Reviewerly/exHarmony huggingface dataset:
Public Key Infrastructure: An Enhanced Validation Framework

Public Key Infrastructure (PKI) is a comprehensive information security framework for providing secure information and communication over the internet.Its need and use has grown over the years and continually grows.This research work examines the current PKI framework's validation process as operated by vendors and subscribers to identify the drawbacks and propose enhanced approaches to its validation mechanism.Using an approach of reviewing secondary data, critical weaknesses of integrity, proof of trust and single point-of-failure were identified with the current PKI framework.This study therefore advances proposed solutions to address the identified weaknesses by specifically introducing multiple Certificate Authorities, storage, visibility and searchability of subscriber information in public repository.A comprehensive detail of its implementation is proposed to address the identified weaknesses of uncertain integrity, trust for certificate authorities and prevent a single point of failure.Furthermore, the proposed enhancements are validated with the protection motivation theory and a framework for empirically testing the enhancements is suggested.Further research would be required to factor in multi-factor authentication without compromising performance.
A CNN-Based Correlation Predictor for PRNU-Based Image Manipulation Localization

For PRNU-based forensic detectors, the fundamental test statistic is the normalized correlation between the camera fingerprint and the noise residual extracted from the image in successive overlapping analysis windows. The correlation predictor plays a crucial role in the performance of all such detectors. The traditional correlation predictor is based on predefined hand-crafted features representing intensity, texture and saturation characteristics of the image block under inspection. The performance of such an approach depends largely on the training and test data. We propose a convolutional neural network (CNN) architecture to predict the correlation from image patches of suitable size fed as input. Our empirical finding suggests that the CNN generalizes much better than the classical correlation predictor. With the CNN, we could operate with a common network architecture for various digital camera devices as well as a single network that could universally predict correlations for content from all cameras we experimented with, even including the ones that were not used in training the network. Integrating the CNN with our forensic detector gave state-of-the-art results.
Image Encryption Based on Fractional Wavelet Transform, Arnold Transform with Double Random Phases in the HSV Color Domain

Objective: In the recent multimedia technology images play an integral role in communication. Here in this paper, we propose a new color image encryption method using FWT (Fractional Wavelet transform), double random phases and Arnold transform in HSV color domain. Methods: Firstly the image is changed into the HSV domain and the encoding is done using the FWT which is the combination of the fractional Fourier transform with wavelet transform and the two random phase masks are used in the double random phase encoding. In this one inverse DWT is taken at the end in order to obtain the encrypted image. To scramble the matrices the Arnold transform is used with different iterative values. The fractional order of FRFT, the wavelet family and the iterative numbers of Arnold transform are used as various secret keys in order to enhance the level of security of the proposed method. Results: The performance of the scheme is analyzed through its PSNR and SSIM values, key space, entropy, statistical analysis which demonstrates its effectiveness and feasibility of the proposed technique. Stimulation result verifies its robustness in comparison to nearby schemes. Conclusion: This method develops the better security, enlarged and sensitive key space with improved PSNR and SSIM. FWT reflecting time frequency information adds on to its flexibility with additional variables and making it more suitable for secure transmission.
Automatic Arabic Dialect Identification Systems for Written Texts: A Survey.

Arabic dialect identification is a specific task of natural language processing, aiming to automatically predict the Arabic dialect of a given text. Arabic dialect identification is the first step in various natural language processing applications such as machine translation, multilingual text-to-speech synthesis, and cross-language text generation. Therefore, in the last decade, interest has increased in addressing the problem of Arabic dialect identification. In this paper, we present a comprehensive survey of Arabic dialect identification research in written texts. We first define the problem and its challenges. Then, the survey extensively discusses in a critical manner many aspects related to Arabic dialect identification task. So, we review the traditional machine learning methods, deep learning architectures, and complex learning approaches to Arabic dialect identification. We also detail the features and techniques for feature representations used to train the proposed systems. Moreover, we illustrate the taxonomy of Arabic dialects studied in the literature, the various levels of text processing at which Arabic dialect identification are conducted (e.g., token, sentence, and document level), as well as the available annotated resources, including evaluation benchmark corpora. Open challenges and issues are discussed at the end of the survey.
Joint Pruning & Quantization for Extremely Sparse Neural Networks.

