Reviewerly/exHarmony huggingface dataset:
Leveraging Hate Speech Detection to Investigate Immigration-related Phenomena in Italy

The presence and integration of immigrants is one of the most controversial issues in our society, and given current worldwide political instabilities, it will likely become ever more prominent in the cultural and political debate. Social media play an increasingly important role in how citizens debate opinions and react to local and global events. However, several studies point out the danger of social media as a breeding ground for online hate speech (or cyberhate). We propose a novel approach to the exploratory analysis of social phenomena based on the integration of automatic detection of cyberhate against immigrants with offline indicators. We gathered data from the Italian Twittersphere and from the main supplier of official statistical data in Italy (ISTAT). We developed a supervised classification model for hate speech detection, trained on a corpus of Italian tweets manually annotated for hate speech against immigrants, and use it to automatically annotate a large sample of geo-tagged tweets over a span of six years. We crossed this data with the ISTAT data, exploring three macro-indicators related to employment, education and crime. We found correlations suggesting an interplay between economical and cultural factors and the expression of hate online.
An Ensemble Rate Adaptation Framework for Dynamic Adaptive Streaming Over HTTP

Rate adaptation is one of the most important issues in dynamic adaptive streaming over HTTP (DASH). Due to the frequent fluctuations of the network bandwidth and complex variations of video content, it is difficult to deal with the varying network conditions and video content perfectly by using a single rate adaptation method. In this paper, we propose an ensemble rate adaptation framework for DASH, which aims to leverage the advantages of multiple methods involved in the framework to improve the quality of experience (QoE) of users. The proposed framework is simple yet very effective. Specifically, the proposed framework is composed of two modules, i.e., the method pool and method controller. In the method pool, several rate adaptation methods are integrated. At each decision time, only the method that can achieve the best QoE is chosen to determine the bitrate of the requested video segment. Besides, we also propose two strategies for switching methods, i.e., InstAnt Method Switching, and InterMittent Method Switching, for the method controller to determine which method can provide the best QoEs. Simulation results demonstrate that, the proposed framework always achieves the highest QoE for the change of channel environment and video complexity, compared with state-of-the-art rate adaptation methods.
Intelligent routing between capsules empowered with deep extreme machine learning technique

A container is a gathering of neurons whose action vector speaks to the instantiation parameters of a particular kind of substance, for example, an item or an article part. We utilize the length of the action vector to speak to the likelihood that the substance exists and its introduction to speak to the instantiation parameters. Compelling cases at one dimension make expectations, utilizing change networks, for the instantiation parameters of more elevated amount containers. At the point when different forecasts concur, a higher amount of container ends up dynamic. We demonstrate that a discriminatively prepared, multilayer case framework accomplishes best in class execution on Modified National Institute of Standards and Technology (MNIST) and is extensively superior to a deep learning algorithm at perceiving exceedingly covering digits. Deep learning algorithms encouraged by the function and structure of the brain. The deep extreme learning machine (DELM) approach is used to construct a compound that has the least error and highest reliability. All layers are jointly or greedily optimized, depending on the strategy. Deep extreme learning learns all the layers. This paper shows research on the expectation of the MNIST dataset using a DELM. In this article to predict digits better, we have used feedforward and backward propagation deep learning neural networks. When the results were considered, it was observed that deep extreme learning neural network has the highest accuracy rate with 70% of training (42,000 samples), 30% of test and validation (28,000 examples). When comparing the results, it was seen that the intelligent routing between capsules empowered with DELM (IRBC DELM) has the highest precision rate of 97.8%. Simulation results validate the prediction effectiveness of the proposed DELM strategy.
Wearable 3.0: From Smart Clothing to Wearable Affective Robot

With the rapid development of science and technology and the accelerating pace of life, people's mental health problems have become increasingly prominent. Besides, traditional wearable technologies and affective computing technologies (i.e., Wearable 2.0 based on smart clothing) have been unable to meet the requirements brought by computation-intensive affective analysis and latency-sensitive affective interaction. Therefore, in this article, Wearable 3.0, which integrates brain wearable devices, intelligent affective interaction robots, and intelligent tactile devices, is proposed. Wearable 3.0 can provide users with more personalized mental health monitoring services by collecting EEG data, speech emotion data, and tactile behavioral perception data. Also, the advantages of Wearable 3.0 and performance comparison with Wearable 2.0 are given in this article. Two typical application cases of Wearable 3.0, namely, wearable affective robot and CreativeBioMan, are introduced. Moreover, the detailed architecture design and realization of Wearable 3.0 are also given, which show that our proposed user mental healthcare system based on Wearable 3.0 has deeper cognitive intelligence and provides more flexible affective interaction.
Learning-Based Anomaly Detection and Monitoring for Swarm Drone Flights

