Reviewerly/exHarmony huggingface dataset:
Integer Reconstruction Public-Key Encryption

In [AJPS18], Aggarwal, Joux, Prakash & Santha described an elegant public-key encryption ( $$\mathsf {AJPS}$$ -1) mimicking NTRU over the integers. This algorithm relies on the properties of Mersenne primes instead of polynomial rings. A later ePrint [BCGN17] by Beunardeau et al. revised $$\mathsf {AJPS}$$ -1's initial security estimates. While lower than initially thought, the best known attack on $$\mathsf {AJPS}$$ -1 still seems to leave the defender with an exponential advantage over the attacker [dBDJdW17]. However, this lower exponential advantage implies enlarging $$\mathsf {AJPS}$$ -1's parameters. This, plus the fact that $$\mathsf {AJPS}$$ -1 encodes only a single plaintext bit per ciphertext, made $$\mathsf {AJPS}$$ -1 impractical. In a recent update, Aggarwal et al. overcame this limitation by extending $$\mathsf {AJPS}$$ -1's bandwidth. This variant ( $$\mathsf {AJPS}\hbox {-}\mathsf {ECC}$$ ) modifies the definition of the public-key and relies on error-correcting codes. This paper presents a different high-bandwidth construction. By opposition to $$\mathsf {AJPS}\hbox {-}\mathsf {ECC}$$ , we do not modify the public-key, avoid using error-correcting codes and use backtracking to decrypt. The new algorithm is orthogonal to $$\mathsf {AJPS}\hbox {-}\mathsf {ECC}$$ as both mechanisms may be concurrently used in the same ciphertext and cumulate their bandwidth improvement effects. Alternatively, we can increase $$\mathsf {AJPS}\hbox {-}\mathsf {ECC}$$ 's information rate by a factor of 26 for the parameters recommended in [AJPS18]. The obtained bandwidth improvement and the fact that encryption and decryption are reasonably efficient, make our scheme an interesting post-quantum candidate.
Knowledge in Topological Argumentation Models

We formalise justification in the topological argumentation model and define knowledge and belief based on justification. In addition to revealing that the notions of knowledge and belief based on justification satisfy Stalnaker’s BK system except for the closure principle, the main contribution of this paper is a counter-intuitive result about the notion of knowledge based on justification, which is related to the no false lemmas.
Pricing model for revenue generation using Recurrent Neural Network for Cloud service provider

Success of any product may depend on the price of product. Demand of a product is one of the factors to be considered for deriving price of the product. As many IT companies have started to move towards the cloud computing and cloud resources are delivered as product over internet. There are many companies providing cloud services like salesforce.com, Amazon AWS, Microsoft azure etc. Different cloud service providers have different pricing policies to enhance the revenue and user satisfaction. The cloud providers have pricing schemes for cloud resources under fixed pricing and dynamic pricing. Some of them favor cloud providers, other cloud consumers. The paper presents a model to predict the price of cloud resource using Recurrent Neural Network(RNN) and auctioning method based on the parameters (as demand). The paper would give insight to researchers and cloud service providers to derive the policies based on the demand and other features.
Monitoring Pedestrian Flow on Campus with Multiple Cameras using Computer Vision and Deep Learning Techniques

This paper proposes a robust method for multi-camera person re-identification (ReID), which can accurately track pedestrians across non-overlapping cameras. Closed-circuit television (CCTV) are widely used to capture pedestrian movement in different places. By integrating CCTV with computer vision and deep learning techniques, trajectory of individual pedestrian can be efficiently acquired for analyzing pedestrian walking behaviors. Many existing ReID methods aim to extract discriminative human features to distinguish a person from others. Recent state-of-the-art performance is achieved mostly by obtaining fine features from each body part. However, these part-based feature extraction methods did not consider which parts are more useful for person ReID. Therefore, this paper proposes a weighted-parts feature extraction method, such that features of specific body parts are more influential to identity prediction. After comparing the performances of utilizing each part alone, several parts are considered more view-invariant and discriminative. Higher weights are then imposed on these specific parts to extract more useful human features for person ReID. Experimental results with videos on a college campus show that the ReID accuracy of our proposed method notably outperforms many existing ones.
Specifications and improvements of LPN solving algorithms

