Reviewerly/exHarmony huggingface dataset:
An overview of unsupervised drift detection methods

Abstract Practical applications involving big data, such as weather monitoring, identification of customer preferences, Internet log analysis, and sensors warnings require challenging data analysis, since these are examples of problems whose data are generated in streams and usually demand real‐time analytics. Patterns in such data stream problems may change quickly. Consequently, machine learning models that operate in this context must be updated over time. This phenomenon is called concept drift in machine learning and data mining literature. Several different directions have been pursued to learn from data stream and to deal with concept drift. However, most drift detection methods consider that an instance's class label is available right after its prediction, since these methods work by monitoring the prediction results of a base classifier or an ensemble of classifiers. Nevertheless, this constraint is unrealistic in several practical problems. To cope with this constraint, some works are focused on proposing efficient unsupervised or semi‐supervised concept drift detectors. While interesting and recent overview papers dedicated to supervised drift detectors have been published, the scenario is not the same in terms of unsupervised methods. Therefore, this work presents a comprehensive overview of approaches that tackle concept drift in classification problems in an unsupervised manner. Additional contribution includes a proposed taxonomy of state‐of‐the‐art approaches for concept drift detection based on unsupervised strategies. This article is categorized under: Technologies &gt; Classification Technologies &gt; Machine Learning
A Discrete Hidden Markov Model for SMS Spam Detection

Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.
Weighted bilateral K-means algorithm for fast co-clustering and fast spectral clustering

Bipartite spectral graph partition (BSGP) is a school of the most well-known algorithms designed for the bipartite graph partition problem. It is also a fundamental mathematical model widely used in the tasks of co-clustering and fast spectral clustering. In BSGP, the key is to find the minimal normalized cuts (Ncuts) of bipartite graph. However, the convolutional BSGP algorithms usually need to use the singular value decomposition (SVD) to find the minimal Ncuts, which is computational prohibitive. Under this circumstance, the application range of those methods would be limited when the volume of the dataset is huge or the dimension of features is high. To overcome this problem, this paper proposes a novel weighted bilateral k-means (WBKM) algorithm and applies it for co-clustering and fast spectral clustering. Specifically, WBKM is a relaxation of the problem of finding the minimal Ncuts of bipartite graph, so it can be seen as a new solution for the minimal-Ncuts problem in bipartite graph. Different from the conventional relaxation ways, WBKM relaxes the minimal-Ncuts problem to a Non-negative decomposition problem which can be solved by an efficient iterative method. Therefore, the running speed of the proposed method is much faster. Besides, as our model can directly output the clustering results without any help of post-procedures, its solution tends to be more close to the ideal solution of the minimal-Ncuts problem than that of the conventional BSGP algorithms. To demonstrate the effectiveness and efficiency of the proposed method, extensive experiments on various types of datasets are conducted. Compared with other state-of-the-art methods, the proposed WBKM not only has faster computational speed, but also achieves more promising clustering results.
Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification

The scarcity of labelled time-series data can hinder a proper training of deep learning models. This is especially relevant for the growing field of ubiquitous computing, where data coming from wearable devices have to be analysed using pattern recognition techniques to provide meaningful applications. To address this problem, we propose a transfer learning method based on attributing sensor modality labels to a large amount of time-series data collected from various application fields. Using these data, our method firstly trains a Deep Neural Network (DNN) that can learn general characteristics of time-series data, then transfers it to another DNN designed to solve a specific target problem. In addition, we propose a general architecture that can adapt the transferred DNN regardless of the sensors used in the target field making our approach in particular suitable for multichannel data. We test our method for two ubiquitous computing problems-Human Activity Recognition (HAR) and Emotion Recognition (ER)-and compare it a baseline training the DNN without using transfer learning. For HAR, we also introduce a new dataset, Cognitive Village-MSBand (CogAge), which contains data for 61 atomic activities acquired from three wearable devices (smartphone, smartwatch, and smartglasses). Our results show that our transfer learning approach outperforms the baseline for both HAR and ER.
Privacy preserving based logistic regression on big data

