Reviewerly/exHarmony huggingface dataset:
Learning diffusion on global graph: A PDE-directed approach for feature detection on geometric shapes

Feature and saliency analyses are crucial for various graphics applications. The key idea is to automatically compute and recommend the salient or outstanding regions of concerned models. However, there is no universally-applicable criterion for the detection results stemming from the personalized viewpoints for interest features on each specific model. This paper proposes a human-oriented feature detection framework, learning diffusion on global graph (LDGG), to understand personalized interests in a simple and low-cost way. A user-friendly interaction method is introduced to incorporate specific human interests as detection criteria in a small training set. Given a test model, we model the interest feature detection process as partial differential equations (PDEs)-directed diffusion on the global graph composed of nodes extracted from all training and test models. To infer the real interest points of users, submodular optimization is employed to select the source seeds adaptively for the diffusion system. By introducing diffusion guidance based on interest information, the PDEs become learnable. Extensive experiments and comprehensive comparisons have exhibited many attractive advantages of the proposed framework, such as capable of small-sample learning, easy-to-implement, extendable, self-correction, discriminative power, etc.
An Optimal Haar Wavelet with Light Weight Cryptography Based Secret Data Hiding on Digital Images in Wireless Sensor Networks

Security is the rising concern in this specialized rebellion that attracts the analysts towards research and new commitment in Wireless Sensor Network (WSN) field. This chapter proposes a creative technique for image security in WSN using Steganographic and cryptographic model which secures the selected cover images and secret information. The effective Opposition-based Particle Swarm Optimization (OPSO) with Haar wavelet coefficients from Discrete Wavelet Transform (DWT) was brought into the embedding procedure. From this procedure, the encrypted file was deciphered though the encoded document may hide the information even now. This optimal wavelet attained the most extreme Hiding Capacity (HC) and PSNR rate. Finally, the stego images were considered in the security Model i.e., LWC-based SIMON block cipher. It works on the basis of key and round generation model and towards the end, the reverse procedure occurs with the image decryption and extraction modeling. The usage results demonstrated that the proposed security strategy has the most extreme CC and PSNR values (52.544) with minimum error rate (0.493) in comparison with other conventional strategies.
Surface roughness measurement accuracy analysis of grinded silicon wafer by white light scanning interferometry (WLSI)

White light scanning interferometry (WLSI) is a fast, noncontact, high-precision method to measure three-dimensional (3D) surface profile and extensively used in roughness measurement of ultra-precision machined surface. However, due to Rayleigh criterion, the lateral resolution of WLSI is limited to hundreds of nanometers. It is hard to measure rough surfaces with delicate details that adjacent distance less than lateral resolution. Also, WLSI can’t measure profiles with large surface gradient for no light reflected and received by objective lens. In this work, with a proposed simulation measurement model, surface gradient error and lateral resolution error on measuring result of WLSI is studied by simulating the response characteristics of sinusoidal signal, square signal, sawtooth signal and actual surface profile of grinding silicon wafer measured by AFM respectively. The effectiveness of the simulation model is verified by comparing the simulation results with the experimental results. The mechanism of surface gradient error and lateral resolution error is revealed from the perspective of simulation analysis, which has certain guiding significance for the future research of error analysis on white light scanning interference.
A Study on Ajax in Web Applications to improve Usability of Online Systems

In olden days the web technology was using HTML (Hyper Text Markup Language) for creating web pages. The AJAX has changed the traditional paradigm of Web development by giving partial page update facility. Ajax is short-form of Asynchronous JavaScript and XML. It is a bundle of technologies that combined together to create new, dynamic, responsive and powerful web applications. Most of the giant internet-based companies such as Google, Yahoo, Microsoft and Amazon etc. are developing web applications based on Ajax. Even though major internet based companies working with Ajax, there is still ignorance about this technology among many developers. Many developers find it difficult to handle those bundle of technologies to build Ajax application.
Performance Evaluation of Two Computational Approaches for Vehicle Collision Simulation

