Reviewerly/exHarmony huggingface dataset:
A Light Weight and Hybrid Deep Learning Model Based Online Signature Verification

The augmented usage of deep learning-based models for various AI related problems are as a result of modern architectures of deeper length and the availability of voluminous interpreted datasets. The models based on these architectures require huge training and storage cost, which makes them inefficient to use in real time systems like online signature verification (OSV) and to use in resource restraint devices. As a solution, in this work, our contribution is two-fold. 1) An efficient dimensionality reduction technique, to lessen the number of features to be considered and 2) a state-of-the-art model CNN-LSTM based hybrid architecture for online signature verification. Thorough experiments on the openly accessible datasets MCYT, SUSIG, SVC approves that the proposed framework attains improved precision even with as low as one training sample. The proposed framework produce state-of-the-art performance in various categories of all the three datasets.
Creating your first augmented reality experience with ARCore

course Share on Creating your first augmented reality experience with ARCore Authors: Germain Ruffle View Profile , Eugene Meng View Profile Authors Info & Claims SA '19: SIGGRAPH Asia 2019 CoursesNovember 2019 Article No.: 122Pages 1https://doi.org/10.1145/3355047.3359416Published:17 November 2019Publication History 0citation174DownloadsMetricsTotal Citations0Total Downloads174Last 12 Months9Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
ION EXCHANGE FILTER MEDIA CAPABILITIES REDUCE LEVEL OF NITRATE DUG WELL WATER COASTAL AREA REGIONAL

Ion Exchange Filter Media Capabilities in Well Water Nitrate Levels Lowering Gali in the Areas of the Coastal Zone. Nitrogen compounds in small quantities does not cause interference, whereas in high levels can cause illness and even death. It also can cause methoemoglobinaemia baby through foods containing nitrates come mainly water wells that have not memenuhin conditions. Filtration technology with ion exchange media to reduce the content of nitrogen contained in the community dug well waterThis study aims to determine the ability of ion exchange filtration in lowering the levels of nitrate (NO 3 ) in water wells coastal areas. This research is Praexpriment with Pretest-Posttest Design that begins with a literature review, preliminary studies and research core that is making filtration apparatus with zeolite filter media as ion exchangers. Data on laboratory results were analyzed descriptively explained in the form of tables, graphs and narrative that describes the overall process that occurred during the research. Based on the research that the ion exchange filter media for reducing levels of flat-ratanitrat(NO 3 ) water wellsfrom initial levels of 16.43 mg / l to 5.67 mg / l with efficiency decreased by 10.77 mg / l (65.52%). The result of this processing is compliant with the appropriate standardsMinister of Health Regulation No. 32 year of 2017 about Clean of the Water of  nitrate levels (NO 3 ) as maximum that is allowed is respectively 10 mg/L and Nitrite (NO 2 ) is respectively 1 mg/L. Keywords : Filtration, ion exchange, zeolites, activated carbon, nitrate (NO 3 ).
Improved Inspection Thanks to Machine Learning

Abstract A pilot setup of different machine learning technologies for the real‐time identification of damaged medical containers puts deep learning for the classification of image contents in focus.
An Approach to Abstractive Summarization for Norwegian Bokmål

In this paper we present a new Norwegian language (Bokmaal) dataset for abstractive summarization task. And we also present results for this task for methods like classic TextRank and modern neural networks based ones, the best method, which uses named entity reparation technique, gets 24.3 ROUGE-1 and 0.02 extraction score.
Comparison study of Fibonacci code algorithm and Even-Rodeh algorithm for data compression

Abstract The need for greater storage capacity is the cause of various compression techniques. By compressing, large data will be reduced in size to save storage capacity. This research will use Even-Rodeh code algorithm and Fibonacci code algorithm to compress the data. The performance of algorithms will be measured by Bitrate, Compression Ratio, Space Saving, and Compression Time (millisecond) and Time Decompression (millisecond). Compression is done by reading String in the text file, then Even-Rodeh Code algorithm and Fibonacci code algorithm create String code and perform compression process. The result of compression is the file with extension *. C and *. Fib which contains character information and String Bit. The results of compression can be decompressed to the original text file. The system test used a sample of Strings consisting of one type of character (Homogen String) and String comprising more than one kind of character (Heterogenous String) is stored in the text file by extension * .doc, * .docx, * .pdf. From the compression process on the homogenous String, the Fibonacci code algorithm is more efficient about the compression ratio of 0.25 and decompression time is 0.011 milliseconds compared with the Even-Rodeh code algorithm. While in the compression process on heterogeneous String Fibonacci Code algorithm is better than the Even-Rodeh code algorithm with an average Compression Ratio of 0.65 and decompression time faster with an average of 0.293 milliseconds.
Use of a Method for Contour Analysis of Radiation Images of Malignant Breast Tumors on the Basis of Retrospective Material

