Reviewerly/exHarmony huggingface dataset:
Lattice HIBE with Faster Trapdoor Delegation and Applications

In this paper, we propose a lattice-based HIBE scheme in the standard model with faster trapdoor delegation. It is proven secure under the Learning With Errors assumption. Inspired by Canetti et al.’s transformation (Eurocrypt’03), an HIBE can be converted into a forward-secure public-key encryption (FS-PKE) scheme, and the efficiency of key update relies on the efficiency of trapdoor delegation. For applications, our HIBE with faster delegation can be used to generate a lattice-based FS-PKE with faster key update. Furthermore, we also obtain a lattice-based forward-secure signature (FSS) scheme combining HIBE-like key-update technique with Zhang et al.’s short signature construction in the standard model (Crypto’16).
ARRay-Tracing - A Middleware to Provide Ray Tracing Capabilities to Augmented Reality Libraries

In recent years we saw the increase and popularization of Augmented Reality applications. However, the visual mismatch between real and virtual elements produce the absence of realism, discouraging the use of these applications. Rendering techniques, as Ray Tracing, can produce very photorealistic scenes. Despite its high computational cost, recent advances in graphics hardware allow the use of these techniques in real-time applications, like those of Augmented Reality. In the literature, we can find some works combining these two technologies, AR and Ray Tracing, but rigidly, without modularization, making the solutions dependent on certain frameworks. In this work, we propose a middleware to integrate Augmented Reality and Ray Tracing in a modularized way, allowing the developer to switch between existing libraries and frameworks to better fulfill their needs and expertise. To evaluate the proposal, we build an application using the artoolkitX library and the Optix framework. Through our middleware, we verified that it was possible to integrate these tools in a simple way, maintaining performance and photorealism in our AR application.
Watermarking skin lesion digital color images based on channels

This paper presents a way to secure through copyright medical images. The novelty of this study consists in splitting in color layers of a mole digital image and of a melanoma digital image, where the common mole is considered as the host image and the melanoma image as watermarked image. This work attempts an embedding of the information with DWT (discrete wavelet transformation) of the Haar wavelets on each color layer, decomposing them on one level, selecting the coefficient of level with low frequency to be the watermarking key and using IDWT ( inverse discrete wavelet transform) Haar wavelets for obtaining the invisible watermarked images. The efficiency and robustness of the proposed method of watermarking is checked using the SSIM (Structural Similarity Index). The images are computed in two situations: when the layers images are induced and on non-noise images. The Bland and Altman (B&A) plot indicates that both methods can be used successfully in securing the medical images.
Two-Factor Human Authentication for Mobile Applications

Generally, a two-factor authentication system verifies the user using three credentials in two steps. These three credentials are a password, a private key assigned to the user in his mobile app and a fingerprint. All these three credentials fell into different categories which are "Something you know," "Something you have," and "Something you are." Credentials from these three categories have been used for people to prove who they claimed to be even before the days of the Internet. This application system combines credentials from all these three categories in order to provide a secure way of authentication that is hardly able to be compromised by hackers. This system uses a complex algorithm in order to protect the private key which is assigned to the user during registration. A unique private key is assigned to the user in the mobile app. This key plays an extremely significant role in this system because of its variable for every time-based one-time password, TOTP to be unique. This system requires Android 6.0 APIs for fingerprint authentication in order to secure the TOTP generation. The TOTP algorithm implemented in this system has the capability to generate a unique string of code every 30 s.
A Galois Connection Approach to Wei-Type Duality Theorems

In 1991, Wei proved a duality theorem that established an interesting connection between the generalized Hamming weights of a linear code and those of its dual code. Wei’s duality theorem has since been extensively studied from different perspectives and extended to other settings. In this paper, we re-examine Wei’s duality theorem and its various extensions, henceforth referred to as Wei-type duality theorems, from a new Galois connection perspective. Our approach is based on the observation that the generalized Hamming weights and the dimension/length profiles of a linear code form a Galois connection. The central result of this paper is a general Wei-type duality theorem for two Galois connections between finite subsets of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}$ </tex-math></inline-formula> , from which all the known Wei-type duality theorems can be recovered. As corollaries of our central result, we prove new Wei-type duality theorems for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula> -demi-matroids defined over finite sets and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula> -demi-polymatroids defined over modules with a composition series, which further allows us to unify and generalize all the known Wei-type duality theorems established for codes endowed with various metrics.
A Machine Learning Method for Automatic Copyright Notice Identification of Source Files

