Reviewerly/exHarmony huggingface dataset:
Improving Urdu Recognition Using Character-Based Artistic Features of Nastalique Calligraphy

The state-of-the-art Urdu recognition approaches for Nastalique use features along with the sequence of characters' labels for classification and recognition.In Arabic-like cursive script, the characters are joined together to form a ligature.The conventional methods process the connected stroke of ligatures as a sequence of characters.However, connected stroke of a ligature image has a sequence of pairs of characters and their joiners, instead of a sequence of characters.The character has a distinctive shape that clearly distinguishes it from other characters.The joiner preserves the connecting stroke shape of a character with the next character.In this paper, an implicit Urdu character recognition technique is presented for the Nastalique writing style that is based on recognition of characters and joiners.The detailed analysis of the Nastalique calligraphy is carried out to extract the artistic features of characters and their joiners.The presented technique is tested on Dataset-1 of 1446 ligature classes covering 3 309 762 ligature instances and 91 129 unique Urdu words.In addition, the system is also tested on 1600 text lines of UPTI dataset called Dataset-2.The character recognition accuracies are 95.58% and 98.37% on Dataset-1 and Dataset-2, respectively.The results reveal that the system outperforms the state-of-the-art hidden Markov models and deep learning-based Urdu recognition techniques.
MFO‐based thresholded and weighted histogram scheme for brightness preserving image enhancement

Histogram equalisation (HE) is a simple and effective image enhancement technique. However, it suffers from excessive brightness change and provides degradation in the visual aspect of the image. To overcome the shortcomings in the HE, a novel histogram framework is proposed in this study. The image histogram is first segmented into two parts using the Otsu's thresholding method. Then, both of the upper and lower histograms are constrained to control the level of enhancement. These constraint parameters are computed through moth-flame optimisation algorithm. After constraining the histograms, mean shift correction is performed to ensure there is a minimum level of mean shifting from input to output image. Traditional HE is then applied with a modified histogram to obtain mapping function for lower and upper grey level individually. This enhanced image provides a balance between the level of enhancement and preservation of the important features of the image for high-level processing. The effectiveness of the proposed method is highlighted with a detailed comparison with other closely related schemes. Through the proposed routine, the enhanced images achieve a good trade-off between features enhancement, low contrast boosting, and brightness preservation in addition to the natural feel of the original image.
Image colorization by fusion of color transfers based on DFT and variance features

Color transfer methods usually suffer from spatial color coherency problem. In order to address this problem, this paper develops a fused color transfer method for image colorization. Our idea is to design a local student’s t-test to screen the incoherent colors in the preliminary colorization results obtained by a simple color transfer method with DFT and variance features. Furthermore, we propose a variational fusion model to inpaint these incoherent colors and fuse the other useful colors together. We also present an efficient algorithm for solving the fusion model numerically, and show the convergence of the algorithm. Finally, experimental results are reported to demonstrate the effectiveness of the proposed method, and its performance is competitive with those of the other testing methods.
Automatic Visual Verification of Layout Failures in Responsively Designed Web Pages

Responsively designed web pages adjust their layout according to the viewport width of the device in use. Although tools exist to help developers test the layout of a responsive web page, they often rely on humans to flag problems. Yet, the considerable number of web-enabled devices with unique viewport widths makes this manual process both time-consuming and error-prone. Capable of detecting some common responsive layout failures, the ReDeCheck tool partially automates this process. Since ReDeCheck focuses on a web page's document object model (DOM), some of the issues it finds are not observable by humans. This paper presents a tool, called Viser, that renders a ReDeCheck-reported layout issue in a browser, adjusting the opacity of certain elements and checking for a visible difference. Unless Viser classifies an issue as a human-observable layout failure, a web developer can ignore it. This paper's experiments reveal the benefit of using Viser to support automated visual verification of layout failures in responsively designed web pages. Viser automatically classified all of the 117 layout failures that ReDeCheck reported for 20 web pages, each of which had to be manually analyzed in a prior study. Viser's automated manipulation of element opacity also highlighted manual classification's subjectivity: it categorized 28 issues differently to manual analysis, including three correctly reclassified as false positives.
Data prefetching and file synchronizing for performance optimization in Hadoop-based hybrid cloud

