Reviewerly/exHarmony huggingface dataset:
Gradient Image Super-resolution for Low-resolution Image Recognition

In visual object recognition problems essential to surveillance and navigation problems in a variety of military and civilian use cases, low-resolution and low-quality images present great challenges to this problem. Recent advancements in deep learning based methods like EDSR/VDSR have boosted pixel domain image super-resolution (SR) performances significantly in terms of signal to noise ratio(SNR)/ mean square error(MSE) metrics of the super-resolved image. However, these pixel domain signal quality metrics may not directly correlate to the machine vision tasks like key points detection and object recognition. In this work, we develop a machine vision tasks-friendly super-resolution technique which enhances the gradient images and associated features from the low-resolution images that benefit the high level machine vision tasks. Here, a residual learning deep neural network based gradient image super-resolution solution is developed with scale space adaptive network depth, and simulation results demonstrate the performance gains in both gradient image quality as well as key points repeatability.
MRI Watermarking Technique Using Chaotic Maps, NSCT and DCT

In this paper, magnetic resonance imaging (MRI) watermarking technique using chaotic maps, nonsubsampled contourlet transform (NSCT) and discrete cosine transform (DCT) has been proposed. Here, three chaotic maps, namely logistic map, Arnold's cat map, tent map are applied in multiple ways of watermarking. The number of iterations and the initial values of parameters of these maps are used as confidential keys, which strengthen the security of proposed watermarking. Here, the pixel positions of MRI are shuffled following the chaotic sequence of logistic map. The watermark is scrambled by Arnold's Cat map and then it is further scrambled by applying 3×3 mask and X-OR operation of tent map. NSCT is used in this watermarking scheme, since this transform assures superior performance than other transforms. Besides, the approximation band of NSCT contains considerable visual information of an image. DCT is applied on this approximation band, because NSCT along with DCT makes watermarking technique more imperceptible and robust. So, the DCT coefficients of shuffled watermark are embedded into the DCT coefficients of approximation band of shuffled MRI in this scheme. Furthermore, the proposed scheme is tested on varieties of MRIs and their associated results reveal that this scheme has promising improvements in imperceptibility and robustness against noise and geometric attacks.
Handwritten Bangla Numeral and Basic Character Recognition Using Deep Convolutional Neural Network

In this paper, the problem of recognizing handwritten Bangla characters is addressed. Handwritten character recognition is one of the most practiced tasks in computer vision. Over the past few years Convolutional Neural Network has produced the best results in case of English handwritten character recognition. Although Bangla the official language of Bangladesh and several Indian states with over 200 million native speakers Bangla handwritten character recognition is quite far behind. We present a 9 layer sequential Convolutional Neural Network model to recognize 60 (10 numerals+ 50 basic characters) Bangla handwritten characters. BanglaLekha-Isolated dataset is used as train-validation set. A new dataset of 6000 images is created for cross validation. Our proposed model trained to recognize 60 characters achieves state-of-the-art 99.44% accuracy on BanglaLekha-Isolated dataset and 95.16% accuracy on prepared test set. Experiments on recognizing Bangla numerals separately also show state-of-the-art performance.
A Web Service Composition Approach Based on Planning Graph and Propositional Logic

To improve the computational efficiency of web service automatic composition, this article proposes a novel approach based on a planning graph and propositional logic. This approach has a forward searching stage and a backward combination stage: the former stage searches services in a service storage and performs the composition in a hierarchical architecture based on a planning graph, while the latter stage combines them via propositional logic operations. This method can obtain all composite services that contain no redundant services, and the computational complexity can be significantly reduced. Experiments are done to illustrate the effectiveness of the approach.
Matching UML class models using graph edit distance

The Unified Modelling Language (UML) class model is an essential constituent in the software system development process and a considerable body of knowledge is encompassed in the form of class model designs. A UML class model forms an elaborate specification hierarchy and comparing different class models in order to identify corresponding parts assumes considerable human expertise. To imitate such human capacity an exponentially complex task needs to be addressed. Yet, the research that involves UML class model matching focuses primarily only on a design pattern detection and studies that tackle the problem of matching any class models are rather rare. The aim of this study is to introduce a class model distance computation framework that can be utilised for comparing class models in model repositories. The framework exploits the relational structure between model elements as well as internal element features to devise a distance measure between any pair of class models. The relational structures of two class models in the form of graphs are aligned using the graph edit distance technique. The internal element feature distance computation deploys the Hungarian algorithm for optimal assignment of any two-feature sets. The distance computation framework reduces the comparison task to polynomial time complexity. The study presents experimental performance analysis of the proposed framework conducted using the precision-recall and receiver operating characteristics curves and corresponding areas under the curves. The results of the analysis indicate low false positive rates for both pairwise and pattern detection tasks.
Exploiting Uncertainty of Deep Neural Networks for Improving Segmentation Accuracy in MRI Images

