Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle localization enhancement with VANETs

This paper presents an assisted system for vehicle localization and map-matching by utilizing Vehicle ad-hoc Networks (VANETs). Fusion of the GNSS and odometer measurement is augmented by ranging distance. Ranging is computed by exchanging the data packets between the vehicular nodes equipped with Dedicated Short Range Communication (DSRC) modem and GNSS receiver. Time-of-Arrival (ToA) of exchanged data packet between the two vehicular nodes is converted in distance. Map matching enhances accuracy of localization while projecting the result of multilateration created by numerous ranging queries. Realistic simulations are conducted to test the performance of the algorithm. Test results show bounded and acceptable particle filter positioning results. The scenario of GPS outages and low number of vehicles collaborating for positioning are simulated. Tracking performance of the particle filter is illustrated. Algorithm helps dead reckoning when GPS data is not available temporarily. A simple GPS receiver is fused with odometer data during tests. Particularly, expensive sensors are not used to achieve better price/performance towards commercial usage.
Mountain Peak Detection in Online Social Media

We present a system for the classification of mountain panoramas from user-generated photographs followed by identification and extraction of mountain peaks from those panoramas. We have developed an automatic technique that, given as input a geo-tagged photograph, estimates its FOV (Field Of View) and the direction of the camera using a matching algorithm on the photograph edge maps and a rendered view of the mountain silhouettes that should be seen from the observer's point of view. The extraction algorithm then identifies the mountain peaks present in the photograph and their profiles. We discuss possible applications in social fields such as photograph peak tagging on social portals, augmented reality on mobile devices when viewing a mountain panorama, and generation of collective intelligence systems (such as environmental models) from massive social media collections (e.g. snow water availability maps based on mountain peak states extracted from photograph hosting services).
A RAID reconfiguration scheme for gracefully degraded operations

One distinct advantage of Redundant Array of Independent Disks (RAID) is fault tolerance. But the performance of a disk array in degraded mode is so poor that no one uses the RAID after failure. Continuous operation of RAID in degraded mode is very important in many real time applications, which can not be interrupted in providing continuous services. In this paper, we propose an efficient architectural reconfiguration scheme to enhance the performance of RAID-5 in degraded mode, called reconfigurable RAID-5. It reconfigures RAID-5 to RPTD-0 in degraded mode. Using this scheme, the calculation of the failure data and the generation of parity in writing the new data to the failed disk can be reduced. It also alleviates the small write problem for RAID-5 in degraded mode. We use the phase parallel model to analyze the total execution time of the RAID-5 and of the reconfigurable RAID-5. Through theoretical analysis and benchmark test, we find the performance of the reconfigurable RAID-5 can be 200 times better than conventional RAID-5.
Unsilenced: Black Girls' Stories

Black girls continue to suffer from inequitable treatment in schools resulting in disparate academic and social outcomes. While deficit ideologists have continued to attribute outcomes to cultural deficiencies within the Black community, research has found various systemic issues of racism and sexism seriously affecting Black girls in schools. However, the experiences of this population remain under or uninvestigated. When Black girls’ experiences in school are investigated, they are commonly framed as a group in need of saving and their perspectives and voices eliminated from the work. Further, this group is often homogenized and all their experiences limited to those of the inner-city or urban environments. Using a critical raced-gendered epistemology, grounded in critical race theory and Black feminism/womanism, this qualitative interview study explores Black high school girls’ experiences in a predominately White suburban public school in the southeast. Through the method of storytelling that includes constructing counter narratives, five girls (ages 14-16) relay their experiences in this predominately White suburban educational space. Parent reflections as well as document review augment these girls’ stories to further illuminate their experience. A grounded theory analysis of these data uses my own cultural intuition. This analytic approach foregrounds the intersectionality of Black girls’ understanding of their racial and gendered educational experiences in a predominantly White suburban environment, the systemic barriers that serve to inhibit their success, and the methods of resistance girls use to persist in these spaces. This study is significant in both its methodology as well as results, offering critical insight into how to conduct equitable and liberatory research and create education policies to improve outcomes for this underserved group. INDEX WORDS: Black girls, Black Feminism, Womanism, Critical Race Theory, Emancipatory Research, Intersectionality, Resistance UNSILENCED: BLACK GIRLS’ STORIES by LATOYA RUSSELL OWENS
Probabilistic Demonic Refinement Algebra

We propose an abstract algebra for reasoning about probabilistic programs in a total-correctness framework. In contrast to probablisitic Kleene algebra it allows genuine reasoning about total correctness and in addition to Kleene star also has a strong iteration operator. We define operators that determine whether a program is enabled, has certain failure or does not have certain failure, respectively. The algebra is applied to the derivation of refinement rules for probabilistic action systems.
In Situ Measurement of UHF Wearable Antenna Radiation Efficiency Using a Reverberation Chamber

