Reviewerly/exHarmony huggingface dataset:
An enhanced KNN-based twin support vector machine with stable learning rules

Among the extensions of twin support vector machine (TSVM), some scholars have utilized K-nearest neighbor (KNN) graph to enhance TSVM’s classification accuracy. However, these KNN-based TSVM classifiers have two major issues such as high computational cost and overfitting. In order to address these issues, this paper presents an enhanced regularized K-nearest neighbor-based twin support vector machine (RKNN-TSVM). It has three additional advantages: (1) Weight is given to each sample by considering the distance from its nearest neighbors. This further reduces the effect of noise and outliers on the output model. (2) An extra stabilizer term was added to each objective function. As a result, the learning rules of the proposed method are stable. (3) To reduce the computational cost of finding KNNs for all the samples, location difference of multiple distances-based K-nearest neighbors algorithm (LDMDBA) was embedded into the learning process of the proposed method. The extensive experimental results on several synthetic and benchmark datasets show the effectiveness of our proposed RKNN-TSVM in both classification accuracy and computational time. Moreover, the largest speedup in the proposed method reaches to 14 times.
Multilingual Sentiment Analysis

Sentiment analysis has empowered researchers and analysts to extract opinions of people regarding various products, services, events and other entities. This has been made possible due to an astronomical rise in the amount of text data being made available on the Internet, not only in English but also in many regional languages around the world as well, along with the recent advancements in the field of machine learning and deep learning. It has been observed that deep learning models produce the state-of-the-art prediction results without the need for domain expertise or handcrafted feature engineering, unlike traditional machine learning-based algorithms. In this chapter, we wish to focus on sentiment analysis of various low resource languages having limited sentiment analysis resources such as annotated datasets, word embeddings and sentiment lexicons, along with English. Techniques to refine word embeddings for sentiment analysis and improve word embedding coverage in low resource languages are also covered. Finally, we discuss the major challenges involved in multilingual sentiment analysis and explain novel deep learning-based solutions to overcome them.
An Explicable Keystroke Recognition Algorithm for Customizable Ring-Type Keyboards

In our previous work, we developed an IMU (Inertial Measurement Unit) based smart ring that allows users to type characters without a physical keyboard and adopt well-known pattern recognition algorithms, such as Support Vector Machine (SVM), and Naive Bayes (NB), for keystroke recognition. However, these algorithms always require intensive computing resources or offer limited recognition accuracy. Moreover, they are often seen as black boxes incapable of providing readily comprehensible and visible clues for classification. This hampers the improvement of keystroke recognition accuracy and the ring-type virtual keyboard's character layout design. Here we present a novel algorithm to recognize keystrokes in a fast and accurate manner. Firstly, the standard feature vector, including five attitude angle features and one acceleration feature, is built to express a specific stroke. Then, the feature vector of the testing keystroke is compared with the standard features. The most similar keystroke is matched and recognized after three times of voting. Based on this algorithm, we can identify the easily confused keystrokes and understand the mechanisms behind it. With this interpretability, we will be able to achieve the customized ring-type virtual keyboard application if necessary. The performance of this algorithm was evaluated by using a dataset with 1500 keystrokes of three different subjects. The results show that our algorithm is more effective in keystroke recognition than traditional algorithms for this ring-type keyboard. In addition to its application on virtual keyboards, this algorithm can also be potentially applied on other classification tasks with easy-to-understand results.
Linear complexity of generalized sequences by comparison of PN-sequences

Linear complexity is a much used metric of the security of any binary sequence with application in communication systems and cryptography. In this work, we propose a method of computing the linear complexity of a popular family of cryptographic sequences, the so-called generalized sequences. Such a family is generated by means of the irregular decimation of a single Pseudo Noise sequence (PN-sequence). The computation method is based on the comparison of the PN-sequence with shifted versions of itself. The concept of linear recurrence relationship and the rows of the Sierpinski triangle play a leading part in this computation.
Shape defect measurement by fringe projection profilometry and phase-shifting algorithms

We propose a fringe-projection profilometry technique for shape defects measurement, which can be employed for three-dimensional (3-D) quality inspection. The proposal consists of using a template surface to design phase-shifting algorithms with special-purpose phase response via the frequency transfer function. These algorithms can jointly and directly estimate the spatial phase deviations in a 3-D inspection. Phase deviations correspond to shape differences between the template shape and a testing one. The phase-unwrapping procedure is unnecessary when phase differences are small, as is usual in quality inspections. Experimental results show that our technique is so sensitive that the ripples in a fingerprint can be retrieved.
Adversarial Image Detection in Cyber-Physical Systems