We investigate pruning and quantization for deep neural networks. Our goal is to achieve extremely high sparsity for quantized networks to enable implementation on low cost and low power accelerator hardware. In a practical scenario, there are particularly many applications for dense prediction tasks, hence we choose stereo depth estimation as target. We propose a two stage pruning and quantization pipeline and introduce a Taylor Score alongside a new fine-tuning mode to achieve extreme sparsity without sacrificing performance. Our evaluation does not only show that pruning and quantization should be investigated jointly, but also shows that almost 99% of memory demand can be cut while hardware costs can be reduced up to 99.9%. In addition, to compare with other works, we demonstrate that our pruning stage alone beats the state-of-the-art when applied to ResNet on CIFAR10 and ImageNet.
Development of My Biology App Learning Media Based On Android Materials of Food Digestion Systems as Student Learning Source at MA

The implementation of the kurikulum 2013 requires of innovative and creative learning with the integration of technology in learning process. Teachers should be creative in finding and gathering learning resources, as well as making teaching materials needed in learning that can help students in learning to achieve goals in the curriculum. So it is necessary to develop learning media based on My Android Biology App in schools in learning biology. Especially in the food digestive system material can be motivate the student and the result of learning. The aim of this study is (1) to analyze the validity of the My Biology App according to media experts and material experts, (2) to analyze the feasibility of the my biology app according to teacher and student responses, (3) to analyze student learning motivation and (4) to analyze the result of learning at student. The method of research is Research and Development (R &amp; D). Pre-Posttest Control Group is the research design. The results of the validation from media experts and material experts indicate that the learning media My Biology App is very valid. It appears in teacher responses with an average of 97.62 and student responses with an average of 108 with a percentage of 90% suitable for use in learning activities. The effectiveness is determined from the motivation data and student learning outcomes. 63% of students are highly motivated and 37% are motivated. The results of the posttest in the experimental class with an average of 86.96 greater than the posttest in the control class with an average of 83.25. In addition, the n-gain in the experimental class 0.74 is greater than the control class with an n-gain of 0.70. It can be concluded based on the results of research that the learning media My Biology App can increase motivation and student learning outcomes in the food digestive system material.
Recent Advances in Practical Secure Multi-Party Computation

Secure multi-party computation (MPC) allows a set of parties to compute a function jointly while keeping their inputs private. MPC has been actively studied, and there are many research results both in the theoretical and practical research fields. In this paper, we introduce the basic matters on MPC and show recent practical advances. We first explain the settings, security notions, and cryptographic building blocks of MPC. Then, we show and discuss current situations on higher-level secure protocols, privacy-preserving data analysis, and frameworks/compilers for implementing MPC applications with low-cost.
Authoring mobile tool for improving cursive handwriting learning/writing skills

Owing to the huge amount of pen-based interfaces, many researchers have taken benefit of this intuitive and ergonomic tool so as to facilitate the learning process and help students learn either their mother tongue or even foreign languages more easily and more efficiently. Mobile phones offer a new way for designing pen based user interfaces for supporting learning handwriting activities and, thus, bringing about major contributions to education. In this paper, we have attempted to display and describe a mobile authoring tool. The developed tool can evaluate students' performance and provide instructions adapted to the needs of individual students. A major factor in the success of user interface lies in the fact that it is designed with a set of features that are adapted to a wide range of symbols accompanied by the abilities of mistake-correction. The experimental results allow less modelling complexity and accomplish good interpretation as well as high accuracy.
Las habilidades sociales y el uso de redes sociales virtuales en estudiantes de quinto grado de secundaria de instituciones educativas estatales y no estatales de Lima Metropolitana

DOI: 10.26439/persona2020.n023(1).4831 This research studies the interaction between social habilities and activity on internet social networks in fifth grade high school students of the town of Lima. The participants were 1066 students and the sample was a probabilistics one. We used a correlational design, as it was sought to determine the degree of relationship between two variables, in this case the index of relation between social habilities and the index of use of internet social networks in high school participants from a state and a non state institutions of the city of Lima. Results show a negative and significant interaction between the full score on the scale of social skills and the index of addiction to internet networks, as well as in the scores of most of the components of the results of the variables under study.
Exploiting multigranular salient features with hierarchical multi-mode attention network for pedestrian re-IDentification