Deep neural networks have shown great achievements in solving complex problems. However, there are fundamental challenges which limit their real world applications. Lack of a measurable criterion for estimating uncertainty of the network predictions is one of these challenges. However, we can compute the variance of the network output by applying spatial transformations, distortions or noise injection to network inputs and interpret these variances as uncertainty of the network predictions. In other words, as long as the deformations do not conceptually alter target of interest, we expect the network to produce the same result. Hence, any outputs changes can be a sign of uncertainty in the network predictions. In order to estimate the prediction uncertainty of deep convolutional neural networks we use simple random transformations. By exploiting the network uncertainty, we improve the overall performance of the system. For a real use case, we apply the proposed method to segment left ventricle in MRI cardiac images. Experimental results demonstrate state-of- the-art performance and highlight the potential capabilities of simple ideas in conjunction with deep neural networks.
Toward Secure Resource Allocation in Mobile Cloud Computing: A Matching Game

Mobile Cloud Computing (MCC) is an emerging computing paradigm that provides many advantages to mobile users but entails critical security concerns that slow down its adoption. In this paper, we approach the problem of resource allocation in MCC from a security perspective. The aim is to satisfy users' security requirements and service providers' security constraints, defined in the Security Service Level Agreement, through security integration into the process of resource allocation, to increase the security of the MCC system. The problem is modeled as a decentralized many-to-one matching game, in which mobile users and service providers evaluate their preferences during the resource allocation process in terms of security satisfaction. The game is then solved using an adapted version of the Gale/Shapley algorithm, which provides stability and computational efficiency. Our model can be implemented in large-scale MCC systems in a fully distributed fashion to enable securer offloading of mobile users' data and computational tasks.
The Popular Culture of 3D Printing

As 3D printing technology achieves mainstream adoption, people are forming new relationships with products as they shift from passive consumers to “prosumers” capable of both producing and consuming objects on demand. This is fueled by expanding online 3D printing communities, with new data within this chapter suggesting that prosumers are challenging existing understandings of popular culture as they bypass traditional mass manufacturing. With 3D digital files rapidly distributed through online platforms, this chapter argues that a new trend for “viral objects” is emerging, alongside the “3D selfie,” as digital bits spread via the internet are given physical form through 3D printing in ever increasing quantities. Analysis of these trends will provide academics, educators, and prosumers with a new perspective of 3D printing's socio-cultural impact, and further research directions are suggested to build a broader discourse around the opportunities and challenges of a cyberphysical future.
Risk management standards and the active management of malicious intent in artificial superintelligence

The likely near future creation of artificial superintelligence carries significant risks to humanity. These risks are difficult to conceptualise and quantify, but malicious use of existing artificial intelligence by criminals and state actors is already occurring and poses risks to digital security, physical security and integrity of political systems. These risks will increase as artificial intelligence moves closer to superintelligence. While there is little research on risk management tools used in artificial intelligence development, the current global standard for risk management, ISO 31000:2018, is likely used extensively by developers of artificial intelligence technologies. This paper argues that risk management has a common set of vulnerabilities when applied to artificial superintelligence which cannot be resolved within the existing framework and alternative approaches must be developed. Some vulnerabilities are similar to issues posed by malicious threat actors such as professional criminals and terrorists. Like these malicious actors, artificial superintelligence will be capable of rendering mitigation ineffective by working against countermeasures or attacking in ways not anticipated by the risk management process. Criminal threat management recognises this vulnerability and seeks to guide and block the intent of malicious threat actors as an alternative to risk management. An artificial intelligence treachery threat model that acknowledges the failings of risk management and leverages the concepts of criminal threat management and artificial stupidity is proposed. This model identifies emergent malicious behaviour and allows intervention against negative outcomes at the moment of artificial intelligence's greatest vulnerability.
An energy-efficiency-aware resource allocation strategy in multi-granularity provision for green computing