In this paper, detection of deception attack on deep neural network (DNN) based image classification in autonomous and cyber-physical systems is considered. Several studies have shown the vulnerability of DNN to malicious deception attack. In such attacks, some or all pixel values of an image are modified by an external attacker, so that the change is almost invisible to human eye but significant enough for a DNN-based classifier to misclassify it. This paper proposes a novel pre-processing technique that facilitates detection of such modified images under any DNN-based image classifier as well as attacker model. The proposed pre-processing algorithm involves a certain combination of principal component analysis (PCA)-based decomposition of the image, and random perturbation based detection to reduce computational complexity. Numerical experiments show that the proposed detection scheme outperforms a competing attack detection algorithm while achieving low false alarm rate and low computational complexity.
Enriching Tourist UX via a Location Based AR Treasure Hunt Game

In this research we implemented an Augmented Reality (AR) treasure hunt game, to enhance a visitor's enjoyment and learning while touring a culturally rich city. The city chosen for this study was Valletta, the 2018 European Capital of Culture. Two prototype mobile applications were implemented and deployed to investigate the effectiveness of an AR serious game with in-built navigation tools. A pragmatic mixed-methods research approach was adopted, with data collected, analysed and triangulated using game log data, pre-post test interviews, observations and a survey. It was concluded that AR is useful to provide city information and enriched the tourist's experience. However, the experience needs to be provided by implementing a treasure hunt `learning through fun' approach, and including GPS navigation crucial to elicit interest and achieve a competitive advantage over traditional city tours.
Pixel-wise dictionary learning based locality-constrained representation for noise robust face hallucination

Recently, the sparsity and locality constrained linear coding (SLLC) attracted much attention in image super-resolution (SR) applications. However, the current SLLC based SR methods are too weak to handle the impulse noise problem. Therefore, this work presents a dictionary learning-based locality-constrained representation (DLcR) for robust face hallucination. It simultaneously hallucinates the face images and suppresses the noise (outliers) using the noisy pixel information in the observed faces. It first identifies the correct position of the noisy pixels in the input face image and then learns the position-wise low-resolution (LR) dictionary images with the detected noise. This learning makes the similar noisy structure of LR dictionary images as the input LR face, which minimizes the error in optimal weight reconstruction. In addition, the performance of DLcR is improved further by imposing the appropriate thresholds on the LR dictionary, named T-DLcR. The thresholding of the LR dictionary in T-DLcR leads to represent the input LR patch through its nearest LR dictionary patches with the precise reconstruction weights. The comparison results of T-DLcR with several position-patch based face SR methods and recent deep learning-based SR method show its superiority on standard as well as real-world face datasets.
Automatic Detection of Smishing Attacks by Machine Learning Methods

Phishing is a type of cyber-attack aiming to steal people's confidential information using a bait. Smishing is a form of phishing that is carried out via SMS messages sent to mobile phones. Attackers intend to steal the private information of their victims through the content they send in their SMS messages. Therefore, the detection of smishing messages has a vital role in information security. In this work, a machine learning-based model is proposed to detect smishing messages. Experiments conducted on a relatively large dataset, which contains legitimate and smishing messages, have shown that the proposed model offers a promising detection performance.
Classification of Human Face: Asian and Non-Asian People

Humans have always been recognized as different from neighboring groups, but such distinction has not always been understood to be natural, permanent and universal. These features are recognized as different features of how the abstraction of race classification is used today. In this way, the concept of race classification came about during the documented process of inspection and subjection which brought it as communication with groups from different continents, and of the idea of classification and typology found in the natural sciences. This method helps us to extract various features from the network and can utilize it to distinguish individuals in the world with a precise personality. The proposed method incorporates the essential feature associated with surface, skin color pattern and another secondary characteristic of training images to efficiently rank the strokes. A CNN based model is proposed to create a system that classifies facial images based on a variety of different facial attributes and classified into two distinct categories. The accuracy of 84.91% was achieved by utilizing the CNN model.
Learning Robust Feature Descriptor for Image Registration With Genetic Programming