Driven by the technical factors such as system reliability, bandwidth constraints, data confidentiality and security, as well as the economic factors such as initial capital expenditure and re-occurring operating expenditure, today's cloud computing tends to adopt hybrid cloud model. However, because hybrid clouds scale both numerically and geographically, the network delay becomes the main constraint in remote file system access. To hide network latency and reduce job completion time in Hadoop-based hybrid cloud data access, a scheduling-aware data prefetching scheme to enhance non-local map task's data locality in Hadoop-based centralized hybrid cloud (CHCDLOS-Prefetch) and a file synchronizing method to decrease job execution delay in Hadoop-based distributed hybrid cloud (DHCDLO-Sync) are proposed. In the former, input data for non-local map tasks are fetched ahead of time to target compute nodes by making use of idle network bandwidth. In the latter, considered from job level scheduling, data files with high popularity are proactively synchronized beforehand among sub-clouds to strength intra sub-cloud data locality in distributed hybrid cloud. Extensive experimental results illustrate that compared to the Capacity, the Fair and the DARE algorithms, our proposed algorithms improve hybrid cloud performance more significantly in data locality and job completion time.
Dynamical performance analysis of communication-embedded neural networks: A survey

State estimation and synchronization of neural networks (NNs) have recently received ever-increasing research interests due to a large number of successful applications in various fields such as repetitive learning, classification of patterns, nonlinear control, adaptive control, image processing, and so forth. Owing to limited communication bandwidth, the network-induced phenomena and the adopted communication scheduling may cause inevitable negative effects or the degradation of dynamical performance of NNs. This paper, from the perspective of dynamical behavior, renders a comprehensive summary on the recent advances of communication-embedded NNs and their application in nonlinear control. First, some common network-induced phenomena and communication protocols are roughly introduced from both models and mechanisms. Then, the state estimation and synchronization analysis of various NNs with network-induced phenomena are systematically reviewed and some interesting results are separately presented. Furthermore, the latest advances for the cases with communication protocols are profoundly surveyed from three aspects: state estimation, synchronization analysis and applications in control realm. Finally, some challenged problems are listed and several potential future research directions are highlighted.
Improved Multiple Hypothesis Tracker for Joint Multiple Target Tracking and Feature Extraction

Feature-aided tracking can often yield improved tracking performance over the standard multiple target tracking (MTT) algorithms. However, in many applications, the feature signal of the targets consists of sparse Fourier-domain signals. It changes quickly and nonlinearly in the time domain, and the feature measurements are corrupted by missed detections and misassociations. In this paper, we develop a feature-aided multiple hypothesis tracker for joint MTT and feature extraction in dense target environments. We use the atomic norm constraint to formulate the sparsity of feature signal and use the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm to formulate the sparsity of the corruption induced by misassociations. Based on the sparse representation, the feature signal are estimated by solving a semidefinite program. With the estimated feature signal, refiltering is performed to estimate the kinematic states of the targets, where the association makes use of both kinematic and feature information. Simulation results are presented to illustrate the performance of the proposed algorithm.
A Large-scale Study of Wikipedia Users' Quality of Experience

The Web is one of the most successful Internet application. Yet, the quality of Web users' experience is still largely impenetrable. Whereas Web performances are typically gathered with controlled experiments, in this work we perform a large-scale study of one of the most popular websites,namely Wikipedia, explicitly asking (a small fraction of its) users for feedback on the browsing experience. We leverage user survey responses to build a data-driven model of user satisfaction which, despite including state-of-the art quality of experience metrics, is still far from achieving accurate results, and discuss directions to move forward. Finally, we aim at making our dataset publicly available, which hopefully contributes in enriching and refining the scientific community knowledge on Web users' quality of experience (QoE).
Seven Pillars to Achieve Energy Efficiency in High-Performance Computing Data Centers

Nowadays, data centers and high-performance computing (HPC) systems are crucial for intensive computing environments. The energy efficiency in HPC is an evergreen problem. Moreover, energy-efficient design and energy ecology measures are core challenges in HPC. However, current research focuses on practical methods to measure power utilization to take decisions for green computing without exceeding resources and without compromising on performance. This paper surveys the issues, challenges, and their solutions over the period 2010–2016, by focusing on the energy consumption of data centers and HPC systems. We grouped existing problems in energy efficiency that data centers are currently facing. Our contribution is twofold. Firstly, with this categorization, we aim to provide an easy and concise view of the underlying energy efficiency model adopted by each approach. Secondly, we propose seven-pillar framework for energy efficiency in HPC systems and data centers for the first time.
Research on a Thangka Image Classification Method Based on Support Vector Machine