Cloud computing has strong computing power and huge storage space. Machine learning algorithm, combining with cloud computing, makes the processing of large-scale data practical. Logistic regression algorithm is a widely popular machine learning-based classification algorithm that can be implemented in cloud. However, data privacy cannot be guaranteed in big data processing as privacy leakage of the training data may occur. In order to prevent the privacy leakage of logistic regression algorithm in the cloud and promote the processing efficiency of training data, this paper offers a Privacy Preserving Logistic Regression Algorithm (PPLRA). The homomorphic encryption is used to encrypt the private data when they are uploaded for training. Moreover, the approximation of the Sigmoid function in logistic regression using Taylor's theorem can support the safe calculation using homomorphic encryption. The Experimental results show that PPLRA has significant effects in data privacy preserving, and is more effective in data processing. Comparison with Non-Privacy Preserving Logistic Regression Algorithm (NPPLRA) shows that the computational efficiency is improved by about 1.2 times.
Self-tracking behaviour in physical activity: a systematic review of drivers and outcomes of fitness tracking

Advances in technologies (e.g. smartphones, wearables) have resulted in the concept of 'self-tracking', and the use of self-tracking technologies in physical activity (i.e. fitness tracking) is on the rise. For example, many people track and monitor their fitness-related metrics (e.g. steps walked, distance ran, and calories burned) to change their behaviours or keep themselves active. Despite the widespread application of self-tracking in fitness, relatively little is known about its drivers and outcomes. To address this gap, the current paper provides an overview of the literature (empirical papers) on self-tracking with a focus on the drivers and outcomes of fitness tracking behaviour and offers four important contributions. First, it identifies 19 drivers of fitness tracking technology usage. Second, it discusses four main outcomes of fitness tracking behaviour. Third, by drawing on the existing studies conducted across various fitness tracking technologies (e.g. fitness trackers, apps) and user groups (e.g. patients, seniors, and females), it provides valuable insights that can be generalisable to other settings (e.g. other types of users and fitness tracking products). Finally, the current paper provides important practical implications and addresses avenues for future research.
Reducing Qubit Requirements while Maintaining Numerical Precision for the Variational Quantum Eigensolver: A Basis-Set-Free Approach

We present a basis-set-free approach to the variational quantum eigensolver using an adaptive representation of the spatial part of molecular wavefunctions. Our approach directly determines system-specific representations of qubit Hamiltonians while fully omitting globally defined basis sets. In this work, we use directly determined pair-natural orbitals on the level of second-order perturbation theory. This results in compact qubit Hamiltonians with high numerical accuracy. We demonstrate initial applications with compact Hamiltonians on up to 20 qubits where conventional representation would for the same systems require 40-100 or more qubits.
A Realistic Image Generation of Face From Text Description Using the Fully Trained Generative Adversarial Networks

Text to face generation is a sub-domain of text to image synthesis.It has a huge impact on new research areas along with the wide range of applications in the public safety domain.Due to the lack of dataset, the research work focused on the text to face generation is very limited.Most of the work for text to face generation until now is based on the partially trained generative adversarial networks, in which the pre-trained text encoder has been used to extract the semantic features of the input sentence.Later, these semantic features have been utilized to train the image decoder.In this research work, we propose a fully trained generative adversarial network to generate realistic and natural images.The proposed work trained the text encoder as well as the image decoder at the same time to generate more accurate and efficient results.In addition to the proposed methodology, another contribution is to generate the dataset by the amalgamation of LFW, CelebA and locally prepared dataset.The dataset has also been labeled according to our defined classes.Through performing different kinds of experiments, it has been proved that our proposed fully trained GAN outperformed by generating good quality images by the input sentence.Moreover, the visual results have also strengthened our experiments by generating the face images according to the given query.
“I just went into it assuming that I wouldn't be able to have the full experience”