The robustness and accuracy of feature descriptor are two essential factors in the process of image registration.Existing feature descriptors can extract important image features, but it may be difficult to find enough correct correspondences for sophisticated images.And these feature descriptors often require domain expertise and human intervention.The aim of this paper is to utilise Genetic Programming (GP) to automatically evolve feature descriptors which are adaptive to various images including remote sensing images and optical images.In this paper, a novel GP-based method (GPFD) is proposed to extract feature vectors and evolve image descriptors for image registration without supervision.The proposed method designs a set of simple arithmetic operators and first-order statistics to construct feature descriptors in order to reduce noise interference.The performance of the proposed method is evaluated and compared against five methods including SIFT, SURF, RIFT, GLPM and GP.These results demonstrate that the feature descriptors evolved by GPFD are robust to complex geometric transformation, the illumination difference and noise.
Data Prediction of Mobile Network Traffic in Public Scenes by SOS-vSVR Method

Accurate base station traffic data in a public place with large changes in the amount of people could help predict the occurrence of network congestion, which would allow us to effectively allocate network resources. This is of great significance for festival network support, routine maintenance, and resource scheduling. However, there are a few related reports on base station traffic prediction, especially base station traffic prediction in public scenes with fluctuations in people flow. This study proposes a public scene traffic data prediction method, which is based on a v Support Vector Regression (vSVR) algorithm. To achieve optimal prediction of traffic, a symbiotic organisms search (SOS) was adopted to optimize the vSVR parameters. Meanwhile, the optimal input time step was determined through a large number of experiments. Experimental data was obtained at the base station of Huainan Wanda Plaza, in the Anhui province of China, for three months, with the granularity being one hour. To verify the predictive performance of vSVR, the classic regression algorithm extreme learning machine (ELM) and variational Bayesian Linear Regression (vBLR) were used. Their optimal prediction results were compared with vSVR predictions. Experimental results show that the prediction results from SOS-vSVR were the best. Outcomes of this study could provide guidance for preventing network congestion and improving the user experience.
Implementation of three-qubit quantum computation with pendular states of polar molecules by optimal control

Ultracold polar molecules have been considered as the possible candidates for quantum information processing due to their long coherence time and strong dipole-dipole interaction. In this paper, we consider three coupled polar molecules arranged in a linear chain and trapped in an electric field with gradient. By employing the pendular states of polar molecules as qubits, we successfully realize three-qubit quantum gates and quantum algorithms via the multi-target optimal control theory. Explicitly speaking, through the designs of the optimal laser pulses with multiple iterations, the triqubit Toffoli gate, the triqubit quantum adders, and the triqubit quantum Fourier transform can be achieved in only one operational step with high fidelities and large transition probabilities. Moreover, by combining the optimized Hadamard, oracle, and diffusion gate pulses, we simulate the Grover algorithm in the three-dipole system and show that the algorithm can perform well for search problems. In addition, the behaviors of the fidelity and the average transition probability with respect to iteration numbers are compared and analyzed for each gate pulse. Our findings could pave the way toward scalability for molecular quantum computing based on the pendular states and could be extended to implement multi-particle gate operation in the molecular system.
FITME: BODY MEASUREMENT ESTIMATIONS USING MACHINE LEARNING METHOD

Online shopping platforms have been attracting many customers since they were introduced in the last decade of the 20th century. Using online shopping platforms, customers can purchase any merchandise anywhere and anytime without the need to physically go from store to store to find a product or wait in lines to check out. Despite their advantages in comparison with instore shopping, customers often have concerns when they shop for products that require measurements estimation such as furniture and clothes. Choosing the wrong clothing size, in particular, is a common issue experienced by many online shoppers. Therefore, in this research, we proposed a model that estimates human body measurements from human real-time pictures using Haar Cascade classifier and support vector machines.
Transfer Learning on the Feature Extractions of Sky Images for Solar Power Production

With the increasing popularity of integrating solar energy into the power system, solar power prediction has recently attracted much interest, where the movement of clouds has a crucial impact on solar irradiance and is the major cause of rapid, violent and irregular fluctuations of solar power production. Meanwhile, it is necessary for solar power prediction to capture these fluctuations several minutes ahead in order to facilitate scheduling and operations that maintain the system stability. Considering such importance of the cloud movement, sky images provided by all-sky cameras consist of important data for solar prediction. However, for solar predictors, raw sky images are usually too large to be directly used as the input data. Instead, the features in the sky images related to solar irradiance should be extracted and fed to the predictors. Hence, in this paper, we propose a transfer learning method to extract features from sky images with a Convolutional Neural Network (CNN) to capture the close relationship between clouds and solar irradiance. To accomplish this goal, we first train a classifier on sky images to determine whether the sun is covered by clouds or not, where the classification accuracy achieved is as high as 97.92%. Then, the spectral and textural features of sky images are further extracted by the regression layers in CNN and a nonlinear relationship between sky images and the solar irradiance is revealed. Experimental results confirm that the proposed method can successfully map the sky images to solar irradiance and have significance for future solar prediction applications.
Session-Based Recommendation with Local Invariance

