Reviewerly/exHarmony huggingface dataset:
Adaptive Diagonal Total-Variation Generative Adversarial Network for Super-Resolution Imaging

To address problems that the loss function does not correlate well with perceptual vision in super-resolution methods based on the convolutional neural network(CNN), a novel model called the ADTV-SRGAN is designed based on the adaptive diagonal total-variation generative adversarial network. Combined with global perception and the local structure adaptive method, spatial loss based on the diagonal variation model is proposed to make the loss function can be adjusted according to the spatial features. Pixel loss and characteristic loss are in combination with the spatial loss for the fusing optimization of the total loss function such that high-frequency details of the images are maintained to improve their quality. The results of experiment show that the proposed method can obtain competitive results in objective evaluations. In subjective assessment, images reconstructed by it are clear, delicate, and natural, and it preserved edge- and texture-related details.
A New Automated Forms Generation Algorithm for Online Assessment

Modern learning techniques require technologies to be embedded within the process. The assessment strategy of testing students has been developed through the past few decades and have presented online computer-based examination systems as a replacement for the conventional paper-based testing. However, a major challenge that is still faced by those examination systems is that there is no any guarantee that students sitting adjacently will have different questions or forms, and the questions of exams’ form are not duplicated for any adjacent students within a lab. In fact, this can influence the reliability pertaining to the online examination systems. Consequently, a new algorithm is proposed in this paper for an online examination system that is capable of effectively generating several exam forms with minimal similarity for the constitutional questions, and that guarantees that two adjacent student’s terminals will not have the same forms. Further, the paper provides an explanation for the proposed algorithm and provides a real-life case for the implementation pertaining to the produced algorithm in a conducted experiment. It is found to be proven from the obtained results that the implementation, which is related to the produced form generation algorithm within this research reveals an evidence that is in line with the research assumptions in which an online system’s assessment reliability can be improved based on eliminating adjacent locations of a form similarity.
Image Resampling Detection Based on Convolutional Neural Network

When an image is under tamper, resampling is one of the most common way to cover the tampering artifacts. With the development of tamper tools, it is difficult to detect the trace of resampling through artificial features to verify the integrity of image. Recently, with the great breakthrough of Deep Learning in computer vision, it is necessary to apply it to the field of digital forensic like resampling detection. As is well known that there is a strongly relationship between the pixels and its surroundings in the resampled image, and the Convolutional Neural Network (CNN) is good at learning the underlying relationship. Low-dimensional feature can hardly find out the trace introduced by resampling while high-dimensional feature is capable of doing this. The CNN has excellent feature extraction ability of distinguishing different feature patterns easily in the high-dimensional space. In this paper, we propose a novel resampling detection supervised CNN that can automatically learn the resampling pattern on the basis of residual mapping relationship. Experimental results show that the proposed method has an excellent performance on different resampling factor detection. Moreover, experiments demonstrate the robustness against the noise of the proposed method. After noise is introduced into the resampled images, our method still learn image resampling pattern and effectively distinguish images with different resampling factors. By detecting the resampled image generated by bilinear interpolation, it is shown that our method is aimed at the resampling pattern rather than the feature pattern of cubic interpolation. Finally, the resampling blind detection experiment show that the proposed CNN can indeed detect the resampling feature pattern.
Effectiveness of counseling with cartoon animation audio-visual methods in increasing tooth brushing knowledge children ages 10–12 years

To find out the effectiveness of counseling with cartoon animation audiovisual method in increasing the knowledge of brushing teeth. This study was a pre-experimental study with one group pre-test post-test design with a cross-sectional approach. This study was conducted on children aged 10–12 years old at Toddopuli Superior Primary School in Makassar City, Indonesia. Assessment of knowledge level on tooth brushing was based on the choice of answers to the questionnaire question. The effectiveness of increased tooth brushing knowledge was measured by comparing the mean test scores pre and post-test counseling, using t-paired analysis. The average value of subjects before and after counseling is in a good category. Fifteen (18%) of children are in the category of a sufficient level of knowledge. After counseling, there are 82 (98.8%) of children are in the category of good knowledge level. There are only 1 (1.2%) of children in the sufficient category, and none in the category lack of knowledge. Counseling with cartoon animation audiovisual method effectively increases the knowledge of brushing teeth of children aged ten to twelve years.
Overall and Disease-Free Survival Prediction of Postoperative Breast Cancer Patients using Machine Learning Techniques