The radiation efficiency and resonance frequency of five compact antennas worn by nine individual test subjects was measured at 2.45 GHz in a reverberation chamber. The results show that, despite significant differences in body mass, wearable antenna radiation efficiency had a standard deviation less than 0.6 dB and the resonance frequency shift was less than 1% between test subjects. Variability in the radiation efficiency and resonance frequency shift between antennas was largely dependant on body tissue coupling which is related to both antenna geometry and radiation characteristics. The reverberation chamber measurements were validated using a synthetic tissue phantom and compared with results obtained in a spherical near field chamber and finite-difference time-domain (FDTD) simulation.
PR ] 9 D ec 2 00 8 Alternating I-divergence minimization in factor analysis

In this paper we attempt at understanding how to build an optimal approximate normal factor analysis model. The criterion we have chosen to evaluate the distance between different models is the I-divergence between the corresponding normal laws. The algorithm that we propose for the construction of the best approximation is of an the alternating minimization kind. Institute of Biomedical Engineering, CNR-ISIB, Padova, lorenzo.finesso@isib.cnr. it Korteweg-deVries Institute for Mathematics, Universiteit van Amsterdam, Amsterdam, spreij@uva.nl
Introduction to the Special Issue on AI & Networks

The importance of networks permeates the world today. From biology to social systems, from the brain to the Internet, networks play an important and central role in the way the world works. In the last ten years, due in part to large increases in computational power, large-scale, real-world networks have received much attention from a variety of fields of study. Within the artificial intelligence community, networks appear some form in nearly every subdiscipline: knowledge representation, inference, learning, natural language processing, multiagent systems, analogical reasoning, and many others. The goals of this special issue are to provide a sampling of research efforts focused on how networks can be used in AI systems, and to facilitate cross-communication among subdisciplines that are studying networks from different perspectives. The seven papers we include here cover a broad range of network-inspired AI research—in natural language processing, data mining, the Semantic Web, peer-to-peer networks, multi-agent systems, analog networks, and the modern social network of the “blogophere. ” Each article represents a snapshot of the area it describes; for example, the collective classification problem surveyed by Sen et al. is just one of many problems within the emerging research area of link mining. Moreover, networks are influential in many other areas of AI that are not represented here, including Bayesian networks and graphical models, sensor networks, swarm systems and cellular automata, graphical games, trust and reputation systems, and computational organizational design, just to name a few. Table 1 summarizes the articles in this collection by characterizing the nature of the networks that are the focus of each of the seven papers.
and June Stevens in Communities Study Dietary Intake and the Development of the Metabolic Syndrome : The Atherosclerosis Risk

Background—The role of diet in the origin of metabolic syndrome (MetSyn) is not well understood; thus, we sought to evaluate the relationship between incident MetSyn and dietary intake using prospective data from 9514 participants (age, 45 to 64 years) enrolled in the Atherosclerosis Risk in Communities (ARIC) study. Methods and Results—Dietary intake was assessed at baseline via a 66-item food frequency questionnaire. We used principal-components analysis to derive “Western” and “prudent” dietary patterns from 32 food groups and evaluated 10 food groups used in previous studies of the ARIC cohort. MetSyn was defined by American Heart Association guidelines. Proportional-hazards regression was used. Over 9 years of follow-up, 3782 incident cases of MetSyn were identified. After adjustment for demographic factors, smoking, physical activity, and energy intake, consumption of a Western dietary pattern (Ptrend 0.03) was adversely associated with incident MetSyn. After further adjustment for intake of meat, dairy, fruits and vegetables, refined grains, and whole grains, analysis of individual food groups revealed that meat (Ptrend 0.001), fried foods (Ptrend 0.02), and diet soda (Ptrend 0.001) also were adversely associated with incident MetSyn, whereas dairy consumption (Ptrend 0.006) was beneficial. No associations were observed between incident MetSyn and a prudent dietary pattern or intakes of whole grains, refined grains, fruits and vegetables, nuts, coffee, or sweetened beverages. Conclusions—These prospective findings suggest that consumption of a Western dietary pattern, meat, and fried foods promotes the incidence of MetSyn, whereas dairy consumption provides some protection. The diet soda association was not hypothesized and deserves further study. (Circulation. 2008;117:754-761.)
Analysis and Control of Catalytic Flow Reversal Reactor

