Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Video Watermarking

Watermarking is a technology with the aim of embedding information into carrier (image, video, audio, text, program, web, database, etc.), and related early work can be traced back as far as 1954. The last 10 years has seen considerable interest in digital video watermarking, due in large part to ever growing of digital video content everywhere and concerns on spreading of pirate programs. A general video watermarking system, shown in Fig. 1, is composed of three parts: watermarking embedding, attack channel and watermark detection. According to the structure, the message (watermarking) is embedded into the original signal under the control of the embedding key, which produces the watermarked signal. In transmission channel, the watermarked signal may be attacked or distorted. The attacks include cutting, rotation, scaling or translation, and so on. The distortion includes such signal processing operations as compression, filtering, adding noise, A/D, D/A, AbsTRACT
Hand Gesture Recognition Using Hidden Markov Model Algorithm

A bstract — Recognizing human hand gesture through the use of INS (Inertial navigation System) sensor, Hidden Markov Model (HMM) was used as a tool to recognize pattern statistically. Employing INS sensor to admit data input , it is assumed that hand gesture could be detected by analizing the acceleration and fluctuation from data sensor and the difference of hand-position in 3-axis. The INS sensor that was being used was came with 6 channels to generate signals of a 3-axis gyroscope and a 3-axis accelerometer. The acceleration fluctuated in three perpendicular directions due to different hand gestures was detected by the accelerometer, while the change of hand-position in 3-axis was detected by gyroscope. Data from sensor was exported to computer via USB (Universal Serial Bus) port. During the stage of data collection, a cut algorithm was developed to pick the most significant part of the sensor data. After finishing data comparison stage, DCT (Discrete Cosine Transform) was selected to transform the signal from time domain to frequency domain. Sequences of calculation were performed to analyze the best sampling frequency to select dominant frequency of every gesture to be picked as parameter value. The parameter value used in HMM as the approach to recognize and differs gestures. Index Terms — 3-axis gyroscope, accelerometer, gesture, gesture recognition, hand gesture, human gesture .
Application of Bar-Shalom and Fortmann's Input Estimation for Underwater Target Tracking

Background/Objectives: The Extended Kalman Filter (EKF) using range and bearing measurements is analyzed for undersea target tracking. The Input estimation technique, developed by Bar-Shalom and Fortmann for radar applications is implemented for sonar applications. Methods/Statistical Analysis: Input estimation is used to estimate the target acceleration whenever the target makes a maneuver. Findings: The algorithm estimates target kinematics using zero mean chi-square distributed random sequence residual. Upon detection of target maneuver, this algorithm corrects the velocity and position components using acceleration components. Application/Improvements: Finally, the performance of this algorithm is evaluated in Monte-Carlo simulations and results conform the effectiveness of input estimation technique.
Distributed and hybrid simulations for manufacturing systems and integrated enterprise

Two categories of simulation that have gained prominence in the past decade are discrete event simulation (DES) and system dynamic simulation (SD). DES is used to model a system in detail to comprehend the actual behavior of the individual components. In contrast, SD simulation is used to build models using aggregated data for system wide dynamic flow analysis of managerial decisions. The need for an integrated hybrid simulation environment is highlighted and prototype architecture is presented. An application methodology in the area of hierarchical production planning in a manufacturing enterprise along with a preliminary feasibility analysis is then presented.
BlanketSet - A clinical real word action recognition and qualitative semi-synchronised MoCap dataset

Recent advancements in computer vision, particularly by mak-ing use of deep learning, have drastically improved human motion analysis in videos. However, these improvements have not yet fully translated into improved performance in clinical in-bed scenarios due to the lack of public datasets representa-tive of this scenario. To address this issue, we introduce BlanketSet, an RGB-IR-D action recognition dataset of sequences performed in a hospital bed. This dataset has the potential to help bridge the improvements attained in general use cases to these clinical scenarios. The data that support the ﬁndings of this study and BlanketSet are available on request from the corresponding author, J.P.S.C.
Border-Processing Technique in Grid-Based Clustering

In order to improve accuracy of grid-based clustering,a border-processing technique is proposed,Using restricted k nearest neighbors and concept of relative density.The technique enables us to separate cluster's border points from outliers or noises accurately.Then,a grid-based clustering algorithm with border processing (GBCB) is developed.Experiment results show high accuracy of recognition of border points.Due to the only one data scan,the GBCB algorithm is very efficient with its run time being linear to the size of the input data set,and can discover arbitrary shapes of clusters and scale well.
Efficient and Automated Analysis of Protein Structures

