Reviewerly/exHarmony huggingface dataset:
Efficient solution to the millionaires' problem based on asymmetric commutative encryption scheme

Abstract Secure multiparty computation is an important scheme in cryptography and can be applied in various real‐life problems. The first secure multiparty computation problem is the millionaires' problem, and its protocol is an important building block. Because of the less efficiency of public key encryption scheme, most existing solutions based on public key cryptography to this problem are inefficient. Thus, a solution based on the symmetric encryption scheme has been proposed. In this paper, we formally analyse the vulnerability of this solution, and propose a new scheme based on the decisional Diffie‐Hellman assumption. Our solution also uses 0‐encoding and 1‐encoding generated by our modified encoding method to reduce the computation cost. We implement the solution based on symmetric encryption scheme and our protocol. Extensive experiments are conducted to evaluate the efficiency of our solution, and the experimental results show that our solution can be much more efficient and be approximately 8000 times faster than the solution based on symmetric encryption scheme for a 32‐bit input and short‐term security. Moreover, our solution is also more efficient than the state‐of‐the‐art solution without precomputation and can also compare well with the state‐of‐the‐art protocol while the bit length of private inputs is large enough.
Development of HUMGEOSTAT: A New Geological Tool for Geostatistical Analysis of Mineral Deposit: A Case Study at Sin Quyen Mine (Northern Vietnam)

This paper presents the results of the design and implementation of a new geological tool, named as HUMGEOSTAT, for advanced geostatistical analysis of mineral deposit, a case study at the Sin Quyen, one of the important mines for the Vietnam mining industry in Vietnam. The tool is developed based on the Stanford Geostatistical Modeling Software (SgeMS) platform, which is an open-source computer package for solving problems involving spatially related variables. The tool is capable of supporting users in reducing uncertainty and improving the quality of mining exploration tasks. The tool has all the functions needed to solve a complete geostatistical issues i.e. Variogram models (such as Spherical, Exponential, Gausian, Linear and Pure nugget effect); and four commonly used Kriging methods (Ordinary Kriging, Simple Kriging, Indicator Kriging, Co-Kriging). The tool was applied to test various types of input data and particularly for complex copper ore bodies of Sin Quyen mine. The results of analysis of orebody spatial structure has found that Cu content has non-uniform change, regional anisotropy in north-south (0±22.50) direction, and anisotropic index is around 1.3. The mineral reserves are estimated according to sub-blocks and points in the orebody-space. The results indicate that the Ordinary Kriging (OK) has reliable results, variance estimation is small; the predicted result gained from OK have strong correlation (K=0.905) with ore samples. HUMGEOSTAT was applied at the Sin Quyen mine and various coal mines, the results were adapted with reality of mines.
A Big Data Experiment to Assess the Effectiveness of Deep Learning Neural Networks in the Mining of Sustainable Aspects of the Hotels Clients Opinions

Context: Opinions given by hotel clients in tourism social networks, the ones which can be a great source of knowledge extraction in the Big Data context, including the sustainable aspects of the hotels clients opinions. Objective: Evaluate performance and quality of deep learning neural networks, especially the Target-Connection LSTM (TC-LSTM) and Attention-based LSTM (AT-LSTM) algorithms, aiming to mine and classify the opinions posted on the TripAdvisor and Booking social networks, by considering sustainability aspects. Method: A controlled experiment to compare the efficiency and efficacy of the classifiers was carried out. Results: The AT-LSTM algorithm presented the best results, especially in terms of accuracy, precision, f-measure, average training time and average classification time. The first with 74,58%, the second with 95,54%, the third with 85,37%, then fourth with 7,3 s and the last one with 1,12 s. Conclusion: The AT-LSTM algorithm was expressly more effective than TC-LSTM, making it an option to be considered for mining opinions based on specific aspects of tourism and peculiar market niches.
Crowd Density Estimation Using Hough Circle Transform for Video Surveillance

In this paper, a crowd density estimation method has been proposed, using Hough circle transformation. In this method background and foreground information is segregated using ViBe technique and followed by segmentation of foreground information. These segmented foreground information are used in Hough circle transformation for crowd density estimation. The performance of proposed method is evaluated in terms of various parameters like mean square error, average standard deviation and elapsed time over a number of video sequences. Experimental results shows that the proposed method is better than parallel crowd density estimation technique in literature.
Dynamic tracking algorithm suitable for intelligent video surveillance

