Reviewerly/exHarmony huggingface dataset:
An Identity-Set-Based Provable Data Possession Scheme

Provable Data Possession (PDP) scheme is a cryptographic protocol that allows the users to check the availability and integrity of outsourced data on cloud storage servers (CSS) which are not completely trusted. Most of the PDP schemes are publicly verifiable, while private verification is necessary in some applications to prevent the disclosure of any relevant information. In this work, we consider the scenario of allowing the cloud user to determine whether the clients can use the data or not through controlling their ability to verify the proof correctly, and propose an identity-set-based PDP (ISB-PDP) scheme. Our ISB-PDP scheme is not only proved to be secure under the hardness of the computational Diffie-Hellman (CDH) problem, but also select/cut some clients (verifiers) dynamically to check the proof correctly according to the needs of the cloud user.
Architecture and independence controller for deep learning in safety critical applications

The market potential of safety critical products using AI is very attractive and Deep Learning Neural Networks (NN) have proven strengths to provide important functionality. This paper describes some of the challenges in arguing safety of systems using Deep Learning NN, especially functional improvement in context of SOTIF (Safety of the Intended Functionality) or other approaches to provide the safety case. An architecture and independence controller is proposed which can be used beneficially to reduce residual risk of functional insufficiencies for Deep Learning NN based systems.
A Parallel Adaptive Swarm Search Framework for Solving Black-Box Optimization Problems

This paper presents a framework to support parallel swarm search algorithms for solving black-box optimization problems. Looking at swarm based optimization, it is important to find a well fitted set of parameters to increase the convergence rate for finding the optimum. This fitting is problem dependent and time-consuming. The presented framework automates this fitting. After finding parameters for the best algorithm, a good mapping of algorithmic properties onto a parallel hardware is crucial for the overall efficiency of a parallel implementation. Swarm based algorithms are population based, the best number of individuals per swarm and, in the parallel case, the best number of swarms in terms of efficiency and/or performance has to be found. Data dependencies result in communication patterns that have to be cheaper in terms of execution times than the computing in between communications. Taking all this into account, the presented framework enables the programmer to implement efficient and adaptive parallel swarm search algorithms. The approach is evaluated through benchmarks and real world problems.
Missing Data Processing Based on Deep Neural Network Enhanced by K-Means

This paper proposes a neural network model based on K-means to process the problem of data missing. The method first clusters the samples according to the attributes without missing values to get several clusters, and then puts these clusters into different neural networks to predict the missing values. In this paper, the data can be divided into two types: the continuous numerical type and the discrete numerical type. At the same time, corresponding neural network models are established for these two types. We conduct experiments on the dataset called Human Development Index and Its Components, showing our method to be feasible and superior.
Augmented Cityscape: The GLOWFestival Case as Valuator of the Territory Cultural Heritage

Studio Glowarp gained experiences into the application of the Spatial Augmented Reality (SAR) that allowed creating a cultural exhibition called GLOWFestival ( www.glowarp.com/glowfestival.html ) whose aim was the revaluation of the cultural heritage set in Ostuni, using digital technologies. The enhancement, intended not as a substitute of the historic and cultural value, provides an alternate use through a multimedia installation. The light and its linked technologies promote the culture sustainability infused in an interested audience. Thought as a four-year festival come to an end in 2016, it evoked an event totally dedicated to light as artistic medium. The web had a fundamental role as mean of sharing and diffusion that helped to start important inter-disciplinary collaboration in the ICT area. The share of the event via social network had made possible the involvement of artists from all over the world who merged into an environment open to sharing digital skills. The decisions made brought different age groups closer to the expressive potential that this medium offers as a show through the form of the artistic edutainment. The participation of artists from all over the world was free of charge. This paper wants to show the results, the findings and potentialities of the GLOWFestival.
Teaching the Next Generation of Cryptographic Hardware Design to the Next Generation of Engineers

Evolving threats against cryptographic systems and the increasing diversity of computing platforms enforce teaching cryptographic engineering to a wider audience. This paper describes the development of a new graduate course on hardware security taught at North Carolina State University during Fall 2018. The course targets an audience with no background on cryptography or hardware vulnerabilities. The course focuses especially on post-quantum cryptosystems---the next-generation cryptosystems mitigating quantum computer attacks---and evolves into designing specialized hardware accelerators for post-quantum cryptography, executing sophisticated implementation attacks (e.g., side-channel and fault attacks), and building countermeasures on such hardware designs. We discuss the curriculum design, hands-on assignment's development, final research project outcome, and the results obtained from the course together with the associated challenges. Our experience shows that such a course is feasible, can achieve its goals, and liked by the students, but there is room for improvement.
Data Acquisition and Processing of Breast Cancer Assisted Diagnosis Based on Ultrasound Imaging