Cancer is a disease which is caused by continual unrestrained proliferation of tissues. Breast cancer is a fatal disease commonly found in females. Breast cancer cure is possible with timely diagnosis, prediction of survivability and its treatment. Prediction of survival from breast cancer on basis of patient historical record is a challenging task. The primary goal of this research is to predict the overall survival (OS) and number of years a patient can survive without any symptoms of breast cancer called disease free survival (DFS). For the experimentation, data set of female patients of Pakistan diagnosed with breast cancer have been considered. Six (6) machine learning classifiers have been trained on the data gathered from INMOL hospital of Pakistan. The classifiers include Naïve Bayes, J48 decision tree, SVM, Random forest, AdaBoost and JRip. To the best of our knowledge no such work has been done on the Pakistani female breast cancer data. From the results of experimentation, it has been found that the performance of JRip for both OS and DFS is much better than the others. This prediction will be helpful for patients and paramedical staff (doctors) to foresee and predict the situation which may befall.
On the Impact of Word Representation in Hate Speech and Offensive Language Detection and Explanation

Online hate speech and offensive language have been widely recognized as critical social problems. To defend against this problem, several recent works have emerged that focus on the detection and explanation of hate speech and offensive language using machine learning approaches. Although these approaches are quite effective in the detection and explanation of hate speech and offensive language samples, they do not explore the impact of the representation of such samples. In this work, we introduce a novel, pronunciation-based representation of hate speech and offensive language samples to enable its detection with high accuracy. To demonstrate the effectiveness of our pronunciation-based representation, we extend an existing hate-speech and offensive language defense model based on deep Long Short-term Memory (LSTM) neural networks by using our pronunciation-based representation of hate speech and offensive language samples to train this model. Our work finds that the pronunciation-based presentation significantly reduces noise in the datasets and enhances the overall performance of the existing model.
A threshold secret sharing technique based on matrix manipulation

Secret sharing techniques are basis of construction of cryptographic operations like secure multi party computation and attribute based encryption. It involves distributing secret data as shares such that during reconstruction all of the shares are required. Threshold secret sharing requires a subset of shares to be used for reconstruction of master secret. Langrage interpolation and Chinese remainder theorem based secret shares generation are popular techniques used in literature. However, these techniques suffer from high computational complexity of share generation and secret reconstruction. In this work, we propose a computationally simple threshold secret sharing technique using matrices. The technique enhances security of existing comparable mechanism by generating a larger number of shares for small sized secrets while incurring little overhead of increase in size of each share.
Harvesting Information from Captions for Weakly Supervised Semantic Segmentation

Since acquiring pixel-wise annotations for training convolutional neural networks for semantic image segmentation is time-consuming, weakly supervised approaches that only require class tags have been proposed. In this work, we propose another form of supervision, namely image captions as they can be found on the Internet. These captions have two advantages. They do not require additional curation as it is the case for the clean class tags used by current weakly supervised approaches and they provide textual context for the classes present in an image. To leverage such textual context, we deploy a multi-modal network that learns a joint embedding of the visual representation of the image and the textual representation of the caption. The network estimates text activation maps (TAMs) for class names as well as compound concepts, i.e. combinations of nouns and their attributes. The TAMs of compound concepts describing classes of interest substantially improve the quality of the estimated class activation maps which are then used to train a network for semantic segmentation. We evaluate our method on the COCO dataset where it achieves state of the art results for weakly supervised image segmentation.
Abusive Comments in Online Media and How to Fight Them

While abusive language in online contexts is a long-known problem, algorithmic detection and moderation support are only recently experiencing rising interest. This survey provides a structured overview of the latest academic publications in the domain. Assessed concepts include the used datasets, their language, annotation origins and quality, as well as applied machine learning approaches. It is rounded off by an assessment of meta aspects such as author collaborations and networks as well as extant funding opportunities. Despite all progress, the domain still has the potential to improve on many aspects: (international) collaboration, diversifying and increasing available datasets, careful annotations, and transparency. Furthermore, abusive language detection is a topic of high societal relevance and requires increased funding from public authorities.
Gauge Freedom within the Class of Linear Feedback Particle Filters