For Free and Open Source Software (FOSS), identifying the copyright notices is important. However, both the collaborative manner of FOSS project development and the large number of source files increase its difficulty. In this paper, we aim at automatically identifying the copyright notices in source files based on machine learning techniques. The evaluation experiment shows that our method outperforms FOSSology, the only existing method based on regular expression.
“Waiting for your info”. An explanatory look at the communicative strategies deployed to mitigate potentially face-threatening acts in emails

Emails are perhaps the most common form of communication in business contexts. In spite of their prominence they are, however, still a common source of misunderstanding and stress. Drawing upon the integration of linguistic and genre aspects, a previous study (Poppi 2015) showed that in order to be able to decide how to draft an email, it is not possible to refer to structural characteristics and style of language alone, as in business communication the boundaries and expectations of the genre are often overruled by inventiveness and creativity. In particular, inventiveness and creativity may especially prove useful when composing emails containing potentially face-threatening acts like directives or requests. The present contribution focuses on 41 email chains written and received by the employees of companies dealing with car-trading, manufacturing of tights and socks, ICT (Information and Computer Technologies) assistance, transport and logistics, who were in charge of customer services. At first, reference was made to Goldstein and Sabin’s (2006) categorization of email exchanges on the basis of the speech act they entail. Out of the twelve main categories identified by them, it was decided to concentrate on those messages which proved to be the textualization of requests and directives (requesting someone to do something), with a view to disclosing the strategies employed to downgrade or mitigate the directness of these potentially face-threatening speech acts. In order to perform this latter stage of the analysis, it was decided to refer to the adaptation of the studies by Blum-Kulka  et al. (1989) and Sifianou (1992) provided by Darics and Koller (2018), as well as to Blum-Kulka and Olshtain’s classification of levels of directness. The preliminary results of the analysis confirm that email writers are generally aware of the importance of mitigating the directness of face-threatening speech acts like information requests, and especially directives, as shown by the variety of strategies employed in the samples under scrutiny here.
Facial Image Verification for Government Kiosk System

Some government organizations have been started mega-projects from which Thai citizens can access their welfare data and receive services from the government via kiosk machines. The kiosk machines are typically installed at the government offices and public places. To access any data or services, the user authentication is done by the Thai citizen smart ID card. However, the weakness of the current system is person verification; it is possible that someone uses the other's ID card. In this paper, we propose a facial image verification system prototype that can integrate to the existing well-fare kiosks. The face verification is done by comparing the face image retrieved from the smart ID card and the face image taken from a camera on the kiosk. The prototype system aims to cross-check the ownership of the smart card holder and to protect personal data privacy. We implemented Face Verification APIs based on the ResNet and MobileNet. From the experimental results, when tested with Cross-Age LFW face dataset, the accuracies are 99.996% and 99.988% on the ResNet50 and the MobileNet, respectively. However, the accuracies reduce to 96.296% and 95.556% when tested on the low-quality face images that embedded in the national ID cards. The system is designed as a client-server model using Microservices Architecture (MSA) style that complements “loosely coupled” multiple services which can be leveraged to share important functionality throughout other government agencies.
Competencias mediáticas, lectura crítica de imágenes en estudiantes universitarios desarrolladas desde la gamificación como una estrategia pedagógica

espanolLa ensenanza-aprendizaje en la Comunicacion tienen el reto de la integracion de las tecnologias de la informacion y la comunicacion (TIC), sin embargo, se han centrado en la motivacion del estudiante, sin estudiar su incidencia en el proceso comunicativo e influencia en competencias basicas. Por tanto, el presente trabajo identifico la incidencia de la estrategia gamificacion en el desarrollo de lectura critica de imagenes. Se realizo con metodologia cuantitativa pre-experimental (pretest/postest); se analizaron datos con prueba t para muestras emparejadas y Shapiro-Wilk, evaluacion acumulativa mediante rubrica analitica y Diagnostico de Curso Rapido para apreciaciones cualitativas. Participaron doce estudiantes en el estudio encontrandose incremento significativo en la media postest; se evidencia que la comunicacion activa entre el docente y estudiante, y colaborativa entre estudiante y estudiante influyeron en el desarrollo individual y la gamificacion incide positivamente en el desarrollo de competencias basicas, sin importar la dependencia directa al contexto tecnologico de los participantes EnglishThe teaching-learning processes in Communication have the challenge to integrate IT technologies, however, they have focused on motivational aspects and student behavior, without studying their impact on the teacher-student communication process and the influence on basic competences. Therefore, this work identified the incidence of gamification strategy in the development of critical image reading. It was carried out with pre-experimental quantitative methodology (pretest / posttest); data were analyzed witht-test for standard samples and Shapiro-Wilk, cumulative evaluation by analytical rubric and Rapid Course Diagnosis for qualitative assessments. Twelve students participated in the study, finding a significant increase in the posttest average; it is evident that active communication between the teacher and student, and collaborative between student and student, influenced individual development and gamification has a positive impact on the development of basic competences, regardless of the direct dependence on the technological context of students and teachers.
Face Tracker-Assisted Multi-Person Face Recognition in Surveillance Videos

