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This paper explores the application of a Machine Vision and Machine Learning Algorithm to a Manipulator with six degrees of freedom (6-DOF). A Kinect sensor were used to extract images from a screen and obtain the relevant target information. Image processing was accomplished using a Scale-invariant feature transform (SIFT) Algorithm to capture image of the target object. The processed visual is rendered...
With the rapid growth of the web services technologies, users often leverage various web services to perform their daily activities, such as on-line shopping. Due to the massive amount of web services available, a user faces numerous choices to meet their personal preferences when selecting the desired services from the web services with the similar functionality. Therefore, it becomes tedious and...
Pepper is a key export of the state of Sarawak (Malaysian Borneo); it produces 98% of Malaysia's pepper. At present, processed pepper berries are graded manually. This process is time consuming and error prone as it is very much dependent on the experience of the pepper grader. To overcome these weaknesses, we propose a Pepper Grading System which employs image processing and machine learning approaches...
We develop a new perspective invariant feature space representation of remotely sensed objects, regarding the features themselves as primitive observables of the 3D objects and to estimate them from multiple sensor measurements. This is formulated as an inverse problem in the feature coefficients. Once the coefficients are estimated they may be used to derive higher level features used by machine...
MicroRNAs are one type of noncoding RNA that regulate their target mRNAs before mRNAs are translated into proteins. Although it has been demonstrated that the regulation is through partial binding of the seed region of a miRNA and its targets, the mechanism of this process is not fully discovered. Some biological experiments have shown that even perfect base pairing in the seed region does not always...
Machine learning algorithms have recently attracted much interest for effective link adaptation due to their flexibility and ability to capture more environmental effects implicitly than classical adaptation algorithms. However, past applications are limited to rather simple configurations such as identifying channel condition or link adaptation in fixed or slowly varying channels. Recently, more...
In this paper we apply Machine Learning (ML) techniques on static features that are extracted from Android's application files for the classification of the files. Features are extracted from Android's Java byte-code (i.e.,.dex files) and other file types such as XML-files. Our evaluation focused on classifying two types of Android applications: tools and games. Successful differentiation between...
The classification of encrypted traffic on the fly from network traces represents a particularly challenging application domain. Recent advances in machine learning provide the opportunity to decompose the original problem into a subset of classifiers with non-overlapping behaviors, in effect providing further insight into the problem domain. Thus, the objective of this work is to classify VoIP encrypted...
Total Order Broadcast (TOB) is a fundamental building block at the core of a number of strongly consistent, fault-tolerant replication schemes. While it is widely known that the performance of existing TOB algorithms varies greatly depending on the workload and deployment scenarios, the problem of how to forecast their performance in realistic settings is, at current date, still largely unexplored...
Prepositional phrase attachment is a major disambiguation problem when it's about parsing natural language, for many languages. In this paper a low resources policy is proposed using supervised machine learning algorithms in order to resolve the disambiguation problem of Prepositional phrase attachment in Modern Greek. It is a first attempt to resolve Prepositional phrase attachment in Modern Greek,...
This paper presents the results of an explorative study on predicting aspects of playing behavior for the major commercial title Tomb Raider: Underworld (TRU). Various supervised learning algorithms are trained on a large-scale set of in-game player behavior data, to predict when a player will stop playing the TRU game and, if the player completes the game, how long will it take to do so. Results...
The imbalanced data set has been reported to hinder the classification performance of many machine learning algorithms on both accuracy and speed. But extremely imbalanced data sets (3~5% positive samples) are common for many applications, such as multimedia semantic classification. In this paper, we propose a novel algorithm to automatically remove samples that have no or negative effects on classifier...
For reinforcement learning often show slow convergence speed problem in continuous and complex tasks, this paper proposes a Q(λ) algorithm based on heuristic reward function-Q(λ)-HRF algorithm. This algorithm can extract features from the environment and get the heuristic information, which can be applied to the study by Agent in the form of reward function, which can accelerate the convergence speed...
In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling...
Recent years have witnessed the emergence of Smart Environments technology for assisting people with their daily routines and for remote health monitoring. A lot of work has been done in the past few years on Activity Recognition and the technology is not just at the stage of experimentation in the labs, but is ready to be deployed on a larger scale. In this paper, we design a data-mining framework...
Time series data classification is important in many applications. Learning temporal knowledge in time series data is challenging. In this paper we propose a novel machine learning algorithm, Feature Ensemble (FE), to learn effective subsequences of signal features distributed over time series data streams. Both the FE learning and the FE classification have been applied to an application problem...
Learning-based anomaly detection method is often subject to inaccuracies due to noise, small sample size, bad choice of parameter for the estimator, etc. We propose a novel method using higher-order feature, based on the sequence nonparametric test to assess the reliability of the estimation. The method allows an expert to discover informative features for separation of normal and attack instances...
The approach based on payload signatures presents more accurately than that using port number and machine learning algorithms in network traffic identification. The performance of payload-based approach heavily depends on abundant and real-time signatures database. Existing approaches to payload signatures identification involved a manual process which is time-consulting and complicated. In this paper,...
As Deep Web contains tremendous well-structured data sources, how to integrate data sources in Deep Web has become a hotspot in current research. Accurately discovering and identifying Deep Web data sources related to a specific domain become key issues. We propose a Domain-Oriented Deep Web data source Discovery method (DO-DWD) and a novel Domain Identification strategy of Deep Web data sources (DIDW)...
Owing to the high dimension characteristic of motion in catching original data, the high dimensional original data will be projected into low dimensional sub space. The internal structure of body motion will be revealed through this low dimensional space. The elimination of the related redundant information of high dimensional characteristics becomes key technology for 3D motion capture data. This...
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