Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of decision tree classifiers to computer intrusion detection

There is an increasing demand for techniques to detect intrusions into a computer and network system for information security and assurance. This paper describes our research effort on the application of a data mining technique, decision trees, to automatically learn and recognize intrusion signatures for intrusion detection. In our study, decision tree classifiers are used to classify activities in a computer and network system into different states and determine the possibility of an intrusion based on the state classification. Our design of decision tree classifiers is based on an incremental tree induction algorithm. Two decision tree classifiers are developed. One decision tree classifier examines single events of activities in a computer and network system for intrusion detection (single event version). Another decision tree classifier examines a sequence of events in a moving window at a given time for intrusion detection (moving window version). We use computer audit data for training and testing the decision tree classifiers. The testing results of the two decision tree classifiers are analyzed and compared. The "moving window" version of the decision tree classifier produces better performance in intrusion detection.
SSM/I response to dry snow and land-cover categories

The behavior of the SSM/I-derived brightness temperature of snow-covered terrain vs. dry snow water equivalent and land use is examined for the winters of 1996-97 and 1997-98 in Finland. The effect of several factors to the response is investigated. A method is proposed to correct the brightness temperature variation due to temperature changes.
Rich multi-touch input with passive tokens

This thesis investigates a novel input technique for enriching the gesture vocabulary on a multi-touch surface based on fingers' relative location and passive tokens. The first project, TouchTokens, presents a novel technique for interacting with multi-touch surfaces and tokens. The originality is that these tokens are totally passive (no need for any additional electronic components) and their design features notches that guide users' grasp. The purpose of the notches is to indicate a finger spatial configuration (touch pattern) that is specific to the token. When users hold a token and place it on the surface, touching them simultaneously, the system can recognize the resulting touch patterns with a very high level of accuracy (>95%). This approach works on any touch-sensitive surface and makes it possible to easily build low-cost interfaces that combine no-conductive tangibles and gestural input. This technique supports a new multi-touch input that the system can recognize. However, the interaction is limited to the two-state model of touch interaction as the system only knows the tokens' position and cannot detect tokens that are not touched. In the second project of the thesis, we introduce a laser-cut lattice hinge technique for making the tokens flexible. We then develop a new recognizer that analyzes the micro-movements of the fingers while user are holding and deforming those tokens on the surface. We run three experiments to design and calibrate algorithms for discriminating the three following types of manipulations: (1) when a token is left on the surface rather than taken off it (On/Off); (2) when a token has been bent, and (3) when it is squeezed. Our results show that our algorithms can recognize these three manipulations with an accuracy of: On/Off 90.1%, Bent 91.1% and Squeezed 96,9%. The thesis concludes with the presentation of two tools, TouchTokenBuilder and TouchTokenTracker, for facilitating the development of tailor-made tangibles using a simple direct-manipulation interface. TouchTokenBuilder is a software application that assists interface designers in placing notches on arbitrarily-shaped vector contours for creating conflict-free token sets and warning them about potential conflicts. It outputs two files: a vector-graphics description of all tokens in the set and a numerical description of the geometry of each token. TouchTokenTracker is a software library that takes as input the numerical description produced by TouchTokenBuilder, and enables developers to track the tokens' full geometry (location, orientation and shape) throughout their manipulation on the multi-touch surface.
Energy-Efficient Design of Battery-Powered Embedded Systems

Energy-efficient design of battery- powered systems demands optimizations in both hardware and software. We present a modular approach for enhancing instruction level simulators with cycle-accurate simulation of energy dissipation in embedded systems. Our methodology has tightly coupled component models thus making our approach more accurate. Performance and energy computed by our simulator are within a 5% tolerance of hardware measurements on the SmartBadge (2). We show how the simulation methodology can be used for hardware design exploration aimed at enhancing the SmartBadge with real-time MPEG video feature. In addition, we present a profiler that relates energy consumption to the source code. Using the profiler we can quickly and easily redesign the MP3 audio decoder software to run in real time on the SmartBadge with low energy consumption. Performance increase of 92% and energy consumption decrease of 77% over the original executable specification have been achieved.
Joint Modeling of Multiple Related Time Series via the Beta Process

