Reviewerly/exHarmony huggingface dataset:
A Survey of High Utility Itemset Mining

High utility pattern mining is an emerging data science task, which consists of discovering patterns having a high importance in databases. The utility of a pattern can be measured in terms of various objective criterias such as its profit, frequency, and weight. Among the various kinds of high utility patterns that can be discovered in databases, high utility itemsets are the most studied. A high utility itemset is a set of values that appears in a database and has a high importance to the user, as measured by a utility function. High utility itemset mining generalizes the problem of frequent itemset mining by considering item quantities and weights. A popular application of high utility itemset mining is to discover all sets of items purchased together by customers that yield a high profit. This chapter provides an introduction to high utility itemset mining, reviews the state-of-the-art algorithms, their extensions, applications, and discusses research opportunities. This chapter is aimed both at those who are new to the field of high utility itemset mining, as well as researchers working in the field.
Attend and Imagine: Multi-Label Image Classification With Visual Attention and Recurrent Neural Networks

Real images often have multiple labels, i.e., each image is associated with multiple objects or attributes. Compared to single-label image classification, the multilabel classification problem is much more challenging due to several issues. At first, multiple objects can be anywhere in the image. Second, the importance of different regions in an image is different, and the regions of interest in a multilabel image can be very different from another one. Finally, multiple labels of an image can have label dependencies due to complex image structures. To address these challenges, in this paper, we propose to predict the labels sequentially by applying the recurrent neural networks (RNNs), which are used to encode the label dependencies. When predicting a specific label, we introduce a dynamic attention mechanism to enable the model to focus on only regions of interest in the image. Two benchmark datasets (i.e., Pascal VOC and MS-COCO) are adopted to demonstrate the effectiveness of our work. Moreover, we construct a new dataset, which includes many semantic dependent labels in each image, to verify the effectiveness of our model. Experimental results show that our method outperforms several state-of-the-arts, especially when predicting some semantic relative labels.
Achieving data utility-privacy tradeoff in Internet of Medical Things: A machine learning approach

The emergence and rapid development of the Internet of Medical Things (IoMT), an application of the Internet of Things into the medical and healthcare systems, have brought many changes and challenges to modern medical and healthcare systems. Particularly, machine learning technology can be used to process the data involved in IoMT for medical analysis and disease diagnosis. However, in this process, the disclosure of personal privacy information must receive considerable attentions especially for sensitive medical data. Cluster analysis is an important technique for medical analysis and disease diagnosis. To enable privacy-preserving cluster analysis in IoMT, this paper proposed an Efficient Differentially Private Data Clustering scheme (EDPDCS) based on MapReduce framework. In EDPDCS, we optimize the allocation of privacy budgets and the selection of initial centroids to improve the accuracy of differentially private K-means clustering algorithm. Specifically, the number of iterations of the K-means algorithm is set to a fixed value according to the total privacy budget and the minimal privacy budget of each iteration. In addition, an improved initial centroids selection method is proposed to increase the accuracy and efficiency of the clustering algorithm. Finally, we prove that the proposed EDPDCS can improve the accuracy of the differentially private k-means algorithm by comparing the Normalized Intra-Cluster Variance (NICV) produced by our algorithm on two datasets with two other algorithms.
RF Fingerprinting of IoT Devices Based on Transient Energy Spectrum

Radio frequency (RF) fingerprinting is considered as one of the promising techniques to enhance wireless security in the Internet of Things (IoT) applications. In this paper, a low-complexity RF fingerprinting method for classification of wireless IoT devices is proposed. The method is based on the energy spectrum of the transmitter turn-on transient signals from which unique characteristics of wireless devices are extracted. The number of spectral components to be used is determined through a proposed approach based on the estimated transient duration value. Transient duration estimation is achieved from the smoothed versions of the instantaneous amplitude characteristics of transmitter signals, which are obtained through a sliding windowaveraging method. Classification performance of the proposed spectral fingerprints is assessed using experimental data and described by a confusion matrix. The discrimination effectiveness of the spectral fingerprints is quantified by a class separability criterion and evaluated for different noise levels through Monte Carlo simulations. It is demonstrated that the proposed fingerprints outperform the classification performance of two existing fingerprints especially at low signal-to-noise ratio. Additionally, computational complexity analysis of the classifier using the proposed fingerprints is provided.
Deep Speaker Embedding Learning with Multi-level Pooling for Text-independent Speaker Verification

