Reviewerly/exHarmony huggingface dataset:
Robust Face Super-Resolution via Patch Network of Global Context Prior

The face image captured in real-world scene is the vital object in visual tasks due to its importance in person identity confirmation. However, the face structures is inherently missing due to the degradations in the imaging process, leading to the severe interference in the face enhancement and subsequent face recognition procedure. To remove the degradations and recover a recognizable face, we propose a novel global facial context prior as the guidance to face super-resolution problem. The global facial context prior expands the local structure prior, which is represented as the local patches, into global-face range by modeling the whole facial patch positions into network scheme empirically according to position distributions. The local structure in neighbored positions provides better guidance for reconstruction than the local structure itself, especially when the size of local distortion is larger than the local patch size in input image, leading to the fact that the local input image patch unable to give informative details. Therefore, the global facial context prior improves the robustness of algorithm in dealing with large-range uneven image distortion, e.g. the occlusion. The quantitative and qualitative evaluation on public database demonstrates the superiority of our algorithm.
Hierarchical morphological graph signal multi‐layer decomposition for editing applications

The authors address the problem of editing signals such as 2D colour images or 3D coloured meshes that are represented under the general framework of graph signals. As state-of-the-art editing approaches decompose an image into several layers in order to manipulate them, they propose a hierarchical multi-layer decomposition of graph signals that relies on morphological filtering. Since morphological filtering operators require a complete lattice, a dedicated approach for the morphological processing of vectorial data on graphs is used. By iterating the application of morphological filterings of decreasing sizes, the graph signal is decomposed into several detail layers, each capturing a given detail level. Editing applications such as abstraction, sharpness enhancement and tone mapping are shown to illustrate the benefits of the proposed approach.
Internet of Things Industry in Supporting Industrial Revolution 4.0

Nowadays the Internet has entered various aspects of human life.Seen from the concept of e (electronic), such as e-government, e-commerce, e-news etc. Regarding internet business, obviously very influential on the business environment.Every business that wants to develop must be connected through the internet to be able to reach consumers who spend more time in cyberspace.The list of successful companies in the world is also dominated by communication technology business people connected to the internet, such as Google, Facebook, Microsoft and Apple.Internet of Things (IoT) is a busy term but still lacks understanding.Until now, there is no standard definition of the Internet of Things, in short, IoT is communication between objects through networks such as the internet.Some of the big companies that are working on IoT include Intel, Microsoft, Oracle, etc.The vision of the Internet of Things itself is that the Internet extends to the real world and integrates with everyday objects, both can be controlled from a distance or a distance and is not limited by space and time.Current predictions, IoT will become "the next big thing" in the world of information technology, because it offers a lot of potential that has not been explored.The method used in this Internet of Things technology is wireless or automatic control without knowing distance.The implementation of IoT also follows the wishes of the application developers for example determining the control room, access distance that can be controlled, network speed and other requirements.IPv6, 4G and Wimax technology can help the implementation of IoT be more optimal and enable a wider coverage area.The implementation of IoT in Indonesia so far has been realized through several products from several operators, one of which is Speedy Monitoring provided by PT.Telkom which functions as a facility of capture, recording and monitoring the condition of a particular room or area by utilizing an IP Camera that is connected to the Speedy network.In terms of the benefits of IoT has many benefits that help human activities, but in terms of regulation, IoT especially in Indonesia does not yet have the latest regulatory system (Positive Law).At present there are also IoT devices that use the access concept of Permanent Roaming, which directly occupies the operator network in Indonesia.Therefore it is necessary to study the Indonesian IoT industry and also business analysis related to the implementation and planning of IOT in other countries.
Fine-grained tumor segmentation on computed tomography slices by leveraging bottom-up and top-down strategies

Fully Convolutional Neural Networks (FCNNs) have been widely employed to solve object segmentation tasks effectively in both the computer vision and medical image processing fields in recent years. In object segmentation, FCNNs play a pixel-level prediction role in generating segmented predictions pixel-by-pixel, but they ignore the relationships among generated pixels on the output image. Moreover, blurry boundaries of predicted objects are another common obstacle in this task, because FCNNs usually generate low-frequency components of an image well, but they lack clear high-frequency information inside. In order to solve these problems, we introduce a top-down strategy by globally considering object shapes and context information. Moreover, based on original pixel-wise loss functions which we call a bottom-up strategy, we formulate the tumor segmentation task as a regression problem by using Jaccard Similarity Coefficient (JSC) which is usually one of the main metrics for evaluating the performance of segmentation methods. We directly propose a JSC loss to further optimize network parameters for globally evaluating the whole outputs of tumors. Furthermore, the new loss also alleviates the effect of severe class imbalanced problems between tumor regions and non-tumor regions when training FCNNs. By leveraging the bottom-up and top-down strategies together, our model can obtain more fine-grained tumor segmentation results and also be easily embedded into any FCNN framework for other object segmentation tasks. Detailed experimental results on common metrics demonstrate the superior performance of our proposed method for Kidney Tumor Segmentation Challenge 2019 among more than 100 involved teams.
Data mining approach for digital forensics task with deep learning techniques