Energy efficiency is one of the most important issues in current large-scale server systems for green computing. A reasonable resource allocation is a key factor impacting on the energy efficiency of a system. Existing scheduling mechanisms in Map Reduce environments focus on the fair sharing of cluster resources among multiple users, allocated resources and scheduled jobs by a priority-based strategy. However, such mechanisms have little awareness of users' SLA (Service-Level Agreement). It is difficult to map the user's SLA to a certain priority. Additionally, they can't be sensitive to the energy-efficiency changes while a cluster is running and a work is going. Also users' SLA cannot be satisfied accurately and effectively. In this paper, an energy-efficiency-aware scheduling mechanism of a virtual resource for a big data application is addressed. This mechanism calculates the energy-efficiency value of different granularity resource mappings. Based on the proposed strategy, we also designed an optimized resource allocation algorithm for different service requirements, which can achieve the maximal energy-efficiency value for green computing.
The Relationship between E- banking Services and Bank Profit in Jordan for the Period of 2010-2015

Purpose: this paper aims to discuss the relationship between electronic bank services and the profit and to measure the impact of this relation on the profit size.
Anomaly Detection from Online Monitoring of System Operations Using Recurrent Neural Network

In production systems, the operation data is multivariate time series data including internal state of the system, control variables, control parameters and the like. As monitoring centre collects data intensively, monitoring time differs for each system. The predetermined frequency of data recording per day may not be protected. In this study, we decided to use a RNN which can learn data with missing values. The neural network learns diagnosis of system abnormality from the operation data of the system and the data of the maintenance record. Then we examine the usefulness of prediction of abnormal occurrence of learned neural network.
Anchored Audio Sampling

Many traditional HCI methods, such as surveys and interviews, are of limited value when working with preschoolers. In this paper, we present anchored audio sampling (AAS), a remote data collection technique for extracting qualitative audio samples during field deployments with young children. AAS offers a developmentally sensitive way of understanding how children make sense of technology and situates their use in the larger context of daily life. AAS is defined by an anchor event, around which audio is collected. A sliding window surrounding this anchor captures both antecedent and ensuing recording, providing the researcher insight into the activities that led up to the event of interest as well as those that followed. We present themes from three deployments that leverage this technique. Based on our experiences using AAS, we have also developed a reusable open-source library for embedding AAS into any Android application.
Abstract Machines

Overlaying virtual worlds onto existing physical rides and altering the sensations of motion can deliver new experiences of thrill, but designing how motion is mapped between physical ride and virtual world is challenging. In this paper, we present the notion of an abstract machine, a new form of intermediate design knowledge that communicates motion mappings at the level of metaphor, mechanism and implementation. Following a performance-led, in-the-wild approach we report lessons from creating and touring VR Playground, a ride that overlays four distinct abstract machines and virtual worlds on a playground swing. We compare the artist's rationale with riders' reported experiences and analysis of their physical behaviours to reveal the distinct thrills of each abstract machine. Finally, we discuss how to make and use abstract machines in terms of heuristics for designing motion mappings, principles for virtual world design and communicating experiences to riders.
Shoot high-quality color images using dual-lens system with monochrome and color cameras

In the dual-lens system with monochrome and color cameras, the gray image captured by the monochrome camera has better quality than the color image from the color camera, but does not have color information. To get high-quality color images, it is desired to colorize the gray image with the color image as reference. Due to occlusions, the colorization will inevitably fail in some cases. Thus, evaluating the colorization quality is also of great importance. We solve both problems in this paper. For colorization, we propose a gray-color correspondence prior, i.e. in local regions, if two patches are similar in the gray channel, it is very often that the two pixels centered at these two patches have similar colors. Based on this prior, a deep learning based and coarse-to-fine colorization method is proposed. For evaluating the colorization quality, we propose a symmetry colorization based evaluation method. Experimental results show that our method could largely outperform the state-of-the-art methods and is also efficient in computation.
Preparation of bipartite bound entangled Gaussian states in quantum optics

The positivity of the partial transpose is in general only a necessary condition for separability. There exist quantum states that are not separable, but nevertheless are positive under the partial transpose. States of this type are known as bound entangled states, meaning that these states are entangled but they do not allow distillation of pure entanglement by means of local operations and classical communication. We present a parametrization of a class of $2\ifmmode\times\else\texttimes\fi{}2$ bound entangled Gaussian states for bipartite continuous-variable quantum systems with two modes on each side. We propose an experimental protocol for preparing a particular bound entangled state in quantum optics. We then discuss the robustness properties of this protocol with respect to the occupation number of thermal inputs and the degrees of squeezing.
Argumentation Analysis for Early Career Researchers

