Reviewerly/exHarmony huggingface dataset:
Secure Outsourced Computation of Multiple Matrix Multiplication Based on Fully Homomorphic Encryption

Fully homomorphic encryption allows a third-party to perform arbitrary computation over encrypted data and is especially suitable for secure outsourced computation.This paper investigates secure outsourced computation of multiple matrix multiplication based on fully homomorphic encryption.Our work significantly improves the latest Mishra et al.'s work.We improve Mishra et al.'s matrix encoding method by introducing a column-order matrix encoding method which requires smaller parameter.This enables us to develop a binary multiplication method for multiple matrix multiplication, which multiplies pairwise two adjacent matrices in the tree structure instead of Mishra et al.'s sequential matrix multiplication from left to right.The binary multiplication method results in a logarithmic-depth circuit, thus is much more efficient than the sequential matrix multiplication method with linear-depth circuit.Experimental results show that for the product of ten 32×32 (64×64) square matrices our method takes only several thousand seconds while Mishra et al.'s method will take about tens of thousands of years which is astonishingly impractical.In addition, we further generalize our result from square matrix to non-square matrix.Experimental results show that the binary multiplication method and the classical dynamic programming method have a similar performance for ten non-square matrices multiplication.
#Cambio o #Fuera. Pensar lo nuevo para resetear la escuela

The article proposes to problematize the weight that is usually granted to the universalization of secondary school to explain cultural degradation. To do this, it follows two tracks. On the one hand, the one provided by the reproductivist criticism carried out by sociology in the 1960s, which points out that it is in the epistemological-pedagogical dimension where its discriminating origin resides. The second track followed is the one rooted in the phenomenal technological and cultural change that our societies are experiencing, configuring a world that articulates defectively with the only school we could create. In light of these hypotheses, the article proposes to tune into the digital age, and then consider what bifurcation the school is forced to take in terms of the cultural transmission of the contemporary context. For this, it focuses on the analysis of three transformations that are transcendental for the cultural function that the school is called to fulfill: the digital culture; complexity theories and chaos; the explosion of diversity. The first one allows problematizing the didactic limitations that ICTs overcome; the second has the merit of correcting many of the epistemological biases promoted by classical science; while diversity opens us to the possibility of installing the value of plurality and materializing an integration of otherness based on differences. The work concludes by pointing out that in the virtuous link between pedagogy and ICT lies the ethical imperative that school has today, as well as its future.
Ontology and Semantic Web in Logistic Applications: State of the Art

The field of logistics has often the resolution of complex combinatorial problems. These problems also implicitly refer to processes, actors, activities, and methods concerning different aspects that need to be considered. Thus, a single problem can occur in the processes of sale/purchase, transport/delivery, and inventory management. These processes are so diverse and interconnected that it is difficult for a logistician to manage them. Logistic processes involve different types of flows such as physical flows, financial documents, products, the logistic processes constitute a network of activities of several actors and each of them carries out different activities in order to reach objectives Sometimes, these activities can meet obstacles and difficult situations contradictory to the objectives. Therefore, we propose an approach based on ontology to share the different aspects of logistics in logistics management, and also in order to help logistics experts to understand, implement and evolve the different processes constituting the enterprise. Therefore, they propose ontologies and the Semantic Web to reduce the ambiguity of the logistic processes.
Sensor-based Abnormal Behavior Detection Using Autoencoder

The population of elderly people is increasing, with the development of an aging society all over the world. As a result, the number of people who need to take care of themselves, such as elderly people living alone or suffering from dementia, is also increasing. Caring for these people requires not only social burdens but also economic costs. A system that manages their behavior is essential to reduce the cost of caring for them. In this study, we propose an abnormal behavior detection model using smart home sensor data to manage elderly people living alone and people with dementia. Previous studies have used probability models such as a hidden Markov model (HMM) or support vector machine (SVM) model. However, the HMM requires a process to estimate values such as the initial probability, or to define states. It is also possible to detect behavior using a classification model such as an SVM, but in this study, we used an autoencoder, which is a representative unsupervised learning model, to obtain a pattern from the behavior data. The autoencoder model can detect abnormal behavior by extracting the characteristics of the normal behavior data. The models used in this study were trained and tested with normal behavior data, showing an accuracy of more than 99%. For abnormal behavior data, a loss of about 10-30% was observed. This model is expected to assist in effectively managing elderly or demented patients and reduce the cost of caring for them.
A novel 3D profile measurement method based on digital photoelastic technology