In this paper, we study the closed loop control of Catalytic Flow Reversal Reactors (CFRR) used for oxidation of methane. We present different techniques like logic-based control, scheduling and model predictive control. Comparison of the relative performances of these methods in maximizing the conversion while maintaining the CFRR in the desired region of operation is made. We also analyze the stablity of the CFRR in the sense of lyapunov and non-linearity measure of the process.
Wound Healing Activity of a Traditionally Used Poly Herbal Product in a Burn Wound Model in Rats

Background: Burns are known as one of the most common and destructive forms of injury with a vast spectrum of consequences. Despite the discovery of various antibacterial and antiseptic agents, burn wound healing still has remained a challenge to modern medicine. Plants, with a valuable traditional support, have been considered as potential agents for prevention and treatment of disorders in recent years. However, modern scientific methods should be applied to validate the claims about the therapeutic effects of the herbal products. Objectives: This study was conducted to evaluate the wound-healing activity of a poly herbal cream (PHC), retrieved from Iranian Traditional Medicine (ITM), in a rat burn wound model in Iran. Materials and Methods: In this experimental study, PHC containing aqueous extracts of Malva sylvestris and Solanum nigrum leaves and oily extract of Rosa damascena petals was used. Second-degree burn wounds were induced in four groups of five rats each. Group 1 received no treatment while groups 2, 3 and 4 were given cream base, silver sulfadiazine (SS) 1% and PHC, respectively to compare the efficacy of PHC with the negative and positive control groups. The percentage of wound healing on days 2, 6, 10 and 14 and histopathological parameters of healed wounds on the 14th day were assessed. Antioxidant and antimicrobial activities of PHC were evaluated using 2, 2-diphenyl-1-picrylhydrazyl (DPPH) and micro-dilution methods, respectively. Results: There was a significant improvement in healing percentage of PHC-treated rats in comparison to the other groups at the end of the treatment period (87.0% ± 2.1% for PHC in comparison to 32.2% ± 1.6%, 57.0% ± 5.3% and 70.8% ± 3.5% for the control, cream base and SS groups, respectively). Moreover, the healed wounds in PHC-treated animals contained less inflammatory cells and had desirable re-epithelialization with remarkable neovascularization. In addition to the antioxidant activity, PHC exhibited antibacterial effect against Staphylococcus aureus. Conclusions: Poly herbal cream experimentally and histopathologically revealed a burn wound healing activity probably due to the antioxidant, anti-inflammatory and antimicrobial activities of its phytochemical contents. Therefore, this study confirms the use of M. sylvestris, S. nigrum and R. damascena in burn prescriptions in ITM.
Velodrome: a sound and complete dynamic atomicity checker for multithreaded programs

Atomicity is a fundamental correctness property in multithreaded programs, both because atomic code blocks are amenable to sequential reasoning (which significantly simplifies correctness arguments), and because atomicity violations often reveal defects in a program's synchronization structure. Unfortunately, all atomicity analyses developed to date are incomplete in that they may yield false alarms on correctly synchronized programs, which limits their usefulness. We present the first dynamic analysis for atomicity that is both sound and complete. The analysis reasons about the exact dependencies between operations in the observed trace of the target program, and it reports error messages if and only if the observed trace is not conflict-serializable. Despite this significant increase in precision, the performance and coverage of our analysis is competitive with earlier incomplete dynamic analyses for atomicity.
e-PPI: Locator Service in Information Networks with Personalized Privacy Preservation

In emerging information networks, having a privacy preserving index (or PPI) is critically important for locating information of interest for data sharing across autonomous providers while preserving privacy. An understudied problem for PPI techniques is how to provide controllable privacy preservation, given the innate difference of privacy concerns regarding different data owners. In this paper we present a personalized privacy preserving index, coined ε-PPI, which guarantees quantitative privacy preservation differentiated by personal identities. We devise a new common-identity attack that breaks existing PPI's and propose an identity-mixing protocol against the attack in ε-PPI. The proposed ε-PPI construction protocol is the first without any trusted third party and/or trust relationships between providers. We have implemented our ε-PPI construction protocol by using generic MPC techniques (secure multi-party computation) and optimized the performance to a practical level by minimizing the expensive MPC part.
Quantitative Histological Validation of Diffusion MRI Fiber Orientation Distributions in the Rat Brain

Diffusion MRI (dMRI) is widely used to measure microstructural features of brain white matter, but commonly used dMRI measures have limited capacity to resolve the orientation structure of complex fiber architectures. While several promising new approaches have been proposed, direct quantitative validation of these methods against relevant histological architectures remains missing. In this study, we quantitatively compare neuronal fiber orientation distributions (FODs) derived from ex vivo dMRI data against histological measurements of rat brain myeloarchitecture using manual recordings of individual myelin stained fiber orientations. We show that accurate FOD estimates can be obtained from dMRI data, even in regions with complex architectures of crossing fibers with an intrinsic orientation error of approximately 5–6 degrees in these regions. The reported findings have implications for both clinical and research studies based on dMRI FOD measures, and provide an important biological benchmark for improved FOD reconstruction and fiber tracking methods.
A wireless infrared sensor network for the estimation of the position and orientation of a moving target