In this paper, we propose an end-to-end hierarchical-based multi-mode attention network and adaptive fusion (HMAN-HAF) strategy to learn different-level salient features for re-ID tasks. First, according to each layer’s characteristics, a hierarchical multi-mode attention network (HMAN) is designed to adopt different attention models for different-level salient feature learning. Specifically, refined channel-wise attention (CA) is adopted to capture high-level valuable semantic information, an attentive region model (AR) is used to detect salient regions in the low layer, and fused attention (FA) is designed to capture the salient regions of valuable channels in the middle layer. Second, a hierarchical adaptive fusion (HAF) is constructed to fulfill the complementary strengths of different-level salient features. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on the following challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03.
Blockchain Technology and Emerging Communications Applications

Blockchain is a distributed ledger technology (DLT) which facilitates peer-to-peer transaction, operating on a consensus mechanism. Each data entry is thoroughly verified and is calculated through a hash algorithm by all nodes in the networks to attain a consensus before being inserted in the corresponding block. It can be visualized as a chain of blocks holding permanent, reliable, authenticated, distributed and tamper-proof data. Often misled with the term Bitcoin, blockchain is the foundation on which crypto currencies are built. Having paved the way for crypto currencies which have boomed exponentially over the past years, the blockchain technology has way more to offer. From its creation in 2008 till recent days, this technology has evolved, introducing new possibilities such as smart contracts and smart property. Blockchain promises a massive disruption on how traditional transactions occur in diverse fields including financial technology (FinTech), Smart Cities, Internet of Things (IoT), healthcare, governance, global trade and the telecommunication sector including the upcoming 5G technology. In this chapter, we provide a clear overview on the principle behind blockchain technology followed by the state-of-the-art applications of blockchain in various sectors while laying emphasis on new emerging blockchain applications.
<scp>Multiobjective</scp> genetic programming for reinforced concrete beam modeling

Abstract This paper presents the application of multiobjective genetic programming (MOGP) in engineering issues. An evolutionary symbolic implementation was developed based on a case study on prediction of the shear strength of slender reinforced concrete beams without stirrups including 1942 set of published test results. In the implementation of the MOGP model, the nondominated sorting genetic algorithm II with adaptive regression by mixing algorithm with considering the optimization of mean‐square error as the fitness measure and the subtree complexity was used. The developed MOGP model was compared to previously developed genetic programming models, different building codes, and additional machine learning based approaches. It is clearly shown that the MOGP model outperformed the other algorithms applied on this database and can be a general solution on any engineering problems with the main advantage of prediction equations without assuming prior form of the relevance among the input predictor variables.
Effective Structure Matching Algorithm for Automatic Assessment of Use-Case Diagram

The demand for higher education keeps on increasing. The invention of information technology and e-learning have, to a large extent, solved the problem of shortage of skilled and qualified teachers. But there is no guarantee that this will ensure the high quality of learning. In spite of large number of students, though the delivery of learning materials and tests to the students have become very easy by uploading the same on the web, assessment could be tedious. There is a need to develop tools and technologies for fully automated assessment. In this paper, an innovative algorithm has been proposed for matching structures of two use-case diagrams drawn by a student and an expert respectively for automatic assessment of the same. Zhang and Shasha's tree edit distance algorithm has been extended for assessing use-case diagrams. Results from 445 students' answers based on 14 different scenarios are analyzed to evaluate the performance of the proposed algorithm. No comparable study has been reported by any other diagram assessing algorithms in the research literature.
Automatic Feedback For Physiotherapy Exercises Based On PoseNet

This project helps patients who undergo physio- therapy exercises. It can help patients to do right poses and prevent them getting injured during the exercises. The home- based physiotherapy exercise (HPTE) dataset is used. The dataset consists of eight exercises which focus on shoulder, and knee. It is performed by five different actors with different shapes and sex. Our system uses seven exercises with two sides for more variety and better user experience. The main idea of the system is based on detecting the body key points using one of pose estimation techniques. The key points are extracted from different exercises and build our pickle file. Then, those pickle file’s key points are compared to the key points of the patient using dynamic time warping. The system is implemented as a desktop application with a simple user interface and a 3D avatar is used to guide the patients to perform the exercises correctly. The system gives them a score on the exercise and a feedback on which parts of their body have incorrectly posture. Our system offers high accuracy pose estimation and pose comparison with a minimum accuracy of 80%.
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design