A three-dimensional profile measurement method based on digital photoelastic fringe analysis technology is proposed in this paper. According to the actual stress field of a disc under appropriate load, the photoelastic fringe patterns are generated. These patterns are illuminated on the reference plane and objects through a projector, which are regarded as the structured-light pattern sequence. Then a series of images including normal images and deformed fringe images are captured. These images contain two significant photoelastic parameters, isoclinic parameter and isochromatic parameter, which could be evaluated by the phase shifting method. Therefore, phase differences can be calculated by photoelastic isochromatic parameter after phase unwrapping. Depth information is carried in the phase differences and virtual 3D profile equal to real objects could be reconstructed. Experiments demonstrate that this method is robust and suitable for measuring objects with regular and general shape.
Traceable and Fully Anonymous Attribute Based Group Signature Scheme with Verifier Local Revocation from Lattices

Efficient user and attribute tracing is desirable in Attribute-Based Group Signatures (ABGS). Tracing cost is high in ABGS schemes with verifier-local revocation (VLR) as it requires to scan almost all the users and all the attributes of the identified user. Thus we propose a new lattice-based VLR-ABGS scheme that supports efficient user and attribute tracing.
Automatic Handwritten Digit Recognition On Document Images Using Machine Learning Methods

Context: The main purpose of this thesis is to build an automatic handwritten digit recognition method for the recognition of connected handwritten digit strings. To accomplish the recognition task ...
Activation Map Networks with Deep Graphical Model for Semantic Segmentation

In this research semantic segmentation by the deep convolutional neural network is used. Segmentation of contextual information with the use of patch-patch between the regions and background. The patch-patch context is done with the help of conditional random fields with CNN pairwise potential functions to identify the semantic correlation of adjacent patches. Efficient piecewise training of deep graphical model is then assigned to eschew continuous CRF inference through background propagation. For Patch-background context capturing the network is designed with the help of multi-scale image input and slide pyramid pooling for the efficient performance. In the CRF model we have used both unary and pairwise potential functions. The simulation results are verified in the semantic segmentation dataset like MATLAB contextual modeling. The proposed technique will give better accuracy. The evaluation metrics of invented method has been done in terms of pixel accuracy. The model achieves outperforming innovation than the other algorithms like Deeplab and DPN on semantic segmentation.
Detecting and Correcting Adversarial Images Using Image Processing Operations and Convolutional Neural Networks

Deep neural networks (DNNs) have achieved excellent performance on several tasks and have been widely applied in both academia and industry. However, DNNs are vulnerable to adversarial machine learning attacks, in which noise is added to the input to change the network output. We have devised an image-processing-based method to detect adversarial images based on our observation that adversarial noise is reduced after applying these operations while the normal images almost remain unaffected. In addition to detection, this method can be used to restore the adversarial images' original labels, which is crucial to restoring the normal functionalities of DNN-based systems. Testing using an adversarial machine learning database we created for generating several types of attack using images from the ImageNet Large Scale Visual Recognition Challenge database demonstrated the efficiency of our proposed method for both detection and correction.
How to retrieve the encrypted data on the blockchain

Searchable symmetric encryption (SSE) scheme can perform search on encrypted data directly without revealing the plain data and keywords.At present, many constructive SSE schemes were proposed.However, they cannot really resist the malicious adversary, because it (i.e., the cloud server) may delete some important data.As a result, it is very likely that the returned search results are incorrect.In order to better guarantee the integrity of outsourcing data, and ensure the correction of returned search results at the same time, in this paper, we combine SSE with blockchain (BC), and propose a SSE-on-BC framework model.We then construct two concrete schemes based on the size of the data, which can better provide privacy protection and integrity verification for data.Lastly, we present their security and performance analyses, which show that they are secure and feasible.
Creating new drugs design patterns.Step one. &#x0D; Pharmaceutical development