Feedback particle filters (FPFs) are Monte-Carlo approximations of the solution of the filtering problem in continuous time. The samples or particles evolve according to a feedback control law in order to track the posterior distribution. However, it is known that by itself, the requirement to track the posterior does not lead to a unique algorithm. Given a particle filter, another one can be constructed by applying a time-dependent transformation of the particles that keeps the posterior distribution invariant. Here, we characterize this gauge freedom within the class of FPFs for the linear-Gaussian filtering problem, and thereby extend previously known parametrized families of linear FPFs.
A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in Dynamic Networks

The evolution of communities in dynamic (time-varying) network data is a prominent topic of interest. A popular approach to understanding these dynamic networks is to embed the dyadic relations into a latent metric space. While methods for clustering with this approach exist for dynamic networks, they all assume a static community structure. This paper presents a Bayesian nonparametric model for dynamic networks that can model networks with evolving community structures. Our model extends existing latent space approaches by explicitly modeling the additions, deletions, splits, and mergers of groups with a hierarchical Dirichlet process hidden Markov model. Our proposed approach, the hierarchical Dirichlet process latent position cluster model (HDP-LPCM), incorporates transitivity, models both individual and group level aspects of the data, and avoids the computationally expensive selection of the number of groups required by most popular methods. We provide a Markov chain Monte Carlo estimation algorithm and demonstrate its ability to detect evolving community structure in a network of military alliances during the Cold War and a narrative network constructed from the Game of Thrones television series.
Dynamic Transformation of Prior Knowledge Into Bayesian Models for Data Streams

We consider how to effectively use prior knowledge when learning a Bayesian model from streaming environments where the data come endlessly and sequentially. This problem is highly important in the era of data explosion and rich sources of valuable external knowledge such as pre-trained models, ontologies, Wikipedia, etc. We show that some existing approaches can forget any knowledge very fast. We then propose a novel framework that enables to incorporate the prior knowledge of different forms into a base Bayesian model for data streams. Our framework subsumes some existing popular models for time-series/dynamic data. Extensive experiments show that our framework outperforms existing methods with a large margin. In particular, our framework can help Bayesian models generalize well on extremely short text while other methods overfit. An implementation of our framework is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://github.com/bachtranxuan/TPS</uri> .
Enhanced Interactive Parallel Coordinates using Machine Learning and Uncertainty Propagation for Engineering Design

The design process of an engineering system requires thorough consideration of varied specifications, each with potentially large number of dimensions. The sheer volume of data, as well as its complexity, can overwhelm the designer and obscure vital information. Visualisation of big data can mitigate the issue of information overload but static display can suffer from overplotting. To tackle the issue of overplotting and cluttered data, we present an interactive and touch-screen capable visualisation toolkit that combines Parallel Coordinates and Scatter Plot approaches for managing multidimensional engineering design data. As engineering projects require a multitude of varied software to handle the various aspects of the design process, the combined datasets often do not have an underlying mathematical model. We address this issue by enhancing our visualisation software with Machine Learning methods which also facilitate further insights into the data. Furthermore, various software within the engineering design cycle produce information of different level of fidelity (accuracy and trustworthiness), as well as with different speed. The induced uncertainty is also considered and modelled in the synthetic dataset and is also presented in an interactive way. This paper describes a new visualisation software package and demonstrates its functionality on a complex aircraft systems design dataset.
Massively Scalable Parallel KMeans on the HPCC Systems Platform

Clustering algorithms are an important part of unsupervised machine learning. With Big Data, applying clustering algorithms such as KMeans has become a challenge due to the significantly larger volume of data and the computational complexity of the standard approach, Lloyd's algorithm. This work aims to tackle this challenge by transforming the classic clustering KMeans algorithm to be highly scalable and to be able to operate on Big Data. We leverage the distributed computing environment of the HPCC Systems platform. The presented KMeans algorithm adopts a hybrid parallelism method to achieve a massively scalable parallel KMeans. Our approach can save a significant amount of time of researchers and machine learning practitioners who train hundreds of models on a daily basis. The performance is evaluated with different size datasets and clusters and the results show a significant scalabilty of the scalable parallel KMeans algorithm.
An Empirical Study to Evaluate Structural Similarity for Source Code Translation

