Reviewerly/exHarmony huggingface dataset:
Confusing Image Quality Assessment: Toward Better Augmented Reality Experience

With the development of multimedia technology, Augmented Reality (AR) has become a promising next-generation mobile platform. The primary value of AR is to promote the fusion of digital contents and real-world environments, however, studies on how this fusion will influence the Quality of Experience (QoE) of these two components are lacking. To achieve better QoE of AR, whose two layers are influenced by each other, it is important to evaluate its perceptual quality first. In this paper, we consider AR technology as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">superimposition</i> of virtual scenes and real scenes, and introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual confusion</i> as its basic theory. A more general problem is first proposed, which is evaluating the perceptual quality of superimposed images, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., confusing image quality assessment. A ConFusing Image Quality Assessment (CFIQA) database is established, which includes 600 reference images and 300 distorted images generated by mixing reference images in pairs. Then a subjective quality perception experiment is conducted towards attaining a better understanding of how humans perceive the confusing images. Based on the CFIQA database, several benchmark models and a specifically designed CFIQA model are proposed for solving this problem. Experimental results show that the proposed CFIQA model achieves state-of-the-art performance compared to other benchmark models. Moreover, an extended ARIQA study is further conducted based on the CFIQA study. We establish an ARIQA database to better simulate the real AR application scenarios, which contains 20 AR reference images, 20 background (BG) reference images, and 560 distorted images generated from AR and BG references, as well as the correspondingly collected subjective quality ratings. Three types of full-reference (FR) IQA benchmark variants are designed to study whether we should consider the visual confusion when designing corresponding IQA algorithms. An ARIQA metric is finally proposed for better evaluating the perceptual quality of AR images. Experimental results demonstrate the good generalization ability of the CFIQA model and the state-of-the-art performance of the ARIQA model. The databases, benchmark models, and proposed metrics are available at: https://github.com/DuanHuiyu/ARIQA.
Restart Expedites Quantum Walk Hitting Times

Classical first-passage times under restart are used in a wide variety of models, yet the quantum version of the problem still misses key concepts. We study the quantum hitting time with restart using a monitored quantum walk. The restart strategy eliminates the problem of dark states, i.e., cases where the particle evades detection, while maintaining the ballistic propagation which is important for a fast search. We find profound effects of quantum oscillations on the restart problem, namely, a type of instability of the mean detection time, and optimal restart times that form staircases, with sudden drops as the rate of sampling is modified. In the absence of restart and in the Zeno limit, the detection of the walker is not possible, and we examine how restart overcomes this well-known problem, showing that the optimal restart time becomes insensitive to the sampling period.
SiamMask: A Framework for Fast Online Object Tracking and Segmentation.

In this article, we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method. We improve the offline training procedure of popular fully-convolutional Siamese approaches by augmenting their losses with a binary segmentation task. Once the offline training is completed, SiamMask only requires a single bounding box for initialization and can simultaneously carry out visual object tracking and segmentation at high frame-rates. Moreover, we show that it is possible to extend the framework to handle multiple object tracking and segmentation by simply re-using the multi-task model in a cascaded fashion. Experimental results show that our approach has high processing efficiency, at around 55 frames per second. It yields real-time state-of-the art results on visual-object tracking benchmarks, while at the same time demonstrating competitive performance at a high speed for video object segmentation benchmarks.
Multi-objective optimization oriented policy for performance and energy efficient resource allocation in Cloud environment

Cloud computing is a hybrid paradigm which makes use of utility computing, high performance cluster computing and grid computing and it offers various benefits such as flexibility, expandability, little or almost no capital investment, disaster recovery, moveable work space and much more. However, due to constantly increasing number of data centers worldwide, the issue of energy consumption by these data centers has attracted attention of researchers. Resource allocation and resource utilization are the major criterion in which the problem of energy efficiency can be addressed. In this research, we aim to provide energy-efficient resource allocation using Multi-Objective Optimization (MOO) method. Further, We propose MOO-based virtual machine (VM) allocation policy and implement it in CloudSim environment. The results are compared with existing policies. The results depict that MOO-based policy leads to saving in energy due to efficient resource allocation, without compromising performance of data center operations.
A further examination of word frequency and age-of-acquisition effects in English lexical decision task performance: The role of frequency trajectory.

