Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibrating probabilities for hyperspectral classification of rock types

This paper investigates the performance of machine learning methods for classifying rock types from hyperspectral data. The main objective is to test the impact on classification error rate of calibrating the model's output into class probability estimates. The base classifiers included in this study are: boosted decision trees, support vector machines and logistic regression. The standard algorithm for some of these methods provides a non-probabilistic, hard decision as output. For those methods, posterior class probability estimates were approximated by fitting a sigmoid function to the classifier predictions. To perform multi-class classification, a one-versus-all approach was used. The different methods were compared using hyperspectral data acquired from ore-bearing rocks under different environmental conditions. The calibration of class probabilities improved the overall performance for almost all algorithms tested; an improvement of over 10% was observed in some cases.
Mining Student Data Using Decision Trees

Student performance in university courses is of great concern to the higher education managements where several factors may affect the performance. This paper is an attempt to use the data mining processes, particularly classification, to help in enhancing the quality of the higher educational system by evaluating student data to study the main attributes that may affect the student performance in courses. For this purpose, the CRISP framework for data mining is used for mining student related academic data. The classification rule generation process is based on the decision tree as a classification method where the generated rules are studied and evaluated. A system that facilitates the use of the generated rules is built which allows students to predict the final grade in a course under study.
Application of Virtual Showcase technologies in real scenarios: the case of San Telmo museum

Intuitive access to information in real environments is a challenge for information society technologies. Having this in mind, a new device for mixed reality applications called Virtual Showcase has been developed. It has been conceived to be used in museums and exhibitions, due to its compatibility with traditional museum showcases, offering additional information to the public by means of a simple, direct and intuitive interface. We present the real implementation of a Virtual Showcase passive-stereo prototype exhibited in a museum for three months. Some preliminary results concerning the evaluation process have also been included. Although the technology seems to be promising, implementation aspects such as size should be improved
An Integrated 2D and 3D Shape-based Search Framework and Applications

Traditional computer-aided design tools have lead to proliferation of products. In itself CAD systems have become complex to use with many features. In the light of the use of menu driven systems, this article proposes a freehand sketch driven search and reuse of designs, beautification of sketches, and a new interaction paradigm with navigation. We review our prior work in this area in an integrated fashion. The central part of this paper addresses the representation of both 2D sketches and 3D designs in a manner that is tailored for engineering. The interaction of the user with the sketch based search forms a natural interface for interaction with both 2D and 3D models. In addition, the interface is also integrated with a constraint solver for freehand sketches. The system can also suggest to the user the part-class name allowing the probing of the classes and semantics in the database. Besides, users can discover new knowledge by navigating our 2D/3D visualization interface freely based on the preliminary retrieval of classification results. Similarity search can be performed any time during the navigation process.
Title: Computational Chemical Dynamics of Complex Systems Principal Investigator: Combustion and Low-temperature Oxidation Mechanisms of Biofuels and Related Systems

The goal of this ongoing project is to apply powerful new simulation techniques to tackle computationally challenging problems in chemical dynamics, with special emphasis on the critical problems in environmental science and chemical engineering facing the U. S. Department of Energy (DOE) and the nation. Our studies involve electrochemistry, heterogeneous catalysis, nanoparticles, solid-state dynamics, and photochemistry. Our computations are carried out with new high-throughput integrated software that we are developing. Our up-to-date accomplishments and activities in the fiscal year of 2012/13 are overviewed below. Additional information is available at http://comp.chem.umn.edu/gc/.
Scaling and eliminating non-contact forces and torques to improve bilateral teleoperation

In bilateral teleoperation, the operator experiences forces and torques applied to the slave manipulator. These forces and torques, however, consist of two components: on the one hand, forces and torques due to contacts with the environment, and on the other hand, non-contact forces, i.e., inertial forces, centrifugal forces, Coriolis forces, and associated torques. For several reasons, eliminating these non-contact forces and torques from the force-torque measurements of the slave or scaling them can be advantageous. For instance, in highly-dynamic teleoperation tasks, these forces and torques may contribute to operator fatigue or hamper the detection of contacts with the environment. This paper briefly reviews the estimation of inertial parameters of the slave load, e.g., an end-effector or a gripper. Subsequently, a method for eliminating the non-contact forces and torques from the measurements of a wrist-mounted force-torque sensor or scaling them is presented. After a brief overview of our teleoperation system, experimental results are presented which demonstrate the effectiveness of our approach.
Research on the Automatic Evaluation of Merchandise Comments on Blogs

