Reviewerly/exHarmony huggingface dataset:
Guesswork for Inference in Machine Translation with Seq2seq Model

One-shot inference is used in machine translation today. In practice, the output probability distribution is not concentrated since there might be multiple valid translations. Therefore, we propose to use a multi-shot inference mechanism in this paper. We analyze the Markovian property of sequence to sequence (seq2seq) model. Based on a large deviation principle satisfied by guesswork on Markov process, we derive theoretical upper bounds on the accuracy of the seq2seq model with single correct answer under one-shot inference and multi-shot inference. We establish analogous bounds when there are multiple correct answers in translating. We also discuss the extension of the results to translation with distortion tolerance.
Image Feature Matching Via Parallax Mapping for Close Range Photogrammetric Application

Image feature matching is an important step in close range photogrammetric applications, and implementing a fast, accurate and robust feature-based image matching technique is a challenging task. To solve the problems of traditional image feature matching algorithms, such as their low accuracy and long processing time, we present a parallax mapping-based matching (PMM) method that is able to improve the computation efficiency, accuracy and robustness of feature-based image matching for close range photogrammetric applications. First, the disparity of the initial corresponding points is calculated, and the mismatches are initially removed via local disparity clustering. Then, the local coordinates of the matching points are constrained by an image division grid. To ensure the correctness of the matching points, the fast polynomial transform is used for as a quadratic constraint. The method can extract high accuracy matching points from the original coarse matching points with low accuracy, and also preserve the true matching points to the greatest extent. Using a variety of experimental datasets and current mainstream feature extraction algorithms, we designed and compared commonly used feature-based image matching algorithms. The experimental results show that the proposed method is simple but effective, can meet the real-time calculation requirements, and outperforms the current state-of-the-art methods.
State Estimation of a Quantum System Using Extended Kalman Filter

In this paper we propose to estimate the state of a quantum system dynamically evolving in the presence of white Gaussian noise (WGN) under Schrödinger dynamics using Extended Kalman Filter (EKF) algorithm. In the proposed measurement model the EKF is applied to obtain the conditional expectation of the state at a given observation time up to that time. First the complex Schrödinger equation of dimension N is transformed into a real Schrödinger equation of dimension 2N. MATLAB programs were implemented to plot the wave function, error and noise to signal ratio (NSR). NSR within minimum bounds is obtained.
Optimal Path Selection of UAV in Daily Maintenance of Expressway Slopes

Unmanned aerial vehicles (UAVs) have gained special attention in recent years, among others in monitoring and inspection applications. We used UAV to analyze the characteristics of the slopes of Hanning Expressway and designed different aerial photography solutions. Compared with manual inspection, the work efficiency increased by 43.5%. Then, we selected the suspicious damage points as the collection points for the second inspection, and introduced the adjacency matrix to describe the situation between the collection points. We used the ant colony algorithm, simulated annealing algorithm and genetic algorithm to solve the shortest path. The results showed that the simulated annealing algorithm was the optimal algorithm.
A Comparison Study of Classification Methods and Effects of Sampling on Unbalanced Data

Most of the real-world classification problems exhibit an amount of imbalance of class. With such data sets, an algorithm doesn't get the required information about the minority class to make a correct prediction. Hence, imbalanced classification becomes very important. This paper explores different methods for imbalanced classification which is a supervised learning problem in which one class is larger than the other. This problem occurs often in binary classification problems than multi-level classification problems. An imbalanced classification problem is in which the dependent variable does not have a balanced proportion of classes. We use one such highly imbalanced dataset (made up of credit card transactions carried out in 2 days during September 2013 by customers of a European bank) to study and compare existing classification models by trying to predict fraudulent transactions from the legit ones. We also look at how different sampling methods like undersampling and oversampling will impact the prediction rate of these classifiers and thus try to create an optimal sampling strategy for this dataset. we try to evaluate performance based on new performance metrics like Precision (the measure of correctness achieved in positive prediction), and Recall (the ratio of observations predicted correctly). This paper explores a comparison study between common classification models like Logistic Regression, KNN SVM and Decision Tree Learning and the effects of various sampling rates on unbalanced data.
Tele-Treatment Application Design for Disable Patients with Wireless Sensors

