Reviewerly/exHarmony huggingface dataset:
Driver-car Natural Interaction Method Based on Head-Eye Behaviors

When drivers interact with the car, their hands will leave the steering wheel, causing great safety risks. In order to solve this problem, this paper proposed a natural interaction method based on driver's head-eye moving behaviors by capturing driver's image and detecting multi-feature in driver's face. Firstly, 2D images of the driver's head and 3D point cloud data are collected through the infrared camera installed in front of the driver in the car. By using the SDM minimization nonlinear least square function to detect the human face in 2D infrared images, the features of human face are obtained. And the eye area images are obtained by using the feature point coordinates. The C-V extension model based on feature search was used to extract the pupil and the feature points of the Purkinje image in the eye image. Meanwhile, the spatial coordinates of the points in the three-dimensional point cloud were transformed by using the index of facial feature points to obtain the driver's head posture. Finally, the decision tree model is used to analyze the obtained eye feature vectors and head posture data, by this way, mapping these head-eye behaviors to the gazing object, and driver can use it to interaction with car. The experimental results show that the proposed method in this paper can accurately and quickly identify the driver's interaction intentions on the vehicle.
Bitcoin Fee Decisions in Transaction Confirmation Queueing Games Under Limited Multi-Priority Rule

In the Bitcoin system, transaction fees serve not only as the fundamental economic incentive to stimulate miners, but also as an important tuner for the Bitcoin system to define the priorities in the transaction confirmation process. In this paper, we aim to study the priority rules for queueing transactions based on their associated fees, and in turn users' decision-making in formulating their fees in the transaction confirmation queueing game. Based on the queueing theory, we first analyzed the waiting time of users under non-preemptive limited multi-priority (LMP) rule, which is formulated to adjust users' waiting time over different priorities. We then established a game-theoretical model, and analyze users' equilibrium fee decisions. Towards the end, we conducted computational experiments to validate the theoretical analysis. Our research findings can not only help understand users' fee decisions under the LMP rule, but also offer useful managerial insights in optimizing the queueing rules of Bitcoin transactions.
Query-Based Word Spotting in Handwritten Documents Using HMM

The retrieval of keywords from handwritten documents is tasked between one or more query word in a database. The works involves segment the individual words from the document images and formation of an index to all words, it uses search mechanism to access the query word from the scanned documents. However, unconstrained handwritten document realization remains a challenging problem with inadequate work to providing robust research experience in handwritten document. The proposed work characterizes to focus on keyword retrieval. The input word images are 2 × 2 block, each partition region again sub-block into 4 × 4, 5 × 5 and 6 × 6. In each sub-block, calculate average intensity of pixels and find the maximum average intensity value in horizontal and vertical direction. Thereby 32, 40 and 48 dimensional features are extracted from different sub-block and extracted MAIV features are fed to HMM to construct the models and validation of handwritten keywords. The query words are recognized using the Euclidean distance of the keyword and search keyword word acquire from the index position to retrieve the appropriate words from the document. The performance measure such as precision, recall and F-measure is calculated for keywords in different sub-block from different own handwritten dataset.
Performance Improvement of Naïve Bayes Classifier for Sentiment Estimation in Ambiguous Tweets of US Airlines

Airline deals with wide scale of customers, including family members, business class, business deals, men, women, and youngsters. Every kind of customer is involved in this domain. So their feedbacks are very important; direct feedback of customers is always positive but analyzing their tweets is important, how the Tweets are? Whenever we move toward personal tweets, analysis becomes so much hectic because of customers in very high volume. Most of the times tweets are ambiguous; it depends on thinker. If the thinker is positive, then he always gives positive tweets and negative thinker gives negative tweets. So ultimately, our work is to find sentiments of descriptive tweets and words and address their expression in quantities format whether they are happy or not. The novel factor inside this work is to examine ambiguous tweets and neutralize them according to proposed algorithm. The complete work is based on Twitter dataset for US Airlines and performs different level of mining and processing for most accurate results. Here, improved sentiment analysis model has been proposed based on Naïve Bayes classifier to classify tweets based on sentiments and neutralized tweets from ambiguous to positive or negative. A Java tool has been developed to implement the proposed solution that is tested through manual testing and performance parameters. This work considers recall/accuracy, f-score, and computation time as performance parameter for proposed solution. The complete solution is compared with existing work and concluded that it performs better than conventional algorithms.
Better Development of Safety Critical Systems: Chinese High Speed Railway System Development Experience Report

