Reviewerly/exHarmony huggingface dataset:
Head Pose Estimation with Siamese Convolutional Neural Network

In order to get rid of the dependence on keypoints and improve the accuracy of pose estimation, in this paper, we propose a method of estimating the 3D head pose using a convolutional neural network with Siamese structure. Firstly, the rank labels of head pose which used to train the Siamese network can be automatically generated from the continuous head pose labels. The Siamese network can rank the head pose deflect levels and this step is equivalent to coarse classification. Secondly, after Siamese network is trained, the continuous raw pose labels were used to fine-tuning a branch of Siamese network and let the network regress the continuous pose ground truth. For avoiding duplicate computation caused by Siamese network, we add a ensemble layer to the network. In addition, high intensity brightness adjustment and Gaussian blur are imposed on images to distort images in data augmentation, so our method will achieve perfect performances in low quality images. Experiments show that our method has higher accuracy than the state-of-the-art methods of estimating head pose from RGB images, stronger robustness than the method of head pose estimation with keypoints, and wider application range than the method of head pose estimation with depth data.
Detection of Necrosis in Mice Liver Tissue Using Deep Convolutional Neural Network

Acute Hepatic Necrosis is an early sign of liver dysfunction. Liver dysfunction is one of the major reasons for increasing death rate. Accurate diagnosis in less time, along with proper medication, show a ray of hope in controlling the aggravation of the situation. To overcome the unfavorable effects of harmful drugs, medicinal plant extract has become major thrust area nowadays. This research work has presented a way to show the improvements in mice liver tissue after applying the designated composition of the plant extract. And the performance is measured with our designed deep convolutional neural network (CNN) architecture along with the preprocessing techniques that has shown to be competent to classify microscopic images of mice hepatic tissues. Considering a small database of 30 images, we introduced a preprocessing stage which included the dividing of the original microscopic images to small patches. The accuracy of the classification results using the proposed CNN based classifier was 99.33%.
A Method for Analyzing the Composition of Petrographic Thin Section Image

The recognition and content analysis of the components in petrographic thin section image is a valuable study in geology. In this paper, we propose a two-stage method to segmentation and recognition of petrographic thin section image. In the first stage, we propose an image segmentation algorithm that can adaptively generate superpixel numbers based on SLIC algorithm. The algorithm is able to continually correct the number of superpixels in the iteration and then cluster the pixels of the image into superpixels by both color and spatial features. In the second stage, we designed a convolutional neural network and trained it with mineral grain images, which is then used to classify the superpixels obtained by first-stage. Finally, we count the categories and content of the components in the image based on the segmentation and classification results. We collected some images and invited geologists to label them for experimentation. The experimental results demonstrate the following: (1) Our proposed image segmentation algorithm is capable of dynamically generating the superpixels by the number of mineral grains in the image. (2) The CNN model we designed can accurately identify the categories of superpixel regions and has a small size. (3) The two-stage method is very effective in identifying the category of major components in an image and accurately estimating the content.
A Mobile User Authentication Technique in Global Mobility Network

Anonymous user authentication always is a paramount job in global mobility network (GLOMONET). In GLOMONET, a mobile user can move from one place to another place causes changes of mobile network from one to another. As authentication server can authenticate only its registered users, it cannot verify other mobile users. Furthermore, in global mobility network, communication channel is public. An adversary of the network can get access of all transmitted messages over the channel. In such a situation, several network security attacks can be initiated by an adversary to decrypt the messages. Therefore, mobile users as well as network provider’s information may come in risk. A robust anonymous user authentication and key agreement technique required to ensure the protection of such information. Several authentication protocols have been designed in GLOMONET in recent years. Unfortunately, most of them are unprotected against various network security attacks. Therefore, in this paper, we proposed a rigid authentication protocol in GLOMONET to overcome all the deficiencies of the previous work. Performance analysis of our protocol shows that it is reliable and even more effective compared to other existing protocols.
Measuring the appeal of mobility-augmented reality games, based on the innovative models of interaction: a case study

