Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning for Sequential Behavior Modeling and Prediction

In the information era, as computer networks and related applications become more and more popular, security problems are more and more serious in global information infrastructure. It was reported that in the past two years, large amounts of network attacks and computer viruses caused great damages to global economy and the potential threats to the global information infrastructure have increased a lot. To defend various cyber attacks and computer viruses, lots of computer security techniques have been studied, which include cryptography, firewalls and intrusion detection, etc. As an important computer security technique, intrusion detection [1,2] has been considered to be more promising for defending complex computer attacks than other techniques such as cryptography, firewalls, etc. The aim of intrusion detection is to find cyber attacks or non-permitted deviations of the characteristic properties in a computer system or monitored networks. Thus, one of the central problems for intrusion detection systems (IDSs) is to build effective behavior models or patterns to distinguish normal behaviors from abnormal behaviors by observing collected audit data. To solve this problem, earlier IDSs usually rely on security experts to analyze the audit data and construct intrusion detection rules manually [2]. However, since the amount of audit data, including network data, process execution traces and user command data, etc., increases vary fast, it becomes a time-consuming, tedious and even impossible work for human experts to analyze dynamic, huge volumes of audit data and extract attack signatures or detection rules. Furthermore, detection rules constructed by human experts are usually based on fixed features or signatures of existing attacks, so it will be very difficult for these rules to detect deformed or even completely new attacks. According to the differences in the monitored data, IDSs can be mainly classified into two categories, i.e., network-based intrusion detection and host-based intrusion detection. Network-based intrusion detection observes data from network packets and extracts various features from them, which usually include connection features, traffic features, and content features. A systematic discussion on feature representation in network-based intrusion detection can be found in [3]. For host-based intrusion detection, various observation data from the corresponding operation systems are collected, which mainly include system call data and shell command data [4], etc. Despite of having different observation data, both host-based and network-based intrusion detection need to improve the detection accuracy for large volumes and variability of normal and attack behaviors. Aiming at this problem, O pe n A cc es s D at ab as e w w w . in te ch w eb . o rg
Comparing Prosody Across Many Languages

An empirical metric is developed to measure similarity or distance between the prosodic systems of diverse languages. Two physical variables which carry prosodic information, F0 and amplitude envelope modulation, are used as inputs in a pairwise language discrimination task. A novel neural network model is trained to discriminate among pairs of languages drawn from a set of ten languages in a telephone speech database. Network performance is used to obtain a distance metric for each pair, allowing overall similarity relationships within the 10 language set to be identified. Results are presented for F0 alone, in which Mandarin and Japanese are found to be clearly distinguished from all other languages in the database, and French is found to be clearly distinguishable, even from related Indo-European languages. Using amplitude envelope modulation alone, a cluster of languages usually described as syllable-timed is identified. Vietnamese and Farsi are found to be rhythmically distinct from languages conventionally described as stress-, syllable- or mora-timed. When both variables are provided to the networks, the results provide an empirical measure of prosodic distance among the languages.
Integration of multiple high-throughput data-types in cancer research

This thesis deals with integrative analysis of two (different) multi-dimensional data types in the context of genomic cancer research. Firstly, simultaneous clustering of two high-throughput data sets optimizing the output of iCluster program. Secondly, developing a novel the Gene Set Score (GSS) that uses different data types to identify de-regulated pathways (or gene sets). Regarding the clustering, the key concern is how to process and filter with the two different data types (Gene Expression and Copy Number data) and how consistent the results of iCluster are and how to determine the optimal (best) number of clusters. The output from different number of clusters is systematically studied using Wallace Index and Rand Index. A large sarcoma data set is analyzed, and the survival analysis of the samples of this cancer data with significant p-values is the resulting method of analysis. Altogether it can be said that iCluster is a good method to cluster samples into groups and this methodology is readily applicable to other data sets as well as to genomic areas where multi-dimensional data sets are produced (image analysis, psychological profiles). The Gene Set Score is a novel score that uses Gene Expression and Copy Number data to identify significantly de-regulated gene sets/pathways/groups of genes. For the Gene Set Score, scores for single genes are calculated that merge together to a score of a given gene set/pathway. Events in the data then create the Gene Set Score. The resulting method identifies not only most of differentially regulated gene sets from a popular and widely used method (Gene Set Analysis, GSA) that is based on Gene Expression data only but additional pathways.
ASPEN: High-performance hardware support for distributed shared-memory

