Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bayesian Algorithm to Enhance the Resilience of WAMS Applications Against Cyber Attacks

Monitoring critical infrastructures is highly dependent on the accuracy of the installed sensors and the robustness of estimation algorithms. Data-injection attacks can degrade the operational reliability and security of any cyber-physical infrastructures. An attacker can compromise the integrity of the monitoring algorithms by hijacking a subset of sensor measurements and sending manipulated readings. Such approach can result in wide-area blackouts in power grids. This paper considers several cases of severe data-injections with high probabilities of information loss. To achieve an accurate supervision, a Bayesian-based approximated filter (BAF) has been proposed at each monitoring node using a distributed architecture. To maintain a reduced communication overhead and time complexity, upper and lower bound methods have been developed. The performance of the proposed technique has been demonstrated in a mature synchrophasor application known as the oscillation detection. Two test cases have been generated to examine the immunity of the proposed estimation scheme in New Zealand and Oman power grids. The tests were conducted in the presence of harsh data-injection attacks and multiple system disturbances. Results show the proposed BAF method can accurately extract the oscillatory parameters from the contaminated measurements.
Interactive Impact of Informational and Normative Influence on Donations

Two field experiments manipulating informational and normative influence demonstrate that they interact to yield substantial increases in donations. Informational influence involved a message about the positive consequences of donating (e.g., helping to save lives). These results are contrary to those of several prospective correlational studies that found no relationship between these variables and donation behavior.
Variable Dimensional Local Shape Descriptors for Object Recognition in Range Data

We propose a new set of highly descriptive local shape descriptors (LSDs) for model-based object recognition and pose determination in input range data. Object recognition is performed in three phases: point matching, where point correspondences are established between range data and the complete model using local shape descriptors; pose recovery, where a computationally robust algorithm generates a rough alignment between the model and its instance in the scene, if such an instance is present; and pose refinement. While previously developed LSDs take a minimalist approach, in that they try to construct low dimensional and compact descriptors, we use high (up to 9) dimensional descriptors as the key to more accurate and robust point correspondence. Our strategy significantly simplifies the computational burden of the pose recovery phase by investing more time in the point matching phase. Experiments with Lidar and dense stereo range data illustrate the effectiveness of the approach by providing a higher percentage of correct matches in the candidate point matches list than a leading minimalist technique. Consequently, the number of RANSAC iterations required for recognition and pose determination is drastically smaller in our approach.
Introducing BioSARN – an ecological niche model refinement tool

Abstract Environmental niche modeling outputs a biological species' potential distribution. Further work is needed to arrive at a species' realized distribution. The Biological Species Approximate Realized Niche (BioSARN) application provides the ecological modeler with a toolset to refine Environmental niche models (ENMs). These tools include soil and land class filtering, niche area quantification and novelties like enhanced temporal corridor definition, and output to a high spatial resolution land class model. BioSARN is exemplified with a study on Fraser fir, a tree species with strong land class and edaphic correlations. Soil and land class filtering caused the potential distribution area to decline 17%. Enhanced temporal corridor definition permitted distinction of current, continuing, and future niches, and thus niche change and movement. Tile quantification analysis provided further corroboration of these trends. BioSARN does not substitute other established ENM methods. Rather, it allows the experimenter to work with their preferred ENM, refining it using their knowledge and experience. Output from lower spatial resolution ENMs to a high spatial resolution land class model is a pseudo high‐resolution result. Still, it maybe the best that can be achieved until wide range high spatial resolution environmental data and accurate high precision species occurrence data become generally available.
In vivo gene therapy for pyridoxine‐induced neuropathy by herpes simplex virus‐mediated gene transfer of neurotrophin‐3

Neurotrophic factors have been demonstrated to prevent the development of peripheral neuropathy in animal models, but the therapeutic use of these factors in human disease has been limited by the short serum half‐life and dose‐limiting side effects of these potent peptides. We used peripheral subcutaneous inoculation with a replication‐incompetent, genomic herpes simplex virus‐based vector containing the coding sequence for neurotrophin‐3 to transduce sensory neurons of the rat dorsal root ganglion in vivo, and found that expression of neurotrophin‐3 from the vector protected peripheral sensory axons from neuropathy induced by intoxication with pyridoxine assessed by electrophysiological (foot sensory response amplitude, and conduction velocity, and H‐wave), histological (nerve morphology and morphometry), and behavioral measures of proprioceptive function. In vivo gene transfer using herpes simplex virus vectors provides a unique option for treatment of diseases of the sensory peripheral nervous system.
A parallel genetic algorithm for identifying faults in large diagnosable systems