This paper aims to improve the widely used deep speaker embedding x-vector model. We propose the following improvements: (1) a hybrid neural network structure using both time delay neural network (TDNN) and long short-term memory neural networks (LSTM) to generate complementary speaker information at different levels; (2) a multi-level pooling strategy to collect speaker information from both TDNN and LSTM layers; (3) a regularization scheme on the speaker embedding extraction layer to make the extracted embeddings suitable for the following fusion step. The synergy of these improvements are shown on the NIST SRE 2016 eval test (with a 19% EER reduction) and SRE 2018 dev test (with a 9% EER reduction), as well as more than 10% DCF scores reduction on these two test sets over the x-vector baseline.
Jail-Phish: An improved search engine based phishing detection system

Stealing of sensitive information (username, password, credit card information and social security number, etc.) using a fake webpage that imitates trusted website is termed as phishing. Recent techniques use search engine based approach to counter the phishing attacks as it achieves promising detection accuracy. But, the limitation of this approach is that it fails when phishing page is hosted on compromised server. Moreover, it also results in low true negative rate when newly registered or non-popular domains are encountered. Hence, in this paper, we propose an application named as Jail-Phish, which improves the accuracy of the search engine based techniques with an ability to detect the Phishing Sites Hosted on Compromised Servers (PSHCS) and also detection of newly registered legitimate sites. Jail-Phish compares the suspicious site and matched domain in the search results for calculating the similarity score between them. There exists some degree of similarity such as logos, favicons, images, scripts, styles, and anchorlinks within the pages of the same website whereas on the other side, the dissimilarity within the pages is very high in PSHCS. Hence, we use the similarity score between the suspicious site and matched domain as a parameter to detect the PSHCS. From the experimental results, it is observed that Jail-Phish achieved an accuracy of 98.61%, true positive rate of 97.77% and false positive rate less than 0.64%.
Deep Multi-Modality Adversarial Networks for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to transfer domain knowledge from existing well-defined tasks to new ones where labels are unavailable. In the real-world applications, domain discrepancy is usually uncontrollable especially for multi-modality data. Therefore, it is significantly motivated to deal with a multi-modality domain adaptation task. As labels are unavailable in a target domain, how to learn semantic multi-modality representations and successfully adapt the classifier from a source to the target domain remain open challenges in a multi-modality domain adaptation task. To deal with these issues, we propose a multi-modality adversarial network (MMAN), which applies stacked attention to learn semantic multi-modality representations and reduces domain discrepancy via adversarial training. Unlike the previous domain adaptation methods, which cannot make full use of source domain categories information, multi-channel constraint is employed to capture fine-grained categories of knowledge that could enhance the discrimination of target samples and boost target performance on single-modality and multi-modality domain adaptation problems. We apply the proposed MMAN to two applications including cross-domain object recognition and cross-domain social event recognition. The extensive experimental evaluations demonstrate the effectiveness of the proposed model for unsupervised domain adaptation.
Asynchronous Federated Learning for Geospatial Applications

Federated learning is an emerging collaborative machine-learning paradigm for training models directly on edge devices. The data remains on the edge device and this method is robust under real-world edge data distributions. We present a new asynchronous federated-learning algorithm (‘asynchronous federated learning’) and study its convergence rate when distributed across many edge devices, with hard data constraints, relative to training the same model on a single device. We compare asynchronous federated learning to an existing synchronous method. We evaluate its robustness in real-world situations; for example, devices joining part-way through training or devices with heterogeneous compute resources. We then apply asynchronous federated learning to a challenging geospatial application, namely image-based geolocation using a state-of-the-art convolutional neural network. Our results lay the groundwork for deploying large-scale federated learning as a tool to automatically learn, and continually update, a machine-learned model that encodes location.
Lightweight Cryptography for Internet of Insecure Things: A Survey

This paper surveys Lightweight Cryptographic solutions for Internet of Things (IoT). This survey covers comprehensively a flow of security measures from Lightweight Cryptographic solutions to comparison among different types of block ciphers. It also includes comparison between Hardware vs Software solutions and different recent approaches of the most trusted and researched block cipher, Advanced Encryption Standard (AES) in terms of architecture, Mix-Column/S-box modify strategy and attacks for IoT security. According to the study, lightweight AES has proved to be a good security solution for constrained IoT devices.
A comparative study of digital informal learning: The effects of digital competence and technology expectancy