Breast disease is a common disease in women. The analysis and judgment of B-mode ultrasound images by doctors depend heavily on the operation experience and technical level of doctors. Computer image processing technologies such as natural image classification, target detection and semantic segmentation, represented by deep learning, have been relatively mature, and have been widely used successfully in automatic driving, security, finance and other fields. In this paper, through consultation and cooperation with medical institutions, a large mammary ultrasound image data set is constructed, which basically meets the needs of deep neural network training and validation testing. It is used to develop and validate algorithms for subsequent sub-tasks of ultrasound image analysis.
Learning from PhotoShop Operation Videos: The PSOV Dataset

In this paper, we present the PhotoShop Operation Video (PSOV) dataset, a large-scale, densely annotated video database designed for the development of software intelligence. The PSOV dataset consists of 564 densely-annotated videos for Photoshop operations, covering more than 500 commonly used commands in the Photoshop software. Videos in this dataset are obtained from YouTube, manually watched and annotated precisely to seconds by experts. There are more than 74 h of videos with 29,204 labeled commands. To the best of our knowledge, the PSOV dataset is the first large-scale software operation video database with high-resolution frames and dense annotations. We believe that this dataset can help advance the development of intelligent software, and has extensive application aspects. In this paper, we describe the dataset construction procedure, data attributes, proposed tasks and their corresponding evaluation metrics. To demonstrate that the PSOV dataset has sufficient data and labeling for data-driven methods, we develop a deep learning based algorithm for the command classification task. We also carry out experiments and analysis with the proposed method to encourage better understanding and usage of the PSOV dataset.
Initial investigation into the effect of image degradation on the performance of a 3-category classifier using transfer learning and data augmentation

This paper documents an initial investigation into the effect of image degradation on the performance of transfer learning (TL) as the number of retrained layers is varied, using a well-documented, commonly-used, and well- performing deep learning classifier (VGG16). Degradations were performed on a publicly-available data set to simulate the effects of noise and varying optical resolution by electro-optical (EO/IR) imaging sensors. Performance measurements were gathered on TL performance on the base image-set as well as modified image-sets with different numbers of retrained layers, with and without data augmentation. It is shown that TL mitigates against corrupt data, and improves classifier performance with increased numbers of retrained layers. Data augmentation also improves performance. At the same time, the phenomenal performance of TL cannot overcome the lack of feature information in severely degraded images. This experiment provides a qualitative sense of when transfer learning cannot be expected to improve classification results.
Research on Improvement of Particle Swarm Optimization

Although the particle swarm optimization algorithm has simple principle, few parameters and easy implementation, the particle swarm optimization algorithm is easy to fall into local optimum on multi-mode function and the local search ability is relatively weak. In this paper, the improvement of these two defects is carried out. The particle motion formula with learning model is added, and the generation strategy of a guided vector is added to improve the particle swarm optimization algorithm. The improved algorithm has a two-layer structure, and finally the research direction is prospected.
Automorphisms and isomorphisms of some p-ary bent functions

In the predecessor to this paper Dempwolff (Comm Algebra 34(3):1077–1131, 2006), group-theoretic methods were used to solve automorphism and equivalence questions for (certain) ordinary bent functions, i.e., bent functions over $$\mathbb {F}_2$$. Here, we consider the same problems for p-ary bent functions, p an odd prime and solve these questions for functions analogous to those which appear in Dempwolff (Comm Algebra 34(3):1077–1131, 2006). Although our general analysis is similar to the approach of Dempwolff (Comm Algebra 34(3):1077–1131, 2006), it turns out that the odd characteristic leads to simplifications: Often, the double derivative can be computed (cf. Lemma 2.10) and factorizations of the automorphism group (cf. Lemma 2.3) can be established resulting in restrictions for automorphisms and equivalence maps.
An In Vivo Experimental Assessment of BTrans: An Agile Business Transformation Methodology