This paper deals with the problem of fault identification in large diagnosable systems under the PMC model. Recently, genetic algorithms have been successfully used to solve this system-level fault diagnosis problem; however, they have one major drawback, i.e. even though they have been shown to perform better than the existing diagnosis algorithms, they are still time-consuming especially for large systems composed of hundreds or thousands of nodes. In this paper, we describe a new parallel version of the existing evolutionary diagnosis algorithm, which exploits competing sub-populations to speed up the diagnosis algorithm. The new approach has been implemented using the parallel virtual machine (PVM) environment and has been evaluated on a workstation network using randomly generated large diagnosable systems. Experimental results showed that the new parallel version considerably improved the response time of the diagnosis algorithm, hence, allowing for fast identification of faulty nodes. elhadef@site.uottawa. ca shantdas@site.uottawa. ca
Simultaneous robot/world and tool/flange calibration by solving homogeneous transformation equations of the form AX=YB

The paper presents a linear solution that allows a simultaneous computation of the transformations from robot world to robot base and from robot tool to robot flange coordinate frames. The flange frame is defined on the mounting surface of the end-effector. It is assumed that the robot geometry, i.e., the transformation from the robot base frame to the robot flange frame, is known with sufficient accuracy, and that robot end-effector poses are measured. The solution has applications to accurately locating a robot with respect to a reference frame, and a robot sensor with respect to a robot end-effector. The identification problem is cast as solving a system of homogeneous transformation equations of the form A/sub i/X=YB/sub i/,i=1, 2, . , m. Quaternion algebra is applied to derive explicit linear solutions for X and Y provided that three robot pose measurements are available. Necessary and sufficient conditions for the uniqueness of the solution are stated. Computationally, the resulting solution algorithm is noniterative, fast and robust. >
"De poeta y loco, todos tenemos un poco": Conceptualizations of illness: mexican immigrants

There has been a well-documented history of barriers to mental health services among the Mexican immigrant community in the United States. Some of these barriers include low socioeconomic status (SES), lack of insurance, limited English proficiency, fear of discrimination, lack of residency status, and certain cultural dissonance. Culture-specific conceptualizations of mental illness may act as an additional barrier to effective mental health treatment for this community. The purpose of this qualitative study was to investigate conceptualizations of mental health in the Mexican immigrant community in the United States. Careful consideration was taken to ensure that the research design was developed to respect and consider the participants’ culture and surrounding community. Eight individuals from the Mexican immigrant community were interviewed about their beliefs regarding the causes of mental illness, explanations of mental illness, experiences with treatment providers, and ideas about appropriate treatment. The results were organized into five categories: Causal Factors, Phenomena, Strategies, Mediating Factors, and Consequences. Based on these results, recommendations for treatment providers working with the Mexican immigrant community are provided. Degree Type Dissertation Rights Terms of use for work posted in CommonKnowledge. Comments Library Use: LIH This dissertation is available at CommonKnowledge: http://commons.pacificu.edu/spp/1141 Copyright and terms of use If you have downloaded this document directly from the web or from CommonKnowledge, see the “Rights” section on the previous page for the terms of use. If you have received this document through an interlibrary loan/document delivery service, the following terms of use apply: Copyright in this work is held by the author(s). You may download or print any portion of this document for personal use only, or for any use that is allowed by fair use (Title 17, §107 U.S.C.). Except for personal or fair use, you or your borrowing library may not reproduce, remix, republish, post, transmit, or distribute this document, or any portion thereof, without the permission of the copyright owner. [Note: If this document is licensed under a Creative Commons license (see “Rights” on the previous page) which allows broader usage rights, your use is governed by the terms of that license. ] Inquiries regarding further use of these materials should be addressed to: CommonKnowledge Rights, Pacific University Library, 2043 College Way, Forest Grove, OR 97116, (503) 352-7209. Email inquiries may be directed to:. copyright@pacificu.edu This dissertation is available at CommonKnowledge: http://commons.pacificu.edu/spp/1141 “DE POETA Y LOCO, TODOS TENEMOS UN POCO”: CONCEPTUALIZATIONS OF ILLNESS: MEXICAN IMMIGRANTS A DISSERTATION SUBMITTED TO THE FACULTY OF SCHOOL OF PROFESSIONAL PSYCHOLOGY PACIFIC UNIVERSITY HILLSBORO, OREGON BY HANNAH NIXON IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PSYCHOLOGY JUNE 27, 2013 COMMITTEE: Genevieve Arnaut, Psy.D. , Ph.D. Shahana Koslofsky, Ph. D.
Chains of Distrust: Towards Understanding Certificates Used for Signing Malicious Applications

