Reviewerly/exHarmony huggingface dataset:
Multi-Task Learning Based Network Embedding

The goal of network representation learning, also called network embedding, is to encode the network structure information into a continuous low-dimensionality embedding space where geometric relationships among the vectors can reflect the relationships of nodes in the original network. The existing network representation learning methods are always single-task learning, in which case these methods focus on preserving the proximity of nodes from one aspect. However, the proximity of nodes is dependent on both the local and global structure, resulting in a limitation on the node embeddings learned by these methods. In order to solve this problem, in this paper, we propose a novel method, Multi-Task Learning-Based Network Embedding, termed MLNE. There are two tasks in this method so as to preserve the proximity of nodes. The aim of the first task is to preserve the high-order proximity between pairwise nodes in the whole network. The second task is to preserve the low-order proximity in the one-hop area of each node. By jointly learning these tasks in the supervised deep learning model, our method can obtain node embeddings that can sufficiently reflect the roles that nodes play in networks. In order to demonstrate the efficacy of our MLNE method over existing state-of-the-art methods, we conduct experiments on multi-label classification, link prediction, and visualization in five real-world networks. The experimental results show that our method performs competitively.
Using Intelligent Personal Annotations to Improve Human Activity Recognition for Movements in Natural Environments

Personal tracking algorithms for health monitoring are critical for understanding an individual's life-style and personal choices in natural environments (NE). In order to train such tracking algorithms in NE, however, annotated data is needed, particularly when tracking a variety of activities of daily living. These algorithms are often trained in laboratory settings, with expectations that they will perform equally well in NE, which is often not the case; they must be trained on annotated data collected in NE and wearable computers provide opportunities to collect such data, though the process is burdensome. Therefore, we propose an intelligent scoring algorithm that limits the number of user annotation requests through the confidence of predictions generated by the tracking algorithm and automatically annotating data with high confidence. We enhance our scoring algorithm by providing improvements in our tracking algorithm by obtaining context data from nearable sensors. Each specific context of a user bounds the set of activities that can likely occur, which in turn improves the tracking algorithm and confidence. Finally, we propose a hierarchical annotation approach, where repeated use allows us to ask for detailed annotations that differentiate fine-grained differences in ways individuals perform activities. We validate our approach in a diet monitoring case study. We vary the number of annotations requested per day to evaluate model accuracy; we improve accuracy in NE by 8% when restricting requests to 20 per day and improve F1-score of activities by 11% with hierarchical annotations, while discussing implementation, accuracy, and power consumption in real-time use.
An analytic approach to separate users by introducing new combinations of initial centers of clustering

In the recommender systems, the users evaluate and rate data items and assign quantitative indices to them; consequently, a comprehensive database called rating matrix is created. In the rating matrix, the separation of the gray sheep users is of crucial importance since these users increase the error rate of the recommendations to the white users. The proposed approach involved three main steps. In the first step, we introduced a new algorithm called MKMeans++ by extracting and combining some features of the rating matrix as well as applying these combinations to the initial centers of the KMeans clustering, in which Power Item and Power Weight features were added to the new combinations. These features indicated the superiority of the proposed algorithm over the KMeans++ algorithm. In the second step, we probed into the relationship between features of the rating matrix and the gray sheep users, the results of which revealed that the gray sheep users can be identifiable only through the Distance feature. In the third step, we started separating the gray sheep users by using MKMeans++ algorithm and applying the Distance feature. Experimental results on the MovieLens and FilmTrust datasets show that the proposed algorithm outperforms existing methods in terms of the MAE accuracy and Converge. In the proposed algorithm, on average, the amount of the MAE improvement for MovieLens and FilmTrust datasets were 0.15 and 0.24, respectively, while also increasing the quality of clusters and the precision of the recommender system.
Gaussian mixture probability hypothesis density filter against measurement origin uncertainty

The Gaussian mixture probability hypothesis density (GM-PHD) filter is a promising solution to the multi-target tracking (MTT) problem, which successfully integrates target detection, tracking, and identification. Despite its wide applicability and computational efficiency, the existing GM-PHD filter can lose the estimates of the targets frequently in heavily cluttered and/or low signal-to-noise ratio (SNR) environments. This is mainly attributed to insufficient consideration of uncertainties around whether a measurement is from a target or not in the GM-PHD filter. Specifically, at each time step, the GM-PHD filter generates new Gaussian components corresponding to individual measurements which have the same estimate error covariances regardless of whether the measurement is from a target or not, so that it can lose the estimates of targets when the clutter density is high and/or the detection probability is low. To address this problem, a new covariance update equation is proposed. This equation computes the estimate error covariance of a newly generated Gaussian component corresponding to each measurement conditioned on the uncertainty in the measurement origin, i.e. whether: (1) measurement is clutter; (2) measurement is originated from a target; and (3) there is no measurement. The performance of the proposed GM-PHD algorithm is demonstrated with illustrative MTT scenarios with different noise conditions.
Remote Triggered Dual-Axis Solar Irradiance Measurement System