This paper addresses anomaly detection and monitoring for swarm drone flights. While the current practice of swarm flight typically relies on the operator’s naked eyes to monitor health of the multiple vehicles, this work proposes a machine learning-based framework to enable detection of abnormal behavior of a large number of flying drones on the fly. The method works in two steps: a sequence of two unsupervised learning procedures reduces the dimensionality of the real flight test data and labels them as normal and abnormal cases; then, a deep neural network classifier with one-dimensional convolution layers followed by fully connected multi-layer perceptron extracts the associated features and distinguishes the anomaly from normal conditions. The proposed anomaly detection scheme is validated on the real flight test data, highlighting its capability of online implementation.
SOBA: Semi-automated Ontology Builder for Aspect-based sentiment analysis

This research explores the possibility of improving knowledge-driven aspect-based sentiment analysis (ABSA) in terms of efficiency and effectiveness. This is done by implementing a Semi-automated Ontology Builder for Aspect-based sentiment analysis (SOBA). Semi-automatization of the ontology building process could produce more extensive ontologies, whilst shortening the building time. Furthermore, SOBA aims to improve the effectiveness of its ontologies in ABSA by attaching to concepts the semantics provided by a semantic lexicon. To evaluate the performance of SOBA, ontologies are created using the ontology builder for the restaurant and laptop domains. The use of these ontologies is then compared with the use of manually constructed ontologies in a state-of-the-art knowledge-driven ABSA model, the Two-Stage Hybrid Model (TSHM). The results show that it is difficult for a machine to beat the quality of a human made ontology, as SOBA does not improve the effectiveness of TSHM, achieving similar results. Including the semantics provided by a semantic lexicon in general increases the performance of TSHM, albeit not significantly. However, SOBA decreases by 50% or more the human time needed to build ontologies, so that it is recommended to use SOBA for knowledge-driven ABSA frameworks, as it leads to greater efficiency.
Traffic scene semantic segmentation using self-attention mechanism and bi-directional GRU to correlate context

Context information plays an important role in semantic segmentation of urban traffic scenes, which is one of the key tasks of the intelligent platform’s (such as unmanned vehicles) perceiving environment, and has inspired a wide range of interests from researchers. This paper synthesizes three considerations: feature space correlation, information distributed in the long distance of image plane and long distance sequence information, and proposes a combination of self-attention mechanism and bi-directional gated recurrent unit (GRU) neural network to extract various contextual information on the basis of deep feature network, so as to achieve better semantic segmentation performance. In order to explore the optimal implementation, two kinds of topological connections are attempted. One is self-attention branch and bi-directional GRU branch in series, and the other is in parallel. In addition, in order to train the network better and achieve more precise segmentation results, a cascade refinement supervised method using two losses is proposed. Experiments carried out on Cityscapes, Mapillary, CamVid and KITTI semantic segmentation datasets demonstrate the outstanding performance and robust generalization ability of our method.
Structured Medical Pathology Data Hiding Information Association Mining Algorithm Based on Optimized Convolutional Neural Network

When using traditional algorithms to mine the association of hiding information in medical pathological data, there are some problems, such as low recognition rate of association and poor accuracy of mining results. Therefore, structured medical pathology data hiding information association mining algorithm based on optimized convolution neural network is proposed. Firstly, an information feature is optimized based on rough set relative classification information entropy and ant colony algorithm and the optimized feature matrix is obtained. The information in the optimized feature matrix is weighted, and the weighted features of hiding information are obtained. Secondly, the hiding information feature matrix is transmitted to the convolution neural network for learning, and the weight of the connection layer is extracted. The importance of the corresponding area of the weight is confirmed by the distribution of the weight value, and the feature average matrix is obtained. According to the matrix, the feature of hiding information data is enhanced. The hiding information in the structured medical pathology data is generalized by using the Gaussian Bell function, and the hiding information generalization processing result is combined with the adjacent matrix in the convolution neural network to construct the hiding information classification model. Finally, the classification standard is defined, the cooperative association of hiding information group is obtained, and the mining of association between hiding information of structured medical pathological data is completed. The experimental results show that the proposed algorithm has good feature optimization effect, and the information association recognition rate is high, the anti-interference ability and accuracy are better than the current related results, the highest recall rate is 99.24%, which is much higher than the traditional algorithm, which shows that the algorithm is effective.
Dynamic Controller That Operates Over Homomorphically Encrypted Data for Infinite Time Horizon