To optimize the process of pharmaceutical development, it is necessary to take into account the timeframes of the processes and to track all its key stages. To do this, it is necessary to create an algorithm for conducting research, taking into account the modern approach to pharmaceutical development the concept of Quality by Design.&#x0D; The aim of this work was to create an algorithm of actions in conducting research when developing a new drug that can be used in the production process.&#x0D; Flowcharts were used for depicting algorithms. To account the timeframes of the processes, a Gantt diagram was used.&#x0D; The process of pharmaceutical development when launching a new generic drug has been studied at an operating pharmaceutical company. An algorithm for the actions of staff involved in research is proposed The algorithm minds the concept of Quality by Design. A temporary Gannta scheme and a project manager's control card for the process of pharmaceutical development of a generic drug have been developed.&#x0D; Based on the results of the work, an algorithm of actions in conducting research when developing a new drug that can be used in the production process was proposed.
Opponent-Aware Planning with Admissible Privacy Preserving for UGV Security Patrol under Contested Environment

Unmanned ground vehicles (UGVs) have been widely used in security patrol. The existence of two potential opponents, the malicious teammate (cooperative) and the hostile observer (adversarial), highlights the importance of privacy-preserving planning under contested environments. In a cooperative setting, the disclosure of private information can be restricted to the malicious teammates. In adversarial setting, obfuscation can be added to control the observability of the adversarial observer. In this paper, we attempt to generate opponent-aware privacy-preserving plans, mainly focusing on two questions: what is opponent-aware privacy-preserving planning, and, how can we generate opponent-aware privacy-preserving plans? We first define the opponent-aware privacy-preserving planning problem, where the generated plans preserve admissible privacy. Then, we demonstrate how to generate opponent-aware privacy-preserving plans. The search-based planning algorithms were restricted to public information shared among the cooperators. The observation of the adversarial observer could be purposefully controlled by exploiting decoy goals and diverse paths. Finally, we model the security patrol problem, where the UGV restricts information sharing and attempts to obfuscate the goal. The simulation experiments with privacy leakage analysis and an indoor robot demonstration show the applicability of our proposed approaches.
Discretionary Access Control Method to Protect Blockchain Privacy

Blockchain technology has been widely concerned by scholars and industry since it was put forward, and Banks, Internet of Things, Supply Chain, Government and Medical Industry have proposed using blockchain technology to solve their problems, respectively. However, there are some difficulties in the deployment of blockchain products. One important reason is privacy protection. In order to protect blockchain privacy, discretionary access control method is proposed, and the corresponding model and algorithm are given. Encryption algorithm is used to encrypt the blockchain transaction transactions to privacy transactions. The encryption key and access rights are encapsulated by Lagrange polynomial to form secret information sent to authorized users. Extracting enough secret information, authorized user groups work together to calculate the decryption key, and then obtain the transaction transactions plaintext and finally implement consensus mechanism to verify the transaction. Secret information safely self-destruct immediately if exceed effective time. Authorized users and effective time are entirely determined by the owner of the transaction. This paper realizes key distributed securely, achieves discretionary access control and fine-grained access control and provides strong privacy protection.
Logic and the 2-Simplicial Transformer

We introduce the 2-simplicial Transformer, an extension of the Transformer which includes a form of higher-dimensional attention generalising the dot-product attention, and uses this attention to update entity representations with tensor products of value vectors. We show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning.
CONTRIBUTION OF CULTURAL HERITAGE SITES TO THE ENHANCEMENT OF YOUNG PEOPLE’S CULTURAL LITERACY

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 1374-1379ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.0412Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Violence Detection in Social Media-Review