Objective. To enhance the reliability of visual analysis of X-ray mammograms, by applying the mathematical models of neoplasms and a method for their processing based on the mathematical apparatus of contour analysis. Material and methods. Two data sets were generated from X-ray mammograms obtained from 38–82 year old patients at routine examinations in the Republican Oncology Dispensary. The first set contained 100 packages of X-ray mammographic images that failed to reveal abnormal malignant changes. The second set was represented by 168 packages of X-ray mammographic images showing morphologically verified breast cancer. All the packages of mammographic images are presented in the standard direct craniocaudal and mediolateral oblique projections. The images were obtained using an analog mammograph. Digital copies of images having a resolution of 600 dpi were obtained for subsequent computer processing. The latter of digital mammographic images involved segmentation of space-occupying lesions, determination of the linearity factor of their outlines, and differential diagnosis of space-occupying lesions based on the calculated value of the linearity factor of their outlines. Results. An algorithm was elaborated for identifying the outlines of space-occupying lesions on X-ray mammographic images. The sequence of complex-valued vectors approximating its curve was used as a mathematical model of the outline. The concept on the outline linearity factor, which quantitatively characterizes its shape, was introduced. A method was developed for the objective classification of malignant and benign space-occupying lesions based on the value of the introduced linearity factor. The outlines of benign space-occupying lesions in the breast were ascertained to be characterized by the higher linearity factor (in the region of 0.3–0.4) (BI-RADS category 2), while the outlines of malignant tumors had a much lower value of this factor (in the order of 0.05–0.1) (BI-RADS categories 4–5). The main quantitative measures (sensitivity, specificity, and accuracy) of the informative value of the proposed method were determined. The latter was shown to have a higher specificity than the traditional visual analysis carried out by a radiologist. This allows the proposed method to be used as an additional procedure in the visual analysis of mammograms to enhance the reliability of clinical findings. Conclusion. The practical value of the method is in quantitatively evaluating the shapes of malignant breast neoplasms, in reducing the performance of a mammographic examination, and in increasing its objectivity. The proposed method makes it possible to reduce the time of analyzing X-ray mammograms and to enhance the reliability of clinical findings.
Local Context Embedding Neural Network for Scene Semantic Segmentation

This paper presents a novel and effective architecture for scene semantic segmentation, named Local Context Embedding (LCE) network. Unlike previous work, in this paper we characterize local context by exploiting the content of image patches to improve the discrimination of features. Specifically, LCE passes spatially varying contextual information both horizontally and vertically across each small patch derived from fully convolutional feature maps, through the use of Long Short-Term Memory (LSTM) network. Using the sequences of local patches from different directions can extensively characterize the spatial context. Therefore, this embedding based network enables us to utilize more meaningful information for segmentation in an end-to-end fashion. Comprehensive evaluations on CamVid and SUN RGB-D datasets well demonstrate the effectiveness and robustness of our proposed architecture.
Evaluation of Cybercrime Economy via MCDM and Decision Tree Approaches: The Case of Zonguldak

One of the most important economic problems that developed/ing countries are facing today is the informal economy. Unregistered economic activities are generally defined as all economic activities that are not included in national accounts. The informal economy has an informal structure as well as a criminal structure. Cyber-attack/s (CA) are increasing day by day as an important part of the informal economy due to the economic effects they produce. Credit card fraud, emptying a bank account, cryptolocker viruses that can be used to encrypt data on the user’s computer, the use of websites and e-commerce sites such as DDOS attacks, espionage, information smuggling, and many new computing crimes can be given as examples in this field. This study aims to evaluate the relations/determiners of CA-damages and information technology (IT) investments to firms’ economics and present the findings to the researchers/decision makers.In this sense, IT investments and CA/CA costs to 321 Zonguldak firms having more than 20 employees in 2016 have been surveyed by Turkish Statistical Institute. According to CA-damage severity indexes generated via TOPSIS, statistical methods and decision tree approaches supported by regression analysis are used in the analysis. According to the analysis, CA-damage increased as IT investments increased. IT investments dimensions’ existence of “website/mobile applications” and “IT policy implementation” accepted the main CA-damage determiners. Meanwhile, the firm’s less usage of firewalls/IPS/IDS indicates that CA are not fully understood, and the security policies’ application is also important along with its preparation.
Remote Sensing Analytics in Databases with ISO SQL/MDA