Opinionated content in Blog comments usually has a positive or a negative or a neutral connotation. This paper researches on the automatic evaluation of Blog comments on merchandise. It builds a meta-search engine for retrieving Blog pages with merchandise comments. The retrieved pages are parsed; the comment texts are drawn out and serve as resources for corpus. By means of feature extraction and polarity analysis, these comments text are represented with SVM. In polarity analyzing, a dictionary of merchandise attributes and a lexicon of positive and negative words are constructed to improve the accuracy. The average score of specific product is calculated based on the polarity score of each merchandise attribute and the percentage of people who gives positive comment. We build a prototype system AESBC and conduct comparison study between the result of our experimental system and that of field experts. The experimental result shows the effectiveness of our method.
Hash Visualization: a New Technique to improve Real-World Security

Current security systems su(cid:11)er from the fact that they fail to account for human factors. This paper considers two human limitations: First, people are slow and unreliable when comparing meaningless strings; and second, people have di(cid:14)culties in remembering strong pass-words or PINs. We identify two applications where these human factors negatively a(cid:11)ect security: Validation of root keys in public-key infrastructures, and user authentication. Our approach to improve the security of these systems is to use hash visualization, a technique which replaces meaningless strings with structured images. We examine the requirements of such a system and propose the prototypical solution Random Art. We also show how to apply hash visualization to improve the real-world security of root key validation and user authentication.
Intrusion Detection with Mobile Agents

abstract During the last ten years, the Internet has grown considerably. More interconnected people yields increased information security problems. Indeed, the continuous increase in computer in-terconnectivity and interoperability in a fully open way enhances the intruder's ability to attempt malicious behaviour against computers and networks and furthermore allows intruders to make these attempts extremely efficient. Detecting an intruder in a network environment is hard for a human and even if the amount of circulating information is collected by computers there is still too much information to analyse in real-time. Intrusion Detection Systems' (IDSs) goal is to detect attacks against information systems. Notably it is difficult to guarantee a completely and provably secure information system and to be sure to always maintain it in a secure state during its utilization. This is why IDSs have to monitor the usage of such systems to detect eventual insecure states. For this task, new approaches and designs on IDSs are required which avoid, for example, centralised control and analysis of data to determine if an intruder entered the network. With this perspective and in the scope of a Swiss National Project (ADAMA II-2000-054014.98), we are investigating the use of Mobile Agents (MAs) research to address Intrusion Detection (ID) in an Intranet. After the general description of the different goals of the project, we expose our first stage ID model using MAs based on immune system principles.
CD9-mediated activation of the p46 Shc isoform leads to apoptosis in cancer cells

CD9, a member of the tetraspanin family, has been shown to be involved in a range of cellular activities, including migration, proliferation and adhesion, but the molecular mechanisms by which it mediates such events is unclear. Here, we found that anti-CD9 monoclonal antibody ALB6 inhibited cell proliferation, reduced cell viability and induced not only morphological changes specific to apoptosis but also molecular changes, as evidenced by TUNEL and annexin-V staining. For the possible mechanism of ALB6-induced apoptosis, ALB6 activated the c-Jun NH2-terminal kinase/stress-activated protein kinase (JNK/SAPK) and p38 mitogen-activated-protein kinase (MAPK) within 5-15 minutes, as well as caspase-3 within 24-48 hours. It is noteworthy that ALB6 induced tyrosine phosphorylation of the p46 Shc isoform specifically and that the overexpression of its dominant-negative form completely suppressed the ALB6-induced activation of JNK/SAPK, p38 MAPK and caspase-3, resulting in the inhibition of apoptotic cell death. These results suggest that CD9 might regulate apoptosis through the specialized signals in human cancer cell lines.
Correlators of short multitrace operators in N=4 supersymmetric Yang-Mills theory

Multitrace scalar operators in the (0,k,0) representations of SU(4){sub R} share many properties with their single-trace analogues. These multitrace operators are primary fields of short representations of the N=4 superconformal group. Thus, they have protected dimensions. We show that two- and three-point correlator functions of such operators do not receive radiative corrections at order g{sup 2} in perturbation theory for an arbitrary gauge group. {copyright} {ital 1999} {ital The American Physical Society}
Accurate 3D localization scheme based on active RFID tags for indoor environment

In last decade, indoor localization of target objects has been intensively studied. In particular, schemes that use RFID tags for precise localization of sensor nodes have received more interests. 3D localization based on RFID tags in indoor environments has not been enough studied. In this paper and at the attempt of contributing in this topic, we propose an approach based on MDS for 3D localization using active RFID tags in indoor environments. The considered system is a centralized scheme that involves a server, readers and tags. The propagation model used to estimate the received power considers the path loss and shadowing effects. To show the accuracy of the proposed scheme, a selection of numerical results is used.
Significance of Sentence Ordering in Multi Document Summarization