In order to improve the shortcomings of the existing algorithm and aiming at the problems of classical algorithm in moving object detection and tracking, a moving object detection algorithm combined with improved texture and chromatic information is proposed. The texture and color two kinds of feat ures insensitive to shadow are used to describe the background, and then the background description is applied to the Gauss mixture model to put forward a new method for moving target detection, which can better resist the influence of shadow and background illumination changes. At the same time, the Camshift tracking algorithm jointing LBP texture and color information and target tracking algorithm based on Kalman filter and Blob matching method are put forward, and the actual effect of the algorithm is tested. The test results showed that the algorithm reduces the computational complexity and improves the robustness of target tracking under the complex scene. It summed up that the tracking algorithm has good performance.
Frequent similar pattern mining using non Boolean similarity functions

There are many problems were the objects under study are described by mixed data (numerical and non numerical features) and similarity functions different from the exact matching are usually employed to compare them. Some algorithms for mining frequent patterns allow the use of Boolean similarity f unctions different from exact matching. However, they do not allow the use of non Boolean similarity functions. Transforming a non Boolean similarity function into a Boolean one, and then applying the previous algorithms for mining frequent patterns, could lead to loss some patterns, and even more to generate some other patterns which indeed should not be considered as frequent similar patterns. In this paper, we extend the similar frequent pattern mining by allowing the use of non Boolean similarity functions. Several properties for pruning the search space of frequent similar patterns and a data structure that allows computing the frequency of patterns candidates, are proposed. Also, three algorithms for mining frequent patterns using non Boolean similarity functions are proposed. Experimental results show the efficiency and efficacy of the algorithms. The proposed algorithms obtain better patterns for classification than those patterns obtained by traditional frequent pattern miners, and miners using Boolean similarity functions.
Memetic Algorithm Image Enhancement for Preserving Mean Brightness Without Losing Image Features

In the image processing application, contrast enhancement is a major step. Conventional contrast enhancement methods such as Histogram Equalization (HE) do not have satisfactory results on many different low contrast images and they also cannot automatically handle different images. These problems result in specifying parameters manually to produce high contrast images. In this paper, an automatic image contrast enhancement on Memetic algorithm (MA) is proposed. In this study, simple exploiter is proposed to improve the current image contrast. The proposed method accomplishes multi goals of preserving brightness, retaining the shape features of the original histogram and controlling excessive enhancement rate, suiting for applications of consumer electronics. Simulation results shows that in terms of visual assessment, peak signal-to-noise (PSNR) and Absolute Mean Brightness Error (AMBE) the proposed method is better than the literature methods. It improves natural looking images specifically in images with high dynamic range and the output images were applicable for products of consumer electronic.
A game-theoretic approach for selecting optimal time-dependent thresholds for anomaly detection

Adversaries may cause significant damage to smart infrastructure using malicious attacks. To detect and mitigate these attacks before they can cause physical damage, operators can deploy anomaly detection systems (ADS), which can alarm operators to suspicious activities. However, detection thresholds of ADS need to be configured properly, as an oversensitive detector raises a prohibitively large number of false alarms, while an undersensitive detector may miss actual attacks. This is an especially challenging problem in dynamical environments, where the impact of attacks may significantly vary over time. Using a game-theoretic approach, we formulate the problem of computing optimal detection thresholds which minimize both the number of false alarms and the probability of missing actual attacks as a two-player Stackelberg security game. We provide an efficient dynamic programming-based algorithm for solving the game, thereby finding optimal detection thresholds. We analyze the performance of the proposed algorithm and show that its running time scales polynomially as the length of the time horizon of interest increases. In addition, we study the problem of finding optimal thresholds in the presence of both random faults and attacks. Finally, we evaluate our result using a case study of contamination attacks in water networks, and show that our optimal thresholds significantly outperform fixed thresholds that do not consider that the environment is dynamical.
BWT Tunnel Planning is Hard But Manageable

The Burrows-Wheeler transform is a well known and useful text transformation used for both data compression and text indexing. Recently, a new technique called "tunneling" was presented, improving compression rates of BWT compressors by a vast amount. In this paper, we address the problem of "tunnel planning", that is, find a good choice of Blocks to be tunneled. We show that, if Blocks are allowed to overlap each other, the corresponding Block cover and maximum coverage problem are NP-hard, while the Block cover problem is in P if no overlappings are allowed. Furthermore, we present a simple heuristic which outperforms existing solutions for Block choice in the overlapping case both in compression rate and resource requirements.
Deep learning for automatic ordnance recognition