ASPEN: High-Performance Hardware Support for Distributed Shared-Memory Kenneth Mark Maxham This thesis describes and evaluates an integrated memory and network subsystem designed to provide the abstraction of shared memory among workstations. The work is embodied in Aspeny, the integrated memory and network communication system developed for the purpose of evaluating our design decisions. Aspen combines a mesh of rings with snooping hardware to provide full and localized broadcasting capability. Its coherence mechanism uses an update-with-timeout protocol, a hybrid between pure invalidation and update protocols. A relaxed memory consistency model allows the bu ering of writes to memory. Routing of messages is accomplished with a novel distributed-directory scheme. Synchronization is provided by the hardware in the form of lock and barrier support. Dual ported memory at the network interface reduces contention between the bus and the network for the large secondary cache. In order to evaluate Aspen, we implemented a cycle-level simulator capable of emulating a variety of architectural con gurations. Simulation parameters included the second-level cache line size, the memory consistency model, and the update timeout setting. Using this simulator, we compared Aspen to other approaches to the y A grove of Aspen trees, while appearing to be a collection of individuals, is actually a single organism with a common root system. problem of shared memory computing. In a simulated 64-processor system, Aspen exhibited application speedups ranging from 37 to 61, depending upon the application. We present additional experimental data that suggests Aspen is scalable to larger numbers of processors with comparable performance. Acknowledgments In the process of writing this thesis, I have received invaluable help, of di ering types, from many sources. Credit must begin with Dr. John Bennett, my advisor, who prompted me to begin a research program and who directed me at every point during it. Dr. Alan Cox and Dr. Bart Sinclair, who agreed to be on my committee, provided invaluable critiques of both the research and the writing. Technical guidance outside my committee came frommany patient people, including, but not limited to, Dr. John Carter, Dr. Sandhya Dwarkadas, Kathi Fletcher, Ram Rajamony, and Evan Speight. In particular I would like to thank Jay Greenwood and Rajat Mukherjee for assistance with the simulator. I am greatly indebted to all who listened to my hare-brained questions, wild speculation, stupid jokes, and general ranting. To Grandpa Ken, who instilled a love of tinkering, Grandpa Rus, who made me laugh, Grandma Eve, who taught me to read, Grandma Kay, who taught me to create, and Mom and Pop, who made it possible
Bauteilindividuelle und verfahrensspezifische Kennzeichnung und Identifikation für die Additive Fertigung

Die Verwendung von additiven Fertigungsverfahren ermoglicht insbesondere das profitable Fertigen von geringen Stuckzahlen gegenuber dem Einsatz von konventionellen Fertigungsverfahren. Die Auspragung der Prozesskette ermoglicht somit das Fertigen von kundenindividuellen Bauteilen in einer geringen Stuckzahl bis hinunter zu Losgrose 1. Die hauptsachliche Herausforderung fur das Fertigungsunternehmen besteht darin, dass in einem Bauprozess verschiedene kundenindividuelle Bauteile simultan gefertigt werden. Dies fuhrt zu einer aufwandigen kundenspezifischen Zuordnung von Bauteilinformationen zu den dazugehorenden Kunden. Diese Zuordnung erstreckt sich uber die gesamte Prozesskette der bauteilindividuellen Additiven Fertigung, vom Eingang der Auftragsdaten, uber den Pre-, In- und Postprozess bis hin in die Nutzung und das Lebenszyklusende der Bauteile. Eine durchgangige Methode zur bauteilindividuellen und verfahrensspezifischen Kennzeichnung und Identifikation fur die Additive Fertigung existiert nicht. Zur durchgangigen Identifikation in der bauteilindividuellen und verfahrensspezifischen Additiven Fertigung wird in dieser Dissertation eine Methode zur Kennzeichnung additiv zu fertigender Bauteile und ein Informationsmodell zur Reprasentation relevanter bauteilindividueller Informationen uber den gesamten Prozess hinweg entwickelt. Die Methode zur Bauteilkennzeichnung basiert auf einem bauteilindividuellen und verfahrensspezifischen Kennzeichnungsansatz, bestehend aus Kennzeichnungstrager und Identifikator, sowie der Anbringung dieses ausgewahlten Ansatzes, basierend auf entwickelten Gestaltungsregeln, an der Bauteilgeometrie. Aufbauend auf diesen Entwicklungen wird dem Anwender die durchgangige virtuelle und physische Identifikation und Nachverfolgung von kundenindividuellen additiv gefertigten Bauteilen bereitgestellt. Ausgehend von der Analyse des relevanten Standes der Technik und bestehender Forschungsarbeiten wurde ein Anforderungsprofil definiert. Aufbauend auf diesen Anforderungen wurde in dieser Dissertation ein Konzept zur bauteilindividuellen und verfahrensspezifischen Kennzeichnung und Identifikation fur die Additive Fertigung entwickelt. Das Konzept gliedert sich in zwei wesentliche Konzeptbausteine. Zum einen wird die Methode zur Bauteilkennzeichnung im Pre-Prozess erarbeitet, welche den Anwender in der erweiterten Prozesskette unterstutzt. Zum anderen wird das Informationsmodell zur bauteilindividuellen und verfahrensspezifischen Kennzeichnung und Identifikation fur die Additive Fertigung entwickelt. Hierauf aufbauend wird die Software-Applikation additiv-ID mit dem Nachverfolgungs- und Assistenzsystem zur Bauteilkennzeichnung prototypisch implementiert. Diese Implementierung dient zur Validierung und Verifikation der entwickelten Ansatze und Methoden.
Classification of time series data: a synergistic neural networks approach