This paper consists of the development of a system to help patients with different disabilities, affected by rare or chronic diseases or any kind of dependence through tele assistance, virtual interaction and intelligent monitoring. The main goal is to increase the quality of life of the minorities who cannot take full advantage of the healthcare system by providing an alternative way of monitoring them with the technology embedded in this paper. The result of the paper is not intended to be a single solution, but a modular system that allows the construction of an application that is able to measure the needs of a health administration and the patients. The paper also pursues an educational training to the facultative trainees in a new way to approach patient treatments. It can improve the quality of life of the patients by saving them time and other resources in moving to the Health center and the professionals can also save time as they can take advantage of the online treatments by using the proposed system.
SEFD: A Simple and Effective Single Stage Face Detector

Recently, the state-of-the-art face detectors are extending a backbone network by adding more feature fusion and context extractor layers to localize multi-scale faces. Therefore, they are struggling to balance the computational efficiency and performance of face detectors. In this paper, we introduce a simple and effective face detector (SEFD). SEFD leverages a computationally light-weight Feature Aggregation Module (FAM) to achieve high computational efficiency of feature fusion and context enhancement. In addition, the aggregation loss is introduced to mitigate the imbalance of the power of feature representation for the classification and regression tasks due to the backbone network initialized by the pre-trained model that focuses on the classification task other than both the regression and classification tasks. SEFD achieves state-of-the-art performance on the UFDD dataset and mAPs of 95.3%, 94.1%, 88.3% and 94.9%, 94.0%, 88.2% on the easy, medium and hard subsets of WIDER Face validation and testing datasets, respectively.
Trajectory Estimation of Hypersonic Glide Vehicle Based on Analysis of Aerodynamic Performance

Due to high speed and high maneuverability of hypersonic glide vehicles (HGVs), the state estimation of such targets has always been a research hotspot. In order to improve accuracy of the trajectory estimation, a nonlinear aerodynamic parameter model for target estimation based on aerodynamic performance analysis is proposed. Firstly, the dynamic characteristics of the hypersonic glide vehicle during the hypersonic gliding stage was analyzed. Then, aiming at HTV-2-liked vehicle, the engineering calculation method was used to form the reference aerodynamic model for the target estimation. Secondly, a deviation model described by first-order Markov process was introduced to compensate the uncertainties of the unknown maneuver information from the target. Finally, extended Kalman filter was utilized to estimate the state of the target. The simulation results show that the proposed model is able to improve the accuracy of acceleration estimation for the HTV-2-liked hypersonic gliding vehicles.
Recommendation System Based on Optimal Feature Selection Algorithm for Predictive Analysis

The Internet business model with predictive analysis provides efficient way of accessibility to customers with less resources and minimum expenses. User access log files are considered in this work for predictive analysis. But the log files contain lots of unwanted information and not in a form for predictive analysis. So, the web logs need to be pre-processed and further handled by the recommendation system for predictive analysis. In the processing phase, unwanted details are removed; generated user sequences, session sequences and complete user access path patterns are also generated. In the proposed recommender system, significant feature set is considered using our proposed feature selection algorithm to reduce the computational cost and to improve the quality in the predictive analysis process. In this paper, vizhamurasu news web server user access logs are considered for predictive analysis. Our proposed feature selection algorithm performance is analyzed using the performance metrics. The performance analysis shows that our proposed algorithms significantly improve the prediction accuracy than the existing.
Performance Analysis of Various Spatial Filters on Noisy Images - A Quantitative Approach

Choosing a suitable denoising filter acts a significant role in the image restoration procedure. Smoothing or reduction of noise present in the image is a significant preprocessing task before segmenting and analyzing the features present in an image. This paper is focused on choosing a desired filter for performing denoising process through some of the well-known image quantitative metrics. Some of the traditional filters are taken for experimentation and quantitative results are compared. In order to evaluate the degree of smoothness of the resultant images, quantitative metrics such as MSSIM (Mean Structural Similarity Index), PSNR (Peak Signal-to-Noise Ratio), and MSE (Mean Square Error) are used. This paper gives guidance in selecting an appropriate filter for performing denoising process through quantitative metrics.
Smart Contract-Driven Mechanism Design to Mitigate Information Diffusion in Social Networks