Explosive Ordnance Disposal (EOD) technicians are on call to respond to a wide variety of military ordnance. As experts in conventional and unconventional ordnance, they are tasked with ensuring the secure disposal of explosive weaponry. Before EOD technicians can render ordnance safe, the ordnance must be positively identified. However, identification of unexploded ordnance (UXO) in the field is made difficult due to a massive number of ordnance classes, object occlusion, time constraints, and field conditions. Currently, EOD technicians collect photographs of unidentified ordnance and compare them to a database of archived ordnance. This task is manual and slow - the success of this identification method is largely dependent on the expert knowledge of the EOD technician. In this paper, we describe our approach to automatic ordnance recognition using deep learning. Since the domain of ordnance classification is unique, we first describe our data collection and curation efforts to account for real-world conditions, such as object occlusion, poor lighting conditions, and non-iconic poses. We apply a deep learning approach using ResNet to this problem on our collected data. While the results of these experiments are quite promising, we also discuss remaining challenges and potential solutions to deploying a real system to assist EOD technicians in their extremely challenging and dangerous role.
Bilayer model for cross-view human action recognition based on transfer learning

In cross-view action recognition, there remains a challenge that the action representation will lack the ability of transfer learning when the feature space changes. To solve this problem, a cross-view action recognition approach using a bilayer discriminative model is proposed. We first extract the key poses to capture the essence of each action sequence and represent each key pose by a bag of visual words (BoVW) in a single view. We then construct a bipartite graph between the heterogeneous poses and apply multipartitioning to cocluster the view-dependent visual words for developing the cross view bags of visual words feature, which is more discriminative in the presence of view changes. The novelty is to design a bilayer classifier consisting of SVM and HMM at the frame level and sequence level, respectively, to make up for the loss of temporal information when using a BoVW to represent the whole action sequence. Finally, DTW is used as a pruning algorithm to lessen the number of nodes for searching the Viterbi path. Extensive experiments are performed on two well-known multiple view action datasets IXMAS and N-UCLA, and a detailed performance comparison with the existing view-invariant action recognition techniques indicates that the proposed method works equally well in accuracy and efficiency.
Predicting Students’ Academic Performance Using Utility Based Educational Data Mining

Knowledge extracted from educational data can be used by the educators to obtain insights about how the quality of teaching and learning must be improved, how the factors affect the performance of the students and how qualified students can be trained for the industry requirements. This research focuses on classifying a knowledge based system using a set of rules. The main purpose of the study is to analyze the most influencing attributes of the students for their module performance in tertiary education in Sri Lanka. The study has gathered data about students in a reputed degree awarding institute in Sri Lanka and used three different data mining algorithms to predict the influential factors and they have been evaluated for interestingness using objective oriented utility based method. Subsequently, age of the students, their family background with regard to parents’ occupations, average monthly income of the family, their English language fluency level and knowledge of Mathematics were identified as the interesting factors. The findings of this study will positively affect the future decisions made regarding the progress of the students’ performance, quality of the education process and the future of the education provider.
Derivation of NARX models by expanding activation functions in neural networks

A method was developed to derive an NARX model from a neural network so that the usability of open‐source libraries for network learning was combined with the NARX advantage of revealing the system structure. After the neural network model was trained on input and output data, the sigmoid activation functions were expanded into Taylor series. Candidate parameters in the NARX model were calculated from the connection weights in the neural network and coefficients in the series. The NARX model structure was determined by the extended least‐squares (ELS) method and the Bayesian information criterion (BIC). Correct NARX models were successfully detected in computer simulations and an experiment. The developed method can be used for any activation functions, including the sigmoid function, if they are expanded into Taylor series. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
A Comparative Study of Deep Learning Approaches for Query-Focused Extractive Multi-Document Summarization

Query-focused multi-document summarization aims to produce a single, short document that summarizes a set of documents that are relevant to a given query. During the past few years, deep learning approaches have been utilized to generate summaries in an abstractive or extractive manner. In this study, we employ six deep neural network approaches to solving a query-focused extractive multi-document summarization task and compare their performances. To the best of our knowledge, our study is the first to compare deep learning techniques on extractive query-focused multi-document summarization. Our experiments with DUC 2005-2007 benchmark datasets show that Bi-LSTM with Max-pooling achieves the highest performance among the methods compared.
Design and creation of salar acoustical phonetics parameter database

