Reviewerly/exHarmony huggingface dataset:
Generating Digital Signature using Facial Landmlark Detection

Information security has developed rapidly over the recent years with a key being the emergence of social media. To standardize this discipline, security of an individual becomes an urgent concern. In 2019, it is estimated that there will be over 2.5 billion social media users around the globe. Unfortunately, anonymous identity has become a major concern for the security advisors. Due to the technological advancements, the phishers are able to access the confidential information. To resolve these issues numerous solutions have been proposed, such as biometric identification, facial and audio recognition etc prior access to any highly secure forum on the web. Generating digital signatures is the recent trend being incorporated in the field of digital security. We have designed an algorithm that after generating 68 point facial landmark, converts the image to a highly compressed and secure digital signature. The proposed algorithm generates a unique signature for an individual which when stored in the user account information database will limit the creation of fake or multiple accounts. At the same time the algorithm reduces the database storage overhead as it stores the facial identity of an individual in the form of a compressed textual signature rather than the traditional method where the image file was being stored, occupying lesser amount of space and making it more efficient in terms of searching, fetching and manipulation. A unique new analysis of the features produced at intermediate layers has been applied. Here, we opt to use the normal and two opposites' angular measures of the triangle as the invariance. It simply acts as the real-time optimized encryption procedure to achieve the reliable security goals explained in detail in the later sections.
IMPLEMENTASI MODEL PEMBELAJARAN ROLE PLAYING DALAM MENINGKATKAN KEMAMPUAN MENULIS TEKS PROSEDUR PADA SISWA KELAS VII SEKOLAH MENENGAH PERTAMA (SMP)

The objectives of the study were: 1) to explain the implementation of role playing learning models in learning materials for writing text procedures, and 2) to explain the calculation of the value of pre-test and post-test in the experimental class and control of experimental and control pre-test. This research shows that the implementation of the role playing learning model is carried out in accordance with the Learning Implementation Plan (RPP). The hypothesis of this study is to increase the results of post-test results compared to pre-test in the experimental class, and the acquisition of post-test scores in the experimental group is higher than the control group. The results of statistical calculations show a significance of <0.05, which means that Ho is rejected and Ha is accepted. Both classes experienced an increase in post-test acquisition. However, the experimental class shows more significant results than the control class. The results of the average in the experimental class were 62.16 during pre-test and 75.32 during post-test. Meanwhile, the control class during pre-test was 63.04 and 70.24 at the post-test period. These results indicate that the role playing model is more suitable for use in learning to write procedure texts.
On the Impact of the Length of Subword Vectors on Word Embeddings

This paper hypothesizes that better word embeddings can be learned by representing words and subwords by different lengths of vectors. To investigate the impact of the length of subword vectors on word embeddings, this paper proposes a model based on the Subword Information Skip-gram model. The experiments on two datasets with respect to two tasks show that the proposed model outperforms 6 baselines, which confirms the aforementioned hypothesis. In addition, we also observe that, within a specific range, a higher dimensionality of subword vectors always improve the quality of word embeddings.
Incorporating global software development into advanced placement computer science principles

research-article Share on Incorporating global software development into advanced placement computer science principles Authors: Lee Kenneth Jones Asia Pacific International School Asia Pacific International SchoolView Profile , Robert E. Mahoney Dallas High School Dallas High SchoolView Profile Authors Info & Claims ACM InroadsVolume 10Issue 2June 2019 pp 36–40https://doi.org/10.1145/3324889Published:25 April 2019Publication History 0citation415DownloadsMetricsTotal Citations0Total Downloads415Last 12 Months6Last 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
The Automated Acquisition of Expert Knowledge Using a Service Department as an Example

In this article, an example of the use of ontology for the automated acquisition of expert knowledge in a service department is presented. The first part of the article presents an overview of the literature on knowledge acquisition methods and an evaluation of knowledge tools. The model for the Automated Acquisition of Expert Knowledge (AA-EK) is then presented and a formal representation of it, using ontology, is provided. The last part illustrates how the AA-EK model is used, using a real-time case study.
Workshop on developmental neural networks

The inaugural workshop on Developmental Neural Networks was held at PPSN (Parallel Problem Solving from Nature) 2018 in Coimbra, Portugal. The aim of this workshop is to encourage approaches which focus on the development of artificial neural networks (ANN). In nature, brains are built through a process of biological development in which many aspects of the network of neurons and connections change, shaped by external information received through sensory organs. However, many modern artificial neural networks do not include developmental mechanisms and regard learning as the adjustment of connection weights, while some that do use development restrain it to a period before the ANN is used. The first edition of the workshop demonstrated a broad interest in this nascent topic.
Adaptive Multi-Scale Tracking Target Algorithm through Drone