Statistical Machine Translation (SMT) is a research hotspot in machine translation and natural language processing. Recently, source code translation tasks based on SMT model have been applied to Software Engineering. Unfortunately, there is no automated metric that can effectively detect the accuracy of code translation. Considering the similarity between code similarity detection and machine translation scoring process, this paper proposes Code Semantic Metric (CSM) based on traditional code plagiarism detection metrics to verify its applicability to code translation tasks. Our empirical research shows that the results of different methods of code plagiarism detection are quite different. After specific parameter adjustment, CSM can reflect the correctness of translation code semantics to a certain extent. We confirm that CSM has a high correlation with human judgment in the semantic accuracy of translated code, which surpasses the scores of MOSS and JPlag, the mainstream traditional code plagiarism detection methods.
Exploiting a Large-scale Knowledge Graph for Question Generation in Food Preference Interview Systems

This paper presents a dialogue system that acquires user's food preference through a conversation. First, we proposed a method for selecting relevant topics and generating questions based on Freebase, a large-scale knowledge graph. To select relevant topics, using the Wikipedia corpus, we created a topic-embedding model that represents the correlation among topics. For missing entities in Freebase, knowledge completion was applied using knowledge graph embedding. We incorporated these functions into a dialogue system and conducted a user study. The results reveal that the proposed dialogue system more efficiently elicited words related to food and common nouns, and these words were highly correlated in a word embedding space.
Disk Throughput Controller for Cloud Data-Centers

With the increasing popularity of virtual machine monitoring (VMM) technologies, performance variability among collocated virtual machines (VMs) can easily become a severe scalability issue. Particularly, it becomes a necessary for administrative team to control the performance degradation level in a shared environment when multiple I/O-intensive applications simultaneously request their I/O operations [1]. Nevertheless, adding several logical layers between the running applications and the physical storage system, as seen in contemporary virtualized storage devices, makes it considerably difficult to build a low overhead controlling mechanism for such systems (while each VM may running a separate operating system instance) [2]. In this paper, we propose a strategy based on control theory for managing the performance of several I/O requests, such as mean response times and read/write throughput in a consolidated environment where multiple virtual services can share access to a storage system. This scheme uses an approach for measuring the characterization of read/write performance attributes of each virtual services and also takes into account the run-time quality of service enforcement levels requested by them. This is formulated as an optimization problem where a reward function is defined to reduce the overall QoS violation incidents among all consolidated virtual services. Performance evaluation is carried out by comparing the proposed solution with the default embedded Linux controller across a range of emulated application workloads in scenarios with multiple consolidated virtual containers. The results confirm that the proposed solution can reduce the overall QoS violation incident rates in scenarios in which the platform operates at a significant traffic load comparing to the default policy in LXC engine.
Autonomic Resource Management for Power, Performance, and Security in Cloud Environment

High performance computing is widely used for large-scale simulations, designs and analysis of critical problems especially through the use of cloud computing systems nowadays because cloud computing provides ubiquitous, on-demand computing capabilities with large variety of hardware configurations including GPUs and FPGAs that are highly used for high performance computing. However, it is well known that inefficient management of such systems results in excessive power consumption affecting the budget, cooling challenges, as well as reducing reliability due to the overheating and hotspots. Furthermore, considering the latest trends in the attack scenarios and crypto-currency based intrusions, security has become a major problem for high performance computing. Therefore, to address both challenges, in this paper we present an autonomic management methodology for both security and power/performance. Our proposed approach first builds knowledge of the environment in terms of power consumption and the security tools' deployment. Next, it provisions virtual resources so that the power consumption can be reduced while maintaining the required performance and deploy the security tools based on the system behavior. Using this approach, we can utilize a wide range of secure resources efficiently in HPC system, cloud computing systems, servers, embedded systems, etc.
Application and Development of Virtual Reality Technology in Artificial Intelligence Deep Learning

Abstract Virtual reality technology is a key technology that can support multiple dimensional spaces. Virtual reality technology is often supported by computer graphics, digital image processing, multimedia technology, and sensor technology. Virtual reality technology is often not a single technology or discipline. With the rise of deep learning theory, virtual reality technology has also been vigorously developed with the help of computer graphics and other related theories. Virtual reality technology has been widely used in the commercial field, and products such as virtual VR glasses have become more and more popular. This article will give a more comprehensive introduction to the development of virtual reality technology, and this paper will introduce the relevant theory of deep learning to further introduce virtual reality technology.