The hardness of LPN problems serves as security source of many primitives in lightweight and post-quantum cryptography, which enjoy extreme simplicity and efficiency for various applications. Accordingly there are several LPN solving algorithms proposed over past decade, and received quite a lot of attention recently. In this paper, we propose a new LPN solving algorithm using covering codes in the existing algorithmic framework with a new data structure of numerical value instead of vector quantity for convenience in table look-up, integrate the optimized procedures, and further presenting four main improvements. Firstly, we apply the technique of binary tree sum in Gaussian elimination and new BKW iterations. Secondly, we propose a global BKW collision optimization with tweakable reduction length, which is proved optimized. Thirdly, we extend the covering codes scope in service for lager bias and smaller data requirement with a bias estimation strategy. Finally, we propose a detailed parameter selection principle for given LPN instances. The best known classic results are given for the (512/532/592,1/8)-instances suggested in cryptographic schemes. Besides, we evaluate the performance on low-noise LPN and (k,1/4)-LPN instances, and further correct the lower length bounds of LPN instances with various bias for security levels of NIST's Post-Quantum Call.
Approach to Blockchain and Smart Contract in Latin America: Application in Colombia

Blockchain is a database that allows to develop transactions between two agents without the need of intermediaries, can be public or private under the managements of an organization. One of the uses of Blockchain is the development of Smart Contracts, known as self-executing programs where responsibilities of the interested parts are established and monitored without human direct participation, what allows them to be simple, unmodifiable, quick and fully automated [10]. In addition, it is worth highlighting that in Latin America the lack of regulation has led to barely incursion in a massive way into the uses of Blockchain. However, the great benefits that this technology can bring to the region are recognized. In Colombia, particularly, the Blockchain has taken part in the government, health, industry and educative fields, among others. In fact, according to a report by BBC News, Colombia is the third country in Latin America where the purchase and sale of bitcoins grows the most. Nevertheless, in the face of regulations, its reality is not very distant to the one lived by the rest of the continent in general ways, since currently the country does not count with legislation in the area. In spite of this, the legislative power has highlighted the need to define this controls and law projects are currently underway in order to regulate the cryptoactives use and interchange.
Pole Determination for Takagi-Sugeno Fuzzy Laguerre Models

The goal of this paper is the approximation of a nonlinear, dynamic process. More specifically it is studied whether the nonlinear, dynamic process can be simplified by separating it in its linear dynamic and nonlinear static components. Focus of this work is the approximation of the dynamic proportion with orthonormal basis functions (OBFs). The approximation of the nonlinear static properties is not fixed to a specific technique, but we focus on Takagi-Sugeno fuzzy models. This approach contrasts with other publications like [10], [12], [15], where OBFs are used to model both the dynamic and nonlinear part of the process. By modeling NOBF models, the main task is the optimization of the OBF pole, because it is the hyper-parameter, which affects the model quality most. This contribution focuses on the loss functions used for the optimization of the OBF pole.The loss functions must fulfill several requirements: It has to have a unique minimum, must be computable without prior knowledge of the nonlinear component of the process, be smooth around minima and sufficiently fast to compute. We compare three approaches to approximate the loss function. The first approach is the true loss function, which is the squared error of the nonlinear model and acts as reference. The second approach incorporates the structure of local model networks [3]. Finally, we propose a new approach, which is model free and based on the gamma test. We further analyze for which type of systems the technique is suitable and compare the advantages and disadvantages of the respective approach.
Semantic enrichment and exploration of open dataset tags

This paper proposes an approach for semantic enrichment of dataset tags through the assignment of terms extracted from the dataset content and the association with meaningful external resources complementing existing tags originally attributed. In this approach, a RDF summary graph is generated to support datasets retrieval through the tags graph exploration. The motivation of this study is the need to improve datasets findability on Open Data Portals through the generation of a richer set of interlinked tags. The semantic enrichment approach is divided in four main steps, comprising cleaning, terms extraction and ranking, linking to associated ontologies or vocabularies terms, and the summarization in graph form, providing tag exploration to find other relevant datasets through tag connections. For the process we developed the Relevant Tag Extractor (RTagE), a semi-automatic software that extracts terms from a dataset, ranks and associates them with external resources. We exemplify the approach with datasets from a Web portal about the use of agrochemicals in agriculture, assigning enriched terms from the AGROVOC thesaurus as dataset tags.
Application-Oriented Succinct Data Structures for Big Data