In the past, digital forensic, with its exploration techniques, are a lane to the data recovery as well as the examination of different investigation techniques.It is a line of investigation which includes many stages.In this, the foremost assignment is data collection later than that the outcome amount produced predicted with the dataset.Some authors proposed several supervised machine learning techniques that have not obtained much better results.Therefore, the goal of our study was to perform an investigational work on a forensics dataset task for class-based classification methods like three-layer CNN classifiers, five-layer CNN classifiers, and seven-layer CNN classifiers.The classifiers evaluated with classification performance and accuracy.The experimental plan has been done with fivefold cross-validation with fifty repetitions for deep learning algorithms in order to obtain consistent results.Matching accuracy values for the next to next pixels in the classes are calculated with the class-based predicted labels.There are four classes assigned on CNN, and the four classes are segmented and separated with the same region of interest.Then the same class-based region of interests is segregated, and these four class-based regions are next given to CNN with the clusters.Further, the comparison results are made with the used three algorithms.
The cognitive process and influence in learning

Learning is linked to the interactions of human brain functions. Every teacher or person who is linked to education is faced with the need and obligation to know how the human brain works and at the same time stimulate it with techniques and strategies that facilitate the assimilation of teaching. Cognitive processes play an important role in education, this help to interact preconceptions and knowledge with their natural and social environment, including processes in which it involves learning with which we acquire knowledge daily and routinely, whether it gives implicitly or explicitly, it helps to solve diverse situations, establishing logical and adequate connections with the help of reasoning because it conducts a behavioral and cognitive process of concentration that keeps us attentive.
Deep learning with R, FrançoisChollet, Joseph J.Allaire, Shelter Island, NY: Manning

BiometricsVolume 76, Issue 1 p. 361-362 BOOK REVIEW Deep learning with R, Chollet, François, Allaire, Joseph J., Shelter Island, NY: Manning Sehee Kim, seheek@umich.edu orcid.org/0000-0002-1815-6957 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USASearch for more papers by this author Sehee Kim, seheek@umich.edu orcid.org/0000-0002-1815-6957 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USASearch for more papers by this author First published: 11 March 2020 https://doi.org/10.1111/biom.13224Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume76, Issue1March 2020Pages 361-362 RelatedInformation
THE IMPACT OF TECHNOLOGY HELLO ENGLISH APPLICATION IN EFL CLASSROOM

Teacing English as foreign language in junior hogh school forced teacher to used to technology as an interest media to attract student in learning. Chiefly, An application was blasted off by CultureAlley in 2014; hello English has potential to solve student’s problem of understanding simple past tense. To seeing the using Hello English Aplication giving impact significantly in understanding of simple past tense was the crucial intention of this study. In doing treatment, it used experimental and control class with 46 participants. Crucially, study proven that Data findings and discussion proven 1) there was difference significantly of posttest between at experiemental class; 95.6% and control class; 60.9% 2) Normality distribution figured out experimental group in posttest is 0.200&gt; ? (0.05) and the significance value of control group in posttest is 0.051&gt;? (0.05). The significance value of both groups are higher than ? (0.05). It means that H0 is accepted and H1 is refused. So, the test distribution of both two groups is normal 3) Homogenity test confirmed ? value (significant value) is higher than ? value. ? &gt; ?, 0,955 &gt; 0,05. Back to the hypothesis, H? is accepted if ? &gt; ?. So it means that the classes are homogeny 4) T-Test delighted 0.000 &lt; a (0.05) for this reason H0 is rejected and H1 is accepted. Absolutely this study concludes the using hello English Aplication is an interest effective media in learning simple past tense; encouraging self-learning; gadget’s changing better behaviour.&#x0D; &#x0D; Keywords: impact, hello english aplication, simple past tense&#x0D;
Implementation of speech to-text application for deaf students on inclusive education course

