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We address two important issues in causal discovery from nonstationary or heterogeneous data, where parameters associated with a causal structure may change over time or across data sets. First, we investigate how to efficiently estimate the "driving force" of the nonstationarity of a causal mechanism. That is, given a causal mechanism that varies over time or across data sets and whose...
Many of today's machine learning (ML) systems are composed by an array of primitive learning modules (PLMs). The heavy use of PLMs significantly simplifies and expedites the system development cycles. However, as most PLMs are contributed and maintained by third parties, their lack of standardization or regulation entails profound security implications. In this paper, for the first time, we demonstrate...
In this paper, we study the leader-follower formation control problem of multiple vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) with limited communication. In particular, the leader's trajectory is only accessible to a subset of the followers and the followers only have access to their neighbours' information. Distributed estimators are developed for each VTOL UAV to obtain accurately...
The measurement of perceptually relevant information about textures has been approached through profilometry, vibrometry, and tribometry. Manfredi et al. [1] used a laser Doppler vibrometer to measure skin surface vibrations as a texture sample slides across a fingertip. In our work, we treat the Manfredi et al. measurements as a gold standard, and assess the performance of a simpler and more portable...
On the way to full production control, wire bonding equipment requires data-driven condition-based maintenance of the mechanical setup. In this paper, we identify aspects of regular mechanical equipment setups that severely affect bonding quality and equipment health in mass production. We show that mechanical equipment setups lower process stability in aluminum wire bonding. Typical faults in mechanical...
In current times, there has been a surge in the amount of collected data from computational systems. The vast amount of data can be useful in many applications and fields, particularly so in Big Data Analytics. However with a large collection of data there is a difficulty discovering important information. Automatic Document Summarization (ADS) systems are suitable for the task of outlining useful...
Exception handling is a powerful tool provided by many pro- gramming languages to help developers deal with unforeseen conditions. Java is one of the few programming languages to enforce an additional compilation check on certain sub- classes of the Exception class through checked exceptions. As part of this study, empirical data was extracted from soft- ware projects developed in Java. The intent...
This work contributes towards the vision of a suture platform that is able to objectively quantify suturing skill by integrating data from multiple sensing modalities. A first step towards such a platform is the synchronization of data from multiple sensor streams and perhaps even multiple systems. We present the design of a novel suture platform with force and motion sensors as well as video capture...
Robot manipulation is one of prerequisites capability for service robot. However, autonomous manipulation remains a challenging problem for robot to implement the task, where robot has physical interactions and mechanical contacts with its environment. To date, learning from demonstration (LFD) has been successfully applied to enable robot to acquire new manipulation skill. Researches on LFD mainly...
Using the recent advances in sequencing technology thousands of genomes have been sequenced. This sequence data can be fruitfully employed in diagnosis, drug design, etc. Genome-wide Association Study (GWAS) focuses on this important problem of extracting useful information from genomic data. As an example, a comparison of different genomes could throw light on causes for different diseases. Human...
Object errors affect the time cost and effectiveness in uncertain data clustering. For decreasing the time cost and increasing the effectiveness, we propose two mechanisms for the centroid based clustering, UKmeans. The first mechanism is an improved similarity. Similarity is an intuitive factor that immediately affects the time cost and effectiveness. For example, similarity calculations with integration...
Data mining, through association rules mining, is one of the best known approaches for patterns identification. However, it results most of the time in a huge set of patterns (rules), so their exploitation is not easy and often requires expert analysis. In this paper we describe a new pattern "set of contrasting rules" which, contrary to most state-of-the-art patterns, has the characteristic...
Time series classification (TSC) problem is important due to the pervasiveness of time series data. Shapelet provides a mechanism for the problem by its ability to measure local shape similarity. However, shapelets need to be searched from massive sub-sequences. To address this problem, this paper proposes a novel shapelet learning method for time series classification. The proposed method uses a...
This paper proposes a multi-sensor based method to predict the falling of the humanoid in a reliable and agile manner. The fusion of multi-sensors such as an inertial measurement unit and foot pressure sensors are considered, which can be regarded as human's vestibular and proprioception. We define a set of feature-based fall indicator variables (FIVs) with manually extracted thresholds for four major...
Extracting centerlines of coronary arteries is a challenging but important task in clinical applications of cardiac computed tomography angiography (CTA). In this paper, we propose an inertia-driven path tracking method based on the directional minimal path (DMP) to automatically extract the coronary centerlines. In DMP, the path tracing is divided into a number of segments. The proposed method adopts...
Snort is a popular open-source Intrusion Detection System (IDS). Since rules are updated offline and network environment changes dynamically, Snort has a low detection rate especially for new types of attacks. Since attack signatures are not stored in the system, attackers could intrude without being detected. The aim of this research is to automate rule generation for system by use of logs of performed...
Recovering deleted files play an important role in a digital forensic investigation. When a file is deleted, only pointers that link file's metadata to its content are deleted and metadata entry is marked as deleted. As long as data is not overwritten or wiped, deleted data will remain in unallocated space. One of the methods that can be used to recover these deleted files is file carving. File carving...
A data bank can provide very useful information while mined properly.[27] In order to be optimally extracted, data mining can be done by observing capacity and characteristics of the data; so it can generates Knowledge Discovery in Databases as expected. For instance in Gene Bank, every single record of DNA, there are at least ten thousand sequences recorded. If the data is more than a hundred records,...
In recent years, the use of Graphics Processing Units (GPUs) for data mining tasks has become popular. With modern processors integrating both CPUs and GPUs, it is also important to consider what tasks benefit from GPU processing and which do not, and apply a heterogeneous processing approach to improve the efficiency where applicable. Similarity search, also known as k-nearest neighbor search, is...
For the first time, FEM analysis was utilized to study the ways in which RAMPA affects the human skull. We calculated the deformation of the completed model with ANSYS Software and analyzed the effect of RAMPA on the human skull with the obtained data. Moreover, by comparing to actual patient treatments, we validated the feasibility of this engineering approach and provide suggestions for treatment...
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