Abstract A data structure is called succinct if its asymptotical space requirement matches the original data size. The development of succinct data structures is an important factor to deal with the explosively increasing big data. Moreover, wider variations of big data have been produced in various fields recently and there is a substantial need for the development of more application-specific succinct data structures. In this study, we review the recently proposed application-oriented succinct data structures motivated by big data applications in three different fields: privacy-preserving computation in cryptography, genome assembly in bioinformatics, and work space reduction for compressed communications.
Human Facial Features Detection Of Enhanced Images Using Cascade Object Detector

In this paper, we propose the detection of facial parts from CLAHE enhanced human facial images. In order to identify and detect only face region from the entire image along with its features such as eyes, mouth and nose Cascade Object Detector is used which is based on Viola Jones Face Detection Algorithm.
Real‐time least‐squares ensemble visual tracking

In this study, the authors present a novel ensemble tracking system by formulating the tracking task in terms of a linear regression which is a least-squares problem. A set of weak classifiers are trained using least squares which are solved efficiently using the Moore–Penrose inverse. Then, these weak classifiers are combined into a strong classifier using bagging. The strong classifier is used to recognise the target and locate its position, which is obtained efficiently in the Fourier domain. For obtaining a good ensemble, a novel sampling strategy is proposed to train accurate and diverse weak classifiers. By exploiting historical targets to monitor the training process, pose change and occlusion are well-handled. The proposed method is extensively evaluated using a variety of evaluation protocols on the recent standard datasets including OTB50, OTB100 and VOT2016. Experimental results show that the proposed methodology performs favourably against state-of-the-art methods in terms of efficiency, accuracy and robustness.
A New Fuzzy Rule Generation Scheme based on Multiple-Selection of Influencing Factors

Automatic rule generation of fuzzy systems is a extensively studied topic which is generally handled by using a labeled dataset. This paper presents a new method for this problem which does not rely on training data. It only requests a tiny information which can be roughly acquired from any data and/or domain expert. By using this information, it selects single or multiple influencing factor(s) to determine the consequent part of rules. The experiments were performed on software fault prediction problem, and the resulting rules are compared with the rules obtained from 3 existing rule generation methods which are data-based, expert-based and partially data-based solutions. Results shows that, the proposed method is able to outperform its counterparts in most of the cases.
Progress of Word Embedding in Code Generation

How to use natural language to generate program code directly has become the focus of researchers, and realizing machine's understanding of natural language is an important foundation to solve this problem. In the natural language processing system based on deep learning, word embedding model can encode words and sentences, which significantly improves the capability of a neural network to process text data. Through the analysis of word embedding model, this paper describes its vital role in code generation, elaborates the process of using word embedding and neural network to automatically generate program code, and finally looks forward to its development trend.
Development of a GF(2) Math Coprocessor

The mathematics of Galois fields GF(2) and the extension fields GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) underpin applications in error control coding and cryptology yet these binary operations are not directly supported in most digital computer instruction sets. A set of hardware blocks to perform low level GF(2) operations as a math coprocessor is described. The circuits were elaborated in VHDL and integrated into an FPGA-based soft processor (Nios II). Performance tests and detailed timing measurements are reported for typical GF(2) calculations.
Boosting the efficacy of power attacks on cryptographic circuits with autoencoder

Power attacks are able to leak the secret key of protected cryptographic circuits as long as the number of sampled power data is sufficient to filter the injected noise. However, in this Letter, a novel power attack is proposed to break protected cryptographic circuits when the number of available power data is inadequate for filtering the injected noise. To achieve this goal, a state-of-the-art machine learning technique: autoencoder is utilised for generating a certain number of new power data that are similar to the original power data to assist the noise filtering. Within the proposed autoencoder neural networks, the input and output layers are set with arrays. Furthermore, two convolution layers are used in the encoder block and decoder block, respectively, to achieve similar features between the input layer and the output layer. Under the assistance of autoencoder, the novel power attack reveals the secret key of a protected cryptographic circuit with 300,000 power data. In contrast, the regular power attacks fail to break the cryptographic circuit under the same number of power data.
A Model for Sensorimotor Affordances in Virtual Reality Environments