Session-based recommendation is a task to predict users' next actions given a sequence of previous actions in the same session. Existing methods either encode the previous actions in a strict order or completely ignore the order. However, sometimes the order of actions in a short sub-sequence, called the detailed order, may not be important, e.g., when a user is just comparing the same kind of products from different brands. Nevertheless, the high-level ordering information is still useful because the data is sequential in nature. Therefore, a good session-based recommender should pay different attention to the sequential information in different levels of granularity. To this end, we propose a novel model to automatically ignore the insignificant detailed ordering information in some sub-sessions, while keeping the high-level sequential information of the whole sessions. In the model, we first use a full self-attention layer with Gaussian weighting to extract features of sub-sessions, and then we apply a recurrent neural network to capture the high-level sequential information. Extensive experiments on two real-world datasets show that our method outperforms or matches the state-of-the-art methods.
Alert and Surveillance System for Newborns

This paper aims to develop a monitoring and surveillance system for new-borns. The system contains a data acquisition block compounded of a temperature sensor, a microphone sensor, an SPO2 sensor, and an inertial- movement sensor. The second block involved is a data processing block which determines whether the newborn has the designated parameters within the normal range. Information once processed, is transmitted via Bluetooth to an Android app., which, if the values are out of range, sends an alert on the caregiver's mobile device. This system uniqueness comes from the combination of monitoring function with that of alerting as well as from the wide range of symptoms detected with a minimum number of sensors.
Dynamic Path Planning of Mobile Robot Based on Improved Ant Colony Optimization Algorithm

Aiming at the problem that the traditional ant colony algorithm (ACO) has poor solution quality in the dynamic path planning process, this paper proposes an improved ACO. Firstly, the genetic operator fused with the traditional ACO is proposed, and the genetic operation is used to expand the search space of the solution. Secondly, the fitness function is introduced in the traditional ACO and the safety distance is added. The pros and cons of the comprehensive evaluation algorithm planning path. Then, by introducing the optimization operator, the redundant nodes are eliminated and the smoothness is improved. Finally, the path planning simulation experiment is carried out in the grid map. The results show that the proposed algorithm can find a shorter and smoother in the dynamic environment path.
Face Detection and Segmentation with Generalized Intersection over Union Based on Mask R-CNN

As a research hotspot of computer vision and information security, face detection has been widely developed in the past few decades. However, most of the existing detection methods only realize the location of the bounding box, which leads to background noise in the face features as well as limited accuracy of detection. To overcome these drawbacks, a face detection and segmentation method with Generalized Intersection over Union (GIoU) based on Mask R-CNN is proposed in this paper, which is called G-Mask. In this method, ResNet-101 is used to extract features, RPN is used to generate RoIs, and RoIAlign faithfully retains the exact spatial locations to generate binary mask through Fully Convolution Network. In particular, to achieve better performance in multi-scale face detection tasks, we utilize GIoU as the bounding box loss function. Furthermore, a new face dataset with segmentation annotation information is constructed in this paper to train the model. The experimental results of the well-known benchmark FDDB and AFW show that the proposed G-Mask method achieves promising face detection performance compared with Faster R-CNN and the original Mask R-CNN method, and also can realize the instance-level face information segmentation while detecting.
Self‐adaptive weighted synthesised local directional pattern integrating with sparse autoencoder for expression recognition based on improved multiple kernel learning strategy

This study presents a novel method for solving facial expression recognition (FER) tasks which uses a self‐adaptive weighted synthesised local directional pattern (SW‐SLDP) descriptor integrating sparse autoencoder (SA) features based on improved multiple kernel learning (IMKL) strategy. The authors’ work includes three parts. Firstly, the authors propose a novel SW‐SLDP feature descriptor which divides the facial images into patches and extracts sub‐block features synthetically according to both distribution information and directional intensity contrast. Then self‐adaptive weights are assigned to each sub‐block feature according to the projection error between the expressional image and neutral image of each patch, which can highlight such areas containing more expressional texture information. Secondly, to extract a discriminative high‐level feature, they introduce SA for feature representation, which extracts the hidden layer representation including more comprehensive information. Finally, to combine the above two kinds of features, an IMKL strategy is developed by effectively integrating both soft margin learning and intrinsic local constraints, which is robust to noisy condition and thus improve the classification performance. Extensive experimental results indicate their model can achieve competitive or even better performance with existing representative FER methods.