Understanding the quantity and quality of solar energy that can be harnessed at a site is essential for estimating and optimizing the cost of energy production for most developing and developed nations. This article details the design of an efficient, low-cost system to measure solar irradiance using dual-axis tracking, irradiation sensors, and an algorithm to track the solar position. The sensed data is acquired using a data acquisition system and provides a user-friendly remote access using a collaborative platform to monitor and control real-time data with options to change parameters and graphically display the data and live streaming. Evaluating a potential site for solar energy installation could be studied without acquiring expensive equipment. Positive results from the study conducted to capture the perceived usefulness, overall adaptability and ease in understanding of underlying scientific concepts and their retention for novice users indicate the effectiveness of the system developed.
Nuclei R-CNN: Improve Mask R-CNN for Nuclei Segmentation

Accurate nuclei segmentation plays an essential role in medical research and various clinical applications. Recently, deep learning has demonstrated its superior performance on object segmentation in natural scene images. However, these methods cannot produce fine segmentation in histopathological images. Therefore, this paper improves Mask R-CNN for nuclei segmentation which is called Nuclei R-CNN, mainly focus on the network model, training scheme and the preprocess of histopathological data. Additionally, we found poor prediction accuracy under high-resolution histopathological images and proposed a simple Block-Merging method to deal with it. The quantitative assessment of Nuclei R-CNN has been based on challenging public dataset. Our experimental shows that Nuclei R-CNN outperforms existing methods for segmenting histopathological images.
Deep Spherical Gaussian Illumination Estimation for Indoor Scene

In this paper, we propose a learning-based method to estimate high dynamic range (HDR) indoor illumination from only a single low dynamic range (LDR) photograph of limited field-of-view. Considering the extreme complexity of indoor illumination that is virtually impossible to reconstruct perfectly, we choose to encode the environmental illumination in Spherical Gaussian (SG) functions with fixed centering directions and bandwidth and only allow the weights vary. An end-to-end convolutional neural network (CNN) is designed and trained to build the complex relationship between a photograph and its illumination represented by SG functions. Moreover, we employ a masked L2 loss instead of naive L2 loss to avoid the loss of high frequency information, and propose a glossy loss to improve the rendering quality. Our experiments demonstrate that the proposed approach outperforms the state-of-the-arts both qualitatively and quantitatively.
Achieving the Bayes Error Rate in Synchronization and Block Models by SDP, Robustly

We study the statistical performance of semidefinite programming (SDP) relaxations for clustering under random graph models. Under the ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Synchronization model, Censored Block Model (CBM) and Stochastic Block Model (SBM), we show that SDP achieves an error rate of the form exp [-(1-o(1)) n̅I*] . Here n- is an appropriate multiple of the number of nodes and I* is an information-theoretic measure of the signal-to-noise ratio. We provide matching lower bounds on the Bayes error for each model and therefore demonstrate that the SDP approach is Bayes optimal. As a corollary, our results imply that SDP achieves the optimal exact recovery threshold under each model. Furthermore, we show that SDP is robust: the above bound remains valid under semirandom versions of the models in which the observed graph is modified by a monotone adversary. Our proof is based on a novel primal-dual analysis of SDP under a unified framework for all three models, and the analysis shows that SDP tightly approximates a joint majority voting procedure.
Blind tone mapped image quality assessment with image segmentation and visual perception

With tone mapping, high dynamic range (HDR) image contents can be displayed on low dynamic range (LDR) display devices, in which some important visual information may be distorted. Thus, the tone mapped image (TMI) quality assessment is one of important issues in HDR image/video processing fields. Considering the difference of visual distortion degrees between the flat and complex regions in TMI, and considering that high-quality TMI should preserve as much information as possible of its original HDR image especially in the high/low luminance regions, this paper proposes a new blind TMI quality assessment method with image segmentation and visual perception. First, we design different features to describe the distortion of TMI’s different regions with two kinds of TMI segmentation. Then, considering that there lacks an efficient algorithm to quantify the importance of features, a feature clustering scheme is designed to eliminate the poor effect feature components in the extracted features to improve the effectiveness of the selected features. Finally, considering the diversity of tone mapping operator (TMO), which may cause global and local distortion of TMI, some other global features are also combined. At last, a final feature vector is formed to synthetically describe the distortion in TMI and used to blindly predict the TMI’s quality. Experimental results in the public ESPL-LIVE HDR database show that the Pearson linear correlation coefficient and Spearman rank order correlation coefficient of the proposed method reach 0.8302 and 0.7887, respectively, which is superior to the state-of-the-art blind TMI quality assessment methods, and it means that the proposed method is highly consistent with human visual perception.
Towards Effective Crowd-Powered Online Content Moderation