Study results on virtual reality (VR) environment properties and their impact on presence have been contradictory. And due to the media specificity of VR, which includes place illusion, rules for environment design cannot directly be transferred from 3D computer games or stereoscopic film to VR. This study develops a model for VR environments based on Mel Slater's observation that place illusion in VR is caused by the use of sensorimotor contingencies (SMC). It defines properties of the environment which provide action possibilities for SMC as sensorimotor affordances (SMA). SMC and SMA form a bidirectional feedback loop of perception. This model helps to clarify former contradictory study results on VR environments, and also provides the basis for a framework of preceptual design rules for VR environments.
Event Quantification in Infinitival Complements: A Free-Logic Approach

In this paper, I argue that some infinitival complements can be analyzed as an argument of verbs, in the same way of perception verb analysis (Higginbotham 1983). Then, I consider an event quantification problem in infinitival complements, showing that quantificational event semantics (Champollion 2015) and free logic are the keys to solving it.
Using Ontology Engineering Methods for Organizing the Information Interaction Between Relational Databases

The article describes an example of data consolidation between two relational databases (RDB). The proposed approach involves using of ontological engineering methods for extracting ontologies from RDB data models. The merging of the resulting ontologies is used to organize the information interaction between the RDB. The difference between the traditional and the proposed data consolidation algorithms is shown, their advantages and disadvantages are considered.
Histogram based automatic noisy band removal for remotely sensed hyperspectral images

For accurate classification of remote sensing data, Hyperspectral Images (HSI) have become very popular. It can capture the reflected electromagnetic spectrum from the object in several contiguous spectral bands. But processing of hundreds of bands is computationally expensive and also it contains several noisy and redundant bands. Often the water absorption bands are manually removed by the researchers in advance. In this work, a histogram based automatic noisy band removal algorithm is developed for the HSI. This algorithm can be used as a preprocessing step prior to hyperspectral image classification. At first, by using the histogram information, noisy bands are removed. Next, after obtaining the desired number of non-noisy bands, a Gaussian Filter is applied on obtained bands to extract spatial-spectral features. Finally, to evaluate the algorithm, classification is performed using a SVM classifier. For experimental validation of results, Indian Pines and Salinas datasets are used. The obtained result clearly reveals the effectiveness of the proposed automatic noisy band removal algorithm.
Modified Approach to Problems of Associative Rules Processing based on Genetic Search

The paper presents the main problems of associative classification in terms of the processing unstructured data of large scope. There is a review of the popular algorithms for searching associative rules and patterns. The authors formulated the problem of developing a modified approach to the optimization of searching associative rules on the basis of integrating the methods of genetic search and algorithms for associative classification. The solution of the task of searching associative rules includes finding the regularities between the related events in the datasets. The proposed approach allows us to perform the preliminary selection and the further partition of the data into the related groups. The approach also provides the determination of effective methods of the data processing in terms of the Big Data mining. To improve the computational efficiency and the accuracy of solving the task of associative classification, the paper proposes a modified genetic algorithm. The experimental research of the effectiveness of the developed algorithm are based on the task of optimization of the input data filtering in terms of the benchmark dataset Retai.
Design and Application of a Service Outsourcing Cloud for the Insurance Industry

With the development and maturity of cloud computing technology, many cloud-based solutions for specific industry applications are also rapidly emerging. This study designed and implemented a Service Outsourcing Cloud for the Insurance Industry (SOC-II) for China's huge market demand, especially for Business Process Outsourcing (BPO) companies serving the insurance industry. Firstly, this research presents the cloud computing ecosystem, conducts SOC-II needs analysis, and then proposes the system architecture and logical architecture of SOC-II. Secondly, this paper introduces an image processing case in a SOC-II production operation system, and gives the operating mode and management mode of SOC-II. Thirdly, we summarize the main features of SOC-II and the new changes that SOC-II brings to the insurance industry. Finally, the article discusses the challenges of cloud computing.