Database systems are essential components of modern services and applications. The remote sensing domain has predominantly been an exception to this trend so far, however, as common data types did not fit as well into the standard database model. Instead, various data management challenges have been addressed ad-hoc with custom proprietary / non-portable solutions. This is finally changing with the recent standardization of ISO SQL/MDA which extends SQL, the standard database query language, with support for data cube storage, management, and processing. This paper gives a high-level overview of SQL/MDA and how it can be used for building flexible, high-performance, and scalable remote sensing applications.
Exploitation of the RFID technology for autonomous living

This paper presents the architecture overview and the main features of the project HABITAT (Home Assistance Based on the Internet of Things for the AuTonomy of everybody) which consists of an Internet of things platform based on wireless sensors and RFID technologies. The project aim has been the design and development of smart devices, to sustain elderly people autonomy at home or in assisted communities. Hardware and software technologies are employed to combined and fuse information coming from different smart objects, for real-time actions or for medium term monitoring. In particular the exploitation of customized RFID solutions for continuous tracking of people movements, to be embedded into the platform, is described. A peculiar characteristic of the HABITAT smart objects is their external appearance, being highly technological but non-invasive so that they may part of furnishing accessories, thus allowing the assistive house to be far from a hospitalized aspect.
Firefly Based Word Spotting Technique for Searching Keywords from Cursive Document Images

In the fast pace development of digitized technologies, document images have become more fashionable for an information management system present in libraries, organization and educational institutions. Searching information from the document image is very difficult to perform as it compared with digital text. Optical Character Recognition (OCR) is employed to detect the characters and converts the images into their text format. OCR system is not properly converts the various fonts, styles, size, symbols, dark background and poor quality of the document images, however it’s not an efficient method. For this reason, there is a necessity for a searching strategy to find the user specified keywords from document images. Word spotting is an alternative method, whereas keyword is identified without changing the document images. The primary objective of this research work is to search the keywords from printed cursive English document images using word spotting techniques. In this research work, the Firefly based word spotting technique is proposed to search the keyword based on query given by the user. To estimate the efficiency the Firefly technique is compared with existing Enhanced Dynamic Time Warping (EDTW) technique. From the experimental analysis, the proposed Firefly based word spotting technique has produced high accuracy rate and less execution time compared with existing EDTW technique.
Development of sound wave and light wave e-book physics based on scientific approach to improve science process skills for secondary school students

This study aims to develop Sound Wave and Light Wave e-book based on Scientific approach to improve science process skills for secondary school students, that is suitable to be used as a learning source. The method used in this research is Research and Development (R&D) with Borg and Gall model. The steps are preliminary research, model development planning, validation, evaluation, revision and implementation. This e-book is developed based on scientific approach, which consists of introduction, concept development, application in real life, concept reinforcement, and evaluation. This e-book has been validated with average score of 98% by material experts, 82% by media experts, and 80% by learning experts. Based on the assessment of the effectiveness in the experimental class, the average score of pretest and posttest obtained were 44.38 and 76.1, with N gain score of 0.55. Overall, this e-book is feasible to be used as a learning media and can improve students’ science process skills in sound wave and light wave subject.
Tag2Vec: Tag Embedding for Top-N Recommendation

It is already well known that recommender systems usually suffer from data sparsity issue of user-item interactions. However, representation learning can efficiently measure correlations between objects, which presents an unprecedented opportunity to alleviate this issue. In this paper, we propose a new distributional vector space model, Tag2Vec, for capturing meaningful relationships of users and items to improve the performance of recommender systems. First, we represent users and items as vectors respectively using tag embedding. With this innovative representation, the semantic relationships between users and items can be captured. To be specific, tag2vec learns representations of users and items in low-dimensional space from user-tag-item interactions using the skip-gram model. Second, we measure similarity between both users and items, and collaborative filtering can then be performed in the learned embedding space. To evaluate the performance of Tag2Vec, we conduct extensive experiments with two real world datasets for Top-N recommendation tasks. The results demonstrate that our proposed method significantly outperforms existing approaches.
Land cover classification using Grey Level Co-occurrence Matrix and Naive Bayes

Abstract Land cover data is important information to describe how much of a region is covered by plantation, forest, residential, rice field and river. In many applications the required information relates to the coverage of land cover class in a region, which is generally derived from a count of the pixels allocated to the class of interest in a classification. The design of the system in this study conducted for detecting land cover using Grey Level Co Occurrence (GLCM) method is used as the extraction in process of taking main image and Naive Bayes as a classification of grouping the images based on the types of land cover. Based on the testing data which is consist of 150 images we obtained the best accuracy is 85% with 206.6715 seconds computation time.
Summary of the Sixth Edition of the International Workshop on Eye Movements in Programming