“The preservation of endangered ethnic minority languages” and “application of ethnic minority languages” have become an important research direction and topic for the ethnic minority language researchers. Based on the previous studies, the combined quantitative and qualitative analysis was made to carry out a systematic study on vowels and consonants in the Salar language, especially the strong vowels, weak vowels, voiceless consonants, and voiced consonants, in order to create the “Salar Acoustical Phonetics Parameter Database”. According to the relevant norms, the segmentation, labeling, parameters extraction and database creation were conducted against the collected signals, and the experimental research on the Salar phonetics was implemented by making a comprehensive statistical analysis of the database. Through the study of Salar phonetics based on the Salar Acoustical Phonetics Parameter Database, it not only provides basic parameters of acoustic physiology for preservation and research of Salar language, but also provides the basic data for the future Salar acoustical corpus construction, speech recognition and speech synthesis, and provides a practical theoretical basis for the acoustical research of the national language project under construction.
Solving 0/1 Knapsack Problem using Opposition-based Whale Optimization Algorithm (OWOA)

The 0/1 Knapsack problem is one of the most popular real-world optimization problems that arise in searching space and finding the most optimum solution. Theoretically, the optimum solution problem of the 0/1 Knapsack requires suitable technique to explore the search space effectively. Practically, as many metaheuristic algorithms, Whale Optimization Algorithm (WOA) may fail in local optimum solution. This paper proposes Opposition-based Whale Optimization Algorithm (OWOA) to optimize solution problem in 0/1 Knapsack. The OWOA has been tested original WOA by using twenty cases of Knapsack problem and against other metaheuristic algorithms such as (CGMA) and HS-Jaya. The experimental results indicate a significant performance of the optimization solution and stabilization with minimal standard deviation value. This shows that the OWOA improved the original version WOA and has promising result in comparison with other existing algorithms.
The Return of xorro

Although parity constraints are at the heart of many relevant reasoning modes like sampling or model counting, little attention has so far been paid to their integration into ASP systems. We address this shortcoming and investigate a variety of alternative approaches to implementing parity constraints, ranging from rather basic ASP encodings to more sophisticated theory propagators (featuring Gauss-Jordan elimination). All of them are implemented in the xorro system by building on the theory reasoning capabilities of the ASP system clingo. Our comparative empirical study investigates the impact of the number and size of parity constraints on performance and indicates the merits of the respective implementation techniques. Finally, we benefit from parity constraints to equip xorro with means to sample answer sets, paving the way for new applications of ASP.
Conflict Detection, Avoidance, and Resolution in a Non-Linear RDF Version Control System

The Semantic Web is about collaboration and exchange of information. While the data on the Semantic Web is constantly evolving and meant to be collaboratively edited there is no practical transactional concept or method to control concurrent writes to a dataset and avoid conflicts. Thus, we follow the question, how can we ensure a controlled state of a SPARQL Store when performing non transactional write operations? Based on the Distributed Version Control System for RDF data implemented in the Quit Store we present the Quit Editor Interface Concurrency Control (QEICC). QEICC provides a protocol on top of the SPARQL 1.1 standard to identify, avoid, and resolve conflicts. The strategies reject, branch, and merge are presented to allow different levels of control over the conflict resolution. While the reject strategy gives full control to the client, with branch and merge it is even possible to postpone the conflict resolution and integrate it into the date engineering process.
ARIES: An Autonomous Controller For Multirobot Cooperation

This paper is focused in cooperative robotics research, particularly for space exploration missions. To do this, we proposed an autonomous controller developed to deploy cooperative heterogeneous robot systems which combine and coordinate their functionalities to achieve scientific targets in challenging scenarios. Contrary to other autonomous controllers, ARIES is a multiagent architecture which brings the T-REX framework and the hierarchical leader -follower approach together. This scheme allows ARIES to provide a distributed execution and a centralized deliberation into the leader, as well as scalability and flexibility to control multiple robotic platforms. Also, the hierarchical scheme provides an affordable way to implement and deploy fail recover mechanisms in ARIES to create fault-tolerant instances of the architecture.
Author-Topic Classification Based on Semantic Knowledge

We propose a novel unsupervised two-phased classification model leveraging from semantic web technologies for discovering common research fields between researchers based on information available from a bibliographic repository and external resources. The first phase performs coarse-grained classification by knowledge disciplines using as reference the disciplines defined in the UNESCO thesaurus. The second phase provides a fine-grained classification by means of a clustering approach combined with external resources. The methodology was applied to the REDI (Semantic Repository of Ecuadorian researchers) project, with remarkable results and thus proving a valuable tool to one of the main REDI’s goals: discover Ecuadorian authors sharing research interests to foster collaborative research efforts.