In this article, we present a dynamic feedback controller that computes the next state and the control signal over encrypted data using homomorphic properties of cryptosystems, whose performance is equivalent to the linear dynamic controllers over real-valued data. Assuming that the input as well as the output of the plant is encrypted and transmitted back to the controller, it is shown that the state matrix of any linear time-invariant controller can be always converted to a matrix of integer components. This allows the dynamic feedback controller to operate for infinite time horizon without decryption or reset of its internal state. For implementation in practice, we illustrate the use of a cryptosystem that is based on the Learning With Errors problem, which allows both multiplication and addition over encrypted data. It is also shown that the effect of injected random numbers during encryption for security can be maintained within a small bound by way of the closed-loop stability.
Phase-Based Noncontact Vibration Measurement of High-Speed Magnetically Suspended Rotor

Video-based measurement, as one of the most common noncontact measurement methods for industrial inspection, has been developed rapidly. Digital video cameras offer the benefit of low cost, high automation, and are capable to make the simultaneous full-field measurement. This article focuses on studying the microvibrational signal extraction for high-speed rotating machinery with digital cameras. An improved phase-based motion extraction and learning-based video magnification are proposed for measuring the microvibration with high frequency and small amplitude. The phase-based motion extraction is improved by directly transforming the phase variations into the displacement without the computation of phase gradient. Furthermore, the learning-based motion magnification is utilized to amplify and qualitatively measure the microvibration in specified frequency bands. The phase-based motion extraction is a quantitative measurement, while the learning-based video magnification is a qualitative measurement. Experimental results for microvibration measurement rising from a magnetically suspended motor system validate the effectiveness of the proposed method.
Cellular Signal Identification Using Convolutional Neural Networks: AWGN and Rayleigh Fading Channels

Spectrum awareness is crucial in wireless communications systems for dynamic network environments. It is required for spectrum resource management, adaptive transmissions, and interference detection. Existing spectrum awareness research includes tasks of spectrum sensing, modulation classification, and medium access control protocol (MAC) identification. This paper explores the identification and classification of signals of various cellular networks, including Global System for Mobile (GSM), Universal Mobile Telecommunication Service (UMTS), and Long-Term Evolution (LTE). We utilize deep learning, specifically, convolutional neural networks (CNN), in training and testing wireless fading signals in those cellular networks. Experimentations demonstrate the effectiveness of deep learning in cellular signal identification.
The Dark Web and cannabis use in the United States: Evidence from a big data research design

Cannabis is one of the most commonly sold drugs on cryptomarkets. Because of the anonymity-granting functions of Tor, no study has traced the within-country effect of the Dark Web on cannabis consumption patterns. This article uses a big data research design to examine the association between revealed interest in the Dark Web and self-reported cannabis use within US states from 2011 when Silk Road launched to 2015 when Operation Onymous shuttered nine markets. This study uses mixed effects ordinary least squared regressions to analyze U.S. state/year panel data, using robust standard errors to correct for heteroscedasticity. Marginal effect plots illustrate substantive effects. The dataset consists of state-level variables drawn from the Uniform Crime Report (UCR), the American Community Survey (ACS), the National Survey on Drug Use and Health, the Correlates of State Policy Project, and the Bureau of Justice Statistics Justice Expenditure and Employment Extracts. Data for the Dark Web interest measure are drawn from Google Trends. The proxy for Dark Web interest is an index of eight Dark Web related search queries. The regression analysis indicates that Dark Web interest in US states positively correlates with cannabis consumption rates overall and among older adults (26+), but not youth (12–17) or younger adults (18–25). Additionally, Dark Web interest is positively associated with more frequent cannabis usage rates (i.e. use in the past month, excluding first time use) both overall and among older adults, but not among youth or younger adults. Dark Web interest does not correlate with casual use (i.e. use in the last year, excluding use in the past month) for any age bracket. Interacting Dark Web interest with state-level legalization regimes indicates that the association between Dark Web interest and cannabis consumption in the past year is no different in medically legalized states and amplified in states with recreational legalization. Lastly, the Dark Web interest term does not correlate with first time cannabis either overall or for any age category. Interest in the Dark Web is associated with increased cannabis use in U.S. states from 2011–2015, but the effect is concentrated in states with more frequent cannabis users, older users, and in states with recreational legalization of cannabis.
Social Media and Sentiment Analysis: The Nigeria Presidential Election 2019