The study of eye gaze data has great potential for research in computer programming, computing education, and software engineering practice. To highlight its role for the software engineering community, the Sixth Edition of the International Workshop on Eye Movements in Programming (EMIP 2019) was co-located with the 41st International Conference on Software Engineering. The goal of the workshop was to advance the methodology of using eye tracking for programming, both theoretically and in applications.
Partial Order Structure Based Image Retrieval

Image retrieval plays an important role in the growing computer vision applications. The computation of the unrelated images in large scale image retrieval task seriously reduces the retrieval efficiency. In this paper, a new Partial Order Structure (POS) based image retrieval method is proposed. Partial order structure diagram is an effective visualization tool in Formal Concept Analysis (FCA) theory, including object partial order structure diagram and attribute partial order structure diagram. There are two contributions in this paper. First, we design an association rule according to the object partial order structure (OPOS) method to measure the correlation between the query image and the database, and then improve the database to be retrieved. Second, we perform a query expansion according to the attribute partial order structure (APOS) method to improve the generalization ability of the query information. Experimental results on two databases verify the effectiveness of the proposed algorithm.
Generalized Digital Certificate Based Key Agreement for Initial Ranging in WiMax Network

WiMAX stands for worldwide interoperability for microwave access and it is based on IEEE 802.16 standards. As the wireless communication has been increasing, so has the concern for security in the wireless scenario. The wireless medium is considered to be less secure due to its shared medium. Security attacks are easier to occur in wireless channel because of its shared open channel. In this paper, RNG-RSP attack which cause Denial of Service (DoS) attack in WiMAX network is addressed. A key agreement algorithm using Generalized Digital Certificate (GDC) for defending DoS attack in WiMAX network is proposed.
The determination of the true geometrical form of Great Ephesus theater, Turkey by using field surveys

The current deals with the determination of the ‘true’ geometrical shape of the Great theatre of Ephesus, which is the largest theatre in Asia Minor. To detect the geometrical form of the theatre a precise point dataset was created using terrestrial measurements performed directly on the theatre site. The well-known ellipse and circle fitting algorithms were presented for detecting the precise geometrical shape of the amphitheatre rings. The study reveals that ellipse geometry fits well to the theatre arcs that is amphitheatre rings are generally elliptical in shape. The precise metric values of the parameters of the geometrical shape were computed to characterise theatre features.
Eigenvector Orientation Corrected LeNet for Digit Recognition

Convolutional Neural Networks (CNNs) are being used popularly for detecting and classifying objects. Rotational invariance is not guaranteed by many of the existing CNN architectures. Many attempts have been made to acquire rotational invariance in CNNs. Our approach ‘Eigenvector Orientation Corrected LeNet (EOCL)’ presents a simple method to make ordinary LeNet [] capable of detecting rotated digits, and also to predict the relative angle of orientation of digits with unknown orientation. The proposed method does not demand any modification in the existing LeNet architecture, and requires training with digits having only single orientation. EOCL incorporates an ‘orientation estimation and correction’ step prior to the testing phase. Using Principal Component Analysis, we find the maximum spread direction (Principal Component) of each test sample and then align it vertically. We demonstrate the improvement in classification accuracy and reduction in test time achieved by our approach, on rotated-MNIST [] and MNIST_rot_12k test datasets, compared to other existing methods.
Improved Subspace K-Means Performance via a Randomized Matrix Decomposition

Subspace clustering algorithms provide the capability to project a dataset onto bases that facilitate clustering. Proposed in 2017, the subspace k-means algorithm simultaneously performs clustering and dimensionality reduction with the goal of finding the optimal subspace for the cluster structure; this is accomplished by incorporating a trade-off between cluster and noise subspaces in the objective function. In this study, we improve subspace k-means by estimating a critical transformation matrix via a randomized eigenvalue decomposition. Our modification results in an order of magnitude runtime improvement on high dimensional data, while retaining the simplicity, interpretable subspace projections, and convergence guarantees of the original algorithm.
An Analysis of Budgeted Parallel Search on Conditional Galton–Watson Trees

Recently Avis and Jordan have demonstrated the efficiency of a simple technique called budgeting for the parallelization of a number of tree search algorithms. The idea is to limit the amount of work that a processor performs before it terminates its search and returns any unexplored nodes to a master process. This limit is set by a critical budget parameter which determines the overhead of the process. In this paper we study the behaviour of the budget parameter on conditional Galton–Watson trees obtaining asymptotically tight bounds on this overhead. We present empirical results to show that this bound is surprisingly accurate in practice.