Ensure the correctness of safety critical systems play a key role in the worldwide software engineering. Over the past years we have been helping CASCO Signal Ltd which is the Chinese biggest high speed railway company to develop high speed railway safety critical software. We have also contributed specific methods for developing better safety critical software, including a search-based model-driven software development approach which uses SysML diagram refinement method to construct SysML model and SAT solver to check the model. This talk aims at sharing the challenge of developing high speed railway safety critical system, what we learn from develop a safety critical software with a Chinese high speed railway company, and we use ZC subsystem as a case study to show the systematic model-driven safety critical software development method.
Enabling the Efficient, Dependable Cloud-Based Storage of Human Genomes

Efficiently storing large data sets of human genomes is a long-term ambition from both the research and clinical life sciences communities. For instance, biobanks stock thousands to millions of biological physical samples and have been under pressure to store also their resulting digitized genomes. However, these and other life sciences institutions lack the infrastructure and expertise to efficiently store this data. Cloud computing is a natural economic alternative to private infrastructures, but it is not as good an alternative in terms of security and privacy. In this work, we present an end-to-end composite pipeline intended to enable the efficient, dependable cloud-based storage of human genomes by integrating three mechanisms we have recently proposed. These mechanisms encompass (1) a privacy-sensitivity detector for human genomes, (2) a similarity-based deduplication and delta-encoding algorithm for sequencing data, and (3) an auditability scheme to verify who has effectively read data in storage systems that use secure information dispersal. By integrating them with appropriate storage configurations, one can obtain reasonable privacy protection, security, and dependability guarantees at modest costs (e.g., less than $1/Genome/Year). Our preliminary analysis indicates that this pipeline costs only 3% more than non-replicated systems, 48% less than fully-replicating all data, and 31% less than secure information dispersal schemes.
A hybrid scheme for enhancing recovered image quality in polynomial based secret image sharing by modify-and-recalculate strategy

A hybrid approach for enhancing the recovered image quality in Shamir's polynomial based secret image sharing (SIS) is proposed in this paper. To remain computational efficiency, simple modular arithmetic is still used. The fundamental operation in the proposed method is mod P calculation, where P is a prime. A framework for the proposed method is firstly given. Mod PG and mod PS sharing algorithms are included. Then, a modify-and-recalculate strategy for mod PS sharing is proposed to make sure that each generated modular result is within the interval [0,2N−1]. Theoretical analysis on recovered image quality and extensive experiments are conducted. Recommendation on choosing optimal sharing method for different values of N (8 ≤ N ≤ 17) is given.
A Robust Circular Two-Dimensional Barcode and Decoding Method

With the popularity of two-dimensional (2D) bar-codes, many image correction algorithms for two-dimensional barcodes have been proposed. However, limited by the form of matrix barcodes, the effectiveness of these correction algorithms is limited. Besides, circular 2D barcodes such as ShotCode have better ability to resist image distortion. But, due to the small information capacity, the research on circular 2D barcodes in recent years is limited. Therefore, we propose a robust circular 2D barcode in this paper. By adopting colour-coding and a new decoding method, it can not only achieve the basic information capacity, but also effectively enhance the ability to resist image distortion. In addition, we propose two indicators (maximum distortion ratio and maximum support opening angle) to measure the image distortion of two-dimensional barcodes on geometric bodies. Experiments verify the superiority of the new circular two-dimensional barcode and its decoding method.
Deep Neural Learning for Automated Diagnostic Code Group Prediction Using Unstructured Nursing Notes

Disease prediction, a central problem in clinical care and management, has gained much significance over the last decade. Nursing notes documented by caregivers contain valuable information concerning a patient's state, which can aid in the development of intelligent clinical prediction systems. Moreover, due to the limited adaptation of structured electronic health records in developing countries, the need for disease prediction from such clinical text has garnered substantial interest from the research community. The availability of large, publicly available databases such as MIMIC-III, and advancements in machine and deep learning models with high predictive capabilities have further facilitated research in this direction. In this work, we model the latent knowledge embedded in the unstructured clinical nursing notes, to address the clinical task of disease prediction as a multi-label classification of ICD-9 code groups. We present EnTAGS, which facilitates aggregation of the data in the clinical nursing notes of a patient, by modeling them independent of one another. To handle the sparsity and high dimensionality of clinical nursing notes effectively, our proposed EnTAGS is built on the topics extracted using Non-negative matrix factorization. Furthermore, we explore the applicability of deep learning models for the clinical task of disease prediction, and assess the reliability of the proposed models using standard evaluation metrics. Our experimental evaluation revealed that the proposed approach consistently exceeded the state-of-the-art prediction model by 1.87% in accuracy, 12.68% in AUPRC, and 11.64% in MCC score.
Application and Analysis of Time Series Prediction Efficient Methods Based on the Krichevsky Predictor