In recent years, computational complexity in structural bioinformatics attained a new level with the vast increase in the amount of structural data available. The Protein Data Bank (PDB), which is the single worldwide repository for 3-D macromolecular structure data, contains more than 25k structures as of July 2004. However, existing methods for protein structure analysis are unable to cope with this increase in the amount of available data. Therefore, this wealth of data requires computationally efficient methods to be developed for the analysis of large numbers of protein structures and their associated functions. In this dissertation, we present methods for protein structure analysis that can scale well with the amount of protein structure data available. Our work can be described under three main categories: (1) visualization and surface modelling, (2) structure comparison and similarity search, and (3) automated classification. For efficiently visualizing protein structures using a scene-graph based graphics API, we have developed methods to optimize the constructed scene-graph to enable real-time visualization of very large protein complexes. Our method (FPV) achieves up to 8 times interactive speed compared to existing methods. For generation of molecular surfaces we recently developed a method based on a level set formulation that can compute the surface and interior inaccessible cavities very efficiently (1.5 to 3.14 times faster on the average than compared methods). For comparison and similarity search of protein structures we have developed a method that utilizes local shape signatures based on the theory of differential geometry. Our method (CTSS) is up to 30 times faster than CE, a widely used method for structure comparison, while achieving the similar level of accuracy. We have also developed an integrated sequence and structure analysis method (ProGreSS), which enables biologists to perform joint sequence and structure similarity queries while improving on the accuracy and efficiency of existing methods. For an up-to-date view of the protein structure universe with the help of automated classification, we have developed an ensemble classifier based on decision trees rooted in machine learning. We show that higher classification accuracy can be achieved using the joint hypothesis of the ensemble classifier.
Cryptography for Information Security

This chapter introduces cryptography from information security phase rather than from deep mathematical and theoretical aspects, along with cryptography application in information security. The chapters introduce classical cryptography, block, stream cipher, and public key family, and with the most advance systems as elliptic curve, digital signature, cryptography key managements, and the last part of the chapter are the cryptography application in protocols, communications, e-mails, and the most advance research topics.
Design of Computer System for SINS with Laser Gyro

Computer plays an important role in SINS. The paper presents a computer system for SINS with laser gyro. The system includes a PC104, an interface card and a microcomputer. The software running in PC104 and microcomputer is also introduced. The system cooperates with the laser gyro and other instruments well. The experimental results prove that the system is practical.
Korean Semantic Role Labeling Based on Suffix Structure Analysis and Machine Learning

Semantic Role Labeling (SRL) is to determine the semantic relation of a predicate and its argu-ments in a sentence. But Korean semantic role labeling has faced on difficulty due to its different language structure compared to English, which makes it very hard to use appropriate approaches developed so far. That means that methods proposed so far could not show a satisfied perfor-mance, compared to English and Chinese. To complement these problems, we focus on suffix information analysis, such as josa (case suffix) and eomi (verbal ending) analysis. Korean lan-guage is one of the agglutinative languages, such as Japanese, which have well defined suffix structure in their words. The agglutinative languages could have free word order due to its de-veloped suffix structure. Also arguments with a single morpheme are then labeled with statistics. In addition, machine learning algorithms such as Support Vector Machine (SVM) and Condi-tional Random Fields (CRF) are used to model SRL problem on arguments that are not labeled at the suffix analysis phase. The proposed method is intended to reduce the range of argument instances to which machine learning approaches should be applied, resulting in uncertain and inaccurate role labeling. In experiments, we use 15,224 arguments and we are able to obtain approximately 83.24% f1-score, increased about 4.85% points compared to the state-of-the-art Korean SRL research.
Cheating and Virtual Crimes in Massively Multiplayer Online Games

I declare that this assignment is all my own work and that I have acknowledged all quotations from the published and unpublished works of other people. I declare that I have also read the statements on plagiarism in Section 1 of the Regulations Governing Examination and Assessment Offences and in accordance with it I submit this project report as my own work.1 Executive Summary Massively Multiplayer Online Games (MMOG) have become extremely popular since the birth of the Internet, with many millions of players playing games such as Poker and World of Warcraft. However, they do not seem to be well understood, and academic research into them has been limited. This project explains the nature of MMOG, and the relationship between MMOG and information security. This project discusses the problem of cheating in MMOG – it explains what cheating is, how it occurs, and how information security can be used to prevent it. The nature of virtual economies in MMOG is discussed, and the virtual crimes that have affected MMOG along with preventative measures are examined. The growth of the Internet over the last decade has led to the emergence of a multi billion dollar online gaming industry. A huge variety of games can be played on the Internet: chess, first person shooters, and casino games, such as poker and blackjack. Aside from casino games, Massively Multiplayer Online Games (MMOG) have become a driving force in the rapid growth of the online gaming industry. MMOG are games that are hosted on servers and played by hundreds or thousands of players using the Internet. MMOG allow players to collaborate with, or pit their wits against, thousands of other players located across the globe. The use of the Internet to play games has added an extra dimension to gameplay. Huge online gaming communities have emerged from MMOG, where players can interact with each other and form friendships. Research has shown that being part of such a community forms a major part of the enjoyment for some players [HN06]. There are a number of different MMOG genres, for example first person shooters such as World War II Online: Battleground Europe (www.battlegroundeurope.com) or real time strategy games such as ShatteredGalaxy (www.sgalaxy.com). By far the most popular genre is MMORPG (Massively Multiplayer Online Role Playing Games). Blizzard Entertainment, publishers of the popular World of Warcraft (WoW) game, claim to have over 8 million subscribers [Bl07]. …
Realization of.NET Remoting-based Distributed System