Abstract This study presents the issues why gamers prefer mobility-augmented reality games to other types of game and what specific characteristics cause them to invest a large amount of their time on tireless game-play. Furthermore, the appeal of mobility-augmented reality games was studied to solve the above mentioned issues. Then, how human–computer interaction based on mobility-augmented reality games was promoted to create a new marketing mode was explored. Then, Pokémon GO, as the worldwide major mobility-augmented reality game, was selected as the research target in this study. The researcher interviewed 9 experts, collected 235 Knasei words from 33 articles, and surveyed 335 gamers through a questionnaire to collect the data about users’ preferences. A preference-based study was believed to disclose the motivated reasons for the appeal of mobility-augmented reality games. The researcher analyzed the gathered Kansei concepts and questionnaires using the two-stage procedures, including evaluation grid method (EGM) and Quantification Theory Type I. During the first stage the hierarchy of the relationship among the types of appeal factors, the reasons for users’ preferences, and the explicit design characteristics of Pokémon GO present the semantic structure of appeal and were determined using EGM through the accumulation of the review of articles and the interviews of experts. During the second stage the strongest two original evaluation items of Pokémon GO are determined as “social interaction” and “scenario interaction” based on the statistical analysis of Quantification Theory Type I, and their corresponding “upper-level” and “lower-level” considerations are proved to have influence on them. Finally, the paper found that the popularity of Pokémon GO can be ascribed to the design of the innovative models of game interaction, which targets the psychological preferences of gamers. This means that the interaction model between a customer and an enterprise can be developed outside the box and a new type of marketing can be formed. The study proved that the innovative models of interaction successfully drove gamers’ motivations to play Pokémon GO. Designers and researchers of mobility-augmented reality games can absorb important information through this study. This study enriches the field of mobile communication, online marketing, and human–computer interaction in cyberspace.
Predicting Metropolitan Crime Rates Using Machine Learning Techniques

The concept of smart city has been gaining public interests with the considerations of socioeconomic development and quality of life. Smart initiatives have been proposed in multiple domains, such as health, energy, and public safety. One of the key factors that impact the quality of life is the crime rate in a metropolitan area. Predicting crime patterns is a significant task to develop more efficient strategies either to prevent crimes or to improve the investigation efforts. In this research, we use machine learningMachine learning techniques to solve a multinomial classificationMultinomial classification problem where the goal is to predict the crime categories with spatiotemporal data. As a case study, we use San Francisco crime data from San Francisco Police Department (SFPD). Various classification methods such as Multinomial Logistic Regression, Random Forests, Lightgbm, and Xgboost have been adopted to predict the category of crime. Feature engineering was employed to boost the model performance. The results demonstrate that our proposed classifier outperforms other published models.
On oracle factoring of integers

We present an oracle factorisation algorithm, which in polynomial deterministic time, finds a nontrivial factor of almost all positive integers n based on the knowledge of the number of points on certain elliptic curves in residue rings modulo n.
Proposal-Aware Visual Saliency Detection with Semantic Attention

In this paper, we propose a proposal based method for saliency detection. Our method separates the salient proposals out by assigning them a novel attention mechanism, semantic attention (SeA). The attention are established based on the observation that regions with high attention should have similarly semantic concepts with salient objects. The SeA takes the high-level semantic features from Faster Region-based Convolutional Neural Network (Faster R-CNN) to assist the proposal selection in images with complex background. We select the salient proposals according to their semantic attention probabilities. Quantitative and qualitative experiments on four datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
Students’ Evaluation of the “Web Technologies” Android Application for Higher Education

The use of mobile devices in tertiary education is an emerging trend which introduces new ways of teaching and learning. In this paper, we present the implementation and the evaluation of an educational application, named WebTech. The goal of WebTech is to support the course Web Technologies, which is taught in the first semester in the Department of Informatics at the University of Piraeus – Greece, through the use of smartphones and tablets and by employing game features. The evaluation of WebTech, among others, indicated an easy-to-use, stable and quite efficient application, which does not increase the user’s cognitive load.
Realising the Potential of People Living with Dementia Through Co-designing and Making Interventions

The UK has an ageing population where there are now more people aged over 65 than those under the age of 16. The impact of this creates increased pressures on the National Health Service (NHS), and on local and regional health and social care services. Key concerns in regards to this aging population include the prevalence of the five most common chronic conditions among the over 65s—arthritis, heart disease, stroke, diabetes and dementia—with the latter expected to increase 25% by 2020 and more than 50% by 2050. In order to counteract the increasing pressures of aging health and mental healthcare issues current government policy aims to encourage people to remain active, engage in regular exercise and refrain from behaviours that could have a detrimental effect on their health. The research, presented here, focuses on developing and implementing innovative design interventions, that seek to encourage people to remain active, promote dignity, and encourage independence particularly for people living with dementia.
A Camera Recalibration Method for a Top-View Surveillance System based on Relative Camera Pose and Structural Similarity