Virtual reality (VR) has the potential to transform many aspects of our daily lives, including work, entertainment, communication, and education. However, there has been little research into understanding the usability of VR for people with mobility limitations. In this paper, we present the results of an exploration to understand the accessibility of VR for people with limited mobility. We conducted semi-structured interviews with 16 people with limited mobility about their thoughts on, and experiences with, VR systems. We identified 7 barriers related to the physical accessibility of VR devices that people with limited mobility might encounter, ranging from the initial setup of a VR system to keeping VR controllers in view of cameras embedded in VR headsets. We also elicited potential improvements to VR systems that would address some accessibility concerns. Based on our findings, we discuss the importance of considering the abilities of people with limited mobility when designing VR systems, as the abilities of many participants did not match the assumptions embedded in the design of current VR systems.
Critical Success Factors for Blockchain Technology Adoption in Freight Transportation Using Fuzzy ANP–Modified TISM Approach

The dynamic and uncertain demand forces organizations to provide flexible services in order to fulfill customer demands. Freight transportation, being the key component of the businesses, requires adoption of efficient Information and Communication Technologies which can induce transparent and flexible services. Blockchain Technology (BT) is an emerging technology which has great potential to cater solutions to freight transportation issues. This study identifies different critical success factors of BT adoption in freight transportation. An integrated Fuzzy Analytic Network Process (FANP) is applied to prioritize identified success factors. Further, modified Total Interpretive Structural Modeling (mTISM) is used to represent the interrelationship among different sub-factors. The findings of the study revealed Development Aspects, Customer Services and Technological Aspects as well as Data Transparency, Reliability and Organizational Culture as the most significant factors and sub-factors, respectively. The proposed model will guide freight transport managers to formulate their strategies regarding BT adoption.
Analysis of various data security techniques of steganography: A survey

The amount of data exchanged via the Internet is increasing nowadays. Hence, data security is termed as a serious issue while communication of data is processed over the Internet. Everyone needs their data to be safe during the process of communication. Steganography is the process of hiding a message, audio, image, or video by embedding it into another image, audio, message, or video. It is employed to protect secret data from malicious attacks. This paper presents a categorization of the steganography based on technical and non-technical steganography and also categorized based on its domain. Issues related to steganography are quality of stego images, payload capacity, mean square error, structural similarity (SSIM), Image Fidelity (IF), and Normalized Cross-Correlation (NCC) and robustness. Researchers have already done commendable research in the area of image steganography. Still, it is important to feature the upsides and downsides of the current systems of steganography. The objective of the paper is to examine and scrutinize the different algorithms of steganography based on parameters like PSNR, MSE, and Robustness. Based on the analysis of these parameters and all the issues, this paper concludes with recommendations for producing high-quality stego images, high-payload capacity, and robust techniques of steganography.
NCNet: Neighbourhood Consensus Networks for Estimating Image Correspondences

We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding scene elements and ambiguities generated by repetitive patterns. The contributions of this work are threefold. First, inspired by the classic idea of disambiguating feature matches using semi-local constraints, we develop an end-to-end trainable convolutional neural network architecture that identifies sets of spatially consistent matches by analyzing neighbourhood consensus patterns in the 4D space of all possible correspondences between a pair of images without the need for a global geometric model. Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences. Third, we show the proposed neighbourhood consensus network can be applied to a range of matching tasks including both category- and instance-level matching, obtaining the state-of-the-art results on the PF, TSS, InLoc, and HPatches benchmarks.
Automatic breast cancer detection based on optimized neural network using whale optimization algorithm

Abstract Breast cancer is the second deadliest type of cancer. Early detection of breast cancer can considerably improve the effectiveness of treatment. A significant early sign of breast cancer is the mass. However, separating the cancerous masses from the normal portions of the breast tissue is usually a challenge for radiologists. Recently, because of the availability of high‐accuracy computing, computer‐aided detection systems based on image processing have become capable of accurately diagnosing the various types of cancers. The main purpose of this study is to utilize a powerful image segmentation method for the diagnosis of cancerous regions through mammography, based on a new configuration of the multilayer perceptron (MLP) neural network. The most popular method for minimizing the errors in an MLP neural network is backpropagation. However, this method has certain drawbacks, such as a low convergence speed and becoming trapped at the local minimum. In this study, a new training algorithm based on the whale optimization algorithm is proposed for the MLP network. This algorithm is capable of solving various problems toward the current algorithms for the analyzed systems. The proposed method is validated on the Mammographic Image Analysis Society database, which contains 322 digitized mammography images, and the Digital Database for Screening Mammography, which contains approximately 2500 digitized mammography images. To assess the detection performance of the proposed system, the correct detection rate, percentage of identification with false acceptance, and percentage of identification with false rejection were evaluated and compared using various methods. The results indicate that the proposed method is highly efficient and yields significantly better accuracy compared with other methods.
Blockchain-based anomaly detection of electricity consumption in smart grids