We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our approach is based on the discovery of a set of latent, shared dynamical behaviors. Using a beta process prior, the size of the set and the sharing pattern are both inferred from data. We develop efficient Markov chain Monte Carlo methods based on the Indian buffet process representation of the predictive distribution of the beta process, without relying on a truncated model. In particular, our approach uses the sum-product algorithm to efficiently compute Metropolis-Hastings acceptance probabilities, and explores new dynamical behaviors via birth and death proposals. We examine the benefits of our proposed feature-based model on several synthetic datasets, and also demonstrate promising results on unsupervised segmentation of visual motion capture data.
Hybrid User Behavioral Analysis with Deep Neural Network for Notifications/Pop-ups Recommendation

With the emergence of mobile and wearable devices, push notification becomes a powerful tool to connect and maintain the relationship with App users, but sending inappropriate or too many messages at the wrong time may result in the App being removed by the users. In order to maintain the retention rate and the delivery rate of advertisement, we adopt Deep Neural Network (DNN) to develop a notification/pop-up recommendation system enabled by collaborative filtering-based user behavioral analysis. We further verified the system with real data collected from the product Security Master, Clean Master and CM Browser, supported by Leopard Mobile Inc. (Cheetah Mobile Taiwan Agency). In this way, we can know precisely about users' preference and frequency to click on the push notification/pop-ups, decrease the troublesome to users efficiently, and meanwhile increase the click through rate of push notifications/pop-ups.
Interfacing of MATLAB with Arduino for Object Detection Algorithm Implementation using Serial Communication

The paper explains proposed algorithm for object detection using image processing and manipulation of the output pin state of Arduino board with ATmega 8 controller by tracking the motion of the detected object. The object detection algorithm has been developed on MATLAB platform by the combination of several image processing algorithms. Using the theory of Image Acquisition and Fundamentals of Digital Image Processing, the object has been detected in real time. Various features of an object such as the shape, size and color can be used to detect and track the object. The variation in vertical and horizontal axis of detected object is moderated by serial communication port and using serial data communication, the state of Arduino board pin has been controlled. MATLAB programming develops a computer vision system in the real time for object detection and tracking using camera as an image acquisition hardware. Arduino programming provides an interfacing of a hardware prototype with control signals generated by real time object detection and tracking. Keywords—MATLAB, Arduino board, ATmega 8, Digital Image processing, Serial data communication, Thresholding.
An Extensible Component-based Approach to Simulation Systems on Heterogeneous Clusters

There is an abundance of computing power sitting in computer labs waiting to be harnessed. Previous research in this area has shown promising results networking clusters of workstations together in order to solve bigger problems faster at a fraction of the cost for supercomputer time. There are, of course, challenges to using these sorts of clusters: the communication fabrics linking these machines are not necessarily high-performance, and the differences between individual machines in the cluster require careful load balancing in order to efficiently use them. These problems have only become greater with the introduction of acceleration hardware such as GPUs and FPGAs; however, that hardware also provides even greater computing power at an even lower price point for those that can work around their idiosyncrasies. This dissertation presents an approach to designing software to effectively utilize these heterogeneous computing clusters in a modular, extensible manner. I apply it to the development of a large-scale NeoCortical Simulator(NCS) as well as the engineering of a virtual reality library, caVR.
Detection of Behavior Patterns of Interest Using Big Data which have Spatial and Temporal Attributes

Abstract. New innovative analytical techniques are emerging to extract patterns in Big Data which have temporal and geospatial attributes. These techniques are required to find patterns of interest in challenging circumstances when geospatial datasets have millions or billions of records and imprecision exists around the exact latitude and longitude of the data. Furthermore, the usual temporal vector approach of years, months, days, hours, minutes and seconds often are computationally expensive and in many cases do not allow the user control of precision necessary to find patterns of interest. Geohashing is a single variable ASCII string representation of two-dimensional geometric coordinates. Time hashing is a similar ASCII representation which combines the temporal aspects of date and time of the data into a one dimensional set of data attributes. Both methods utilize Z-order curves which map multidimensional data into single dimensions while preserving locality of the data records. This paper explores the use of a combination of both geohashing and time hashing that is known as “geo-temporal” hashing or “space-time” boxes. This technique provides a foundation for reducing the data into bins that can yield new methods for pattern discovery and detection in Big Data.
Verificação de Locutores Independente de Texto: uma Análise de Robustez a Ruído

