Reviewerly/exHarmony huggingface dataset:
A visual saliency based sample augmentation method for image patch similarity comparison

In this paper, we propose a sample augmentation method for Convolutional Neural Networks (CNN) based image patch similarity comparison methods, which have presented obvious superiority compared with the conventional methods. In our method, we augment the learning samples by pre-processing them with their visual saliency maps. By using the saliency enhancement samples rather than the raw samples, the trained CNN models are more robust to the background changes and disorderly minor interferes, and therefore achieve higher comparison accuracy. In the experiments, we test our method by combining it with the 2-channel CNN model, and then compare with the primary 2-channel CNN, Siamese and Pseudo-Siamese CNN models. The experimental results demonstrate the superiority of the proposed method: it makes the learning networks obtain higher comparison accuracy and have stronger stability at the same time.
Polarity Detection Using Digital Media

People now use digital mediums for expressing their opinion on any matter. This interaction caused a tremendous increase in the amount of data generated online. Automatic sentiment extraction provides an effective method for analysing public opinion on any topic. This paper proposed an approach for summarising large documents and for automatically extracting sentiments. Twitter and news feeds are the 2 primary sources of data used in this paper .In this work Latent Semantic Analyser extracted the mostly discussed topics and are visualised using a word cloud. Valence Aware Dictionary and Sentiment Reasoner is the lexicon and rule based sentiment analysis tool used to extract sentiments from collected data. Valence Aware Dictionary and Sentiment Reasoner extracts the polarity expressed on text and the results are visualised for better analysis. The topics selected for data collection are Kerala flood, Election 2019 and Sabrimala issue.
Multimedia Forensics - An approach to detect and analyze Human faces in multimedia files

Manual Security is being replaced by video surveillance systems by the advent of multimedia forensics. Face detection is an important technique in multimedia forensics. Many proposed methods identify and localize the faces in an image or in an image sequence. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Since, innumerable video data that is collected from video surveillance are to be automatically processed and get valuable information out of it, we need to scale the processing framework accordingly. In this paper, we propose face recognition system and minimize the overall processing time of large data set using parallel processing method. Given a video from a video surveillance, we need to convert it to frames, detect the human faces in these frames and recognize the face by comparing with a given face or comparing against database entries where criminal data is present. We use cascade classifier that takes a vector of features (decision variables) and results the probability that the vector belongs to the class. Using cascade classifier, we identify the faces from a given image and finally compare the faces with database entries.
Improved D*Lite path planning algorithm based on slip prediction

Path planning algorithm is an important algorithm in navigation system which are commonly used in mobile robots, unmanned vehicles and automated driving. the effectiveness and speed of planning processing determine whether the target can be successfully reached . In different environments and application scenarios, the planning algorithms need to be designed based on different environmental characteristics . Based on the application scenario of the lunar rover, this paper adds the slip variable into account, designs the slip prediction module, and improves the D*Lite algorithm to more effectively carry out the path planning in the complex environment.
Ontology-Driven Design and Development of Situation Assessment Software in Cyber-Physical Systems

Evolution of cyber-physical systems (CPS) and extension of their application areas complicate, among other things, their software design and development. This requires improvements in programming techniques used to build CPS. One of the important tasks arising in complex CPS is a situation assessment (SA) based on data received from diverse sources. In the chapter, an ontology-driven approach for CPS SA software design and development automation is proposed. The approach is based on the JDL data fusion model and flexible enough to be applied for any class of CPS applications. In contrast to known approaches, ontologies are used not only for domain knowledge representation but also for SA calculation process formalization. It provides a higher level of automation of SA software synthesis and, in the end, increases design and development efficiency.
Secure Video Steganography Technique using DWT and H.264

Sharing of mixed media data has turned out to be brisk because of headway in data innovation. In any case, data security breaking has expanded by the innovation progression. The concealing limit and power against assaults are three principle prerequisites used in video steganography technique should think about. In this paper, a secure video steganography calculation usingDiscrete wavelet transform(DWT) space dependent on the motion object detection calculation and H.264 is proposed utilizing Mat lab programming. The mystery message is pre-prepared by applying bit moving and H.264 utilized for encoding the mystery information. To begin with, motion object detection calculation is actualized on host recordings to recognize the locales of enthusiasm for the moving articles. At that point, the information concealing procedure is tasked by inserting mystery message image into the Discrete wavelet transform planes of all movement areas in the video relying upon foundation subtraction. Our test result improves the installing limit as well as upgrades its security against different assaults.
The Grey Markov Chain Model Based on Sliding Window in Vertical Steering Locus Forecasting for Shearers