Persons with hearing impairment is one of the minorities who is still marginalized from the right, including the needs of access to communications and information technology.Because of that, people with hearing impairment are not able to participate in instructional activities.Based on the observation and the interview, to graduate and undergraduate students with hearing impairment of Special Education department, they said that they still have problems in communicating with lecturers or other students, because of their inability to understand the explanation of the lecturer.This paper aims at providing an analysis of using speech to text app in an inclusive education course, which can record the voice of a lecturer and converted to text that is displayed through the power point, so that, the students with hearing impairment can read the text when the learning occurred.A classroom action research with tw0 cycle is used to recognize a summary of the instructional which attended by two students with hearing impairment and twenty-eight regular students.Observation, test, and interview conducted by team teaching show that speech to text based on power point has an impact in increasing comprehension of students with hearing impairment on inclusive education course.
Real-time Integrated Human Activity Recognition System based on Multimodal User Understanding

This paper presents our real-time human activity recognition system that understands human behavior using multimodal sensor data at multiple levels. Our system consists of a multimodal data acquisition framework and a user understanding algorithm including user identification, activity recognition, and health monitoring components.
A Novel Software Defect Prediction Method Based on Isolation Forest

Software defect prediction (SDP) is a hot topic in the modern software engineering research community to analyze software quality and reliability. Many data mining and machine learning methods were used to judge whether software models are defective or non-defective by analyzing software source codes or developing processes, extracting some critical metrics and then building predictor. Isolation forest, being an anomaly detection method, was modified and introduced firstly in this application community, a novel SDP method based on isolation forest was proposed in this paper. Experiment conducted on five real NASA datasets demonstrated the proposed method effective
Pseudo Labels and Soft Multi-Part Corresponding Similarity for Unsupervised Deep Hashing

In recent years, unsupervised deep hashing methods have achieved great success in large-scale image retrieval.However, these approaches still suffer two major problems in real world applications.On the one hand, due to the lack of effective supervision information, hash codes of different categories are easily judged to be similar.On the other hand, binary semantic similarity matrices can not reflect ranking relationship and the internal structure information of different images.To solve these problems, we propose a novel unsupervised deep hashing method, named Pseudo labels and Soft multi-part Corresponding similarity based Hashing (PSCH), to ensure the heterogeneity of the hash codes.Specifically, we propose a ''pseudo labels'' method that use k-means clustering and a distance threshold to generate the pseudo labels.Further, in order to reflect the hash codes similarity between instances within the same class, we propose a novel soft multi-part corresponding similarity method to learn better hash codes.This method can divide deep feature maps into several groups and compute the attention map for multi-part similarity matrices.In addition, a novel loss function is proposed to support learning with pseudo labels and soft multi-part corresponding similarity for achieving better performance.Comprehensive experiments on CIFAR-10, NUSWIDE, and Flickr demonstrate that our method can generate high-quality hash codes and outperform state-of-the-art unsupervised hashing methods by a large margin.
Technological Determinism: Breakthrough Into The Future

Modern technogenic civilization has entered a new, informational stage of its development. At the same time, the transition from the industrial stage to the informational one was not spontaneous, chaotic or accidental. To a large extent, it was theoretically justified by technological determinism, according to which technics and technology are the determining factors of social development. The materials for the study were the works of theorists, classics of technological determinism and publications of modern authors studying the problems of technics and technology. In the process of research, such methods as: analysis and synthesis were used. Today, technics and technology are an integral part of people's daily lives. Moreover, they determine social relations and communication. Globalization brings States and people closer together, strengthens political, economic and cultural ties, and forms various international organizations. Information technology, artificial intelligence lay the foundations of a society of knowledge, a society in which people, clones and androids will exist together. Technological determinism not only stimulates the development of technics and technology, but is also capable of constructive acceptance of criticism to solve the problems of humanization of technology, environmental and other problems.
An Efficient Non-local Attention Network for Video-based Person Re-identification