The personal identification of individuals is a task executed millions of times every day by organizations from diverse fields. Questions such as "Who is this individual? " or "Is this person who he or she claims to be? " are constantly made by organizations in financial services, health care, e-commerce, telecommunication systems and governments. Biometric identification is the process of identifying people using their physiological or behavioral characteristics. These characteristics are generally known as biometrics and examples of these include face, fingerprint, iris, handwriting and speech. Speaker recognition is a biometric modality which makes the personal identification by using speaker-specific information from the speech. This work focuses on the development of text-independent speaker verification systems. In these systems, speech from an individual is used to verify the claimed identity of that individual. Furthermore, the verification must occur independently of the pronounced word or phrase. The main challenge in the development of speaker recognition systems comes from the mismatches which may occur in the acquisition of the speech signals. The techniques proposed to mitigate the mismatch effects are referred as compensation methods. They may operate in three domains: in the feature extraction process, in the estimation of the speaker models and in the computation of the decision score. Besides presenting a wide description of the main techniques used in the development of text-independent speaker verification systems, this work presents the description of the main feature-, model- and score-based compensation methods. In the experiments, this work shows comprehensive comparisons between the conventional techniques and the alternatively compensations methods. Furthermore, two compensation methods are proposed: one operates in the model domain and the other in the score-domain. The scoredomain proposed compensation method is based on the Normal cumulative distribution function and, in some contexts, outperformed the performance of the main score-domain compensation techniques. On the other hand, the model-domain compensation technique proposed in this work is based on a method presented in the literature which combines two concepts: the multi-condition training and the Missing Data Theory. The formulation proposed by the authors is based on the Posterior Union models and is not completely appropriate for the text-independent speaker verification task. This work proposes a more appropriate formulation for this context which combines the concepts used by the authors with a type of modeling using Universal Background Models (UBMs). The proposed method outperformed the usual GMM-UBM modeling technique, based on Gaussian Mixture Models (GMMs).
Comparing content-filter techniques for stopping spam

There are many new theoretical techniques for detecting spam e-mail based upon the message contents. Although Bayesian methods are the most wellknown, there are other approaches for classifying information. This paper establishes some criteria for measuring spam filter effectiveness and compares the Boosting and Support Vector Machine approaches with some well-known existing filter software. It also examines ways of transforming e-mail messages into a form which is more readily processable by such algorithms.
Effective and efficient analysis on internet-based social networks

In the last decade there has been a shift towards online user-generated content. Services such Twitter, Facebook, Instagram or Tumblr allow users to share their content and ideas in a simple manner. The lower barrier of access (more non-technical users), ubiquity and social capabilities generate the creation of content that is user-centered: opinions, reviews, discussions and interests. Effective and Efficient exploration and analysis of user-generate content is an open and evolving question, which gained increased interest in recent years. The aim of my dissertation was to explore method and algorithms to analyze and leverage the user-generated content. In particular our objectives are as follows: 1) Study how we can leverage social network data for prediction, recommendation or classification tasks; 2) Define novel features that can be used to improve the efficiency/efficacy of the models generated from user-content; and 3) Define tools and algorithms that facilitate the use of user generated content. This work has studied: how to collaboratively annotate user-generated content in specific domains, how to define features that improve the prediction accuracy of time series based on the social network activity, how to define algorithms and measures to detect spatio-temporal changes of topics, and how to improve accessibility to the online social data, which is publicly available using a restrictive API provided by the content provider.
Artificial Intelligence Education using Java Applet Examples and WEKA Tool