Multi-document summarization represents the information in a concise and comprehensive manner. In this paper we discuss the significance of ordering of sentences in multi document summarization. We show experimental results on DUC2002 dataset. These results show the ordering of summaries before and, improvement in this, after applying sentence ordering. For this purpose we used a term frequency based summarizer [9] and a centroid based summarizer [4] for multi document summarization and Kendall’s Tau co-efficient [11] as performance evaluation metrics.
25.3 A 96dB SFDR 50MS/s Digitally Enhanced CMOS Pipeline A/D Converter

Analog-to-digital (A/D) converters for "universal" base-stations have stringent spurious-free dynamic range (SFDR) and speed requirements. High speed pipelined data converters can be combined with digital resolution enhancement techniques as ideal candidates for this application. In our design, the linearity of the overall pipeline converter has been improved by increasing the linearity of the input sample-and-hold (S/H) and then digitally post-processing the raw output for improved performance.
Accelerated Variational Infinite Mixture Models

Infinite mixture models, such as the Dirichlet process mixture, are promising candidates for clustering applications where the number of clusters is unknown a priori. Due to computational considerations these models are unfortunately unsuitable for large scale data-mining applications. We propose a class of deterministic accelerated infinite mixture models that can routinely handle millions of data-cases. The speedup is achieved by incorporating kd-trees into a variational Bayesian algorithm for infinite mixture models in the stick breaking representation. Besides kd-trees, this algorithm is also different from Blei and Jordan (2005) in the way we handle truncation: we only assume that the variational distributions are fixed at their priors after a certain truncation level. Experiments show that speedups relative to the standard variational algorithm can be significant.
The Quest for Low Latency

Low latency processing is usually a goal in real-time audio applications; however, it is not clear how little latency is to be considered low enough. We discuss currently available experimental data on human perception and argue that somewhat high latencies (around 20–30ms) are probably perfectly acceptable for typical musical applications. We also argue that it should be possible to accept various levels of latency on a system if we can be aware of the effects of this latency on the users of the system; therefore, we still need more experimental data on latency perception to be able to better assess the effects of latency on musical applications. Such an experi-
A Theory of Rational Inflationary Inertia

“. if there is such a thing as an economy with a rock-solid inflation rate of 40 per cent, plus or minus 2 per cent, per year, institutions would surely adapt, so that prices would be announced in catalogs and wage contracts with smooth growth paths paralleling the smooth aggregate price path. Nominal rigidity would set in about this price path in much the same form as we see around the zero inflation rate in low-inflation economies. ” (Sims, 1988, p. 77)
Software testing: a machine learning experiment

Testing is a critical part of the somare development process. As the cost of so&are development has escalated, attempts to accurately estimate the cost (and time) of so&are testing have become more important. Research is being done to predict soware development costs and to develop tools to help in cost estimations. In this research, machine learning techniques are applied to determine which sojlware testing attributes are important in predicting soBware testing costs and more specifically soflware testing time. Testing data on 25 sofrware projects were collected. Using this database, a machine karning system identifies the factors that affect testing time by creating a classification tree in which nodes share similar values. By analyzing the classification tree we are able to determine the salient factors which placed a program into its group. 77ze factors that we consider include code complexity measures, measures of programmer and tester experience, measures of the use of sohare engineering principles such as structured programming techniques, andstatistics collected during actual testing of the projects. 'Ihe programs in a node have an average testing time that is reflective of any program whose attributes would place it into this group. Thus, the tree can be used, among other things, to predict testing time. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copyright is by permission of the Association of Computing Machinery. To copy otherwise, or to republish, requires a fee and/or swcific txxmission. I. INTRODUCTION The cost of developing a computer-based system consists primarily of the cost of developing the software. It is estimated that software development is 80% of the cost of developing a computer-based system. Testing is a critical part of the software development process. As the cost of software development has escalated, attempts to accurately estimate the costs of all phases of the software development process, including software testing, have become more important. Since resources are limited, it is critical to determine how they should be allocated throughout the software life-cycle. Tools are being developed to estimate software development costs. The purpose of this research is to demonstrate a technique using machine-learning to identify attributes that are important in predicting software testing costs and more specifically software testing time …
Dimension Reduction of Chemical Process Simulation Data