A spatial and temporal attention strategy based on Non-local Networks is proposed for video-based person re-identification. The most existing methods design attention mechanisms on high-level features, which ignore the low-level features with more details. The proposed method adopts non-local networks which can aggregate features according to feature correlation at any level. There are two contributions of this work can be summarized as follows: (i) The spatial and temporal redundancy in video-based person Re-ID is analyzed in this work; (ii) An Efficient Non-local Attention Network is designed to reduce the computation complexity by exploring spatial and temporal redundancy for video-based person Re-ID. We conduct extensive experiments on two large-scale benchmarks, i.e. MARS and DukeMTMC-VideoReID. The experiments show that our model achieve 85.2% mAP, 88.3% rank-1 accuracy on MARS dataset and 95.4% mAP, 95.6% rank-1 on DukeMTMC-VideoReID without re-ranking, which significantly outperforms the state-of-arts.
A CP-ABE Scheme Supporting Arithmetic Span Programs

Attribute-based encryption achieves fine-grained access control, especially in a cloud computing environment. In a ciphertext-policy attribute-based encryption (CP-ABE) scheme, the ciphertexts are associated with the access policies, while the secret keys are determined by the attributes. In recent years, people have tried to find more effective access structures to improve the efficiency of encryption systems. This paper presents a ciphertext-policy attribute-based encryption scheme that supports arithmetic span programs. On the composite-order bilinear group, the security of the scheme is proven by experimental sequence based on the combination of composite-order bilinear entropy expansion lemma and subgroup decision (SD) assumption. And, it is an adaptively secure scheme with constant-size public parameters.
MAP CHARTS: VISUALISATION OF STATISTICAL DATA ON A BACKGROUND MAP – CASE STUDY

Data visualizations take a variety of forms, from classic bar charts to three-dimensional presentations on globe maps.The purpose of this paper is a comparative analysis of selected techniques for visualizing statistical data on a background map.The exploratory tests were carried out to study the design possibilities of three software tools: Visualization: GeoChart, GIS and JavaScript and 3D Maps (MS Office), as well as the functionality and usability of applications created with them.In addition, selected technical attributes of the applications were measured, including the size and number of component files, loading time in the web browser window, as well as performance.It has been shown that the tested tools are predisposed to create data visualizations on administrative maps, that they have different design possibilities, that they differ in the degree of service advancement, and that they can also be useful in creating small visualizations, so-called "ad-hoc map".
Design and Implementation of Sign Language Translation Program Using Motion Recognition

About 5% of the world population, 466million people in figure (432million adults and 34 million children), have hearing impairment.Most people with hearing impairment use sign language as a means to communicate not only with people without disability but also with people with disability.However, there are hundreds of different sign languages and most people without a disability do not know how to use sign language at all.Thus, it is difficult for people without disabilities to communicate with people with hearing impairment in daily living.This study aims to help those with hearing impairment for easier communication.To do this, this study designed a sign language translation program and implemented some of the design to recognize the user's motion by using motion recognition sensors and translating the recognized motions after finding the right words for the motions followed by displaying the words on the screen.
Privacy Preserving Sensitive Data Publishing using (k,n,m) Anonymity Approach

Open Science movement has enabled extensive knowledge sharing by making research publications, software, data and samples available to the society and researchers. The demand for data sharing is increasing day by day due to the tremendous knowledge hidden in the digital data that is generated by humans and machines. However, data cannot be published as such due to the information leaks that can occur by linking the published data with other publically available datasets or with the help of some background knowledge. Various anonymization techniques have been proposed by researchers for privacy preserving sensitive data publishing. This paper proposes a (k,n,m) anonymity approach for sensitive data publishing by making use of the traditional k-anonymity technique. The selection of quasi identifiers is automated in this approach using graph theoretic algorithms and is further enhanced by choosing similar quasi identifiers based on the derived and composite attributes. The usual method of choosing a single value of ‘k’ is modified in this technique by selecting different values of ‘k’ for the same dataset based on the risk of exposure and sensitivity rank of the sensitive attributes. The proposed anonymity approach can be used for sensitive big data publishing after applying few extension mechanisms. Experimental results show that the proposed technique is practical and can be implemented efficiently on a plethora of datasets.
Distributed Information Filter for Linear Systems with Colored Measurement Noise

This paper considers the distributed filtering problem for discrete-time linear systems with colored measurement noise obeying an autoregressive process in sensor networks. For the considered system in the centralized fusion framework, a novel information-type filter is proposed based on the measurement difference approach. Here, the dimension of the estimate error covariance (i.e., the information matrix) in the proposed information filter is the same as that of the original system state, with the help of the block matrix inverse operation. Then, the average consensus-based distributed implementation is designed, to ensure that the final state estimate in each sensor node is asymptotically consistent with the centralized filtering result as closely as possible. An example about target tracking with colored measurement noise in sensor networks validates the proposed method.