In this paper, we present a camera recalibration method for a discontinuous top-view stitched image. A top-view stitched image using multiple camera images is efficient for surveillance camera monitoring because it enables monitors to understand surroundings easily by spatial continuity. However, the top-view stitched image loses the advantage even if only one camera used in the stitched image is shifted by physical contact or its own weight. A simple solution such as returning shifted cameras physically or calibrating cameras with calibration markers again takes time and personnel. To address this problem, we propose the recalibration method based on relative pose of camera and image structural similarity in the top-view image. The proposed method performs recalibration without changing camera pose physically or using calibration markers. As a result of an experiment, we found out that our method restores a discontinuous top-view image stitched closing to the original one.
Single-Binocular Vision Conversion Strategy for Obstacle Detection at Non-signalized Intersections

Taking full account of the advantages and disadvantages of monocular vision and binocular vision in environmental perception, this paper proposes a single-binocular vision conversion strategy based on monocular camera for obstacle detection at non-signalized intersections. Using this strategy we can get fast and accurate identification of obstacles on the lateral anterior of the vehicle, and achieve fast and accurate detection for subsequent distances and speeds of obstacles. To ensure driving safety at the intersections on the premise of effectively detecting cross-conflict, first, the monocular camera conversion model, the number of cameras and the installation position are determined. Second, a single-binocular vision conversion strategy is made: the single-binocular visual composition is determined in real time according to the distance parameter and the angle parameter, and the obstacle information on the lateral anterior of the vehicle is acquired. In the end, compared with the complete monocular vision and complete binocular visual environmental perception method, the comparison results prove the rationality of the conversion strategy, and prove that the strategy is efficient and accurate in environmental perception and obstacle recognition, ranging, and speed measurement.
Improving NoSQL's Performance Metrics via Machine Learning

As the NoSQLs are becoming more and more popular in the big data era, a few auto-tuning tools have been developed to optimize its application performance. However, the majority of them to date has focused on improving the performance of a single objective-throughput. As such, these tools cannot cover the diverse optimization requirements because that many applications need to make a trade-off between the response time(latency) and the throughput. In this paper, we propose a novel approach, called Multiple Objective Optimization for NoSQL (MOTuner) applications from the angle of parameter tuning. The key is an accurate performance model and a search algorithm along with a searching direction, which determined by the weights of optimization objectives. The model takes configuration parameters as input and outputs performance metrics (the throughput and latency). The Generic Algorithm (GA) takes this model as input, along the specified direction determined by the weights, search the unique optimal configuration in the huge configuration space for a given application. We validate MOTuner in a HBase cluster with 10 nodes by using 5 typical applications from YCSB. The experimental results show that MOTuner can improve throughput by 48% on average and up to 89% compared to the default configurations with different weight pairs of optimization objectives. At the same time, the latency of HBase operations is reduced by 13.1% on average and up to 52.4%. MOTuner is a powerful tool for operators to make a trade-off between the throughput and latency.
Variational Bayesian Iterative Estimation Algorithm for Linear Difference Equation Systems

Many basic laws of physics or chemistry can be written in the form of differential equations. With the development of digital signals and computer technology, the research on discrete models has received more and more attention. The estimates of the unknown coefficients in the discretized difference equation can be obtained by optimizing certain criterion functions. In modern control theory, the state-space model transforms high-order differential equations into first-order differential equations by introducing intermediate state variables. In this paper, the parameter estimation problem for linear difference equation systems with uncertain noise is developed. By transforming system equations into state-space models and on the basis of the considered priors of the noise and parameters, a variational Bayesian iterative estimation algorithm is derived from the observation data to obtain the parameter estimates. The unknown states involved in the variational Bayesian algorithm are updated by the Kalman filter. A numerical simulation example is given to validate the effectiveness of the proposed algorithm.
Robust keystroke transcription from the acoustic side-channel

The acoustic emanations from keyboards provide a side-channel attack from which an attacker can recover sensitive user information, such as passwords and personally identifiable information. Previous work has shown the feasibility of these attacks given isolated key strokes, but has not demonstrated robust keystroke detection and segmentation in the presence of realistic noise and fast typing speeds. Common problems include noises like doors closing or speech as well as overlapping keystroke waveforms. Prior work has assumed that isolating the waveform of individual key strokes can be achieved with near 100% accuracy, but we show that these techniques generate a large number of misses and false positives, drastically impacting the downstream keystroke classification task.
Development of a Computer Player for Seejeh (A.K.A Seega, Siga, Kharbga) Board Game with Deep Reinforcement Learning