Digital certificates are key component of trust used by many operating systems. Modern operating systems implement a form of digital signature verification for various applications, including kernel driver installation, software execution, etc. Digital signatures rely on digital certificates that authenticate the signature, which then verify the validity of a given signature for a signed binary. Malware attempts to subvert the chain of trust through several techniques to achieve execution, evasion, and persistence. In this paper, we examine a large corpus of malware ($3.3$ million samples) to extract digital signatures and their corresponding certificates. We examine several characteristics of the digital certificates to study features in the process of malware authorship that will potentially be used for characterizing and classifying malware. We look at many features including the certificate's chain length, the issue and expiration year, the validity duration of a certificate, the issuing country, validity, top issuing certificate authorities (CAs), and others, highlighting potentially discriminatory features.
The pursuit of positive affect in task advice networks: Effects on individual performance

Research has demonstrated that people in organizations form advice networks not only for access to task knowledge, but also for the positive affect they derive from social interaction, so much so that they are willing to trade off task competence for the personal liking, pleasantness and energy experienced in interactions with colleagues. The effects of such relational choices on individual performance are poorly understood, however. I propose a theory of affect in advice networks according to which an individual’s propensity to form task-related advice ties based on positive affect experienced when interacting with a colleague enhances individual task performance. I argue that this effect changes depending on the level of activation (a feeling of being energized) generated by those interactions. Compared to forms of positive affect with neutral-to-moderate levels of activation (feelings of enjoyment), the preferential selection of work partners that elicit high-activation positive affect enhances task pe.
Real-Time Curvature Defect Detection on Outer Surfaces Using Best-Fit Polynomial Interpolation

This paper presents a novel, real-time defect detection system, based on a best-fit polynomial interpolation, that inspects the conditions of outer surfaces. The defect detection system is an enhanced feature extraction method that employs this technique to inspect the flatness, waviness, blob, and curvature faults of these surfaces. The proposed method has been performed, tested, and validated on numerous pipes and ceramic tiles. The results illustrate that the physical defects such as abnormal, popped-up blobs are recognized completely, and that flames, waviness, and curvature faults are detected simultaneously.
Identifying fast-food restaurants using a central register as a measure of the food environment

Objective: To validate the identification and location of fast-food restaurants according to a government list of inspected food stores and restaurants. Methods: Fast-food restaurants in the Capital Region of Denmark were identified using a government list of inspected food stores and restaurants (the Smiley register, spring 2010). Ground-truthing was used as the validation method and was performed in May and June 2010 in 125 randomly selected 250 × 250 m grid cells. Results: A total of 186 fast-food restaurants was identified by ground-truthing and 99% of these were registered in the same grid cell by the Smiley register. However, only 152 restaurants of these were categorised as fast-food restaurants by both methods. The sensitivity was 82% and the positive predictive value was 92%. The mean ± standard deviation position accuracy was 15 ± 24 m. Conclusions: Using a government list of inspected restaurants was found to be a valid and useful alternative to expensive and time-consuming field observation and provided a relatively accurate tool for identifying and locating fast-food restaurants in communities.
Inferring Human Interaction from Motion Trajectories in Aerial Videos

People are adept at perceiving interactions from movements of simple shapes but the underlying mechanism remains un-known. Previous studies have often used object movements deﬁned by experimenters. The present study used aerial videos recorded by drones in a real-life environment to generate de- contextualized motion stimuli. Motion trajectories of displayed elements were the only visual input. We measured human judgments of interactiveness between two moving elements, and the dynamic change of such judgments over time. A hierarchical model was developed to account for human per-formance in this task, which represents interactivity using la- tent variables, and learns the distribution of critical movement features that signal potential interactivity. The model provides a good ﬁt to human judgments and can also be generalized to the original Heider-Simmel animations (1944). The model can also synthesize decontextualized animations with controlled degree of interactiveness, providing a viable tool for studying animacy and social perception.
A Probability Model for the Measurement of Ecological Segregation

IN RECENT years many indexes have been proposed to measure ecological segregation and many criteria for the evaluation of such a measure have been set forth. ' As Jahn, Schmid, and Schrag point out, there is theoretically virtually no limit to the number and variety of indexes of ecological segregation which might be constructed. The purpose of this paper is to examine the underlying logic of one set of these segregation indexes, namely, the Shevky-Williams index of isolation and group segregation ratio. A probability model is proposed which estimates the probable interaction between the members of the same racial, nationality, or cultural group and another is proposed which estimates the probable interaction among the members of different groups. The chief advantage of the probability model for the measurement of ecological segregation is that it provides a context for the direct interpretation of scores at all points along the continuum from "no segregation" to "complete segregation. " Minor revisions in the ShevkyWilliams indexes are suggested which are consistent with this probability interpretation.
Behavioral and physiological findings of global-local mental rotation

