the ratio of negative to positive instance in pair-wise data from 1:1 to k:1 to ensure the data construction in line with the real decision making process. We propose a new negative sampling method taking the geographical distance and POI categorical distance into consideration jointly for enhancing the quality of training data. For our specialized pair-wise data, we propose a new optimization criterion for implementing effective preference learning. Finally, we conduct extensive experiments on two real-world datasets to validate the effectiveness of our proposed approach. The experiment results show that our approach outperforms the state-of-the-art models by at least 19.7% on F1-Score and 24.4% on nDCG. Additionally, our approach can be easily generalized to other domains, such as commodities, news, and movie recommendation.
Quantum-based privacy-preserving sealed-bid auction on the blockchain

Sealed-bid auction is one of the major protocols used in the electronic commerce industry. Recently, many schemes have been proposed to implement sealed-bid auction protocols based on quantum computing, while other schemes have adopted the blockchain. However, each of the previous proposals has focused on a few sealed-bid auction features while simply ignoring others. A robust sealed-bid auction protocol should comprise all important features and satisfy all requirements. We design a sealed-bid auction protocol using quantum-based blockchain, in which the transactions of the sealed-bid auction are stored using the blockchain and supported by quantum computation and communication to enhance security and protect privacy. Our proposed protocol takes advantage of both quantum computing and blockchain technology to ensure essential features and requirements. Security analysis, evaluation, and comparisons are presented to demonstrate the advantages of our protocol.
A fast graph modification method for social network anonymization

Privacy on social networks is one of the most important and well-known issues. Various algorithms have been proposed to preserve the privacy of social network, all of which try to change the graph structure such that the utility of the graph is maintained. Although these algorithms have been successful in protecting the privacy and the utility of social networks, they are not suitable for anonymizing big data, because of the high cost of processing. Some of these algorithms have a high runtime. In addition, they should be improved from the aspect of preserving graph utility. In this paper, an effective algorithm has been introduced to increase the anonymization speed, as well as improving the graph utility. This algorithm uses number factorization to remove the best edges from the graph in the graph modification step of the algorithm. Since the appropriate edges are selected just through one scan of the edges, the runtime is reduced. In order to add edges to the graph, all the appropriate edges are selected simultaneously and added to the graph using NaFa algorithm. Some limitations are applied to the selection of edges to choose the correct ones. Taking into account the appropriate criteria to select edges in both the removal and addition steps improves the graph utility. The evaluation results of the proposed algorithm on real data sets show the efficiency of the algorithm to sharply reduce the runtime and increase the graph utility, simultaneously.
Adaptive priority-based data placement and multi-task scheduling in geo-distributed cloud systems

With the rapid development and the widespread use of cloud computing in various applications, the number of users distributed in different regions has grown exponentially. Therefore, the Geo-distributed cloud systems have become a research hotspot and big data processing technology has also emerged. Nowadays, the most widely used big data processing framework is Spark. However, massive amounts of data are generated every moment, and the processing procedure becomes more and more complex, the execution efficiency of Spark has been greatly affected. In the Spark frame of geo-distributed cloud systems, aiming at the data placement problem, the data placement strategy based on RDD dynamic weight is introduced. The target node is selected with a strong computation capacity to place the data. Aiming at the problems of multi-task scheduling, a task will be scheduled to a node whose computation capacity can satisfy the requirement of this task. And then considering job classification and computing node performance, the optimized task scheduling strategy is in traduced. Experiments show that our algorithms can effectively adjust the weight of node data placement according to the actual task execution information, and shorten the task execution time.
Safe Navigation in Human Occupied Environments Using Sampling and Control Barrier Functions

Sampling-based methods such as Rapidly-exploring Random Trees (RRTs) have been widely used for generating motion paths for autonomous mobile systems. In this work, we extend time-based RRTs with Control Barrier Functions (CBFs) to generate, safe motion plans in dynamic environments with many pedestrians. Our framework is based upon a human motion prediction model which is well suited for indoor narrow environments. We demonstrate our approach on a high-fidelity model of the Toyota Human Support Robot navigating in narrow corridors. We show in simulation results that our proposed online method can navigate safely in the presence of moving agents with unknown dynamics.
Reinforcing learning in Deep Belief Networks through nature-inspired optimization

Deep learning techniques usually face drawbacks related to the vanishing gradient problem, i.e., the gradient becomes gradually weaker when propagating from one layer to another until it finally vanishes away and no longer helps in the learning process. Works have addressed this problem by introducing residual connections, thus assisting gradient propagation. However, such a subject of study has been poorly considered for Deep Belief Networks. In this paper, we propose a weighted layer-wise information reinforcement approach concerning Deep Belief Networks. Moreover, we also introduce metaheuristic optimization to select proper weight connections that improve the network’s learning capabilities. Experiments conducted over public datasets corroborate the effectiveness of the proposed approach in image classification tasks. • Novel DBN with weight-based residual connections between layers. • Reinforcement and regularization of the information flow. • Application of metaheuristic optimization to fine-tune Res-DBN hyperparameters;
Robust Automatic Modulation Classification in the Presence of Adversarial Attacks

Automatic modulation classification (AMC) is used in intelligent receivers operating in shared spectrum environments to classify the modulation constellation of radio frequency (RF) signals from received waveforms. Recently, deep learning has proven capable of enhancing AMC performance using both convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, deep learning-based AMC models are susceptible to adversarial attacks, which can significantly degrade the performance of well-trained models by adding small amounts of interference into wireless RF signals during transmission. In this work, we present a two-fold defense mechanism to withstand adversarial interference on modulated radio signals. Specifically, our method consists of (1) correcting misclassifications on mild attacks and (2) detecting the presence of an adversary on more potent attacks. We show that our proposed defense is capable of withstanding adversarial interference injected into RF signals while maintaining false positive detection rates on CNNs and RNNs as low as 3%.
An intelligent system to help deaf students learn Arabic Sign Language

This paper proposes a new system to translate an Arabic Sign Language (ArSL). The system consists of two sub-systems: the first, Speech to ArSL translation Subsystem. This sub-system is mainly based on the speech recognition engine. The second is ArSL to speech translation subsystem to translate the images of signs into speech, mainly based on GTTS library. This system will be easily used by both groups (the hearing disabled and normal persons) who want to learn ArSL and will support more communication between them. Among the advantages of this system is that it is expandable by adding new signs to the database and can be accessed online at: https://sr.gravita-demo.com/. The proposed system was evaluated using several methods. The findings show that the system can translate ArSL with a recognition rate of 99%. An experimental approach was used to evaluate the effectiveness of the proposed system. Results show that the proposed system was effective and the ability of Arab deaf students to learn improved significantly. In addition, the system's performance evaluation questionnaire revealed that system users preferred this approach to ArSL learning and teaching and acquired new concepts, which predicts a promising future for this system in academic environments.