The location of a moving person or vehicle in a virtual reality environment is a critical issue. A wireless infrared sensor network capable of estimating the position of a target on a plane and its orientation is presented in this paper. This is actually an extension of the position estimation system that was presented in [1-2] where the target was allowed to move on a plane but not to rotate. In [1-2] the estimation of the position was based on a network consisting of a few low cost infrared transmitters and a pair of infrared receivers mounted on the target. In this paper we discuss how a third infrared sensor properly positioned at the side of the receiver can also allow the estimation of the target orientation.
New approach of model based T-S fuzzy predictive control using LMI approach

In this work, a model based T-S fuzzy predictive control LMI optimization is introduced. The aim of discrete T-S fuzzy predictive controller is to drive the state of the system to the original state where a stabilizing controller is ensured. The stability of the controlled systems is studied using non quadratic case of the Lyapunov function and adopting of Non-PDC controller. The stability is guaranteed based on the conditions expressed of terms of LMIs. The optimal solution has been obtained at each sampling time. The results are shows the effectiveness of this strategy.
Employment Horizon and the Choice of Performance Measures: Empirical Evidence from Annual Bonus Plans of Loss-Making Entities

We examine the extent to which employment horizon concerns affect the relative emphasis on financial versus nonfinancial performance measures in annual bonus plans. We argue that managers of loss-making firms are likely to voluntarily or forcibly depart in the near future and, consequently, have a shorter employment horizon. Loss-making firms then need to increase the emphasis on forward-looking nonfinancial performance measures to motivate long-term effort of their managers. Thus, we hypothesize that the emphasis on nonfinancial performance measures is greater in loss making than in profitable firms even after controlling for the informativeness of earnings. We find consistent support for our hypothesis using different (archival, survey, and field) data sources and various proxies for short employment horizon and the emphasis on nonfinancial performance measures.
A comparative analysis of cultural diversity satisfaction scores of undergraduate students in an online learning environment

The purpose of this quantitative, causal-comparative study was to analyze the differences of perceived overall satisfaction scores (organizational structure, technology usage, and curriculum design) between Caucasian, Latino-American, and African-American undergraduate students enrolled in an online program, as measured by the Cultural Diversity Satisfaction Survey (CDSS) instrument. This study compared the differences between three distinct diverse groups for overall satisfaction in an undergraduate online general elective course. The study participants were undergraduate students enrolled in an online general elective course in Virginia, n = 433. The study utilized a one-way ANOVA to determine if there was a statistically significant difference in means between the dependent variables, (a) organizational structure of the course, (b) learner’s understanding and usage of technology, and (c) curriculum design and the independent variable, the student’s ethnicity. It was found that there was a statistically significant difference in the satisfaction scores between two ethnic groups (African-American and Caucasians) in the design scores. Results also showed no statistical difference in the other two dependable variables between all three ethnic groups. It was concluded that two ethnic groups, African-American and Hispanics, view communication with faculty and peers very highly in an online course. Since it is expected that the Asian community in the U.S. may surpass Hispanics by the year 2065, it will be helpful to conduct a similar study comparing the satisfaction scores of all four ethnic groups in an online course.
Quantifying knowledge exchange in R&D networks: a data-driven model

We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale data sets, extracting unique information about 7500 R&D alliances and 5200 patent portfolios of firms. These data are used to calibrate the model parameters for network formation and knowledge exchange. We obtain probabilities for incumbent and newcomer firms to link to other incumbents or newcomers able to reproduce the topology of the empirical R&D network. The position of firms in a knowledge space is obtained from their patents using two different classification schemes, IPC in eight dimensions and ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange assumes that collaborating firms approach each other in knowledge space at a rate μ for an alliance duration τ. Both parameters are obtained in two different ways, by comparing knowledge distances from simulations and empirics and by analyzing the collaboration efficiency Ĉn$\mathcal {\hat {C}}_{n}$. This is a new measure that takes in account the effort of firms to maintain concurrent alliances, and is evaluated via extensive computer simulations. We find that R&D alliances have a duration of around two years and that the subsequent knowledge exchange occurs at a very low rate. Hence, a firm’s position in the knowledge space is rather a determinant than a consequence of its R&D alliances. From our data-driven approach we also find model configurations that can be both realistic and optimized with respect to the collaboration efficiency Ĉn$\mathcal {\hat {C}}_{n}$. Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.