Face recognition is a method to recognize people from image and video sources. It can be used for several applications by law enforcement agencies such as surveillance systems and person authentications. Multi-person face recognition is the task of identifying multiple people approaching camera view at same time. Performing real-time and accurate multiple person face recognition is highly challenging due to the uncontrolled face poses, illuminations and expressions. Due to these factors of unconstrained environment, the accuracy gets degraded across video frames. In this paper, we propose improved face recognition framework for multiple people in unconstrained movement/walking towards camera which is implemented on the NVDIA graphics processing unit (GPU)-enabled computer systems. We propose the use of a face tracker to save the GPU computational power and also to increase the overall accuracy of the system. The idea is to do the detection of the faces once through face detector model and subsequently use the face tracker for further frames until the face disappears from camera view. We do combined the use of this tracked faces information along with a face recognition model over same faces. This has achieved face recognition rate improvements on videos of people walking across camera.
Autonomous motor vehicle categorisation using a convolutional neural network

We present an automatic approach in recognising the classification type of motor vehicles. Over the years, governments and private organisations have implemented schemes in which motor vehicles are categorised according to their size, weight, scale, and axel. Such schemes are used for various purposes, mainly for the implementation of new regulations, generation of statistics as well as urban development. This investigation proposes an autonomous system that is able to detect motorcycles, light motor vehicles (LMV's) and heavy motor vehicles (HMV's) from static images. This was established by training a Convolutional Neural Network (CNN) on preprocessed subsets of an existing dataset using the transfer learning technique. Our model achieved an F1 Score of 94.27% and an Accuracy Rate of 89.28% and challenging scenarios were identified.
Hardware Implementation and Performance Evaluation of DOCSIS 3.1 Upstream System

DOCSIS 3.1 is designed to enable data services up to 10 Gbps down to 1 Gbps for high-speed data services such as 4K UHD, multi-screen video streaming services, and cable IPTV. In this paper, we design and implement such an upstream system of DOCSIS 3.1. In addition, the BER characteristics of the DOCSIS 3.1 upstream module implemented through various laboratory tests are compared and analyzed.
Use Case Points (UCP) with 3 Point in Program Evaluation and Review Technique (PERT) to Estimate Effort Software

Abstract Software has an important influence in business to accelerate and manage business processes better. Software development projects and projects in general require precise estimates for good business management and running smoothly. The method of Use Case Points is one of the methods used to estimate object-based software utterances, but there are some disadvantages such as subjective assessment of Technical Complexity Factor (TCF) and Environmental Complexity Factor (ECF) for novice developers who are inexperienced. Three points from the Program Evaluation and Review Technique (PERT) are used at UCP to help estimate the two factors better by modifying both assessment factors. By using 3 PERT points in UCP, it is expected to help novice developers in estimating the size of the software as predicted by experienced developers. Results of the calculation of the estimated software information systems sales of goods in the Madrasah Istiqlal foundation obtained an effort value is 1,011 man-hours.
Improving Distribution-Based Discrete Particle Swarm Optimization Using Lévy Flight

Some metaheuristic algorithms such as particle swarm optimization (PSO) are extended and have been shown to perform very well in a wide range of optimization domains though they are originally designed for continuous optimization. In discrete optimization, some extended algorithms handle continuous parameters of a probability distribution, which assumes variable values of a candidate solution instead of directly handling discrete variables. These distribution-based discrete PSOs (DDPSO) sample a variable value from a distribution for every variable to generate a candidate solution. This procedure can be considered as a kind of local search centered on an intended solution, which has the highest probability to be generated. Step length from the intended solution increases proportionally and the probability of producing an intended solution decreases exponentially in high-dimensional problems. We propose a novel sampling method to control the step size for DDPSO. In this paper, we describe our new sampling method to control the step size with Lévy distribution in a similar way to Lévy flight. The proposed method is applied to three representative methods of DDPSOs and performance is compared with original algorithms. In our discrete optimization experiments, we demonstrate that our algorithm increases DDPSO’s search performance and robustness to dimensionality.
An Approach towards Implementation of Active and Passive voice using LL(1) Parsing