Word frequency is an important predictor of lexical-decision task performance. The current study further examined the role of this variable by exploring the influence of frequency trajectory. Frequency trajectory is measured by how often a word occurs in childhood relative to adulthood. Past research on the role of this variable in word recognition has produced equivocal results. In the current study, words were selected based on their frequencies in Grade 1 (child frequency) and Grade 13 (college frequency). In Experiment 1, four frequency trajectory conditions were factorially examined in a lexical-decision task with English words: high-to-high (world), high-to-low (uncle), low-to-high (brain) and low-to-low (opera). an interaction between Grade 1 and college frequency demonstrated that words in the low-to-high condition were processed significantly faster and more accurately than words in the low-to-low condition, whereas the high-to-high and high-to-low conditions did not differ significantly. In Experiment 2, an advantage for words with an increasing frequency trajectory was also supported in regression analyses on both lexical decision and naming times for 3,039 items selected from the English Lexicon Project (Balota et al., 2007). This was replicated in Experiment 3, based on a regression analysis of 2,680 words from the British Lexicon Project (BLP; Keuleers, Lacey, Rastle, & Brysbaert, 2012). In all analyses, rated age-of-acquisition also significantly impacted word recognition. Together, the results suggest that the age at which a word is initially learned as well as its frequency trajectory across childhood impact performance in the lexical-decision task. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Benchmarking parts based face processing in-the-wild for gender recognition and head pose estimation

Multiple in-the-wild face processing approaches suffer from degraded performance due to, among other factors, facial expressions, occlusions, accessories, and variations in lighting and in the head pose. To better understand these issues, four different face parts, each eye, the nose, and the mouth, are studied for performing face processing tasks in challenging environments. Additionally, an automatic pipeline based on convolutional neural networks is proposed for detecting the available regions, processing them, and combining the results generated from each, resulting in a robust solution. The pipeline is evaluated on two common face processing tasks: head pose estimation, and gender recognition. Experiments are performed using two different object detectors, five popular, and one custom convolutional network architecture, for the classification step, and two datasets, one for each task, with different overall difficulty, representing a wide range in the unconstrained scenario spectrum. Results are detailed for each region and their combination, comparisons are performed against the state-of-the-art and in-depth discussions are provided. In particular, experiments indicate that the nose and the mouth play a major role in challenging scenarios, due to their robustness to self occlusion. The complete pipeline outperforms state-of-the-art works when estimating the head pose in unconstrained scenarios, and achieves competitive performance for recognizing gender. By evaluating each region separately, degraded parts are excluded from processing, favoring the use of reliable face information, resulting in increased performance.
The Value of Values and Norms in Social Simulation

Social simulations gain strength when agent behaviour can (1) represent human behaviour and (2) be explained in understandable terms. Agents with values and norms lead to simulation results that me ...
Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural Networks

In this paper, the radial basis function neural network (RBFNN) is used to generate a prospectivity map for undiscovered copper-rich (Cu) deposits in the Finnmark region, northern Norway. To generate the input data for RBFNN, geological and geophysical data, including up to 86 known mineral occurrences hosted in mafic host-rocks, were combined at different resolutions. Mineral occurrences were integrated into “deposit” and “non-deposit” training sets. Running RBFNN on different input vectors, with a k-fold cross-validation method, showed that increasing the number of iterations and radial basis functions resulted in: (1) a reduction of training mean squared error (MSE) down to 0.1, depending on the grid resolution, and (2) reaching correct classification rates of 0.9 and 0.6 for training and validation, respectively. The latter depends on: (1) the selection of “non-deposit” training data throughout the study area, (2) the scale at which data was acquired, and (3) the dissimilarity of input vectors. The “deposit” input data were correctly identified by the trained model (up to 83%) after proceeding to classification of non-training data. Up to 885 km2 of the Finnmark region studied is favorable for Cu mineralization based on the resulting mineral prospectivity map. The prospectivity map can be used as a reconnaissance guide for future detailed ground surveys.
Remote Sensing Image Enhancement Via Edge-Preserving Multiscale Retinex

Recently, an image enhancement receives a considerable research attention due to its wide application.In this paper, we propose a novel algorithm to enhance the contrast of remote sensing image while avoiding color distortion and detail loss.First, based on the Sobel operator strength, the local adaptive weighting parameter is obtained to penalize the fixed regularization parameter of original guided filtering.Then, the grayscale difference between flat patch and edge patch, which is caused by the fixed regularization parameter is amplified meanwhile without sacrificing the denoising performance.Finally, the Gaussian filtering of original multiscale retinex with color restoration is replaced with the guided filtering based on the Sobel edge detection operator to estimate the incident component of an image.Comparing with the previous algorithms, the proposed one can obtain more edge details and better color fidelity but with lower computational complexity and higher efficiency.
Human Action Recognition Using Multilevel Depth Motion Maps