In order to solve the influence of scale change on target tracking using the drone, a multi-scale target tracking algorithm is proposed which based on the color feature tracking algorithm. The algorithm realized adaptive scale tracking by training position and scale correlation filters. It can first obtain the target center position of next frame by computing the maximum of the response, where the position correlation filter is learned by the least squares classifier and the dimensionality reduction for color features is analyzed by principal component analysis. The scale correlation filter is obtained by color characteristics at 33 rectangular areas which is set by the scale factor around the central location and is reduced dimensions by orthogonal triangle decomposition. Finally, the location and size of the target are updated by the maximum of the response. By testing 13 challenging video sequences taken by the drone, the results show that the algorithm has adaptability to the changes in the target scale and its robustness along with many other performance indicators are both better than the most state-of-the-art methods in illumination Variation, fast motion, motion blur and other complex situations.
Reduction of Impulse Noise by using k-mean Clustering Segmentation Approach

The main object of our research work is to look at the execution of different division systems for shading pictures. Division by K-implies bunching and Thresholding methods are thought about for their execution in division of shading pictures. Division of a picture involves the division or partition of the picture into areas of comparative property. The most essential characteristic for division of a picture is its luminance adequacy for a monochrome picture and shading segments for a shading picture. This paper proposes a shading based division strategy that utilizes K-implies grouping system. The k-implies calculation is an iterative method used to parcel a picture into k groups. The standard K-Means calculation produces exact division results just when connected to pictures characterized by homogenous areas as for surface and shading since no neighborhood requirements are connected to force spatial congruity. At first, the pixels are grouped dependent on their shading and spatial highlights, where the bunching procedure is cultivated. At that point the grouped squares are converged to a particular number of districts. This methodology along these lines gives a doable new answer for picture division which might be useful in picture recovery. The test results clear up the viability of our way to deal with improve the division quality in parts of accuracy and computational time. The recreation results show that the proposed calculation is promising.
Mathematical simulation of countermeasures to attacks of “denial of service” type with the use of game theory approach

The paper proposes to use the game theory approach for modeling of interaction between a defender and an intruder by deploying network information decoys as a tool for attraction of intruder's attention and obtaining of information on its real intentions.
A 135-mW 1.70TOPS Sparse Video Sequence Inference SoC for Action Classification

An inference system-on-chip (SoC) is designed to extract spatio-temporal features from videos for action classification. The SoC contains an inference core that implements a recurrent neural network in three processing layers. High sparsity is enforced in each layer of processing, reducing the complexity by two orders of magnitude and allowing multiply accumulates to be replaced by select accumulates. Spatio-temporal kernel and activation compression are applied to reduce memory by 43% and 64%, respectively. The design is demonstrated in a 2.53-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 40-nm CMOS chip with an OpenRISC core, providing control and classification. With the inference core extracting spatio-temporal features and a soft-max classifier programmed on the OpenRISC core, the SoC classifies KTH Human Action Data Set at a 76.7% accuracy. At 0.9 V and 250 MHz, the SoC achieves 1.70TOPS, dissipating 135 mW.
Efficient Enhancement of Hepatic and Splenic Carcinoma in Canine using Image Processing

Image processing finds application in many fields, especially in the field of biomedical engineering for tumor and cancer detection. Research has been going since a very long time for identification of different types of cancer in humans and the research is still on its path. However, pathologic detection of hepatic and splenic cancer in canine is very costly and diagnosing becomes a tedious task in many cases. So, a cost effective and easier technique has to be developed in order to detect the cancer and start the treatment. Here image processing techniques are made use to pre-process the cancer-causing cells in dogs using ultrasonic (US) images. The proposed system frame work consists of image pre-processing stage where enhancement of the raw image is aimed. Here different image enhancement algorithms are applied, of which the best algorithm is selected based on the evaluation parameters like PSNR, SNR, MSE, RMSE.
Developing Multimedia Enhanced Content to Raise HIV/AIDS Awareness to Children