Mainly discusses the realization of.NET remoting-based distributed system.First,introduces the architecture and principle of.NET Remoting.Second,introduces proxy,channel,remote object,configure file,the deployment of remote object and other related core objects and technologies.Finally,points out that it can simplify the development of distributed remote object by giving an example of remote object processing.
Mergers between Spanish credit cooperatives as a mechanism to prevent insolvencies (1996-2016)

The objective of this research is to determine if the mergers between Spanish Rural Savings Banks is the usual strategy of the management team to avoid a future insolvency. In this sense, within the scarce academic research on the insolvencies of credit cooperatives, the different authors agree that insolvency processes within the credit cooperative sector are not common. In fact, Porath (2006), Cabo et al (2010), Lima (2012) and Madera (2017) observe that merger processes between credit unions are more common, asking us whether these mergers are indeed a strategy to avoid situations of future insolvency. Both discriminant analysis and the estimation of logit model in its traditional version and the variation proposed by Shumway (2001), confirm that mergers of rural banks in Spain were motivated not only by business factors, but factors related to short-term sustainability played a decisive role and a strategy.
What's the big deal about big data?

This position paper is based on a major cooperative research and development proposal to form a Big Data Research, Analytics, and Information Network (BRAIN). Challenges presented by Big Data research are introduced and several projects are sketched in four important Big Data research theme areas, the solutions of which will further decision making in these areas of investigation. The four themes are large-scale data analytics and cloud computing, computational biology, health informatics, and interactive content analytics. These theme areas are certainly not inclusive, rather indicative of the wide variety to which Big Data now occupies decision analytics. The importance of training highly qualified personnel (HQP), knowledge mobilization and novelty are discussed.
Research on K-Means Algorithm Analysis and Improvement

传统的k-means算法采用的是随机数初始化聚类中心的方法，这种方法的主要优点是能够快速的产生初始化的聚类中心，其主要缺点是初始化的聚类中心可能会同时出现在同一个类别中，导致迭代次数过多，甚至陷入局部最优出现错误的聚类结果。 针对传统的k-means算法初始聚类中心的缺点，本文提出了p-K-means算法，该算法采用了数学几何距离的方法改进k-means算法中初始聚类中心分布不均匀的现象多个聚类中心出现在同一类簇中的现象，这种方法能避免k-means聚类算法聚类过程中陷入局部最优，另一方面降低了聚类过程中的反复迭代次数。 本文通过实验的方式来对两个算法进行分析比较后发现改进的算法在收敛速度上优于传统k-means算法，也不容易陷入局部最优。 Traditional k-means algorithm uses a random number to initialize the cluster center, the main advantage of this method is the ability to quickly produce cluster center initialization, its main drawback is initializing cluster centers may appear in the same a category, leading to excessive iterations, errors and even local optimum clustering result. For the shortcomings of traditional k-means algorithm initial cluster centers, this paper presents the pK-means algorithm, which uses a mathematical geometric distance method for improving the k-means clustering phenomenon of multiple algorithms initial cluster centers unevenly distributed Center appear in the same class cluster phenomenon, this approach avoids k-means clustering algorithm clustering process into local optimization, on the other hand reduces the clustering process repeated iterations. After analyzing and comparing two algorithm experimentally, the article found that the improved algo-rithm is better than the traditional k-means algorithm converges quickly, not easy to fall into local optimum.
Integer sparse distributed memory and modular composite representation