LL (1) is a top down parser; it is subset of CFL (context free language). Initially, we do some research work on LL (1) parsing, syntactic parsing, syntactic structure, roman language conversion etc. This was observing that there is less work or research on active passive conversion. This paper aims to explain conversion of active into passive sentences. So, we proposed a prototype that can convert active sentence into passive sentences. And also do some research that can help in further implementation of conversion of active into passive. In order to make it more efficient there are various different techniques that we can use in it in this research. We can use more POS tagging and Speech recognition to make it more efficient. We suggest the new technology such as Artificial Intelligence in order to make it successful in converting complex active voice into passive voice by using AI dictionary and Google Vocab dictionary in order to get family of words and to deal with complex sentences.
Learning More Accurate Features for Semantic Segmentation in CycleNet

Contextual information is essential for computer vision tasks, especially semantic segmentation. Previous works generally focus on how to collect contextual information by enlarging the size of receptive field, such as PSPNet, DenseASPP. In contrast to previous works, this paper proposes a new network – CycleNet, which considers assigning a more accurate representative for every pixel. It consists of two modules, Cycle Atrous Spatial Pyramid Pooling (CycleASPP) and Alignment with Deformable Convolution (ADC). The former realizes dense connections between a series of atrous convolution layers with different dilation rates. Not only the forward connections can aggregate more contextual information, but also the backward connections can pay more attention to important information by transferring high-level features to low-level layers. Besides, ADC generates accurate information during the decoding process. It draws support from deformable convolution to select and recombine features from different blocks, thus improving the misalignment issues caused by simple interpolation. A set of experiments have been conducted on Cityscapes and ADE20K to demonstrate the effectiveness of CycleNet. In particular, our model achieved 46.14% mIoU on ADE20K validation set.
Autonomous Hybridization of Agent-Based Computing

Using agent-based systems for computing purposes, where agent becomes not only driver for realizing computing task, but a part of the computing itself is an interesting paradigm allowing for easy yet robust design of metaheuristics, making possible easy parallelization and developing new efficient computing methods. Such methods as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) or Evolutionary Multi Agent-System (EMAS) are examples of such algorithms. In the paper novel approach to hybridization of such computing systems is presented. A number of agents doing their computing task can agree to run other algorithm (similarly to high level hybrid proposed by Talbi). The paper focuses on presenting the background and the idea of such algorithm along with firm experimental results.
Extracting finite state representations from recurrent models of Industrial Cyber Physical Systems

Neural networks are being broadly explored for the identification of Industrial Cyber Physical Systems (ICPS) models from data sequences. However, learned representations typically lack explainability, representing a major challenge of deep learning. Interpreting the information structured across the synaptic links is particularly challenging for recurrent neural networks (RNN), encoding input features and observed system dynamics within a continuous latent space. In this work, we target the representations built within RNNs while learning behavioral models of a class of discrete dynamical systems. To this end, we propose a method to extract the symbolic knowledge structured by the continuous state, based on Gaussian Mixture Model clustering, handling latent activations characterized by partially overlapping and non-isotropic distributions. Experiments are performed on a pilot remanufacturing plant, by learning the model of a conveyor controller from process data. We show the capability of the proposed method to extract the hidden finite state machine from the trained RNN, providing a human interpretable representation of the input conditioned computations performed through the continuous latent space.
Prediction of Shoulder Surfing in E-Wallet

Shoulder surfing is one of the major problem in today's world as any intruder can easily hack the data from our mobile phone and most of the people store all the confidential details in mobile phone by which they can easily access the data as the keypad consist only a numerical values i.e. from 0 to 9.This paper exhibits the prevention of shoulder surfing in E-Wallet to solve the problem of shoulder surfing in the E-Wallet a technique which consists of colours or graphical method in place of the numerical values which is proposed earlier. The graphical method uses the colours in place of the numbers in keypad with the help of neuro fuzzy algorithm. The neuro fuzzy algorithm is mainly proposed for interchanging the colours. Neuro fuzzy algorithm is executed in wamp server as it is used for web improvement and inner testing. Wamp server uses a php and SQL and also access less space for any type of computation. The proposed method of shoulder surfing has improved the efficiency and security for the E-Wallet as it may be difficult for the gate crasher to assume the code using graphical representation method.