This study was aimed to explore the cognitive mechanism of global advantage on mental rotation. Using event-related potential and divided-attention paradigm of compound stimuli, we observed that subjects` response to global rotation was faster than their response to local rotation, and the parietal P300 amplitude was inversely related to the character orientation. What’s more, the local rotation task delayed the onset of the mental-rotation-related negativity at frontal and parietal electrodes. None clear effect was found for occipital N150. These findings suggest mental rotation of the global level occurs earlier than processing of the local one and further provide convergent evidence for our previous finding using a focused-attention paradigm.
Millimeter wave material characterization using FMCW-transceivers

In this paper, ultra-wideband frecuency modulated continuous wave (FMCW) radar transceivers are used to measure the complex permittivity of dielectric materials. The radar transceivers are used to perform fast and accurate measurements from 200 GHz to 250 GHz within milliseconds. The dielectric constant and losses are determined using a free-space reflection measurement at normal incident on a given material sample. The FMCW transceiver is calibrated using adopted methods from vector network analysis.
Performance Comparison and Evaluation of AdaBoost and SoftBoost Algorithms on Generic Object Recognition

Abstract—SoftBoost is a recently presented boosting algorithm,which trades off the size of achieved classiﬁcation margin andgeneralization performance. This paper presents a performanceevaluation of SoftBoost algorithm on the generic object recog-nition problem. An appearance-based generic object recognitionmodel is used. The evaluation experiments are performed usinga difﬁcult object recognition benchmark. An assessment with re-spect to different degrees of label noise as well as a comparison tothe well known AdaBoost algorithm is performed. The obtainedresults reveal that SoftBoost is encouraged to be used in caseswhen the training data is known to have a high degree of noise.Otherwise, using Adaboost can achieve better performance.Keywords– SoftBoost algorithm, AdaBoost algorithm, Genericobject recognition. I. I NTRODUCTION Boosting methods and algorithms have been used suc-cessfully in many pattern recognition applications such asfeature selection [12], face detection [14] and generic objectrecognition [7], [3]. AdaBoost is one of the most popularboosting algorithms. It generates a combined hypotheses withlarge margin and works well on data with low degree of noise[10], but not good on data with high degree of noise [15]. In such a case, a large margin on all training data cannotbe achieved without affecting the generalization performance[10], [15]. Due to this reason, many variants of AdaBoostappeared to cope with this problem and to trade off the numberof margin errors and the size of the margin. This is achievedby restricting the weighting maintained by the algorithm to notconcentrate too much on the most difﬁcult (hard to classify)examples [15]. Examples of these algorithms are AdaBoostwith soft margin [8] and LPBoost [1]. On the other hand, and as previously mentioned, the hypothe-ses combination produced by AdaBoost has a large marginon the data. This margin is not necessarily the maximum hardmargin. Therefore, many new versions of AdaBoost, which tryto provide a maximum hard margin, have been developed suchas AdaBoost
Purely Structural Protein Scoring Functions Using Support Vector Machine and Ensemble Learning

The function of a protein is determined by its structure, which creates a need for efficient methods of protein structure determination to advance scientific and medical research. Because current experimental structure determination methods carry a high price tag, computational predictions are highly desirable. Given a protein sequence, computational methods produce numerous 3D structures known as decoys. Selection of the best quality decoys is both challenging and essential as the end users can handle only a few ones. Therefore, scoring functions are central to decoy selection. They combine measurable features into a single number indicator of decoy quality. Unfortunately, current scoring functions do not consistently select the best decoys. Machine learning techniques offer great potential to improve decoy scoring. This paper presents two machine-learning based scoring functions to predict the quality of proteins structures, i.e., the similarity between the predicted structure and the experimental one without knowing the latter. We use different metrics to compare these scoring functions against three state-of-the-art scores. This is a first attempt at comparing different scoring functions using the same non-redundant dataset for training and testing and the same features. The results show that adding informative features may be more significant than the method used.
Fast Collision Detection Between Cloth and a Deformable Human Body

This paper addresses the problem of performing collision detection between two flexible objects, the skin and the cloth in real-time animation of virtual humans,. In order to achieve fast collision detection we propose a suitable oriented bounding boxes (OBB) hierarchy for deforming bodies that can be constructed in a pre-processing phase, and then updated efficiently during the animation. These OBBs give a coarse approximation of the human body that can be used for a preliminary test and therefore reduce the number of triangles against which collision detection needs to be tested.