The scenario of great competition among companies has awakened the constant need to optimize the way their internal processes are conducted. The continuous process improvement cannot be achieved in any way without strategic alignment, without digital transformation and without the analysis of the human resources responsible for carrying out the activities. The three dimensions of management mentioned above need to be synchronized for the perfect functioning of the organizational gears. This article presents a Business Transformation Methodology that integrates the business process management (BPM) with the strategic objectives, digital transformation and people management to improve the operational efficiency of the business. In addition, an in vivo experiment on the implementation of the methodology using agile project management in a food distributor company is also described. The results of the experiment showed significant improvements in the goods receipt process at a food distributor company, reducing the total execution time of the process, reducing the number of errors and acquiring more control of the critical activities of the company.
Achieving useful AI explanations in a high-tempo complex environment

Based on current capabilities, many Machine Learning techniques are often inscrutable and they can be hard for users to trust because they lack effective means of generating explanations for their outputs. There is much research and development investigating this area, with a wide variety of proposed explanation techniques for AI/ML across a variety of data modalities. In this paper we investigate which modality of explanation to choose for a particular user and task, taking into account relevant contextual information such as the time available to them, their level of skill, what level of access they have to the data and sensors in question, and the device that they are using. Additional environmental factors such as available bandwidth, currently usable sensors and services are also able to be accounted for. The explanation techniques that we are investigating range across transparent and post-hoc mechanisms and form part of a conversation with the user in which the explanation (and therefore human understanding of the AI decision) can be ascertained through dialogue with the system. Our research is exploring generic techniques that can be used to underpin useful explanations in a range of modalities in the context of AI/ML services that operate on multisensor data in a distributed, dynamic, contested and adversarial setting. We define a meta-model for representing this information and through a series of examples show how this approach can be used to support conversational explanation across a range of situations, datasets and modalities.
Anapedia – An Open Web-based Encyclopedia for Indonesian Children

Background: The limited number of web and child applications becomes a challenge especially providing web or education app that can be a source of information referral to help the school task, especially for the needs of children in Indonesia.Objective: This study answers the challenge by designing a special encyclopedia for children called Anapedia, an open web-based encyclopedia for children.Methods: Anapedia designed for the quality of content for education and design for entertainment that is suitable for children's needs by considering the children cognitive abilities.Results: The features are full-color, simple layout, large font sizes, and simple language with illustrated drawing to make it easier. For maintain the quality of the articles, this app involves the educator as a contributor who have better understand the characteristic and need of school-level children. This also provided a search feature based on keywords using information retrieval system with spell checker.Conclusion: The results of testing result with Black Box Method, it can be concluded that the entire system being tested has been functioning properly in accordance with expectations.
Sensor Data Cleaning Using Particle Swarm Optimization

Sensors play an important role in the monitoring and decision making of autonomous systems. However, the sensor measurements suffer from the addition of random noise due to various reasons. These noisy observations affect the decisions taken by the sensory system. Removal of noise is a critical aspect of any such system. This paper presents an optimization based novel procedure for cleaning the noisy sensor measurements. The proposed algorithm is designed with the help of a well-known particle swarm optimization technique. The developed algorithm is tested with the simulated standard data having different noise intensities and validated with the help of real-time measurements taken using the gyroscope sensor. Results show the feasibility of using the optimization based algorithm for cleaning the sensory data.
Tradition and Innovation Dialectics in Russian Dissertation Culture

Today in Russia the system of the state scientific certification undergoes significant changes: the powers of federal executive authorities are delegated to the educational and scientific organizations. As a result, independence in the choice of mechanisms, orders and procedures of formation of dissertation committees and criteria of awarding academic degrees is allowed. At the same time reinterpretation of axiological bases of dissertation culture through overcoming the problems connected with authenticity and quality of dissertation researches, by increase in reputation responsibility of the organizations and scientists takes place. On May 31, 2019 it will be a year to the moment of transition of the MGIMO University to a new model of the state scientific certification. According to the model developed by the MGIMO University, awarding an academic degree comes by the results of the defense of a thesis at a meeting of the dissertation committee of 5 doctors of science, three of which are employees of the MGIMO University, and two are invited scientists from other organizations. Two of five are experts with publications in the scope of the research combining the role of a member of the dissertation committee and an official opponent. The result of the defense is politically variable: alongside with the possibility of taking the negative decision awarding an academic degree without completions of the text and also under the condition of insignificant and considerable completions of the text of a research is allowed. The lack of such opportunities in the practice of awarding academic degrees existing in the domestic dissertation culture excluding modification of the manuscript submitted to defence after holding a meeting of dissertation committees, as well as other defects of the historically developed model of awarding academic degrees, defined the fastest transition of the MGIMO University to a new model of certification. «MGIMO Review of International Relations» discussed the main milestones of formation and key problems of development of the domestic dissertation culture with P. Kasatkin and E. Antyukhova.
Pre-Primary Education: Its Impact on Academic Achievement of the Learners Learning at an Elementary Stage in Odia Language Subject in Odisha