It is estimated that more than two-thirds (70%) of all the 35 million people living with Human Immunodeficiency Virus (HIV) in the world are in Sub-Saharan Africa. Tanzania as one of the country in sub-Sahara region is no exceptional, more than 60% of all new HIV infections in Tanzania occur among young people. While almost all children in Tanzania aged 15 years and below have heard about HIV/AIDS, less than half have enough knowledge to protect themselves against infections. It is known that HIV/AIDS is taught in primary schools, although majority of the pupils demonstrated a very low level of knowledge about HIV/AIDS. This raised a concern on the effectiveness of HIV/AIDS and life skills education in primary schools. Information and Communication Technology (ICT) has a great potential for HIV/AIDS education to children because they like and enjoy to use ICT resources for entertainment, learning, networking and communication. Hence, it is important to apply technology for educational in teaching HIV/AIDS through the use of multimedia contents in accordance to the local context of Tanzanian curriculum for primary schools. Therefore, the main objective of this paper is to develop multimedia enhanced contents to raise HIV/AIDS awareness to children. ADDIE model, Adobe Flash Professional 5, Action Script programming language and articulate storyline2 authoring tool, were used in the development of multimedia enhanced contents. Results show that level of HIV/AIDS knowledge among pupils after using the interactive multimedia has been enhanced in the tested three knowledge areas (causes, prevention and effects). Results in this paper suggest that, multimedia enhanced contents can be used to complement text book based learning approach in enhancing HIV/AIDS awareness for children in primary schools in Tanzania.
Assessing the source and spatial distribution of chemical composition of a rift lake, using multivariate statistical, hydrogeochemical modeling and remote sensing

The chemical composition of a freshwater surface depends on the sources of input that can be of natural or anthropogenic origin. This study examined the spatial variability of the water quality from Chapala Lake and discussed the possible sources to this freshwater surface which is the largest rift lake in Mexico. The methodology included multivariate statistical techniques to analyze the possible relationship between water quality and the natural and anthropogenic factors of the area. The outcome showed the existence of four groups of water mixture in the lake. Each one showed two or three end members that characterized its chemical nature. The different groups showed a spatial distribution and a particular spectral behavior was detected from the analysis of a Landsat 7 ETM+ image. The spectral signatures extracted from the satellite image showed a high reflectivity in the range of 830 - 1300 nm for the water provided by the Lerma-Chapala (group IV) system, which represents the most polluted water in the lake. This spectral behavior is not present in the rest of the group. The Western portion of the lake is fed mainly by open water. Contributions from groundwater dominate the Central part, and in the Eastern region, the primary source is the contaminated water from the Lerma river system. A high hydraulic head (hydraulic barrier) prevents the highest level of pollution from the Lerma river. This hydraulic barrier constituted by a significant portion of groundwater is at the Central part of the lake. Furthermore, the most polluted water flows directly to the Metropolitan Region of Guadalajara through the Santiago river. The predominant role played by the groundwater in the dynamics of Chapala Lake is explained, in part, from its tectonic origin. The dynamics of water in the Chapala Lake suggest the need to implement some management plans considering the tectonic origin of the Lake as an advantage for the control of pollution because of the significant contribution of groundwater in the Chapala Lake freshwater system.
Design Labs for Data-Driven Multivalence

Designing products, spaces, and services for the Everyday has become the focus of designerly research and practice over the last decade. As designers create complex systems that are space-aware, grow and dynamically morph, their tools and methods have to undergo a similar parallel transformation and extension–often towards and borrowing from technical disciplines. Current designers work with complexity and let data shape all facets and modalities of designed artifacts. This article investigates new challenges for design operating at macro, meso, and micro scales. A translational perspective is proposed together with new types of labs and design research infrastructure that address challenges and emerging needs of the design community.
In situ estimation of fatigue crack parameters by digital image correlation under cyclic loading with single overload

The paper represents the investigation of crack propagation in aluminium alloy AA2024 in a case of constant cyclic loading with single overload. The parameters of the process were evaluated by means of optical and noncontact digital image correlation technique which allows precise in situ measurements and provides local strain data. There have been analysed strain fields, local strains at the crack tip and crack closure level in order to reveal the effect of overload on crack growth parameters. Results are consistent with published data and developed mathematical models - the highest impact is emerged when crack extended into overload plastic zone by 30%.
Digital video watermarking method based on heteroassociative image compression and its implementation by artificial neural netwoks

In this paper, we consider video steganography method based on using heteroassociative compressive transformation to embed user information into fragments of video frames. The results of testing the method in relation to visual distortion evaluation and accuracy of hidden data extraction using a number of known video codecs for compressing watermarked videos are presented.
EuroGP vs EvoCOP