Aiming at the current situation that the coal-rock interface identification in the coal mining process is difficult to realize, the solution of predictive algorithm to realize the locus prediction of the shearer is proposed, so that the intelligent control of the shearer is realized. The gray Markov chain predictive model based on sliding window is proposed due to the small number of data samples in the process of locus prediction of the shearer. The model has the characteristics of relatively simple algorithm, ability to respond to random events, high prediction accuracy and fast calculation speed. Through the simulation experiment, the sliding window width was determined and the predictive algorithm was verified at the same time. The simulation experiment results indicated that the predictive algorithm has fast response speed and high prediction accuracy, which is suitable for practical applications.
Human Operator Modelling with Interval-valued Takagi-Sugeno Fuzzy Models

The dynamic model of a Human Operator (HO) inherits not only nonlinear dynamics but also a large amount of uncertainty due to the reaction time and expertize level of humans. In this study, the Interval valued Takagi-Sugeno (TS) fuzzy modelling approach is employed to efficiently capture the dynamics of HOs. For this purpose, we have conducted experimental studies in which a simple target tracking computer game is used as a platform. We have collected input-output data from individual players that have different talent levels. Then, by using symbolic data analysis, we have obtained interval valued TS fuzzy models of the HOs and evaluated their performances. It is concluded that, via the interval-valued TS fuzzy modelling approach, HOs can be modelled successfully as the predicted upper and lower bounds can successfully envelope the uncertainties and nonlinearities of HOs.
Use of Digital Objects for Improving the Learning Process

Effective integration of virtual technologies is aimed at improving the quality of education. It is very important to form trends of study at the initial stage of education. This document presents the first steps in the project “Development of a Virtual Learning Environment in Technical Higher Education,” jointly funded by the Erasmus program, which includes institutions from four countries: Estonia, Lithuania, Turkey, and Poland, for 24 months. It aims to create a digital house simulator and a set of training materials for construction specialties freshmen. The tasks of this simulator are to give a learner the opportunity to independently obtain knowledge about the details of the structure through the internet without special equipment. Learning through the virtual house model will help students develop spatial thinking, which is necessary for success in graphics and engineering in general. This, in turn, will increase their professional competence, since spatial visualization refers to the curriculum for all industrial technologies and engineering programs.
Bystander: QoE Perception for Dynamic Video Streaming from Encrypted Traffic

In this paper we introduce a new application layer objective QoE metric and propose a machine learning approach to infer the introduced metric for QoE estimation based on features abstracted from encrypted traffic. Experimental results show that our method can infer video users QoE with an accuracy higher than 80%.
Morphological Feature Extraction from Low Quality of Thick Blood Smear Microphotographs

Identification of the parasite presence in a blood smear is the most crucial part of the diagnosis of malaria. In the case of the low quality of smear, the diagnosis process becomes more complex because the parasites are difficult to read. The identification accuracy can be improved using morphological features that microscopist commonly used in diagnosing malaria. In this paper, we proposed a strategy to reduce false-positive identification of malaria parasites using rule-based on the features. This paper shows how to extract the important features and determine the rule of identification of malaria parasites. Moreover, a splitting algorithm was developed for cases where core and cytoplasm are joined in segmentation result. The proposed method was tested on 50 candidate parasite. The experimental results show that the proposed method can reduce false positives greater than 70% compared to the results of the previous system.
Improving Animal Recognition Accuracy using Image De-fencing

When a computer recognizes an animal on an image, this paper, we aim to improve the recognition rate of objects an accuracy of animal recognition decreases if a part of the animal's body is covered by fences. On the other hand, when a human recognizes an object on the image, it is known to be possible to remove the fences and supplement the removed areas, and recognize the object, even if a part of the object is covered by fences. In this research, we aim to improve the accuracy of hidden by fences. animal recognition using de-fencing on computer.
Estimation of Timewise Varying Boundary Heat Flux via Bayesian Filters and Markov Chain Monte Carlo Method

This chapter addresses the reconstruction of timewise varying functions within a Bayesian framework. The Markov Chain Monte Carlo method implemented with the Metropolis-Hastings sampler is implemented with a total variation prior model and compared against the results obtained with the Bayesian filter known as SIR (Sampling Importance Resampling). Besides, it is proposed a combination of the Bayesian filter solution (supposed to be obtained online) with the Markov Chain Monte Carlo method solution, consisting of employing the SIR filter solution as the initial state for the Markov Chain Monte Carlo method, allowing for an offline solution refinement with reduced CPU time. An application is presented considering the reconstruction of a boundary heat flux applied to a thermally thin plate. The good results obtained for this application indicate the feasibility of the proposed methodology.
Performance of Classification Models on Various Types of Character Data