Social media has become a vital part of humans’ day to day life. Different users engage with social media differently. With the increased usage of social media, many researchers have investigated different aspects of social media. Many examples in the recent past show, content in the social media can generate violence in the user community. Violence in social media can be categorised into aggregation in comments, cyber-bullying and incidents like protests, murders. Identifying violent content in social media is a challenging task: social media posts contain both the visual and text as well as these posts may contain hidden meaning according to the users’ context and other background information. This paper summarizes the different social media violent categories and existing methods to detect the violent content. Keywords: Machine learning, natural language processing, violence, social media, convolution neural network
Creating algorithmic audio-visual narratives through the use of augmented reality prints

Abstract This article investigates the hypothesis of creating non-linear audio-visual narratives, through an unanticipated use of traditional print-based games, enriched with videos, via augmented reality (AR) possibilities. A ludic system has been created and presented. Based on a traditional card game, a non-linear cinematic narrative occurs. We attempt to examine the following questions: in which way can we bring together different forms of visual communication, such as graphic design and video? Can the above forms create a complex narrative whole and what kind of rules will be needed for this? How can we enrich traditional forms of gaming with the potentials of AR? Gaming itself demands a set of rules. Can these rules play the role of algorithms in the combined universe that we have designed and created? In which way can the designer on the one hand and the user on the other influence the overall output of the system? What will the user experience be like? The printed card system chosen for this is Tarot and more precisely the Great Arcana, which makes use of the 22 fundamental Tarot figures.
The Leadership Imperatives for Assessment Excellence: Imperative #5, Sustaining a Culture Supportive of Assessment Excellence

Assessment UpdateVolume 31, Issue 6 p. 3-14 Editor's Notes The Leadership Imperatives for Assessment Excellence: Imperative #5, Sustaining a Culture Supportive of Assessment Excellence Stephen P. Hundley, Stephen P. HundleySearch for more papers by this author Stephen P. Hundley, Stephen P. HundleySearch for more papers by this author First published: 17 December 2019 https://doi.org/10.1002/au.30190Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume31, Issue6November/December 2019Pages 3-14 RelatedInformation
Option Attentive Capsule Network for Multi-choice Reading Comprehension

In this paper, we study the problem of multi-choice reading comprehension, which requires a machine to select the correct answer from a set of candidates based on the given passage and question. Most existing approaches focus on designing sophisticated attention to model the interactions of the sequence triplets (passage, question and candidate options), which aims to extract the answer clues from the passage. After this matching stage, a simple pooling operation is usually applied to aggregate the matching results to make final decisions. However, a bottom-up max or average pooling may loss essential information of the evidence clues and ignore the inter relationships of the sentences, especially dealing with complex questions when there are multiple evidence clues. To this end, we propose an option attentive capsule network with dynamic routing to overcome this issue. Instead of pooling, we introduce a capsule aggregating layer to dynamically fuse the information from multiple evidence clues and iteratively refine the matching representation. Furthermore, we design an option attention-based routing policy to focus more on each candidate option when clustering the features of low-level capsules. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on RACE dataset.
ICT IN PRESCHOOL EDUCATION

1 Technical University of Civil Engineering of Bucharest (ROMANIA)2 Kindergarten 119 Bucharest (ROMANIA)
Phase-only optical image encryption and hiding based on normalization and orthogonalization phase-shifting algorithm

Optical encryption technology using phase-shifting interferometry (PSI) is widely used in many security fields, showing the advantages of parallel processing and the high security due to the information can be encrypted in multiple dimensions. The traditional PSI- based encryption methods are realized by the traditional multi-step phase-shifting algorithms (PSAs), although they are fast and accurate, the phase shifts deviation (PSD) caused by phase shifter detuning often affect the performance of the above methods. In order to solve this problem, a phase-only encryption method combining a normalization and orthogonalization phase-shifting algorithm (NOPSA) is proposed to eliminate the influence of PSD on the effective on decryption. First, the original image is phase encoded and encrypted by a phase key loaded on a liquid crystal spatial light modulator (SLM). Subsequently, the modulated reference light will be overlapped with the object wave to form interference pattern. Finally, the encrypted image hiding can be implemented based on NOPSA. Compared with the current PSI- based encryption methods, its obvious advantage is that when the phase shifts are not accurate or unknown, the proposed method can still achieve high quality encryption and hiding, which broadens the working conditions of PSI- based encryption methods.