A vehicle collision can be divided in three distinct time phases, the pre-collision, the collision and the post-collision phase. Usually during a traffic accident reconstruction the collision and post-collision phases are investigated in order for the accident reconstructionist to draw conclusions concerning the causes and the events that lead to the vehicle collision. The investigation of both phases is usually a repetitive procedure which terminates when the investigation results match the physical evidence drawn from the accident scene. The objective of the analysis of the collision phase is the determination of the velocities of both vehicles prior and post collision. For the computational simulation of the collision phase two main approaches exist in the literature, the energy-based approach, developed by McHenry, and the momentum based one, developed by Brach, both in the late 1970s. The objective of the analysis of the post collision phase is the reconstruction of the trajectories of both vehicles from the point of collision to the point of rest. For the computational simulation of the vehicle trajectories different approaches exist, such as their approximation using geometric curves and the application of the equations of motion for each vehicle after collision. In the present paper two algorithms for vehicle collision reconstruction have been set up in Matlab. Each one utilizes a different approach for both the collision and the post collision phase. In more details the momentum-based approach has been coupled with geometric approximation of the trajectories while the energy-based approach has been coupled with the equations of motion for the post-collision phase. Both algorithms incorporate a suitable optimization method in order to provide optimized results in terms of collision geometry, collision physics and post-collisional trajectories of the vehicles. In order to evaluate the performance of both algorithms, the vehicle collisions described in details in the RICSAC database have been used. The results of each algorithm are compared with each other as well as with the measured quantities existing in RICSAC database.
A Study on Fuzzy Keyword Search Over Web Traffic Analysis

As Cloud Processing becomes prevailing, extra and extra sensitive and painful information ar being centralized to the cloud. For the defense of data solitude, sensitive and painful data often need to be forced to be secured before outsourcing, which makes efficient data employment a very hard task. however old searchable development systems help a person to strongly research around secured data through keywords and by variety obtain documents of curiosity, these methods help only specific keyword search. That's, there is number patience of slight typos and structure inconsistencies that, on the contrary give, ar common consumer seeking conduct and occur very often. the internet machine traffic evaluation resources construct the employment of web Entry Records that ar created on the machine while an individual is opening the internet site. a web accessibility wood includes of diverse items just as the title of an individual, his IP handle, selection of bytes moved timestamp etc. The job of web traffic evaluation resources becomes harder when the internet traffic quantity is huge and maintains on growing. in this report, we are likely to propose a numerous design to get and analyze beneficial information from the available diary data and conjointly supplies a relative examine of type of Wood analyser resources occur that assists in studying the traffic on web server.
Statistical HOG on Multi-temporal Depth Motion Maps Approach for Human Action Recognition

This work proposes an efficient spatiotemporal compact descriptor for action representation from depth map sequences. The feature descriptor is intended to resolve the problems of distinguishing different posture shapes with temporal order. The proposed work is composed of three phases. In the first phase, a depth sequence is partitioned into three non-overlapping temporal depth parts, which are utilized to produce three depth motion maps (DMMs) to capture the shape and motion cues leading to a multi-temporal DMMs representation. In the following phase, the Histogram of Oriented Gradients (HOG) is adopted from DMMs. Time-frequency statically features then extracted from DMM-HOG descriptor and concatenated in order to feed L2-CRC in the last phase. Comprehensive experiments on the known datasets clarify how the proposed approach exceeds action recognition related approaches. Experimental results achieved 97.93% and 95.97% for MSR Action3D, MSR Gesture3D respectively.
Computer Implementation of the Fuzzy Model for Evaluating the Educational Activities of the University

The article describes the computer implementation of an objective assessment of the university’s educational activities. It is expertly established that the effectiveness of educational activity is an integral indicator, consisting of three integrated characteristics: The effectiveness of educational activities in terms of methodological support, the quality of the educational process, and the effectiveness of educational activities in the part of the level of vocational training of graduates. Each of these characteristics consists of individual indicators, expressed in different units of measurement. Different perceptions by individuals of the importance of each of these indicators, as well as a wide variation in understanding how effective educational activity should look, make the development of such a valuation model relevant. The model of estimation is based on the ideas of the theory of fuzzy logic and fuzzy sets. The model is developed in the Fuzzy Logic Toolbox package of the MATLAB software environment. The model is open for modification. Any registered user can change the model at his discretion by adding or removing evaluation parameters, linguistic rules. The work is of interest to a wide range of readers, since the evaluation of the educational activity of universities is a question that many people, somehow connected with the sphere of higher education, are asking.
Optimal Data Hiding Key in Encrypted Images Based Digital Image Security in Wireless Sensor Networks