Content moderation is an important element of social computing systems that facilitates positive social interaction in online platforms. Current solutions for moderation including human moderation via commercial teams are not effective and have failed to meet the demands of growing volumes of online user generated content. Through a study where we ask crowd workers to moderate tweets, we demonstrate that crowdsourcing is a promising solution for content moderation. We also report a strong relationship between the sentiment of a tweet and its appropriateness to appear in public media. Our analysis on worker responses further reveals several key factors that affect the judgement of crowd moderators when deciding on the suitability of text content. Our findings contribute towards the development of future robust moderation systems that utilise crowdsourcing.
A Precise High Count-Rate FPGA Based Multi-Channel Coincidence Counting System for Quantum Photonics Applications

Coincidence counters play the role of gating events from the background noise in almost every quantum photonics setup.Precise multi-channel and high count-rate coincidence counting tools have become desirable due to an increase in the complexity of quantum photonics experiments.However, timing analyzers are struggling to meet these needs.We are proposing a Field Programmable Gate Array (FPGA) based coincidence counting system which provides 8 operational channels with 8.9 ps root mean square (RMS) resolution (with a bin width of 7.7 ps) and a count-rate of 320 million counts per second (MCPS) (with 40 MCPS per channel).We have successfully tested our design in different quantum photonics scenarios such as the detection of two-photon interference and pseudo-photon-number resolving detection of a coherent state of light where it has shown its capability of working beyond the saturation of detectors.Also, we have introduced a Dual Data Rate Registration TDC, which improved the linearity of the time tagging operation by using both clock edges without increasing the dead-time or using the space excessively.1.2 LSB in max DNL error, 1.8 LSB in max INL error, 10 ps in FWHM and 3.2 ps in RMS resolution improvements were achieved.
Image encryption with cross colour field algorithm and improved cascade chaos systems

In this study, the critical factor of the chaos system has been analysed to improve the randomicity of the encryption keyspace. Several chaos systems have been integrated together with a linear function to form a much more efficient key sequence generator. This study also presents the cross colour field confusion method which scrambles the pixels among the R G B colour matrixes. In this way, the range of the pixels scrambling map is extended to three matrixes compared to the traditional pixels scrambling schemes which do pixels scrambling in its own colour matrix. The experiment results show that the improved cascade system has better bifurcation diagrams. The cross colour field diffusion algorithm makes the encrypted image has little pixel correlation and no information leakage. The experiment results justify that the novel cross colour confusion scheme with improved chaos systems has a good ability to resist brute force attack, known plain attack, chosen plain text attack, chosen ciphertext attack and differential attack. The security analysis demonstrates that the proposed approach has satisfactory properties in image encryption.
Simplified Workflow Simulation on Clouds based on Computation and Communication Noisiness

Many researchers rely on simulations to analyze and validate their researched methods on Cloud infrastructures.However, determining relevant simulation parameters and correctly instantiating them to match the real Cloud performance is a difficult and costly operation, as minor configuration changes can easily generate an unreliable inaccurate simulation result.Using legacy values experimentally determined by other researchers can reduce the configuration costs, but is still inaccurate as the underlying public Clouds and the number of active tenants are highly different and dynamic in time.To overcome these deficiencies, we propose a novel model that simulates the dynamic Cloud performance by introducing noise in the computation and communication tasks, determined by a small set of runtime execution data.Although the estimating method is apparently costly, a comprehensive sensitivity analysis shows that the configuration parameters determined for a certain simulation setup can be used for other simulations too, thereby reducing the tuning cost by up to 82.46 percent, while declining the simulation accuracy by only 1.98 percent on average.Extensive evaluation also shows that our novel model outperforms other state-of-the-art dynamic Cloud simulation models, leading up to 22 percent lower makespan inaccuracy.
Spin cat-state family for Heisenberg-limit metrology