Abstract The objective of this paper is to investigate the effects of digital competence and technology expectancy and how they interact with cultural differences on digital informal learning (DIL) of university students have different cultural backgrounds. Empirical data collected from a survey conducted both in China and Belgium, the total sample consists of 335 Chinese and 197 Belgians. Partial least square with formative assessment was employed to test the proposed model and multi‐groups comparison analysis was conducted to examine the moderation effects of cultural difference on influential factors related to students’ DIL behaviors. The results highlighted the essential role of digital competence and technology expectancy in DIL. Cultural difference revealed a strong influence on the motive pattern of DIL behaviors. This study raised some major implications for educational researchers and practitioners alike. Specifically, the paper introduced and stressed the role of digital competence in informal learning context which was rarely considered in prior studies in terms of learning ecology. The study expanded current theoretical understanding of technology adoption in informal learning environments across different cultural contexts. The finding has shown both a homogeneous informal learning behavior and motive variance between different two cultural backgrounds. The study also provided a set of practical implications, including the need for digital competence training in today’s digital era, and the need for a satisfactory user interface design globally.
Convolutional Neural Network and Guided Filtering for SAR Image Denoising

Coherent noise often interferes with synthetic aperture radar (SAR), which has a huge impact on subsequent processing and analysis. This paper puts forward a novel algorithm involving the convolutional neural network (CNN) and guided filtering for SAR image denoising, which combines the advantages of model-based optimization and discriminant learning and considers how to obtain the best image information and improve the resolution of the images. The advantages of proposed method are that, firstly, an SAR image is filtered via five different level denoisers to obtain five denoised images, in which the efficient and effective CNN denoiser prior is employed. Later, a guided filtering-based fusion algorithm is used to integrate the five denoised images into a final denoised image. The experimental results indicate that the algorithm cannot eliminate noise, but it does improve the visual effect of the image significantly, allowing it to outperform some recent denoising methods in this field.
Can we gauge forecasts using satellite-derived solar irradiance?

Satellite-derived irradiance data, as an alternative to ground-based measurements, offer a unique opportunity to verify gridded solar forecasts generated by a numerical weather prediction model. Previously, it has been shown that the mean square errors (MSE) evaluated against ground-based measurements and satellite-derived solar irradiance are comparable, which might warrant the use of satellite-based products for regional forecast verification. In this paper, the 24-h-ahead hourly forecasts issued by the North American Mesoscale forecast system are verified against both ground-based (Surface Radiation Budget Network, or SURFRAD) and satellite-based (National Solar Radiation Data Base, or NSRDB) measurements, at all 7 SURFRAD stations over 2015–2016. Three different MSE decomposition methods are used to characterize—e.g., through association, calibration, refinement, resolution, or likelihood—how well the two types of measurements can gauge the forecasts. However, despite their comparable MSEs, NSRDB is found suboptimal in its ability to verify forecasts as compared to SURFRAD. Nonetheless, if a new forecasting model produces significantly better forecasts than the benchmarking model, satellite-derived data are able to detect such improvements and make conclusions. This article comes with supplementary material (data and code) for reproducibility.
Peak Ground Displacement Saturates Exactly When Expected: Implications for Earthquake Early Warning

Abstract The scaling of rupture properties with magnitude is of critical importance to earthquake early warning systems that rely on source characterization using limited snapshots of waveform data. ShakeAlert, a prototype earthquake early warning system that is being developed for the western United States, provides real‐time estimates of earthquake magnitude based on P wave peak ground displacements measured at stations triggered by the event. The algorithms used in ShakeAlert assume that the displacement measurements at each station are statistically independent and that there exists a linear and time‐independent relation between log peak ground displacement and earthquake magnitude. Here we challenge this basic assumption using the largest data set assembled for this purpose to date: a comprehensive database of more than 140,000 vertical‐component waveforms from M4.5 to M9 earthquakes occurring near Japan from 1997 through 2018 and recorded by the K‐NET and KiK‐net strong‐motion networks. By analyzing the time evolution of P wave peak ground displacements for these earthquakes, we show that there is a break, or saturation, in the magnitude‐displacement scaling that depends on the length of the measurement time window. We demonstrate that the magnitude at which this saturation occurs is well‐explained by a simple and nondeterministic model of earthquake rupture growth. We then use the predictions of this saturation model to develop a Bayesian framework for estimating posterior uncertainties in real‐time magnitude estimates.
Automotive radar and camera fusion using Generative Adversarial Networks