An effective optimal Data Hiding (DH) key with Light Weight Encryption (LWE) is proposed in this chapter. There are numerous issues in wireless sensor organize like client authentication just as information travel in the system isn’t so much secure. The image was encrypted with a secret key; yet conceivable to insert extra information for secret key. On sender side, the cover image was considered for the encryption procedure by hash function after which the data hiding method was used to hide the images utilizing the optimal data hiding keys. For key selection the current research utilized the Enhanced Cuckoo Search Optimization (ECS) method. The most extreme embedding capacity and security level were achieved. In reversible process, at the receiver side, the embedded image undergoes decryption procedure to recouped image and the data is selected by public key (Shankar and Eswaran in J Circuits Syst Comput 25(11):1650138, 2016) [1]. It can be applied in various application situations; moreover, the data extraction and image recovery are free of any errors. From this security model, the hided image containing the installed information can be then sent to combination focus utilizing sensor networks. At the combination focus, the hidden information is separated from the picture, the required handling is performed and choice is taken consequently. From the implementation procedure, the proposed model attained the most extreme Hiding Capacity (HC) (91.073%) and the greatest PSNR (59.188 dB), compared to other conventional techniques.
Secret Information Transmission Model based on Non-complete Orthogonal Basis

The secret information transmission model is studied and proved based on orthogonal basis space. In order to maintain the main information of the secret information's carrier, which is usually media form, paper gets the principal component of media information by Principal Component Analysis (PCA) as the orthogonal basis. Due to the reasonable compression of the carrier by PCA, paper constructs two non-complete orthogonal basis spaces based on the principal component to reduce construction's difficulty and complexity. The experiment verified the efficiency and feasibility about the model. And the form of the secret information is flexible. The capacity of the secret information is more. So it has Obvious advantages.
Improved Chinese Complexly Arranged Scene Text Detection

At present, the existing text detection algorithms in natural scenes have made great progress, but most of them are for text line detection. On the one hand, they have limitation on the effective detection of complexly arranged (such as vertical arrangement, circular arrangement) text lines. On the other hand, the length of text lines varies greatly, which makes text detection very challenging. In this paper, an optimized SSD detection algorithm is proposed for single Chinese text detection. Convolution enhanced module is used to improve the detection of small target text. The introduction of deformable convolution improves the detection of deformable text. A text region connectivity algorithm is proposed to connect complexly arranged single text into readable text lines. In the natural scene Chinese text datasets on CTW and RCTW 17 datasets, mAP of 73.1% and F1 scores of 61.3% were achieved respectively.
A Survey: Lower Limb Ambulation Modes

For the past several decades, devices likeexoskeletons and other orthoses have been progressed from the unbelievable component to nearly an available commercial products which anyone can opt in today's scenario. But still there are many challenges associated with these lower extremity skeletons and other prosthesis improvement are yet to be corrected, even though their improvements in the scientific world have been large. In this survey, we will see the history of prosthesis and we will discuss more about the assisted devices of lower limb prostheses in detail. We also suggested an overview of hardware design of these rehabilitation aids, mechanical actuators, sensory devices, and their controlling systems for most of the available designs have been described and the importance of biosignals in improving the device design and feasibility of robotics in overcoming the hurdles which was the dream of many lower limb disabled patients. In the end, we discussed some of the future works which can be done in this field of ambulation.
Interacting With Human Simulations: A Prototype Application

The process of interacting and executing agent-based simulations of human phenomena is complex and difficult to follow for lay people. This is due to (1) limited access to computing power, (2) difficulty in writing analytics scripts and (3) the size and complexity of the simulation space. Consequently, agent-based simulations are normally used once to answer a limited set of questions instead of exploring all the potential paths that the model offers. Here, we describe an approach for developing user experiences that automate the analytic process and rely on interactive visualizations and life histories of societies and agents. This approach helps the user visualize simulation results, pin-point consequences of different initial conditions on the societies' evolution and agents' behavior, and ultimately allow the user to build a narrative or story-telling to present complex information. We present a prototype application that we implemented in the R language.
Dynamic <i>Ad Hoc</i> Social Networks in Improvised Intelligence/Counter-Intelligence Exercises: A Department of Homeland Security Red-Team Blue-Team Live-Action Roleplay