Using an online database with bibliographic information on major computer science publications we have constructed collaboration networks for the two main EvoStar (the he leading European event on Bio-Inspired Computation) conferences: EuroGP (European Conference on Genetic Programming) and EvoCOP (Evolutionary Computation in Combinatorial Optimisation). In these networks two authors are connected if they have coauthored one or more papers appearing in these conferences since their inception until 2018. The networks are then visualised and analysed using a number of network statistics. Our main focus is to reveal and contrast the patterns of collaboration and the most active researchers in both conferences. EuroGP's network shows a large central component of connected authors, wherease EvoCOP authors appear to work in small groups without direct interaction between groups. This could be explained by the different origins and composition of these two communities.
A dimensionality reduction method of continuous dependent variables based supervised Laplacian eigenmaps

Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper we propose a supervised manifold learning method, it makes use of the information of continuous dependent variables to distinguish intrinsic neighbourhood and extrinsic neighbourhood of data samples, and construct two graphs according to these two kinds of neighbourhoods. Following the idea of Laplacian eigenmaps, we reveal that on the low-dimensional manifold the neighbourhood structure can be preserved or even improved. Our approach has two important characteristics: (i) it uses dependent variables to find an informative low-dimensional projection which is robust to noisy independent variables and (ii) the objective function simultaneously enlarges the distance between dissimilar samples and pushes similar samples close to each other according to the graph constructed with the help of continuous dependent variables. Our experiments demonstrate that the effectiveness of our method is over their traditional rivals.
Lesson Exemplars In Teaching Pop- Up Writing To Literature Students

This study assessed the effectiveness of lesson exemplars in the teaching of pop-up writing to AB English students of Cebu Technological University- Main Campus during the Academic Year 2016-2017. This study utilized the quasi- experimental research design: the lesson exemplar is the independent variable while the students’ level of creativity in writing is the dependent variable. The study revealed that the entry level pop-up writing performance of the respondents was neutral in all criteria. This performance is numerically highest for story structure and lowest for creativity. Moreover, the LE1 pop-up writing performance of the respondents is good for story structure and topic development but remained neutral for language use and creativity, the LE2 popup writing performance of the respondents is good for story structure but remained neutral for language use, topic development, and creativity, and the LE3 pop-up writing performance of the respondents is good for all four (4) criteria. The increase in LE1 pop-up writing performance of the respondents is significant for story structure and topic development but the change is negligible for language use and creativity, the increase in LE2 pop-up writing performance of the respondents is significant for story structure; yet the changes are negligible for language use, topic development, and creativity, and there were no significant differences in the LE3 pop-up writing performance. Therefore, teachers may design more learning activities that would reinforce the aspects of pop-up writing.
Optical character recognition of typeset Coptic text with neural networks

Abstract Digital Humanities (DH) within Coptic Studies, an emerging field of development, will be much aided by the digitization of large quantities of typeset Coptic texts. Until recently, the only Optical Character Recognition (OCR) analysis of printed Coptic texts had been executed by Moheb S. Mekhaiel, who used the Tesseract program to create a text model for liturgical books in the Bohairic dialect of Coptic. However, this model is not suitable for the many scholarly editions of texts in the Sahidic dialect of Coptic which use noticeably different fonts. In the current study, DH and Coptological projects based in Göttingen, Germany, collaborated to develop a new Coptic OCR pipeline suitable for use with all Coptic dialects. The objective of the study was to generate a model which can facilitate digital Coptic Studies and produce Coptic corpora from existing printed texts. First, we compared the two available OCR programs that can recognize Coptic: Tesseract and Ocropy. The results indicated that the neural network model, i.e. Ocropy, performed better at recognizing the letters with supralinear strokes that characterize the published Sahidic texts. After training Ocropy for Coptic using artificial neural networks, the team achieved an accuracy rate of &amp;gt;91% for the OCR analysis of Coptic typeset. We subsequently compared the efficiency of Ocropy to that of manual transcribing and concluded that the use of Ocropy to extract Coptic from digital images of printed texts is highly beneficial to Coptic DH.
How to Understand Three Types of Cognitive Models

This paper aims to explore more efficient information processing methods through quasi-holographic space, knowledge and language cognitive systems. The method is as follows: First, the spatial computing system is understood as a formal abstract cognitive system; further, the five-loop traversal system is understood as a conceptual knowledge query system; finally, the language cognitive system is understood as a tabular text reusing system. The result is: quasi-holographic space, five-loop traversal and bit-list logic as three thinking modes or three types of cognitive systems, in the object form and knowledge content information processing on the same path. The significance is that not only the three types of cognitive systems, such as quasi-holographic space, five-loop traversal and order-sequence structure, all of them are difficult to understand, now are easily understood, and a new cognitive paradigm that is simplified is obtained.