In this paper we present the algorithm of calculation of the Krichevsky's predictor and R-measure based on it. This algorithm allows to compute the predictor in linear time. We present the description of the algorithm, some of its features and the results of its application. In addition, we present the results of comparative analysis of R-measure and other prediction methods application on real time series problems.
Facilitating Electric Vehicle Charging Across the UAE Using Blockchain

Electric Vehicles are becoming popular among car owners now a day with several car manufacturing companies expanding their productivity with different models and sizes. However, there is a lack of proper charging infrastructure to support their wide spread. Blockchain as a technology first was used for money transactions while not relying on any trusted third parties such as central banks to securely approve and validate such exchanges. This paper is focusing on procedures of Blockchains in controlling and sharing charging stations and billing records to permit customers, organizations/authorities, and other power stakeholders to share information among themselves, and increment the ability of systems to exchange and make use of information. Despite the fact that the use of Blockchains may diminish repetition and provide power organizations with constant records about their customers, regardless it accompanies few difficulties which could invade customer's privacy. In this paper, we study different blockchain constructions, discuss the challenges and provide a possible framework to facilitate Electric Vehicles charging across the UAE.
Feature Selection based on Random Forests and its Application to a Modeling of Sewage Treatment Plant

When solving a regression or classification problem, we often need to identify relevant input variables, rather than predict the output value. A procedure for selecting the relevant input variables is referred to as the feature selection. In this study, we propose a new feature selection method that uses the importance measure available in the random forest. Through numerical experiments, we confirmed that the proposed method shows a higher sensitivity but a lower specificity. The proposed method is then applied to an actual regression problem in order to identify relevant input variables.
Metric-Based Regularization and Temporal Ensemble for Multi-Task Learning using Heterogeneous Unsupervised Tasks

One of the ways to improve the performance of a target task is to learn the transfer of abundant knowledge of a pretrained network. However, learning of the pre-trained network requires high computation capability and large-scale labeled datasets. To mitigate the burden of large-scale labeling, learning in un/self-supervised manner can be a solution. In addition, using un-supervised multi-task learning, a generalized feature representation can be learned. However, un-supervised multi-task learning can be biased to a specific task. To overcome this problem, we propose the metric-based regularization term and temporal task ensemble (TTE) for multi-task learning. Since these two techniques prevent the entire network from learning in a state deviated to a specific task, it is possible to learn a generalized feature representation that appropriately reflects the characteristics of each task without biasing. Experimental results for three target tasks such as classification, object detection and embedding clustering prove that the TTE based multi-task framework is more effective than the state-of-the-art (SOTA) method in improving the performance of a target task.
A two-fold fusion fuzzy framework to restore non-uniform illuminated blurred image

Despite significant progress in the field of imagery, the image captured may experience some deterioration that is mainly summarized in non-uniform illumination and blur. This paper proposes a new two-fold fusion fuzzy framework to restore non-uniform illuminated blurred image: Fuzzy Rule-Based Method to enhance Illumination (FRMI) and Fuzzy Rule-Based Method for Deblurring (FRMD). The purpose of the restoration is to approximate an image as similar as possible to the original image from the observed object. In the first stage (FRMI), a fuzzy rule-based methodology is proposed to sharpen the non-uniform illuminated image which is based on the local brightness differences among pixels by distinguishing the dark and bright image regions. To deblur the image, blind deconvolution method is used. The goal of the second stage (FRMD) is to use the proposed edge detection and fuzzy model to find the correct size of the point spread function (PSF) which is needed for the blind deconvolution method. Implementation of the proposed method on two different datasets consists of non-uniform illuminated blur image shows that the proposed method outperforms the other enhancement techniques. To measure the restored image quality, seven image quality measurement metrics are used. The proposed fusion method produces an even illuminated deblurred image with improvement in the details and contrasts.
Guidelines for Writing a Case Study

Case studies are an effective publication method used to document the facts and analysis of a specific observation or incident. The content and format of a case study can vary depending on several factors, including the purpose of the case study, the degree of analysis available at the time of the report, the derived lessons learned, and any recommendations. This commentary outlines the guidelines for writing an effective case study.
Key Exchange and E-Mail Authentication Using Lagrange Interpolation