Challenging AI applications, such as cognitive architectures, natural language understanding, and visual object recognition share some basic operations including pattern recognition, sequence learning, clustering, and association of related data. Both the representations used and the structure of a system significantly influence which tasks and problems are most readily supported. A memory model and a representation that facilitate these basic tasks would greatly improve the performance of these challenging AI applications. Sparse Distributed Memory (SDM), based on large binary vectors, has several desirable properties: auto-associativity, content addressability, distributed storage, robustness over noisy inputs that would facilitate the implementation of challenging AI applications. Here I introduce two variations on the original SDM, the Extended SDM and the Integer SDM, that significantly improve these desirable properties, as well as a new form of reduced description representation named MCR. Extended SDM, which uses word vectors of larger size than address vectors, enhances its hetero-associativity, improving the storage of sequences of vectors, as well as of other data structures. A novel sequence learning mechanism is introduced, and several experiments demonstrate the capacity and sequence learning capability of this memory. Integer SDM uses modular integer vectors rather than binary vectors, improving the representation capabilities of the memory and its noise robustness. Several experiments show its capacity and noise robustness. Theoretical analyses of its capacity and fidelity are also presented. A reduced description represents a whole hierarchy using a single high-dimensional vector, which can recover individual items and directly be used for complex calculations and procedures, such as making analogies. Furthermore, the hierarchy can be reconstructed from the single vector. Modular Composite Representation (MCR), a new reduced description model for the representation used in challenging AI applications, provides an attractive tradeoff between expressiveness and simplicity of operations. A theoretical analysis of its noise robustness, several experiments, and comparisons with similar models are presented. My implementations of these memories include an object oriented version using a RAM cache, a version for distributed and multi-threading execution, and a GPU version for fast vector processing.
Research on intelligent vehicle moving obstacle avoidance control using dynamic target position concept

In order to simulate truthfully behavior pattern of driver in moving obstacle avoidance in a dynamic environment,this paper proposes a dynamic target position concept,and employes cubic spline curves as path fitting curves of moving obstacle avoidance.In this paper,a lateral controller of vehicle is designed by using the fuzzy logic as a control strategy,the T-S fuzzy model as its structure and an adaptive neural network as a means of adjusting membership function.The simulation result shows that the designed controller based on dynamic target position can simulate well moving obstacle avoidance characteristic of vehicle in actual traffic situation.
Kajian Unified Theory of Acceptance and Use of Technology Dalam Penggunaan Open Source Software Database Management System

Perkembangan perangkat lunak computer dewasa ini terjadi sedemikian pesatnya, perkembangan tidak hanya terjadi pada perangkat lunak yang memiliki lisensi tertentu, perangkat open source pun demikian. Perkembangan itu tentu saja sangat menggembirakan bagi pengguna computer khususnya di kalangan pendidikan maupun di kalangan mahasiswa, karena pengguna mempunyai beberapa pilihan untuk menggunakan aplikasi. Perangkat lunak open source juga menawarkan produk yang umumnya gratis, diberikan kode programnya, kebebasan untuk modifikasi dan mengembangkan. Meneliti aplikasi berbasis open source tentu saja sangat beragam seperti aplikasi untuk pemrograman (PHP, Gambas), Database Management System (MySql, SQLite), browsing (Mozilla, Firefox, Opera). Pada penelitian ini di kaji penerimaan aplikasi DBMS ( Database Management System ) seperti MySql dan SQLite dengan menggunakan sebuah model yang dikembangkan oleh Venkantes(2003) yaitu UTAUT ( Unified Theory of Acceptance and Use of Technology ). Faktor – faktor tertentu juga mempengaruhi dalam melakukan kegiatan pembelajaran aplikasi open source ini, salah satu faktor atau yang disebut dengan moderating yang bisa mempengaruhi efektifitas dan efisiensi. Dengan demikian akan mendapatkan hasil yang bisa membuat kelancaran dalam pembelajaran aplikasi berbasis open source ini. Kata kunci— open source , Database Management System (DBMS), Modereting
Anti-bias Association Algorithm for Automatic Identification System and Radar Based on Bias Detection

The classical multi-sensor anti-bias association algorithms require complex calculation procedure, can not be used in real-time condition, for instance the algorithms based on image matching and reference topology feature. On the basis of tracks distribution feature on sea surface and Cell-Average Constant False Alarm Rate (CA-CFAR) detection theory, a real-time anti-bias association algorithm for Automatic Identification System (AIS) and the data track of radar is proposed, named confidential-association algorithm, to make real-time radar systematic error registration and multi-sensor information fusion come true. Monte-Carlo simulation results show that the accuracy of confidential-association algorithm maintains on a high level on the sea-surface environment, it has the superiority of simply calculation procedure and substantially reduces run-time in comparison to current anti-bias association algorithms. Radar automatically registration technique based on confidential-association algorithm reduces average error of the measured data by nearly 90%.
A method of traffic lane detection and tracking

In order to obtain the better edge images of traffic lane and the better traffic lane recognition,wavelet is used in the lane edge detection and edge connection.Then the Hough transform methods are used to get the traffic lane parameters,and finally the Kalman filter is used to achieve real-time traffic lane tracking.The experimental results show that this method has good reliability,robustness and real-time which ensures smart driving and solves the security.