The advent of depth sensors opens up new opportunities for human action recognition by providing depth information. The main purpose of this paper is to present an effective method for human action recognition from depth images. A multilevel frame select sampling (MFSS) method are proposed to generate three levels of temporal samples from the input depth sequences first. Then, the proposed motion and static mapping (MSM) method is used to obtain the representation of MFSS sequences. After that, this paper exploits the block-based LBP feature extraction approach to extract features information from the MSM. Finally, the fisher kernel representation is applied to aggregate the block features, which is then combined with the kernel-based extreme learning machine classifier. The developed framework is evaluated on three public datasets captured by depth cameras. The experimental results demonstrate the great performance compared with the existing approaches.
Detection and classification of normal and abnormal patterns in mammograms using deep neural network

Summary Breast cancer detection is the most challenging aspect in the field of health monitoring system. In this paper, breast cancer detection was assessed by employing Mammographic Image Analysis Society (MIAS) dataset. The proposed approach contains four major steps, namely, image‐preprocessing, segmentation, feature extraction, and classification. Initially, Laplacian filtering was utilized to identify the area of edges in mammogram images and, also, it was very sensitive to noise. Then, segmentation was carried‐out using modified‐Adaptively Regularized Kernel‐based Fuzzy‐C‐Means (ARKFCM); it was a flexible high level machine learning technique to localize the object in complex template. In conventional ARKFCM, it was hard to segment the ill‐defined masses in mammogram images. To address this concern, the Euclidean distance in ARKFCM was replaced by correlation function in order to improve the segmentation efficiency. The hybrid feature extraction (Histogram of Oriented Gradients (HOG), homogeneity, and energy) was performed on the segmented cancer region to extract feature subsets. The respective feature values were given as the input for a multi‐objective classifier: Deep Neural Network (DNN) for classifying the normal and abnormal regions in mammogram images. The experimental outcome shows that the proposed methodology improved accuracy in breast cancer classification up to 3% to 9% compared to other existing methods.
Who should make decision on this pull request? Analyzing time-decaying relationships and file similarities for integrator prediction

In pull-based development model, integrators are responsible for making decisions about whether to accept pull requests and integrate code contributions. Ideally, pull requests are assigned to integrators and evaluated within a short time after their submissions. However, the volume of incoming pull requests is large in popular projects, and integrators often encounter difficulties in processing pull requests in a timely fashion. Therefore, an automatic integrator prediction approach is required to assign appropriate pull requests to integrators. In this paper, we propose an approach TRFPre which analyzes Time-decaying Relationships and File similarities to predict integrators. We evaluate the effectiveness of TRFPre on 24 projects containing 138,373 pull requests. Experimental results show that TRFPre makes accurate integrator predictions in terms of accuracies and Mean Reciprocal Rank. Less than 2 predictions are needed to find correct integrator in 91.67% of projects. In comparison with state-of-the-art approaches cHRev, WRC, TIE, CoreDevRec and ACRec, TRFPre improves top-1 accuracy by 68.2%, 73.9%, 49.3%, 14.3% and 46.4% on average across 24 projects.
Blockchain Solutions for Agency Problems in Corporate Governance

As a foundational technology, blockchain technology creates the infrastructure for decentralized networked governance that, over time, creates the environment that enables the removal of internal and external monitoring mechanisms previously necessitated by agency problems in corporate governance. Blockchain technology facilitates a substantial increase in efficiency in the agency relationship and lowers agency costs in orders of magnitude.
Blockchains, Coordination, and Forks

Blockchains are distributed ledgers. Their protocol aims at ensuring that the miners in charge of recording transactions reach a consensus about a unique ledger. In this paper, we highlight that the game induced by the blockchain proof-of-work protocol generates several equilibria. In some equilibria, different versions of the ledger or “branches” coexist, breaching consensus. Such forks arise because of the interplay of miners' incentives to coordinate on the same branch, and miners' incentives to protect their vested interests on a given branch. We illustrate that these elements were present in the recent hard forks that occurred on Bitcoin.
Assessing Migration of a 20-Year-Old System to a Micro-Service Platform Using ATAM

Architecture evaluation is a systematic approach to evaluate target architectures and ATAM (Architectural Trade-off Analysis Method) is one of the available methods. Migration of software systems imply many architectural decisions that should be systematically evaluated to assess concrete trade-offs and risks. This article reports on the ATAM usage at a mid-size Chilean specialty software development company to assess the migration of its 20-year old flagship product to a micro-service platform. Over three days, 10 key architectural decisions, addressing 35 scenarios, were considered. Since almost all requirements were deemed essential for legal reasons, the evaluation scheme used a modified importance dimension distinguishing among business-key (first line deal breakers), legally-mandated (second-line deal breakers), and desirable requirements. Key lessons learned include the very positive client feedback on the introduction of systematic evaluation of architectural choices using business criteria.
Retaining Privileged Information for Multi-Task Learning