Abstract We discuss a Red Team-Blue Team (RT-BT) study conducted to examine the formation and efficacy of social networks in self-organizing, ad hoc , or crowd-sourced intelligence and counter-intelligence operations in grassroots, improvised communities. Student volunteers were sorted into two teams: one team (Blue) was asked to find puzzle pieces using clues provided by the organizers, with the goal of reconstructing a message contained therein, while the opposing team (Red) was tasked with disrupting this process. While the Blue Team quickly organized into an efficient, centrally-governed structure, the Red Team instead adopted a decentralized, distributed operational network to hinder puzzle completion, using creative and diverse infiltration and disruption methods to interfere in the more centralized, hierarchical organization of their opponents. This exercise shows how untrained, unaffiliated individuals may self-organize into different types of social organizations to accomplish common tasks when aware of potential adversarial organizations, and how these choices may affect their efficacy in accomplishing collaborative clandestine goals.
Conceptual Security System Design for Mobile Platforms Based on Human Nervous System

We present the conceptual security system design for mobile platform based on the human nervous system in order to achieve security resilience against threats. The reason for imitation of the human nervous system is to achieve distributed decision making and instinctive reaction against threats by observing the entire device activities. The system is adapted to have the functionalities of the nervous system through the creation of event detection and controlling mechanisms. The two main subsystems, brain and spinal cord, are created through sharing of security related operations in order to act independently without contradicting each other‘s decisions. The brain comprises of modules that handles the incidents and learning of behaviours to realise holistic view of security against ongoing activities. The spinal cord is responsible for spawning receptor and effector pairs against application activities to establish a medium to communicate and exercise control over them. Some of the factors that influence the method of implementation are usability, acceptability and the type of learning algorithms.
Parameter Discovery in Unsupervised Clustering

Analyzing massive amounts of data and extracting value has become key across different disciplines. A plethora of approaches has been developed to analyze the deluge of data. Using these approaches, however, is not straightforward and many require a priori knowledge of the dataset to set parameters, such as the number of clusters, making their use challenging. Lack of knowledge about the dataset means either that the clustering algorithm has to be run multiple times with different parameters or expensive human intervention and in-depth analysis is required, significantly delaying the analysis and reducing its reproducibility. In this paper, we introduce the idea of simple assumptions about the global distribution of some property of the data leading to local, actionable insights. More specifically, we derive configuration parameters for a clustering method from global distribution properties of a dataset.
Building a Sentiment Analysis system using automatically generated training Dataset

In this paper, we describe a procedure for extracting annotated Arabic negative and positive tweets. We use these extracted annotated tweets to build our sentiment system using Naive Bayes with TF-IDF enhancement. The large size of training data for a highly inflected language is necessary to compensate for the sparseness nature of such languages. We present our techniques and explain our experimental system. We automatically collect 200 thousand annotated tweets. The evaluation shows that our sentiment analysis system has high precision and accuracy measures compared to existing ones.
Automatic Question Generation with Natural Language Processing and Genetic Algorithm

This thesis report describes an intelligent multiple- choice question examination system, named Automatic MCQ Generation, for students . It requires lots of educators efforts  and time since it is tedious and meticulous which can sometimes leads to human mistake . The purpose of this research is to ease the educators work      in the process of preparation exam questions and giving them more time to concentrate on teaching materials and strengthen their teaching techniques without being burdened with the exam questions preparations.The area of research is multiple choice questions, which are commonly used in educational tests, social surveys, market research and other areas We introduce a rule- based automatic question generation for the task, as well as im- plement statistical sentence selection and various configurations of named entity recognition . Three types of WH-questions (What, Who, and Where) can be produced by our system .
A Discrete Cosine Transform Based Evolutionary Algorithm and Its Application for Symbolic Regression

A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies, the energy-compaction property of which makes itself very suitable for symbolic regression problems with noisy data samples. In this paper, we propose a DCT based genetic programming (DCT-GP) for symbolic regression or other optimization problems with noisy experimental observations. Firstly, a sequence of experimental samples was changed from time domain into frequency domain with DCT. Secondly, the boundary between the low frequency components and high frequency components was decided, by which most of the noise inserted into the sample data can be discarded. The experimental sample sequence was rebuilt with the low frequency components. Finally, the new obtained samples were looked on as another fitness function and were used to direct the evolutionary procedure of GP. In this way, DCT was integrated into the GP algorithm. The major advantage of the DCT-GP algorithm is that, on the one hand, it can deal with the regression problem with noisy data samples and obtain accurate solutions. On the other hand, if the sample scale is very small, it can deal with the over-fitting problem in the regression progress and can avoid losing of structure information when the available samples were departed into training set and validating set. The performance of the proposed DCT-GP algorithm with several regression problems indicates that the proposed algorithm can find the solution more efficiently and effectively than traditional sample-partitioning based GP algorithms.