The big data generated by Industry 4.0 is expected to increase 20-fold in the next ten years and it has raised various challenges in Industrial Wireless Sensor Networks (IWSNs). Among these challenges, detecting different types of anomalies of industrial electricity consumption in an accurate and timely manner is a priority. If not handled properly, these anomalies could lead to serious consequences, such as irregular fire and paralyzed power system components. While existing anomaly detection techniques may be efficient for old systems, they are now faced with big transmitted data. Therefore, it is important to design new methods that can detect the electricity consumption anomaly and carry out appropriate actions. In this article, we first review several existing work on anomaly detection schemes, and then introduce the system and monitoring models. Then, we present a new framework that aims to detect electricity consumption anomalies accurately and timely using sensor processing, smart meter readings, machine learning and blockchain.
Dynamic Knowledge Graph based Multi-Event Forecasting

Modeling concurrent events of multiple types and their involved actors from open-source social sensors is an important task for many domains such as health care, disaster relief, and financial analysis. Forecasting events in the future can help human analysts better understand global social dynamics and make quick and accurate decisions. Anticipating participants or actors who may be involved in these activities can also help stakeholders to better respond to unexpected events. However, achieving these goals is challenging due to several factors: (i) it is hard to filter relevant information from large-scale input, (ii) the input data is usually high dimensional, unstructured, and Non-IID (Non-independent and identically distributed) and (iii) associated text features are dynamic and vary over time. Recently, graph neural networks have demonstrated strengths in learning complex and relational data. In this paper, we study a temporal graph learning method with heterogeneous data fusion for predicting concurrent events of multiple types and inferring multiple candidate actors simultaneously. In order to capture temporal information from historical data, we propose Glean, a graph learning framework based on event knowledge graphs to incorporate both relational and word contexts. We present a context-aware embedding fusion module to enrich hidden features for event actors. We conducted extensive experiments on multiple real-world datasets and show that the proposed method is competitive against various state-of-the-art methods for social event prediction and also provides much-need interpretation capabilities.
WavingSketch: An Unbiased and Generic Sketch for Finding Top-k Items in Data Streams

Finding top-k items in data streams is a fundamental problem in data mining. Existing algorithms that can achieve unbiased estimation suffer from poor accuracy. In this paper, we propose a new sketch, WavingSketch, which is much more accurate than existing unbiased algorithms. WavingSketch is generic, and we show how it can be applied to four applications: finding top-k frequent items, finding top-k heavy changes, finding top-k persistent items, and finding top-k Super-Spreaders. We theoretically prove that WavingSketch can provide unbiased estimation, and then give an error bound of our algorithm. Our experimental results show that, compared with the state-of-the-art, WavingSketch has 4.50 times higher insertion speed and up to 9 x 106 times (2 x 104 times in average) lower error rate in finding frequent items when memory size is tight. For other applications, WavingSketch can also achieve up to 286 times lower error rate. All related codes are open-sourced and available at Github anonymously.
Automated Searching and Identification of Self-Organized Nanostructures

Currently, researchers spend significant time manually searching through large volumes of data produced during scanning probe imaging to identify specific patterns and motifs formed via self-assembly and self-organization. Here, we use a combination of Monte Carlo simulations, general statistics, and machine learning to automatically distinguish several spatially correlated patterns in a mixed, highly varied data set of real AFM images of self-organized nanoparticles. We do this regardless of feature-scale and without the need for manually labeled training data. Provided that the structures of interest can be simulated, the strategy and protocols we describe can be easily adapted to other self-organized systems and data sets.