This paper presents a new direction in privacy preserving techniques for social networks based on consensus-driven blockchain and mechanism design principles. Privacy problem is among the class of the most important and fundamental problems in social networks. The most commonly accepted privacy solution is to incorporate a perfect data privacy policy and central system, which inherently lacks transparency and trust. All existing privacy techniques deny undesired users access to the information directly, but, in reality, the information may be forwarded to them from other users who possess the information. Our user-controlled privacy mechanism aims to control such data dissemination using simple game theoretic concepts combined with blockchain technology. Our mechanism applies to DAG structured networks (directed acyclic graphs), and our reward policy incentivizes the receivers if they do not diffuse the message in the network. We establish blockchain powered smart contracts to enable the flow of incentives in the system, without the involvement of a trusted third party. The owner of the message has to pay for the rewards, but our mechanism makes sure that the payment is minimum. In fact, the owner will have more utility when he/she pays. Our mechanism satisfies the necessary constraints of mechanism design, namely individual rationality, incentive compatibility, and weakly budget balance while ensuring privacy.
Extremal solutions to fuzzy relation equations and inequalities with three unknowns

A large number of studies have investigated the systems of fuzzy relation equations and inequalities, which have a much wider field of application. In this paper, we study several types of systems of fuzzy relation equations and inequalities consisting of a given family of k-ary fuzzy relations, w here natural number k ≥ 2, and three unknown fuzzy relations over complete residuated lattices and meet-continuous lattices. Their solutions are triples of fuzzy relations. For the systems of fuzzy relation inequalities, we give the greatest solutions contained in a given triple of fuzzy relations, the least solutions containing a given triple of fuzzy relations, or give maximal solutions contained in or containing a given triple of fuzzy relations, or belonging to a given interval of triples of fuzzy relations over complete residuated lattices and complete meet-continuous lattices. For the systems of fuzzy relation equations, we present a method of computing maximal solutions contained in a given triple of fuzzy relations and a method of computing minimal solutions containing a given triple of fuzzy relations. Furthermore, we provide some conditions under which there exist the greatest solutions contained in and the least solutions containing a given triple of fuzzy relations.
Dynamic Virtual Machine Scheduling Approach for Minimizing the Response Time Using Distance Aware Virtual Machine Scheduler in Cloud Computing

The virtual machine is a virtual model of a physical computer. Virtualization technology supports to generate multiple Virtual Machines (VMs) on a physical server; every VM is generated with their private OS and application. VM is a fundamental unit of cloud computing that is used to execute various types of applications successfully in a cloud environment. The propagation time and the response time are increased according to the distance between the customer region and the cloud server. Therefore, to solve the above-mentioned issue and for the efficient cloud services, the VM scheduling approach is necessary to manage the distance, resources and to reduce the propagation time and response time. In this paper, the Distance Aware VM Scheduler is proposed for mapping the VMs to the physical core processors and to provide the resources dynamically from the nearest data center. The proposed Distance Aware VM Scheduler is used to provide the resources from the nearest data center; thereby it decreases the propagation time and response time. Therefore, the cloud services are provided quickly with improved performance. The motivation of the proposed Distance Aware VM Scheduler is to generate an efficient VM allocation policy to physical servers and provide the resources from the nearest data center.
Smart Cards with Biometric Influences: An Enhanced ID Authentication

Management of flow of all kinds of objects including human beings signifies their real time monitoring. This paper outlines the advantages accrued out of biometrics integration with Smartcards. It showcases the identity authentication employed through different biometric techniques. Biometric key considerations influencing the essence of this technology in Smartcards have been discussed briefly in this paper. With better accuracy and highly reliable support system this technology finds itself today in widespread deployment. However, there are still some concerns with human interfaces along with important factors in implementations of biometrics with smartcards which have been highlighted in this article. This paper also examines the privacy concerns of users in addressing their apprehensions to protect their confidentiality through biometric encryption and proposes DNA technology as a best possible biometric solution. However, due to inherent limitations of its processing time and an instant requirement of authentication, it has been suggested in the proposed modal to use it with combination of one or more suitable biometric technologies. An instant access has been proposed to the user with limited rights by using biometric technology other than the DNA as a primary source of authentication. DNA has been proposed as secondary source of authentication where only after due sample comparison full access rights to the user will be granted. This paper also aims in highlighting the number of advantages offered by the integration of biometrics with smartcards. It also discusses the need to tackle existing challenges due to restrictions in processing of different biometric technologies by defining certain specific future scopes for improvements in existing biometric technologies mainly against the time taken by it for sample comparisons.
A Method of Hybrid Intelligence for UAV Route Planning Based on Membrane System