Radar sensors are considered to be very robust under harsh weather and poor lighting conditions. Largely owing to this reputation, they have found broad application in driver assistance and highly automated driving systems. However, radar sensors have considerably lower precision than cameras. Low sensor precision causes ambiguity in the human interpretation of the measurement data and makes the data labeling process difficult and expensive. On the other hand, without a large amount of high-quality labeled training data, it is difficult, if not impossible, to ensure that the supervised machine learning models can predict, classify, or otherwise analyze the phenomenon of interest with the required accuracy. This paper presents a method for fusing the radar sensor measurements with the camera images. A proposed fully-unsupervised machine learning algorithm converts the radar sensor data to artificial, camera-like, environmental images. Through such data fusion, the algorithm produces more consistent, accurate, and useful information than that provided solely by the radar or the camera. The essential point of the work is the proposal of a novel Conditional Multi-Generator Generative Adversarial Network (CMGGAN) that, being conditioned on the radar sensor measurements, can produce visually appealing images that qualitatively and quantitatively contain all environment features detected by the radar sensor.
Incorporating text dispersion into keyword analyses

Keyword analysis has become an indispensable tool for discourse analysts, being applied to identify the words that are especially characteristic of the texts in a target discourse domain. But, surprisingly, the statistical computation of keyness makes no reference to those texts. Rather, once a corpus has been constructed, it is treated as a homogeneous whole for the computation of keyness. As a result, the keywords in such lists are relatively frequent in the corpus, but they are often not widely dispersed across the texts of that corpus and are thus not truly representative of the target discourse domain. The purpose of this study is to propose a new method for keyword analysis – text dispersion keyness – that is based on text dispersion, rather than corpus frequency. We compare the effectiveness of this measure to four other methods for computing keyness, carrying out a series of case studies to identify the keywords that are typical of online travel blogs. A variety of quantitative and qualitative analyses are carried out to compare these methods based on their content-generalisability and content-distinctiveness, demonstrating that text dispersion keyness is a superior measure for generating keyword lists.
Acting your avatar’s age: effects of virtual reality avatar embodiment on real life walking speed

When immersive virtual reality users employ digital self-representations, i.e., avatars, they may be subject to the Proteus effect. This effect describes changes in attitudes and behaviors in accordance with identity cues derived from the employed avatar's appearance, which can persist after leaving virtual reality. Individual reactions to the experience can affect the strength of observed Proteus effects. Especially the experienced illusions of body ownership of avatars and of being in the virtual environment (spatial presence) have been discussed in this context. This study investigated a Proteus effect of avatar age on post-embodiment walking speed, with special focus on how body ownership and spatial presence moderated this effect. Participants who had previously embodied older avatars took significantly longer to walk a set distance than either young avatar or control group participants. This was only apparent during the first half of the walking phase, which may indicate fast decay rates of the effect after embodiment ended. The reported body ownership could not be shown to impact the strength of the Proteus effect. Participants reporting more pronounced spatial presence were subject to stronger Proteus effects, with only the two-thirds of the sample with higher spatial presence showing evidence of the effect.
Photonic Synapses for Ultrahigh‐Speed Neuromorphic Computing

Neuromorphic computing based on a non‐von Neumann architecture is a promising way for the efficient implementation of artificial intelligence. A neuromorphic computing system is mainly composed of artificial synapses and neurons. Various electronic neuromorphic devices have been developed by different technologies. Neuromorphic photonics combines the efficiency of neural networks and the speed of photonics to build computing systems. Compared with their electronic counterparts, photonic neurons and synapses have the potential advantages of ultrafast operation speed, large bandwidth, and no electrical interconnect power loss, inherent to the computing by optical means. Therefore, photonic neuromorphic devices are attracting more and more attention. This work reviews the recent advances in the development of photonic synapses. Both purely photonic synapses and photoelectric synapses are described. Their structures and working mechanisms are discussed in detail.
Managing QoS of Internet-of-Things Services Using Blockchain

Owing to the exponential growth of Internet of Things (IoTs), ensure that the Quality of Service (QoS) over IoT becomes challenges at the network edge or on cloud. The traditional mechanisms for QoS measurements rely on the centralized trusted third parties who use specialized agents to collect data and measure the performances of services. However, these mechanisms are ineffective to deal with highly dynamic and distributed nature of IoT-based services. Moreover, the dynamism of QoS needs to collect, update, and access reliable quality relevant data frequently, while lacking trust becomes a major hurdle for data utilization. It is our argument that the QoS measurement of IoT-based services would be decentralized and the trusts be built from collectively trusted subnetworks. In this paper, we propose to integrate the blockchain technologies (BCT) with a multi-agent approach to warrantee the trustiness of real-time data for the measurement of QoS in the IoT environment. The proposed approach is verified by some demonstrative examples in addressing QoS specification patterns commonly found in service-based applications (SBAs), where qualitative analyses are conducted for the evaluation of the patterns.