In the analysis of combustion processes, simulation is a costefficient tool that complements experimental testing. The simulation models must be precise if subtle differences are to be detected. On the other hand, computational evaluation of precise models typically requires substantial effort. To escape the computational bottleneck, reduced chemical schemes, for example, ILDM-based methods or the flamelet approach, have been developed that result in substantially reduced computational effort and memory requirements. This paper proposes an additional analysis tool based on the Machine Learning concepts of Subgroup Discovery and Lazy Learning. Its goal is compact representation of chemical processes using few variables. Efficacy is demonstrated for simulation data of a laminar methane/air combustion process described by 29 chemical species, 3 thermodynamic properties (pressure, temperature, enthalpy), and 2 velocity components. From these data, the reduction method derives a reduced set of 3 variables from which the other 31 variables are estimated with good accuracy.
ComForWaRD: Complex Fourier-Wavelet Regularized Deconvolution in Computerized Tomography

The inversion of the Radon transform in the presence of noise is numerically unstable in tomographic image reconstruction and is said to be ill conditioned. We propose an efficient hybrid Complex Fourier-Wavelet Regularization (ComForWaRD) that comprises blurring operator inversion followed by noise suppression via scalar shrinkage in both Fourier and Wavelet domains. The Fourier shrinkage exploits the structure of the noise inherent in deconvolution, while the Wavelet shrinkage exploits the piecewise smooth structure of real world signals and images. Moreover the Complex Discrete Wavelet Transform (CWT) has an additional properties of nearly shift invariant and good directional selective. The CWT is particularly suitable for representation of images and other multi-dimensional signals. The proposed hybrid regularization yields state of the art mean squared error (MSE) performance in practice. Further for certain problems the hybrid algorithm guarantees an optimal rate of MSE decay with increasing resolution. All the algorithms are implemented in Matlab.
Lipopolysaccharide-mediated modulation of cytochromes P450 in Stat1 null mice.

Signal transducer and activator of transcription (Stat), a family of transcriptional factors, has been demonstrated to play a critical role in gene regulation in response to inflammatory cytokines, such as interferon and interleukin-6. Inflammatory cytokines and bacterial endotoxin are known to suppress, in most of cases, the constitutive or induced cytochromes P450 (P450) in animals and humans. However, it is not clear if the suppression of P450 by cytokines is through the Stat-signaling pathway. In the present study, we determined whether Stat1 is involved in lipopolysaccharide (LPS)-mediated modulation of P450 in mouse liver. In both Stat1(+/+) (wild type) and Stat1(-/-) (null) mice, a single dose of LPS treatment (1 mg/kg of body weight, i.p.) significantly reduced the expression of CYP3A11, 2C29, and 1A2 mRNA to 8 to 40% of the control levels as determined by real-time quantitative reverse transcription-polymerase chain reaction. The reduction was supported by Western blot analysis. In contrast, LPS significantly induced the level of CYP4A10 mRNA in both Stat1(+/+) (338% of control) and Stat1(-/-) mice (264% of control). Although suppression of mRNA levels of CYP2E1, and 2D9 was not observed in either LPS-treated Stat1 null or wild-type animals, LPS treatment resulted in a reduction of CYP2E1 protein content, which was more significant in Stat1(+/+) (23% of control) than in Stat1(-/-) mice (67% of control). Consistent with this result, the chlorzoxazone 6-hydroxylase and lauric acid 11-hydroxylase activities, as CYP2E1 representative activities, were reduced markedly by LPS in Stat1(+/+) but not in Stat1(-/-) mice. The ethoxyresorufin O-deethylase activity, as a representative CYP1A activity, was also reduced significantly only in LPS-treated Stat1(+/+) mice. These data clearly demonstrate that LPS-mediated modulation of CYP3A11, 2B10, 2C29, 1A2, and 4A10 in mouse liver is Stat1-independent. However, the significant difference between the LPS-treated Stat1(+/+) and Stat1(-/-) mice in the levels of CYP2E1 protein and activity as well as in the activity level of CYP1A suggests that Stat1 may be indirectly involved in the post-transcriptional modulation of these two mouse P450 enzymes.
Data Clustering Using Group Search Optimization with Alternative Fitness Functions

Data clustering is an important tool for statistical data analysis and exploration, and it has been successfully applied in many fields like image understanding, bioinformatics, big data mining, and so on. From the past few decades, Evolutionary Algorithms (EAs) have been introduced to deal with clustering task, given their global search capabilities and their mechanisms to escape from local minima points. EAs execution is driven in an attempt to optimize a criterion function, also known as fitness function. In this work, we evaluate the influence of the fitness function on Group Search Optimization (GSO) meta-heuristic when applied to data clustering. Three different fitness function are proposed to GSO. Experiments are performed on twelve benchmark data sets obtained from UCI Machine Learning Repository to evaluate the performance of all alternative GSO models in comparison to other well-known partitional clustering methods from literature.