As an art image, Thangka images have rich themes, various forms of expression, complex picture content and many layers of color representation. This paper mainly constructs a multi-core support vector machine (SVM) based on the information entropy feature-weighted radial basis kernel function. In this paper, the kernel function is optimized, and the feature reduction is performed by using the random forest feature selection algorithm with average accuracy degradation. Finally, the effective classification of the icon image and the mandala image in Thangka is realized. The research results provide support for the follow-up study of Thangka image annotation and retrieval.
Detect DRAM Disturbance Error by Using Disturbance Bin Counters

DRAM disturbance errors are increasingly a concern to computer system reliability and security. There have been a number of designs to detect and prevent them; however, there lacks any design that guarantees 100 percent detection (no false negative) with a small and fixed hardware cost. This paper presents such a design based on a novel idea called disturbance bin counter (DBC). Each DBC is a complex counter that maintains an upper bound of disturbances for a bin of DRAM rows. Their access is not in the critical path of processor execution and thus incurs no performance overhead. The design is optimized at the circuit level to minimize the storage requirement. Our simulation results using multi-core SPEC CPU2006 workloads show that no false positive occurs with a 1,024-entry DBC table, which requires only 4.5 KB storage. The design can be incorporated into a memory controller to guarantee the detection of DRAM disturbance errors or row hammering by malicious programs.
Traffic Scene Classification on a Representation Budget

Visual cues can be used alongside GPS positioning and digital maps to improve understanding of vehicle environment in fleet management systems. Such systems are limited both in terms of bandwidth and storage space, so minimizing the size of transmitted and stored visual data is a priority. In this paper, we present efficient strategies for computing very short image representations suitable for classifying various types of traffic scenes in fleet management systems. We anticipate that the set of interesting classes will change over time, so we consider image representations that can be trained without knowing the labels of the target dataset. We empirically evaluate and compare the presented methods on a contributed dataset of 11447 labeled traffic scenes. Our results indicate that excellent classification results can be achieved with very short image representations, and that fine-tuning on the target dataset image data is not mandatory. Image descriptors can be as short as 128 components while still offering good performance, even in presence of adverse weather or illumination conditions.
Visual analytics for collaborative human-machine confidence in human-centric active learning tasks

Abstract Active machine learning is a human-centric paradigm that leverages a small labelled dataset to build an initial weak classifier, that can then be improved over time through human-machine collaboration. As new unlabelled samples are observed, the machine can either provide a prediction, or query a human ‘oracle’ when the machine is not confident in its prediction. Of course, just as the machine may lack confidence, the same can also be true of a human ‘oracle’: humans are not all-knowing, untiring oracles. A human’s ability to provide an accurate and confident response will often vary between queries, according to the duration of the current interaction, their level of engagement with the system, and the difficulty of the labelling task. This poses an important question of how uncertainty can be expressed and accounted for in a human-machine collaboration. In short, how can we facilitate a mutually-transparent collaboration between two uncertain actors—a person and a machine—that leads to an improved outcome? In this work, we demonstrate the benefit of human-machine collaboration within the process of active learning, where limited data samples are available or where labelling costs are high. To achieve this, we developed a visual analytics tool for active learning that promotes transparency, inspection, understanding and trust, of the learning process through human-machine collaboration. Fundamental to the notion of confidence, both parties can report their level of confidence during active learning tasks using the tool, such that this can be used to inform learning. Human confidence of labels can be accounted for by the machine, the machine can query for samples based on confidence measures, and the machine can report confidence of current predictions to the human, to further the trust and transparency between the collaborative parties. In particular, we find that this can improve the robustness of the classifier when incorrect sample labels are provided, due to unconfidence or fatigue. Reported confidences can also better inform human-machine sample selection in collaborative sampling. Our experimentation compares the impact of different selection strategies for acquiring samples: machine-driven, human-driven, and collaborative selection. We demonstrate how a collaborative approach can improve trust in the model robustness, achieving high accuracy and low user correction, with only limited data sample selections.
Physical dimensions of children's touchscreen interactions: Lessons from five years of study on the MTAGIC project

Touchscreen interaction is nearly ubiquitous in today's computing environments. Children have always been a special population of users for new interaction technology: significantly different from adults in their needs, expectations, and abilities, but rarely tailored to in new contexts and on new platforms. Studies of children's touchscreen interaction have been conducted that focus on individual variables that may affect the interaction, but as yet no synthesis of studies replicating similar methodologies in different contexts has been presented. This paper reports the results across five years of focused study in one project aiming to characterize the differences between children's and adults’ physical touchscreen interaction behaviors. Six studies were conducted with over 180 people (116 children) to understand how children touch targets and make onscreen gestures. A set of design recommendations that summarizes the findings across the six studies is presented for reference. This paper makes the entire set available for reference in one place and highlights where the findings are generalizable across platforms. These recommendations can inform the design of future touchscreen interfaces for children based on their physical capabilities. Also, this paper outlines the future challenges and open questions that remain for understanding child-computer interaction on touchscreens.
SWAT: Seamless Web Authentication Technology