During the last six decades, numerous studies on the topic of pattern recognition in machines have been conducted. An important problem in pattern recognition with various real-life applications is character recognition. This study aims to compare the performance of five classification models on different character datasets. The models presented are K-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forests, and Extra Trees. We concluded that Random Forests and Extra Trees have comparable results and they outperform the other models.
A Consideration on Generating Uniform Binary Sequence with High Linear Complexity

Pseudorandom sequences of having a uniform distribution of bit patterns and high linear complexity play a key role in many security applications. However, only a few pseudorandom sequence possesses both of these vital features. In this paper, the authors propose and evaluate a geometric sequence that has a uniform distribution and high linear complexity.
Image Segmentation Algorithm for feature recognition in Satellite Imageries – A case Study

Remote sensing and satellite information's are very useful for the geospatial analysis of surface and subsurface features. In the modern day-to-day scenario, the landforms and its features to be monitored and managed is a very challenging one and Remote Sensing plays a crucial role in the society. Analysis of Satellite imageries using geospatial techniques is playing a vibrant role in the earth science system. Earth contains rivers, streams, tanks, water bodies as natural formations and delineating the features itself with the image segmentation algorithm is an elegant task over remote sensing imagery. Feature extraction and image segmentation are extensively used in the field of medicine for X-ray and scan images, satellite imaging, pattern identification, features and face interpretation and various applications of engineering.
Detecting Twitter Compromised Accounts Using Anomalous User Behavior

In today's world the use of social media play an important role. Account trading off is a genuine danger to clients of Online Social Networks (OSNs). Rather than investigating client profile substance or message substance, present framework look to reveal the conduct oddity of bargained accounts by utilizing their authentic proprietors' history social action designs, which can be seen in a lightweight way. To comprehend client all movement OSN give assortment of online highlights to their clients to participate in, for example, building associations, sending messages, transferring photographs, perusing companions' most recent updates, and so forth. In any case, how a client includes in every action is totally determined by close to home interests and social propensities. While a client will in general fit in with its social examples, a programmer of the client account who thinks minimal about the client's conduct propensity is probably going to wander from the examples. This paper shows a novel technique to recognize Account Compromisation as and when it occurs by profiling on the web social practices.
Performance Comparison of Phoneme Modeling using PNCC and RASTA-PLP Features with various Optimization Algorithms for Neural Network Architectures

Speech is the basic means of human communication. It contains a lot of information in addition to the content one is conveying and words being spoken. These days, humans try to effectively communicate with machines. So, understanding of speech has become potentially useful. To communicate with machines, conversion of speech into text is required. This is the task of Automatic Speech Recognition (ASR) system, which is a subfield of Artificial Intelligence (AI). Phonemes are distinct speech sounds that a language uses. This work focuses on phoneme modeling in English language using Artificial Neural Networks (ANN). Experiments are carried out using PNCC (Power Normalized Cepstral Coefficients) and RASTA-PLP (Relative Spectral Transform Perceptual Linear Prediction) features and the results for different input sizes (corresponding to the number of adjacent frames of feature vectors used, containing different amount of contexual information), optimizing training functions, number of hidden layers and number of hidden nodes for the ANN are compared.
Studying impressive parameters on the performance of Persian probabilistic context free grammar parser

Studying impressive parameters on the performance of Persian probabilistic context free grammar parser
Classification for Web Page Prediction

The uncontrolled nature of Web content presents additional challenges to Web page classification as compared to traditional text classification, but the interconnected nature of hyper text also provides features that can assist the process. Web pages are not visited by the users in their accesses, the limited bandwidth of network and services of server will not be used efficiently and may face the access delay problem. Therefore, it is critical that we have an effective prediction method during perfecting. The Markov models have been widely used to predict and analyze user‘s navigational behavior. We propose new two- tier prediction frameworks that classify the user sessions, based on the KNN algorithm and then the Kth Markov Model is applied to predict the next web page. The discovered patterns can be used for better web page access prediction. Prediction model are better prediction of next web page the user want to visit. Using web page access prediction, the right advertisement will be placed in the website according to the users' browsing patterns. In Web page prediction, the next action corresponds to predicting the next page to be visited.
Action Recognition from Depth Video Sequences Using Microsoft Kinect