For current intelligent path optimization method, when the intelligent optimization algorithm searches for the optimal solution, it cannot simultaneously take into account the defects of global and local search capabilities. This paper takes advantage of the parallelism of membranes structure and the advantages of information interaction within the membrane, nested cell membrane structure, firefly algorithm and bee colony algorithm are used as intra-membrane optimization rules, present a hybrid intelligent optimization method based on cell membrane structure, and apply it to route planning optimization. The results show that the proposed algorithm can accurately find the optimal path planning scenarios, and the algorithm can balances local and global search capabilities.
Reducing high variability in medical image collection by a novel cluster based synthetic oversampling technique

In general, there are two open challenges for domain specific visual concept detection. First is the high intra-class variations and second is to collect large collection of sample training data covering the wide variety. In this research we present a novel medical image sampling approach to handle these two challenges. For huge intra-class variations present in the data collection we propose a unique clustering method to group similar data samples. We propose to measure similarity on the basis of membership degree between candidate groups. At the same time we handled the issue of large data collection over wide variety. We synthetically re-sampled the data on the basis of membership score. The membership score helped to make use of minor groups which has small sample size. Experiments show that our proposed method can achieve promising results and outperforms existing approaches particularly for medical image concept detection.
EPMS: A Framework for Large-Scale Patient Matching

The healthcare industry, through digitization, is trying to achieve interoperability, but has not been able to achieve complete Health Information Exchange (HIE). One of the major challenges in achieving this is the inability to accurately match patient data. Mismatching of patient records can lead to improper treatment which can prove to be fatal. Also, the presence of duplicate overheads has caused inaccessibility to crucial information in the time of need. Existing solutions to patient matching are both time-consuming and non-scalable. This paper proposes a framework, namely, Electronic Patient Matching System (EPMS), which attempts to overcome these barriers while achieving a good accuracy in matching patient records. The framework encodes the patient records using variational autoencoder and amalgamates them by performing locality sensitive hashing on an Apache spark cluster. This makes the process faster and highly scalable. Furthermore, a fuzzy matching of the records in each block is performed using Levenshtein distances to identify the duplicate patient records. Experimental investigations were performed on a synthetically generated dataset consisting of 44555 patient records. The proposed framework achieved a matching accuracy of 81.15% on this dataset.
Obstacle Detection Using Combination Between Gradient Analysis and Threshold Process

Wheelchair is very important for disability person. Currently a smart wheelchair has been developed that can move autonomously. Generally vision systems are used to control Smart Wheelchair autonomously. Detecting obstacles is one of the important parts of autonomous Smart Wheelchair. Obstacle detection can give an information for Smart Wheelchair to take a decision. The problem of obstacles detection using camera is a shadow. We uses a combination between Prewitt and threshold to detect an obstacle. The Prewitt operation was used for detecting edge of object in image. However, sometimes an object in the image contains shadow. Furthermore, the elimination of shadow object in an image using threshold process. Result of accuracy on in this research is 80.36% from the 8 data testing. Later, the smart wheelchair can decide the right obstacle and the left obstacle, if the obstacle in the right of wheelchair, wheelchair should be turn left.
Password is Not Dead and Can be Used in Local Identification for Remote Authentication

Password is the most famous authentication technology in electronic networks due to its convenience for applications. This paper investigates and points out that sharing-based password authentication leads to privacy leakages and guessing attacks. In order to overcome the vulnerabilities, this paper proposes a reformed password authentication method. It shares random credential and localizes password with multiple factors using terminal devices. Furthermore, the security of password authentication is demonstrated with hybrid privacy optimizations. Finally, the analysis illustrates that password not only achieves secure authentication, but also helps to protect privacy.
Goal Control for UGV Path Planning in Complex Environment

With the development of science and technology, the environment where the Unmanned Ground Vehicles (UGVs) patrol is complex for the cooperative and adversarial interactions. The actions performed by the UGVs can be observed by others which might be cooperators or adversaries. In order to fulfill the battle field patrol task, the goals of path planning for UGVs should be controlled in the mixed cooperative and adversarial environment. In this paper, we first define the goal control problem among three different roles (actor, cooperative observer, and adversarial observer), where the actor (UGV) need to reveal the real goal tied with the truthful path to the cooperative observer while hide the real goal tied with the deceptive path to the adversarial observer. Therefore, we propose one method of generating truthful or deceptive path depending on the judgement of the current observer type. Finally, indoor robot demonstration has been performed to verify the applicable of our proposed method.