Application of neural networks have been plentiful in past decades. One of the development in recent years is the synergy of neural networks. There have been reported in literature under different terms and scopes, there has yet to be an overview study of such approach. Even though classification is probably most prolific application area of neural networks, relatively few have been carried out on times series data. One such application is in classification of time series patterns which are commonly occurred, especially in real time control system. This paper presents an overview of synergistic approach to neural networks. It systematically classifies the approach according to 2 facets. It follows by presenting how a synergy of neural networks could improve the performance in classification of time series control chart patterns when signals are highly noisy. This work illustrates the benefit of synergistic approach in neural networks and points out the importance of selecting appropriate neural network architecture for such classification.
Power-conscious software design-a framework for modeling software on hardware

Modeling software on an abstract machine, we study the power consumption (energy) requirement in solving two of the most popular software problems: search and sorting. Sorting consumes /spl sim/N times more energy than search where N is the data size. For the same problem and the same data size, the energy requirement for different algorithms could also vary by orders of magnitude.
A 1.4-million-element CCD image sensor

A 2/3" format 1.4 megapixel full-frame CCD image sensor with a cell size of6.8×6. 8μm, that achieves a quantum efficiency of 50% at 550nm, will be disclosed. Output-amplifier noise is 15rms electrons. Amplifier uses buried channel LDD NMOSFETs. Input-referred sensitivity is 15μV/electron over a 71MHz bandwidth. Device has been fabricated using a two-phase, two-level polysilicon process.
Constraints on intervals for reducing the search space of geometric configurations

The combination of task and motion planning presents us with a new problem that we call geometric backtracking. This problem arises from the fact a single symbolic state or action can be geometrically instantiated in infinitely many ways. When an action cannot be geometrically validated, we may need to backtrack in the space of geometric configurations, which greatly increases the complexity of the whole planning process. In this paper, we address this problem using intervals to represent geometric configurations, and constraint propagation techniques to shrink these intervals according to the geometric constraints of the problem. After propagation, either (i) the intervals are shrunk, thus reducing the search space in which geometric backtracking may occur, or (ii) the constraints are inconsistent, indicating the non-feasibility of the sequence of actions without further effort. We illustrate our approach on scenarios in which a two-arm robot manipulates a set of objects, and report experiments that show how the search space is reduced.
Using articulatory data to inform speech pathology theory and clinical practice.

This paper outlines new developments and recent research in electropalatography (EPG) and discusses how they have contributed to theoretical issues in speech pathology and clinical practice. A central theoretical issue in speech pathology is defining the articulatory processes underlying speech disabilities. EPG data are providing increasing evidence that abnormal speech motor control underlies at least some disorders that were previously thought to have a linguistic origin. The technique also provides visual feedback of tongue-palate contacts, which has been shown to be an effective approach to the treatment of articulation disorders in children and adults. Portable units have increased the effectiveness of feedback therapy because they allow for more frequent practice. Recent initiatives have successfully widened access to EPG therapy. Case illustrations show how the new Windows® EPG provides researchers and clinicians with a more technological advanced system and a wider range of facilities than previous versions.
Direct methods for sparse linear systems

Preface 1. Introduction 2. Basic algorithms 3. Solving triangular systems 4. Cholesky factorization 5. Orthogonal methods 6. LU factorization 7. Fill-reducing orderings 8. Solving sparse linear systems 9. CSparse 10. Sparse matrices in MATLAB Appendix: Basics of the C programming language Bibliography Index.
Saliency Detection Integrated with Depth Information

Most bottom-up saliency detection methods only perform outstandingly on simple image but when operating on complex one, results are not satisfactory. In a complex scene image, more image features demand being taking into consideration, the most important one of which is the spatial relation of different objects. Aiming at this problem, a new image saliency detection method is proposed based on frequency-tuned (FT) salient region detection. In our method, depth map of the image are constructed and then depth information of every object are added to the calculation of saliency to improve FT. Experimental results illustrate that our proposed method outperforms the original one and thus the salient regions are more evident in the ultimate saliency map. Keywords-image saliency detection; complex scene image; spatial relation; depth information
LMI based control for balancing an Inverted Pendulum Mobile Robot