We present a seamless challenge-response authentication protocol which leverages on the variations of html5 canvas rendering made by the software and hardware stacks. After a training phase that leads to feature extraction with deep learning techniques, a server becomes able to authenticate a user based on fresh canvasses, hence avoiding replay attacks. The whole authentication process is natively supported by any mainstream browser, stateless on client side and can be transparent to the user. We argue that those features facilitate deployment and composition with other authentication mechanisms without lowering the user experience. We present the threat model against which our protocol is expected to live and discuss its security. We also present a prototype implementation of our protocol and report on a real-word experimentation that we ran in order to analyze its efficiency and effectiveness.
A Family of Fuzzy Orthogonal Projection Models for Monolingual and Cross-lingual Hypernymy Prediction

Hypernymy is a semantic relation, expressing the “is-a” relation between a concept and its instances. Such relations are building blocks for large-scale taxonomies, ontologies and knowledge graphs. Recently, much progress has been made for hypernymy prediction in English using textual patterns and/or distributional representations. However, applying such techniques to other languages is challenging due to the high language dependency of these methods and the lack of large training datasets of lower-resourced languages.
Method Level Refactoring Prediction on Five Open Source Java Projects using Machine Learning Techniques

Introduction: Identifying code segments in large and complex systems in need of refactoring is non-trivial for software developers. Our research aim is to develop recommendation systems for suggesting methods which require refactoring. Materials and Methods: Previous research shows that source code metrics for object-oriented software systems are indicators of complexity of a software system. We compute 25 different source code metrics at the method level and use it as features in a machine learning framework to predict the need of refactoring. We conduct a series of experiments on a publicly available annotated dataset of five software systems to investigate the performance of our proposed approach. In this proposed solution, ten different machine learning classifiers have been considered. In order to handle issues related to class imbalance, three different data sampling methods are also considered during implementation. Conclusion: Our analysis reveals that the mean accuracy for the SMOTE and RUSBoost data sampling technique is 98.47% respectively. The mean accuracy for the classifier AdaBoost is 98.16% and the mean accuracy for the classifier ANN+GD is 98.17% respectively. Hypothesis testing results reveals that the performance of different classifiers and data sampling techniques are statistically significant in nature.
Learning Relationship-Aware Visual Features

Relational reasoning in Computer Vision has recently shown impressive results on visual question answering tasks. On the challenging dataset called CLEVR, the recently proposed Relation Network (RN), a simple plug-and-play module and one of the state-of-the-art approaches, has obtained a very good accuracy (95.5%) answering relational questions. In this paper, we define a sub-field of Content-Based Image Retrieval (CBIR) called Relational-CBIR (R-CBIR), in which we are interested in retrieving images with given relationships among objects. To this aim, we employ the RN architecture in order to extract relation-aware features from CLEVR images. To prove the effectiveness of these features, we extended both CLEVR and Sort-of-CLEVR datasets generating a ground-truth for R-CBIR by exploiting relational data embedded into scene-graphs. Furthermore, we propose a modification of the RN module – a two-stage Relation Network (2S-RN) – that enabled us to extract relation-aware features by using a preprocessing stage able to focus on the image content, leaving the question apart. Experiments show that our RN features, especially the 2S-RN ones, outperform the RMAC state-of-the-art features on this new challenging task.
From Small-scale to Large-scale Text Classification

Neural network models have achieved impressive results in the field of text classification. However, existing approaches often suffer from insufficient training data in a large-scale text classification involving a large number of categories (e.g., several thousands of categories). Several neural network models have utilized multi-task learning to overcome the limited amount of training data. However, these approaches are also limited to small-scale text classification. In this paper, we propose a novel neural network-based multi-task learning framework for large-scale text classification. To this end, we first treat the different scales of text classification (i.e., large and small numbers of categories) as multiple, related tasks. Then, we train the proposed neural network, which learns small- and large-scale text classification tasks simultaneously. In particular, we further enhance this multi-task learning architecture by using a gate mechanism, which controls the flow of features between the small- and large-scale text classification tasks. Experimental results clearly show that our proposed model improves the performance of the large-scale text classification task with the help of the small-scale text classification task. The proposed scheme exhibits significant improvements of as much as 14% and 5% in terms of micro-averaging and macro-averaging F1-score, respectively, over state-of-the-art techniques.