Artificial intelligence is defined as a discipline that a machine works well in the field of sensing, thinking, and decision like as people. The introduction of intelligent systems and devices has developed an interest in artificial intelligence among university instructors and students. Because artificial intelligence is a wide topic, the course being taught tends to focus on theoretical aspects of the subject rather than its practical uses. The experiment of using Java applet examples based multimedia courseware and a well-known data mining tool called WEKA has been conducted to incorporate both the theoretical and the practical uses of artificial intelligence in college education. After the experiment has been conducted, students have completed surveys to whether or not this way of learning was effective. The results showed the increase in students’ understanding of this subject area than before.
Analysis and Development of Consensus-based Estimation Schemes

In the last few decades we assisted to an extraordinary expansion of the Internet and of wireless technologies. These interconnection technologies allow a continuously increasing number of devices to exchange information. This fact, together with the parallel increase in the availability of inexpensive nodes carrying a wide range of sensing capabilities, attracts the interest in developing large-scale sensing platforms, which could be used to measure a variety of physical phenomena. However, these huge networks of simple devices are subject to tight energy and bandwidth constraints, making efficient distributed estimation and data fusion algorithms a strong need, to avoid unmanageable computational and communicational burden on network bottleneck nodes. %In this thesis we address some issues in this research avenue In this thesis we address some issues in this field, presenting and analyzing distributed algorithms to solve specific distributed estimation problems and carrying out the analysis of some other recently-proposed algorithms. To perform data fusion in a distributed fashion, we relay on consensus algorithms, namely algorithms that achieve agreement on a common value in the network. Using consensus as a basic brick to build estimation algorithms we can take advantage of the solid understanding on this problem that many recent contributions deepened and sharpened, and we can leverage for our analysis on powerful and effective tools. In the thesis we propose a distributed algorithm for offset removal and an algorithm for least-square identification of the wireless-channel parameters, motivated by the application of localization and tracking of a moving object. We present moreover a novel linear algebra inequality, useful in the analysis of randomized algorithms. This result comes into play when we carry out an analysis of a recently-proposed distributed Kalman filtering algorithm. Finally, we look at the intriguing set up of a network cooperation to estimate different but correlated quantities, proposing and analyzing a distributed algorithm that performs inference over a simple Gauss-Markov random field.
Protein-Protein Interaction Extraction from Biomedical Literatures Based on a Combined Kernel ?

Automatically extracting protein-protein interaction (PPI) from biological literature is an important and challenging task in natural language processing (NLP). In this paper, we use an ensemble kernel to extract the PPI information. This ensemble kernel is composed with feature-based kernel and structurebased kernel using the parse tree of a sentence containing two protein names. Experiments conducted on the IEPA corpus show that this ensemble kernel is e‐cient at extracting protein-protein interaction information. The recall, precision and f-score on the IEPA corpus are 73.03%, 82.09% and 77.28% respectively, which outperform most of the state-of-the-art systems.
Transparency of reporting in CALL meta-analyses between 2003 and 2015

Abstract Since its introduction by Glass in the 1970s, meta-analysis has become a widely accepted and the most preferred approach to conducting research synthesis. Overcoming the weaknesses commonly associated with traditional narrative review and vote counting, meta-analysis is a statistical method of systematically aggregating and analyzing empirical studies by following well-established procedures. The findings of a meta-analysis, when appropriately conducted, are able to inform important policy decisions and provide practical pedagogical suggestions. With the growing number of publications employing meta-analysis across a wide variety of disciplines, it has received criticism due to its inconsistent findings derived from multiple meta-analyses in the same research domain. These inconsistencies have arisen partly due to the alternatives available to meta-analysts in each major meta-analytic procedure. Researchers have therefore recommended transparent reporting on the decision-making for every essential judgment call so that the results across multiple meta-analyses become replicable, consistent, and interpretable. This research explored the degree to which meta-analyses in the computer-assisted language learning (CALL) discipline transparently reported their decisions in every critical step. To achieve this aim, we retrieved 15 eligible meta-analyses in CALL published between 2003 and 2015. Features of these meta-analyses were extracted based on a codebook modified from Cooper (2003) and Aytug, Rothstein, Zhou and Kern (2012). A transparency score of reporting was then calculated to examine the degree to which these meta-analyses are compliant with the norms of reporting as recommended in the literature. We then discuss the strengths and weaknesses of the methodologies and provide suggestions for conducting quality meta-analyses in this domain.