We identify a class of superposition spin coherent states that enable measurements with a Heisenberg-limit (HL) precision. We demonstrate that a superposition of any two antipodal points on the Bloch sphere (two antipodal spin coherent states) can provide the HL precision. This finding suggests a universal strategy for the generation of quantum states for HL metrology and offers a physically clear perspective on the relation between the HL precision and two-mode cat states. We show that antipodal cat states suitable for HL metrology can be generated using cavity quantum electrodynamics structures.
A Framework for Real-Time Face-Recognition

The advent and wide use of deep-learning technology has enabled tremendous advancements in the accuracy of face recognition under favourable conditions. Nonetheless, the reported near-perfect performance on classic benchmarks like lfw, does not include complications in unconstrained application. The research reported in this paper addresses some of the critical challenges of face recognition under adverse conditions. In this context, we introduce an end-to-end framework for real-time video-based face recognition. This system detects, tracks and recognizes individuals from live video feed. The proposed system addresses three key challenges of video-based face recognition systems: end-to-end computational complexity, in the wild recognition and multi-person recognition. We exploit sophisticated deep neural networks for face detection and facial feature extraction, while minimizing the computational overhead from the rest of the modules in the recognition pipeline. A comprehensive evaluation shows that the proposed system can effectively recognize faces under unconstrained conditions, at elevated frames per second rates.
High-precision human body acquisition via multi-view binocular stereopsis

It remains challenging how to acquire a human body shape with high precision and evaluate the reconstructed models effectively, because the results can be easily affected by various factors (e.g., the performance of the capture device, the unwanted movement of the subject, and the self-occlusion of the articulated body structure). To tackle the above challenges, this research presents a passive acquisition system, which comprises 60 spatially-configured Digital Single Lens Reflex (DSLR) cameras and a carefully devised algorithmic pipeline for shape acquisition in a single shot. Different from traditional multi-view stereo solutions, the constituent cameras are synchronized and organized into 30 binocular stereo rigs to capture images from multiple views simultaneously. Each binocular stereo rig is regarded as a depth sensor. The acquisition pipeline consists of three stages. First, camera calibration is performed to estimate intrinsic and extrinsic parameters of all cameras, especially for paired binocular cameras. Second, depth inference based on stereo matching is employed to recover reliable depth information from RGB images. A novel hierarchical seed-propagation stereo matching framework is proposed, resulting in 30 dense and uniform-distributed partial point clouds. Finally, a point-based geometry processing step composed of multi-view registration and surface meshing is carried out to obtain high-quality watertight human body shapes. This research also proposes an elaborate and novel method to assess the accuracy of reconstructed non-rigid human body model based on anthropometry parameters, which solves the synchronization of the ground-truth values and the measured values. Experimental results show that the system can achieve the reconstruction accuracy within 2.5 mm in average.
Learning with Small Data

In the era of big data, it is easy for us collect a huge number of image and text data. However, we frequently face the real-world problems with only small (labeled) data in some domains, such as healthcare and urban computing. The challenge is how to make machine learn algorithms still work well with small data? To solve this challenge, in this tutorial, we will cover the state-of-the-art machine learning techniques to handle small data issue. In particular, we focus on the following three aspects: (1) Providing a comprehensive review of recent advances in exploring the power of knowledge transfer, especially focusing on meta-learning; (2) introducing the cutting-edge techniques of incorporating human/expert knowledge into machine learning models; and (3) identifying the open challenges to data augmentation techniques, such as generative adversarial networks. We believe this is an emerging and potentially high-impact topic in computational data science, which will attract both researchers and practitioners from academia and industry.
Application of Deep Learning Approaches for Sentiment Analysis

Social media platforms, forums, blogs, and opinion sites generate vast amount of data. Such data in the form of opinions, emotions, and views about services, politics, and products are characterized by unstructured format. End users, business industries, and politicians are highly influenced by sentiments of the people expressed on social media platforms. Therefore, extracting, analyzing, summarizing, and predicting the sentiments from large unstructured data needs automated sentiment analysis. Sentiment analysis is an automated process of extracting the opinionated from data and classifying the sentiments as positive, negative, and neutral. Lack of enough labeled data for sentiment analysis is one of the crucial challenges in natural language processing. Deep learning has emanated as one of the highly sought-after solutions to address this challenge due to automated and hierarchical learning capability inherently supported by deep learning models. Considering the application of deep learning approaches for sentiment analysis, this chapter aims to put forth taxonomy of traits to be considered for deep learning-based sentiment analysis and demystify the role of deep learning approaches for sentiment analysis.