This paper provides a fast and effective method for understanding human actions. The object performs various actions such as sitting, hand-wave, theft, and walking. The movements are captured in real time using the Microsoft Kinect sensor where the recorded video is in the mode of depth. Individual frames are taken from the video of specific action and SURF characteristics are extracted based on the frames interest points. A common approach known as the Bag of features method is used for recognition purposes, which uses k-means clustering method to create a visual vocabulary by reducing the number of features by quantizing, which in turn makes the results more accurate. For the classification of the computationally efficient depth features obtained, a multi-class SVM classifier is used. The overall classification accuracy is around 97%.
Precision in High Dimensional Optimisation of Global Tasks with Unknown Solutions

High dimensional optimisation is a challenge for most of the available search methods. Resolving global and constrained task seems to be even harder and exploration of tasks with unknown solutions can be seen very rare in the literature and requires more research efforts. This article analyses optimisation of high dimensional global, including constrained, tasks with unknown solutions. Reviewed and analysed are experimental results precision, possibilities for trapping in local sub-optima and adaptation to unknown search spaces.
Research on Extending Person Re-identification Datasets Based on Generative Adversarial Network

Person re-identification(Person Re-ID) means that images of a pedestrian from cameras in a surveillance camera network can be automatically retrieved based on one of this pedestrian's image from another camera. The appearance change of pedestrians under different cameras poses a huge challenge to person re-identification. Person re-identification systems based on deep learning can effectively extract the appearance features of pedestrians. In this paper, the feature enhancement experiment is conducted, and the result showed that the current person reidentification datasets are relatively small and cannot fully meet the need of deep training. Therefore, this paper studied the method of using generative adversarial network to extend the person re-identification datasets and proposed a label smoothing regularization for outliers with weight (LSROW) algorithm to make full use of the generated data, effectively improved the accuracy of person re-identification.
Monocular Vision-Based Collision-Free Navigation for Flying Robots

In this paper, a systematic navigation structure for flying robots is proposed. In particular, the large scale direct SLAM (LSD-SLAM) is implemented to provide self-localization and semi-dense depth maps. The collected depth maps are then integrated into a three-dimensional occupancy map with the Octomap, and the occupancy information of the currently built map is utilized for the navigation of the flying robot. Dealing with the scale recovery problem of monocular vision-based systems, such as the LSD-SLAM system, an estimate of the scale is calculated with the aid of an ultrasound sensor. Given the estimated scale, the flying robot can then be navigated with the re-scaled pose provided by the LSD-SLAM system. The proposed system avoids collision of the robot with any obstacle, and the paths are only generated within the area free of collision. Experiments in both simulation and the real world are provided to verify the proposed system.
Singular value decomposition based functional link learning machine and its application to process modeling

Process modeling plays a very important role in process system engineering. However, it is more and more difficult to develop an accurate model for a complex chemical process due to the highly nonlinear and complicated process data. For the purpose of handling this problem, an improved functional link learning machine using singular value decomposition (SVD-FLLM) is presented to develop accurate models for complex chemical processes. In the proposed SVD-FLLM model, singular value decomposition is adopted to reduce the expanded variable dimension. Then, the least square algorithm is used to build a regression model between the reduced outputs and the expected vectors. For the sake of validating the testing effectiveness of the proposed SVD-FLLM method, a UCI dataset named Airfoil Self-Noise is first selected. Then the proposed SVD-FLLM is used to develop a model for an actual complex chemical system. Simulation results indicate that, compared the traditional functional link neural network (FLNN), the improved SVD-FLLM can achieve higher accuracy and faster convergence.
Medical Image Registration Using Coral Reef Optimization for Substrate Layer

Image registration is process of alignment of two or more images. It is used to get more information about the obtained image which is useful for the analysis of the disease. In the proposed method we use three different types of image registration. They are demon registration, image registration by mutual information and difference method. In these methods the alignment is achieved by changing some parameters manually. The degree of alignment of two images is directly proportional to the amount of information obtained. In order to maximize the alignment an optimization algorithm is used. The conventional image registration methods are constrained by many limitations. Hence we use a bio-inspired meta-heuristics and high performance Coral Reef optimization with Substrate Layer (CRO-SL) algorithm. CRO-SL is an advanced method of coral reef optimization based on natural behavior of coral reef. The image registration process comprises of various steps like transformation of registering image, evaluation of performance metrics and finding the optimized value for transformation. A uni-model affine transformation is used in the proposed method. The experimental results show that CRO-SL is a very efficient approach in case of alignment of image in higher degree.