Social media has become an inevitable tool in many sectors including politics. On February 23, Africa's largest economy and most populous country, Nigeria, conducts its presidential elections. Many Nigerians used the social media to express their opinion in favour or against the various presidential candidates. Research has shown that their shared sentiments can influence the opinions of others and hence who eventually wins the presidential election. This paper therefore aims to identify and analyze public sentiments towards two popular candidates with the aim of determining their chances of being elected into the highest position of authority in Nigeria based on social media comments. First, we perform sentiment analysis on election-related posts from Nairaland (a social network targeted at Nigerians) using lexicon-based and supervised machine learning (ML) techniques with the aim of detecting their sentiment polarity (i.e. negative or positive). We collected 118,421 posts between January 1 and February 22, 2019. Second, we implemented and compared the performance of three lexicon-based classifiers and five ML-based classifiers. The best performing classifier is then used in determining the sentiment polarity of posts. Third, we conducted thematic analysis on both positive and negative posts to further understand and reveal public opinions about each candidate. Finally, we discuss our analytical findings and the possibility of a candidate receiving more votes than the other. Our findings relate considerably to the actual election results released by the Independent National Electoral Commission (INEC).
Mortadelo: Automatic generation of NoSQL stores from platform-independent data models

In the last decade, several NoSQL systems have emerged as a response to the scalability problems manifested by classical relational databases when used in Big Data contexts. These NoSQL systems appeared first as physical-level solutions, initially lacking any design methodologies. After this initial batch of systems, several design methodologies for NoSQL have been recently created. Nevertheless, most of these methodologies target just one NoSQL paradigm. In addition, as each methodology uses a different conceptual modeling approach, NoSQL database designers would need to remake conceptual models as they switch from one NoSQL paradigm to another. Moreover, most of these design processes provide just a set of design heuristics and guidelines that database designers need to apply manually, which can be a time-consuming and error-prone process. To overcome these limitations, this article presents Mortadelo, a model-driven NoSQL database design process where, from a high-level conceptual model, independent of any specific NoSQL paradigm, an implementation for a concrete NoSQL database system can be automatically generated. Moreover, this database generation process can be customized, so that some design trade-offs can be managed differently according to each context needs. We evaluated Mortadelo's capabilities by generating database implementations for several typical NoSQL case studies. In these cases, Mortadelo was able to generate implementations for the Cassandra and MongoDB NoSQL systems from the same conceptual data model. These implementations were similar to the ones generated by design methodologies specifically developed for a single paradigm. Therefore, design quality is not sacrificed by our approach in favor of generality.
Novel Scheme for Image Encryption Combining 2D Logistic-Sine-Cosine Map and Double Random-Phase Encoding

In this paper, we present a 2D Logistic-Sine-Cosine map (2D-LSCM) which is based on the classical 2D Logistic, Sine and Cosine maps.Performance analysis shows that it has larger chaotic range, bigger Lyapunov exponent and more complex chaotic behavior than the classical 2D Logistic map.By means of 2D-LSCM, Discrete Wavelet Transform (DWT) and Double Random-Phase Encoding (DRPE), we proposed an effective image encryption algorithm.In this scheme, after conducting DWT on the original image, the wavelet coefficients are scrambled by using the 2D-LSCM, and then the DRPE is employed to encode the scrambled result.Next, the Inverse Discrete Wavelet Transform (IDWT) is conducted on the encoded results.At last, the result from IDWT is diffused and confused with the chaos sequences generated from 2D-LSCM.Simulation results and security analysis reveal the fact that the proposed algorithm has good statistical property, large key space and effective resistance to common cryptanalytic attacks.
On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery

X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on the use of automated screening approaches, including the potential anomaly detection as a methodology for concealment detection within complex electronic items. Here we address this problem considering varying segmentation strategies to enable the use of both object level and sub-component level anomaly detection via the use of secondary convolutional neural network (CNN) architectures. Relative performance is evaluated over an extensive dataset of exemplar cluttered X-ray imagery, with a focus on consumer electronics items. We find that sub-component level segmentation produces marginally superior performance in the secondary anomaly detection via classification stage, with true positive of ~98% of anomalies, with a ~3% false positive.