Proving processes in mathematics classrooms follow their own peculiar rationale, which raises the question of how to reconstruct and analyse the complex argumentative structures that arise in classroom discussion. In this chapter we describe a method of analysis of argumentation processes in the mathematics class, following the structure of our workshop at the ICME-13 Early Career Researcher Day. The method builds on Toulmin's theory of argumentation (Toulmin 1958) and allows the description of both global argumentation structures and local argumentations. A three stage process is followed: reconstructing the sequencing and meaning of classroom talk; analysing local argumentations and global argumentation structures; and finally comparing these argumentation structures and revealing their rationale. The second stage involves two moves, first analysing local arguments on the basis of Toulmin's functional model of argumentation, and second analysing the global argumentative structure of the proving process. We provide an example of the use of the method to analyse a transcript from a mathematics classroom.
Neural and statistical predictors for time to readmission in emergency departments: A case study

The prediction of readmissions in the healthcare system, i.e. patients that are discharged and come back in a short interval of time, has taken great importance as readmissions have been taken as a measure of the system quality of service. Most studies in the literature follow a classification approach predicting the occurrence of the readmission event, however in this paper we are concerned with the prediction of the time until readmission, which can be studied in the framework of survival analysis. We report the performance of several neural and statistical prediction models on a large real dataset, finding approaches (weighted k-NN and regression tree based rule system) which provide a smooth approximation of the observed survival function, thus encouraging further research in this direction.
HistoryTracker

The sport data tracking systems available today are based on specialized hardware (high-definition cameras, speed radars, RFID) to detect and track targets on the field. While effective, implementing and maintaining these systems pose a number of challenges, including high cost and need for close human monitoring. On the other hand, the sports analytics community has been exploring human computation and crowdsourcing in order to produce tracking data that is trustworthy, cheaper and more accessible. However, state-of-the-art methods require a large number of users to perform the annotation, or put too much burden into a single user. We propose HistoryTracker, a methodology that facilitates the creation of tracking data for baseball games by warm-starting the annotation process using a vast collection of historical data. We show that HistoryTracker helps users to produce tracking data in a fast and reliable way.
Fast Convolutional Distance Transform

We propose “convolutional distance transform”- efficient implementations of distance transform. Specifically, we leverage approximate minimum functions to rewrite the distance transform in terms of convolution operators. Thanks to the fast Fourier transform, the proposed convolutional distance transforms have O(N log N) complexity, where N is the total number of pixels. The proposed acceleration technique is “distance metric agnostic.” In the special case that the distance function is a p-norm, the distance transform can be further reduced to separable convolution filters; and for Euclidean norm, we achieve O(N) using constant-time Gaussian filtering.
SINE: Side Information Network Embedding

Network embedding learns low-dimensional features for nodes in a network, which benefits the downstream tasks like link prediction and node classification. Real-world networks are often accompanied with rich side information, such as attributes and labels, while most of the efforts on network embedding are devoted to preserving the pure network structure. Integrating side information is a challenging task since the effects of different attributes vary with nodes and the unlabeled nodes can be influenced by diverse labels from neighbors, not to mention the heterogeneity and incompleteness. To overcome this issue, we propose Side Information Network Embedding (SINE), a novel and flexible framework using multiple side information to learn a node representation. SINE defines a flexible and semantical neighborhood to model the inscape of each node and designs a random walk scheme to explore this neighborhood. It can incorporate different attributes information with particular emphasis depending on the characteristics of each node. And label information can be both explicitly and potentially integrated into the representation. We evaluate our method and existing state-of-the-art methods on the tasks of multi-class classification. The experimental results on 5 real-world datasets demonstrate that our method outperforms other methods on the networks with side information.
A Modern Approach for Image Forgery Detection using BRICH Clustering based on Normalised Mean and Standard Deviation

with the fast growth of digital technology and user friendly image editing tools, duplicating the contents of digital images becomes easier. The main aim of a digital image forensic is to determine the originality of digital images. Copy move forgery is one of the common methods to forge the digital content. In this paper we introduce a passive method to identify the copy move forgery. It decomposes the image and extracts the moments as feature vector to detect the forgery. The experimental results are studied to demonstrate that the proposed technique detects the forgery and classify the related forged images as clusters based on hierarchical method by BIRCH algorithm. The authentic image even after the post processing operations done on images is identified. Computer vision related with automatic analysis and innovating the useful information from digital information is implemented. It is mainly applied in image understanding.