This paper discussed the impact of Pre-Primary Education on the academic achievement of the learners learning at an elementary stage in Odia language subject. The study dealt with the Descriptive Survey method in order to study the problem. The sample of 120 elementary learners of the class consisted of 60 boys and 60 girls using random sampling technique for this study. The investigator prepared a tool of Achievement test questionnaire with the help of the investigator and experts. The test questionnaire was prepared on Odia language subject for Class-I. This test measured the performance of the learners with and without Pre-Primary education in Odia Language subject. For the present study, the investigator used statistical techniques such as mean, standard deviation and t-test in order to compare the academic achievement of the learners. The findings of the study revealed that the learners learning in elementary level receiving Pre-Primary Education do better in their oral and written test in Odia Language subject than the learners receiving no Pre-Primary Education. There exists a significant difference between the performance of boys learning at elementary school level in Odia language subject both written and oral test with and without receiving pre-primary education. There exists a significant difference between the achievement of girls learning at elementary school level in Odia language subject both written and oral test with and without pre-primary education.
Application-oriented type synthesis and coupling kinematics analysis of a 1Rx2Tyz parallel stabilized platform for tunnel detection system

For collecting image high precisely, the application-oriented type synthesis and kinematics analysis for parallel vehicle-based stabilized platform are systematically carried out. Firstly, taking multi-camera install center and vehicle trajectory offset as research objects, the influence of external perturbation on visual system is analyzed quantitatively by numerical calculation and field testing. On this basis, a new type of 2SPR/RPS parallel mechanism with two-translational and one-rotational degree of freedoms is presented. Secondly, by analyzing the motion characteristic of 2SPR/RPS parallel mechanism, the motion adjoint transformation and input–output mapping for parallel vehicle-based stabilized platform are proposed by screw description of rigid-body movement. In addition, an experimental prototype is developed, and the results between numerical examples and semi-physical simulations are compared in order to prove the validity of theoretical analysis. Finally, based on the concept of influence of external perturbations on tunnel detecting system, an external parameter identify method for coupling pose and object distance of visual system is studied. The results can be extended to development, application, and performance evaluation for parallel vehicle-based stabilized platform in next step.
Novel Scheme for Express Sheet Design and Realization Based on Portrait Storage with QR Code

The article introduces a novel scheme of express sheet design and realization based on portrait storage with QR code. For a given portrait image, it is first preprocessed then compressed and embedded into a QR code. The aim of the proposed scheme is mainly to address the problems of storing high quality images within limited memory space and preserving good visual quality of reconstructed images. Experiments are conducted and the results show the proposed scheme is both feasible and effective.
Augmented Reality Application for Home Shopping in M-Commerce Using Markerless Tracking

M-commerce is constantly changing and those wanting to get ahead in the market need to have their finger on the pulse. More than half the number of shoppers abandon their carts before completing a purchase or return a particular product saying that it was not as expected. The Augmented Reality Application for Home Shopping will help users to get a better view of the product by providing a virtual representation. It gives the user a mocked-up version of how their home could look when fitted out with various items or products. The entire scene that users see is a virtually generated version of a home, and how various products would appear in the real world. The current market works on marker-based tracking which hampers the true value of augmented reality. The proposed methodology provides an idea of using markerless tracking which is more efficient and requires less effort from user's side.
Nearly Markovian maps and entanglement-based bound on corresponding non-Markovianity

We identify a set of dynamical maps of open quantum system, and refer to them as "$ \epsilon $-Markovian" maps. It is constituted of maps which, in a higher dimensional system-environment Hilbert space, possibly violate Born approximation but only a "little". We characterize the "$\epsilon$-nonmarkovianity" of a general dynamical map by the minimum distance of that map from the set of $\epsilon$-Markovian maps. We analytically derive an inequality which gives a bound on the $ \epsilon$-nonmarkovianity of the dynamical map, in terms of an entanglement-like resource generated between the system and its "immediate" environment. In the special case of a vanishing $\epsilon$, this inequality gives a relation between the $\epsilon$-nonmarkovianity of the reduced dynamical map on the system and the entanglement generated between the system and its immediate environment. We numerically investigate the behavior of the similar distant based measures of non-Markovianity for classes of amplitude damping and phase damping channels.