Recent years have proven the existing room of deep reinforcement learning (DRL) applications. DRL has been utilized as an AI computer player in many board games. Seejeh is an ancient board game, where no one attempts to create an AI system that is able to learn to play it. Seejeh is a two-player, zero-sum, discrete, finite and deterministic game of perfect information. Seejeh board game is different from all other strategic board games. It has two stages; Positioning and moving. Player place two tiles at each action in stage one. A player might have a sequence of moves in the second stage unlike Othello and Go. In this work, we develop an automated player based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. This paper presents a self-play algorithm utilizing DRL and search algorithms. The system starts with a neural network that knows nothing about the game of Seejeh. It then plays games against itself, by combining this neural network with powerful search algorithms. To the best of our knowledge, we are the first who develop an agent that learns to play Seejeh game.
An Open Dataset of Pediatric Activity and Energy Expediture and Deep Learning Approach for Analysis

Obesity in school children has increased and posed an adverse impact on the development at their adolescent phase. Physical activity and energy expenditure are important factors for diagnosing and treating obesity. To obtain such metrics, we conduct a study on the preschool children to quantitatively measure their activity using two types wearable devices (ActiGraph GT9X and our low-cost one) and metabolic instruments. In such a way, we have obtained a unique dataset that consists of motion characteristics and energy expenditure of preschool children performing daily activities. We also validate that our low-cost devices produce equivalent sensor data as ActiGraph. In our preliminary study, we use a Long Short-Term Memory model to classify activity types using raw sensor data. We expect this dataset will enable research and validation of models and algorithms to quantify pediatric activities and intensity using wearable sensors.
Quantum ultra-walks: Walks on a line with hierarchical spatial heterogeneity

We discuss the model of a one-dimensional, discrete-time walk on a line with spatial heterogeneity in the form of a variable set of ultrametric barriers. Inspired by the homogeneous quantum walk on a line, we develop a formalism by which the classical ultrametric random walk as well as the quantum walk can be treated in parallel by using a "coined" walk with internal degrees of freedom. For the random walk, this amounts to a $2^{{\rm nd}}$-order Markov process with a \emph{stochastic} coin, better known as an (anti-)persistent walk. When this coin varies spatially in the hierarchical manner of "ultradiffusion," it reproduces the well-known results of that model. The exact analysis employed for obtaining the walk dimension $d_{w}$, based on the real-space renormalization group (RG), proceeds virtually identical for the corresponding quantum walk with a $unitary$ coin. However, while the classical walk remains robustly diffusive ($d_{w}=\frac{1}{2}$) for a wide range of barrier heights, unitarity provides for a quantum walk dimension $d_{w}$ that varies continuously, for even the smallest amount of heterogeneity, from ballistic spreading ($d_{w}=1$) in the homogeneous limit to confinement ($d_{w}=\infty$) for diverging barriers. Yet for any $d_{w}<\infty$ the quantum ultra-walk never appears to localize.
Cloud-assisted Adaptive Stream Processing from Discriminative Representations

As the streaming data generated by Internet of Things (IoT) ubiquitous sensors grow in massive scale, extracting interesting information (anomalies) in real-time becomes more challenging. Traditional systems which retrospectively perform all the processing in the cloud do not capture real-time changes in the data. Similarly, real-time solutions which rely on human monitors have the tendency to miss the anomalies due to their rare nature. In recent times, several machine learning techniques have been proposed for stream processing. Approaches based on supervised or semi-supervised learning fail to adapt to changing patterns of the streaming data and the data labelling costs are huge. To address these limitations, we propose a cloud-assisted framework where an intermediary node (edge) is introduced between the end devices and the cloud to assist in stream processing. A model deployed on the edge is designed to learn in an iterative manner to discriminate between similar and dissimilar data representations, making it easier to distinguish the anomalies. In this work, we have proposed an iterative method that combines the capabilities of deep clustering and l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -normalisation to achieve better discriminative representations. Experimental results demonstrate the proposed method achieves robust performance over state-of-the-art discriminative representation algorithms and sets new benchmark accuracy on transformation invariant image dataset.