Efficient design of biological sequences will have a great impact across many industrial and healthcare domains. However, discovering improved sequences requires solving a difficult optimization problem. Traditionally, this challenge was approached by biologists through a model-free method known as "directed evolution", the iterative process of random mutation and selection. As the ability to build models that capture the sequence-to-function map improves, such models can be used as oracles to screen sequences before running experiments. In recent years, interest in better algorithms that effectively use such oracles to outperform model-free approaches has intensified. These span from approaches based on Bayesian Optimization, to regularized generative models and adaptations of reinforcement learning. In this work, we implement an open-source Fitness Landscape EXploration Sandbox (FLEXS: github.com/samsinai/FLEXS) environment to test and evaluate these algorithms based on their optimality, consistency, and robustness. Using FLEXS, we develop an easy-to-implement, scalable, and robust evolutionary greedy algorithm (AdaLead). Despite its simplicity, we show that AdaLead is a remarkably strong benchmark that out-competes more complex state of the art approaches in a variety of biologically motivated sequence design challenges.
[Paper] Multiple Human Tracking with Alternately Updating Trajectories and Multi-Frame Action Features

In this paper, we propose a multiple human tracking method with alternately updating trajectories and mult iframe action features (MHT-MAF). Even though occlusion or motion blur occurs due to the sudden movement of the drone, ID switches are prevented by the stable MAF. In the experiments, we verified the effectiveness of the proposed method using the Okutama-Action dataset. Our code is available online (https://github.com/hitottiez/mht-paf).
Explaining Recurrent Machine Learning Models: Integral Privacy Revisited

Abstract We have recently introduced a privacy model for statistical and machine learning models called integral privacy. A model extracted from a database or, in general, the output of a function satisfies integral privacy when the number of generators of this model is sufficiently large and diverse. In this paper we show how the maximal c -consensus meets problem can be used to study the databases that generate an integrally private solution. We also introduce a definition of integral privacy based on minimal sets in terms of this maximal c -consensus meets problem.
TimeTracer

Ensuring correct trace links between different types of artifacts (requirements, architecture, or code) is crucial for compliance in safety-critical domains, for consistency checking, or change impact assessment. The point in time when trace links are created, however, (i.e., immediately during development or weeks/months later) has a significant impact on its quality. Assessing quality thus relies on obtaining a historical view on artifacts and their trace links at a certain point in the past which provides valuable insights on when, how, and by whom, trace links were created. This work presents TimeTracer, a tool that allows engineers to go back in time - not just to view the history of artifacts but also the history of trace links associated with these artifacts. TimeTracer allows easy integration with different development support tools such as Jira; and it stores artifacts, traces, and changes thereof in a unified artifact model.
Perceptions of pre-service primary school teachers regarding the application of Interactive educational Software in classroom teaching

The Interactive Educational Software in Classroom Teaching (IESCT) represents computer programs used for the independent design of educational content that is to be acquired.The starting point of this research was the general assumption that IESCTs significantly contribute to the effectiveness of classroom teaching and the learning and participation of students in classes.The starting points were also the specific assumptions that the research would identify the components that indicate the most common contributions of IESCTs and that it would establish a significant difference in the estimates of the pre-service teachers regarding the contribution of the IESCTs relative to their academic achievement.To establish this, the research presented in this paper was conducted in the second half of 2019 on a sample of 83 pre-service primary school teachers at the Faculty of Education in Prizren (Republic of Serbia).The data were generated by the IESCT Scale (α = .912).The factor analysis identified two components that indicate the contribution of the IESCT: 1) the efficiency of 12 1 zivorad.milenovic@pr.ac.rs 2 This paper was written as part of science project Kosovo and Metohija between national identity and European integrations, ID III-47023 which has been
Konvolüsyonel Sinir Ağı Tabanlı YOLO Yöntemi ile Orta Kulak Otoskop Görüntülerinde Timpanik Membran Bölgesinin Belirlenmesi

Due to inflammation of the middle ear, various deformations occur in the eardrum.In order to diagnose the disease, it is necessary to examine the tympanic membrane in detail with an otoscope.In recent years, deep learning has been applied in many areas including biomedical field and very effective results have been achieved.Deep learning based methods are used successfully in automatic object detection.In this study, a deep learning based object detection method namely You Only Look Once (YOLO), is used for automatic detection of tympanic membrane in eardrum images obtained using otoscope device.To enable automatic detection of tympanic membrane by YOLO, experimental studies were conducted with AlexNet, VGGNet, GoogLeNet and ResNet.According to the performance results, the most efficient results were obtained with ResNet and VGGNet architectures.Tympanic membrane region detection with YOLO, was performed with an accuracy rate of 93%.