Inverted pendulum on a mobile robot system is a paradigm example taken into consideration for studying the dynamics related to huge complex systems over many years. The major problems that arise with this system are to stabilize the unstable equilibrium point of it, which can be readily achieved by the moving cart underneath. This paper tries to implement an Linear Matrix Inequality (LMI) based optimal controller for this system, to ensure robust stability and optimal performance with respect to the other known techniques in the literature, being devised using convex optimization procedure.
Named Entity Recognition : A Survey of Machine-Learning Tools

It is well known that the rapid growth and dissemination of the Internet has resulted in huge amounts of information generated and shared, available in the form of textual data, images, videos or sounds. This overwhelming surge of data is also true for specific areas such as biomedicine, where the number of published documents, such as articles, books and technical reports, is increasing exponentially. For instance, the MEDLINE literature database contains over 20 million references to journal papers, covering a wide range of biomedical fields. In order to organize and manage these data, several manual curation efforts have been set up to identify, in texts, information regarding entities (e.g. genes and proteins) and their relations (e.g. protein-protein interactions). The extracted information is stored in structured knowledge resources, such as Swiss-Prot [1] and GenBank [2]. However, the ef‐ fort required to continually update these databases makes this a very demanding and ex‐ pensive task, naturally leading to increasing interest in the application of Text Mining (TM) systems to help perform those tasks.
Preserving process: conservation of a Helen Frankenthaler maquette for Card

ABSTRACT Contemporary American print publishers, such as Universal Limited Art Editions (ULAE), collaborate with artists to produce prints using high quality materials for their editions. However, for developing their imagery, artists often use more affordable materials that may be prone to degradation, discoloration, and structural instability. This article discusses the treatment of a maquette by Helen Frankenthaler, a multi-layered assemblage of paper and transparent film joined together with pressure-sensitive tape that is one of the several preliminary works for her 1971 lithograph Card, now in the collection of The Art Institute of Chicago. Treatment of the maquette was undertaken to reduce degraded tape adhesive and replace it with a stable pressure-sensitive alternative while preserving the integrity of the work and its original materials. FTIR analysis in reflectance and transmittance modes identified the transparent films as cellulose acetate and the tape carriers as both cellulose acetate and regenerated cellulose (cellophane); these findings informed treatment and storage decisions. An analytical survey of several works in the ULAE collection also confirmed the use of cellulose acetate in works by other artists from the 1960s and 1970s. Therefore, concerns for the stability of this material would ultimately factor into the broader preservation plan for the archive.
The State of Educational Data Mining in 2009: A Review and Future Visions.

We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries (“discovery with models”), and the reduction in the frequency of relationship mining within the EDM community. We discuss two ways that researchers have attempted to categorize the diversity of research in educational data mining research, and review the types of research problems that these methods have been used to address. The mostcited papers in EDM between 1995 and 2005 are listed, and their influence on the EDM community (and beyond the EDM community) is discussed.
Jaguaruna: a trajectory planner and executor for the Kanguera Robot Hand

The human hand is an extremely versatile instrument for manipulation and communication. If it becomes possible to copy a part of its sophisticated manipulation abilities, this would provide robotics with much more flexible end-effectors than there are used today. This is a great challenge due to the high complexity of the hand which results from its many degrees of freedom. In this master thesis a trajectory planner was developed for the anthropomorphic robot hand Kanguera of the Mechatronics Laboratory at the School of Engineering of São Carlos, University of São Paulo. This trajectory planner should become the first part of a grasping and manipulation system, which is broken down into several levels, in order to manage the complex problem of coordinately moving all fingers. The work comprises the dynamic modelling of the actuation system and an analysis of the current prototype. A position controller was implemented. To visualize the hand, a 3D model of Kanguera has been implemented in the GraspIt! simulation programme. For the trajectories, two types of paths were elaborated and the methods to calculate them. One of the path types is probably a new concept, that was named Squeezed Screw. It is the combination of the movement on an ellipse with a constant translation in an arbitrary direction. The other path type consists of a series of movements that are linear in configuration space. For the path generation, the performance of a Newton, a Cyclic Coordinate Descent and a look-up table algorithm was compared, to solve the inverse kinematics. The look-up table method performed the best. The spline interpolated trajectories are executed in a hybrid position-velocity control. The trajectory executor contains a method to detect and escape blocked movements. The evaluation of the executions on the real system showed that the executed trajectories are smooth and the end point is reached with a precision of less than 0.5 mm. The thesis closes with suggestions, how to improve the hand prototype and the trajectory planner and how the current system could be enhanced.