All corporate and government sectors like the aviation, banking, military, etc., need to determine genuineness of an authenticated user. This paper proposes an identity-based secure authentication system for e-mail security using Lagrange interpolation based on the concept of Zero-Knowledge Protocol. This protocol makes the user to prove to the server that he/she has password without sending any information, either encrypted password or clear text to the server and also without intervention of the third party (authentication server). In this protocol, one party allows the other party to confirm whether the statement is true without revealing any other information. Mutual identification of two users is done using this protocol, and it involves exchange of pseudorandom numbers to generate secret key or session key which in turn is used for encryption and decryption. Polynomial-based session key is generated using Lagrange interpolation by sender and receiver.
Improved Binarization Using Morphology-driven Image Resizing and Decomposition

This paper presents a novel binarization algorithm for stained decipherable patterns. First, the input image is downsized, of which the reduction ratio is determined by iteratively applying binary morphological Closing operation. Such morphology-driven image downsizing not only saves the computation time of subsequent processes, but the key features necessary for the successful decoding is preserved. Then, high or low contrast areas are decomposed by applying the grayscale morphological Closing and Opening operators to the downsized image, and subtracting the two resulting output images from each other. If necessary, these areas are further subjected to decomposition to obtain finer separation of high and low regions. Having done the preprocessing, two approaches are proposed to do the binarization: (1) GMM is used to estimate a binarization threshold for each region (2) the binarization problem is treated as an image-translation task and hence a deep learning approach based on the conditional generative adversarial network (cGAN) is trained using the high or low contrast areas as conditional inputs. Our method solves the difficulty of choosing a proper preset sampling mask in conventional adaptive thresholding methods. Extensive experimental results show that the binarization algorithm can efficiently improve the decipher success rate over the other methods.
Introduction to Genetic Algorithm with a Simple Analogy

Genetic Algorithm (GA), which is now considered as a well-established and one of the most widely applied optimization techniques, started its journey when Von Neumann first forwarded the theory of self-reproducing automata during fifties (Fellenius W: Calculation of the stability of earth dams. Trans, of 2nd congress on Large Dams, vol 4, pp 445--459, 1936). However, implementation of this ideas came into application during eighties (Baker R: Determination of the critical slip surface in slope stability computations. Int J Numer Anal Methods Geomech 4:333--359, 1980; Bishop AW: The use of slip circle in the stability analysis of slopes. Geotechnique London 5:7--17, 1955; Chen Z-Y, Shao C-M: Evolution of minimum factor of safety in slope stability analysis. Canadian Geotech J Ottawa 25:735--748, 1988; Celestino TB, Duncan JM: Simplified search for noncircular slip surface. In: Proceedings of the 10th international conference on SMFE, pp 391--394, 1981). Because of the obvious advantage of using GA in optimizing even complex non-linear functions, it has now been used by many researchers for solving varieties of optimization problems. Basically GA is a computerized search optimization algorithms based on the principles of survival of the fittest, first laid down by Charles Darwin. Concept of GA has so far been presented by different researchers in different forms, mostly in a mathematical framework. The terms like gene, chromosome, cross over, mutation and generation, which are basically derived from biological origin, many a time, become confusing to readers of other disciplines and also it become difficult to appreciate how these processes will lead to an optimal solution. Therefore, in this article, the basic philosophy of GA is presented by highlighting how it works and how the natural biological processes can help in obtaining the most optimal or at least near optimal solution through a simple analogy.
Construction of new codes in term‐rank metric

This study investigates codes in the term-rank metric. These codes can be used to correct crisscross errors in a () matrix. These errors can be found in memory chip arrays, in magnetic tape recordings, or in the parallel-channel system with interference. In this study, the authors will build an algorithm for computing term-rank of a matrix and new construction of term-rank codes. Single error-correcting codes in the term-rank metric are considered and help us build a generator matrix and parity-check matrix for term-rank codes. Research results derived from this study show that the term-rank metric is suitable for correcting crisscross errors.
Cryptanalysis of Security Analysis and Enhancements of a Remote User Authentication Scheme

Abstract The main purpose of user authentication schemes is to verify the authorized user using a server via an insecure channel. With the authentication, a server and a user could have a mutual authentication. In 2019, Cao proposed an improvement of a user authentication scheme. The scheme was postulated in that it could protect from the several possible attacks and have the following advantages: Identity preservation, not only to resist the slow wrong password detection, to resist the user masquerading, the password guessing, and the sever masquerading attacks, but also to have a mutual authentication between servers and users. However, we will show his scheme is not with the capacity to against the denial of service and on-line password guessing attacks in this article. In order to improve that authentication scheme, this work proposes an enhanced remote authentication scheme with the capacity to resist those vulnerabilities as shown in Cao-Sun-Cao’s scheme.