Knowledge transfer has been of great interest in current machine learning research, as many have speculated its importance in modeling the human ability to rapidly generalize learned models to new scenarios. Particularly in cases where training samples are limited, knowledge transfer shows improvement on both the learning speed and generalization performance of related tasks. Recently, Learning Using Privileged Information (LUPI) has presented a new direction in knowledge transfer by modeling the transfer of prior knowledge as a Teacher-Student interaction process. Under LUPI, a Teacher model uses Privileged Information (PI) that is only available at training time to improve the sample complexity required to train a Student learner for a given task. In this work, we present a LUPI formulation that allows privileged information to be retained in a multi-task learning setting. We propose a novel feature matching algorithm that projects samples from the original feature space and the privilege information space into a joint latent space in a way that informs similarity between training samples. Our experiments show that useful knowledge from PI is maintained in the latent space and greatly improves the sample efficiency of other related learning tasks. We also provide an analysis of sample complexity of the proposed LUPI method, which under some favorable assumptions can achieve a greater sample efficiency than brute force methods.
Accident Recognition via 3D CNNs for Automated Traffic Monitoring in Smart Cities

Automatic recognition of road accidents in traffic videos can improve road safety. Smart cities can deploy accident recognition systems to promote urban traffic safety and efficiency. This work reviews existing approaches for automatic accident detection and highlights a number of challenges that make accident detection a difficult task. Furthermore, we propose to implement a 3D Convolutional Neural Network (CNN) based accident detection system. We customize a video game to generate road traffic video data in a variety of weather and lighting conditions. The generated data is preprocessed using optical flow method and injected with noise to focus only on motion and introduce further variations in the data, respectively. The resulting data is used to train the model, which was then tested on real-life traffic videos from YouTube. The experiments demonstrate that the performance of the proposed algorithm is comparable to that of the existing models, but unlike them, it is not dependent on a large volume of real-life video data for training and does not require manual tuning of any thresholds.
“When Numbers Matter!”: Detecting Sarcasm in Numerical Portions of Text

Abhijeet Dubey, Lakshya Kumar, Arpan Somani, Aditya Joshi, Pushpak Bhattacharyya. Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 2019.
Comparison of depth cameras for three-dimensional reconstruction in medicine

KinectFusion is a typical three-dimensional reconstruction technique which enables generation of individual three-dimensional human models from consumer depth cameras for understanding body shapes. The aim of this study was to compare three-dimensional reconstruction results obtained using KinectFusion from data collected with two different types of depth camera (time-of-flight and stereoscopic cameras) and compare these results with those of a commercial three-dimensional scanning system to determine which type of depth camera gives improved reconstruction. Torso mannequins and machined aluminium cylinders were used as the test objects for this study. Two depth cameras, Microsoft Kinect V2 and Intel Realsense D435, were selected as the representatives of time-of-flight and stereoscopic cameras, respectively, to capture scan data for the reconstruction of three-dimensional point clouds by KinectFusion techniques. The results showed that both time-of-flight and stereoscopic cameras, using the developed rotating camera rig, provided repeatable body scanning data with minimal operator-induced error. However, the time-of-flight camera generated more accurate three-dimensional point clouds than the stereoscopic sensor. Thus, this suggests that applications requiring the generation of accurate three-dimensional human models by KinectFusion techniques should consider using a time-of-flight camera, such as the Microsoft Kinect V2, as the image capturing sensor.
Curvature-constrained path elongation with expected length for Dubins vehicle

Path elongation is a basic means of adjusting the time for a curvature-constrained vehicle to reach its destination, which is very common in the maneuvering control of high-speed vehicles or the coordinated control of their fleet. This paper studies the path elongation problem of a Dubins vehicle which moves in a two-dimensional plane and is subject to a maximum curvature. The aim of the paper is to answer the following question arising from the path elongation of Dubins vehicles: Can a Dubins vehicle reach a given destination by path elongation with a predefined path length? It is discovered and proved by theoretical analysis that, when the destination point is located in a special region, there is a special length interval for which no proper path exists. For all realizable length intervals, we provide an example of feasible path patterns for expected elongation. The results provide ideal reference trajectories with expected length for Dubins vehicles to follow for the sake of accurate arrival-time control.