Miracle-dz/UniFAR-FTUs huggingface dataset:
Relative class number of imaginary Abelian fields of prime conductor below 10000

In this paper we compute the relative class number of all imaginary Abelian fields of prime conductor below 10000. Our approach is based on a novel multiple evaluation technique, and, assuming the ERH, it has a running time of O(p2 log 2(p) log log (p)), where p is the conductor of the field
Configurable real-time simulation suite for coaxial rotor UAVs

This paper describes an accurate and extendable rotorcraft dynamics simulator developed to support the design and control of autonomous coaxial rotor vehicles. This simulator is capable of accurately predicting the dynamic flight response of coaxial rotor vehicles purely based on geometric, inertial and aerodynamic specifications. The simulator is fully configurable and implements the typical mechanical layouts found in model-size coaxial helicopters. The corresponding software framework as well as the underlying theory is presented in detail. System parameters for a coaxial rotor prototype have been estimated and the resulting simulation results compared with real flight-data demonstrating the capabilities of the presented simulation software.
Photometry of Polar-Ring galaxies

Se obtuvo fotometria en B y R en siete galaxias con anillo polar, probables o con armadas, del Catalogo de Anillos Polares (PRC) de Whitmore et al. (1990). Los anillos tienen un amplio intervalo de colores de B􀀀R 0:6 hasta B􀀀R 1:7. Los anillos m as azules contienen brillantes regiones H II, las cuales son evidencia directa de formaci on estelar reciente. La edad m nima del anillo m as rojo, el de PRC B-20, es algo incierta debido a la falta de conocimiento del enrojecimiento interno y la metalicidad en este sistema, pero parece ser al menos de 1.2 Gyr. Por lo tanto, este anillo parece ser estable, al menos durante varios periodos rotacionales. Este anillo es un excelente candidato para estudios futuros que podr an determinar de mejor manera si en realidad es un anillo viejo
A novel technique for automated red eye detection and correction

In this study we propose a new system to detect and correct red eye defect in images. The proposed system works in three phases namely face localization, eye localization and red eye removal. Face localization involves method for extracting face from the image. Eye localization locates eye in the face extracted. The red eye defect, if present, is automatically detected and corrected using a simple and straightforward approach.
ISO-TimeML: An International Standard for Semantic Annotation

In this paper, we present ISO-TimeML, a revised and interoperable version of the temporal markup language, TimeML. We describe the changes and enrichments made, while framing the effort in a more general methodology of semantic annotation. In particular, we assume a principled distinction between the annotation of an expression and the representation which that annotation denotes. This involves not only the specification of an annotation language for a particular phenomenon, but also the development of a meta-model that allows one to interpret the syntactic expressions of the specification semantically.
Coop-DMAC: A Cooperative Directional MAC protocol for wireless networks

Directional antennas in wireless networks can provide substantial performance gain and opportunities. In the case of high-frequency systems, such as emerging 60 GHz networks, they are inherent feature of the system design. In contrast to the traditional omni-directional antennas, directional antennas are capable of reducing the interference level, increasing the communication range, and improving spatial reuse. However, new challenges need to be solved, such as the deafness problem and the asymmetric-in-gain. In this paper, we propose Cooperative-Directional MAC (Coop-DMAC) protocol to address the new challenges. The Coop-DMAC is completely distributed and relies on limited cooperation between networked nodes. In Coop-DMAC, nodes are supposed to exchange local information including the angle of arrival of their neighboring nodes and the duration of data transmission to improve their spatial reuse. In this paper, we analyze and evaluate the performance of Coop-DMAC, and pay particular attention to the performance and network topology. Simulation results validate our theoretical analysis and show that the proposed Coop-DMAC protocol outperforms the standard IEEE 802.11ad MAC protocol in terms of higher goodput in most common network topologies considered in the paper.
The ART of adaptive pattern recognition by a self-organizing neural network

The adaptive resonance theory (ART) suggests a solution to the stability-plasticity dilemma facing designers of learning systems, namely how to design a learning system that will remain plastic, or adaptive, in response to significant events and yet remain stable in response to irrelevant events. ART architectures are discussed that are neural networks that self-organize stable recognition codes in real time in response to arbitrary sequences of input patterns. Within such an ART architecture, the process of adaptive pattern recognition is a special case of the more general cognitive process of hypothesis discovery, testing, search, classification, and learning. This property opens up the possibility of applying ART systems to more general problems of adaptively processing large abstract information sources and databases. The main computational properties of these ART architectures are outlined and contrasted with those of alternative learning and recognition systems. <<ETX>>
Keeping up appearances: understanding the dimensions of incidental information privacy

We conducted a survey of 155 participants to examine privacy concerns relating to the viewing of incidental information (i.e. traces of previous activity unrelated to the task at hand) in web browsers. We have identified several dimensions of privacy for this domain. Results revealed the scope of this problem and how location and device affect web browsing activity and contribute to the types of incidental information that may be visible. We found that there are different privacy comfort levels inherent to the participant and dependent on the context of subsequent viewing of incidental information, including the sensitivity of the content, their relationship to the viewer and the level of control retained over input devices.
Identifying the Factors Contributing to Students’ Difficulties in the English Language Learning

In today’s ever-changing world, the English language has been placed to be one of the important languages in communication, as well as the main medium of instruction in education. To be in line with this acknowledgement, the Ministry of Education of Malaysia has placed the importance of mastering the English language and hashighlighted the need for students to excel in the language as early as in the primary school years. To reach this aim, the Ministry of Education has introduced the GPMP (Gred Purata Mata Pelajaran/ Subject Grade Point Average) target for English to schools in every state to ensure every school performs well in the subject. However, there are still a handful of schools that are unable to pass this GPMP target for English due to the poor performance of their students in the subject. Therefore, this study aims to investigate the factors that interfere with the students’ performance in learning English. The participants of this study were the 116 students from the seven schools that were unable to reach the state’s GPMP in the English language. This study used questionnaire as the instrument to explore the factors that hindered them from performing well in the subject. Overall, the findings show that there are three major themes that contribute to the students’ difficulties in the English language learning; the lack of English vocabulary, the influence of the first language and the socio-economic status of the family. The findings and discussion of this study would be able to help the pertinent parties such as teachers, school administrators and parents to make decisions for the learners’ improvement in their English language learning.
Boosting the performance of PC-based software routers with FPGA-enhanced network interface cards

The research community is devoting increasing attention to software routers based on off-the-shelf hardware and open-source operating systems running on the personal-computer (PC) architecture. Today's high-end PCs are equipped with peripheral component interconnect (PCI) shared buses enabling them to easily fit into the multi-gigabit-per-second routing segment, for a price much lower than that of commercial routers. However, commercially-available PC network interface cards (NICs) lack programmability, and require not only packets to cross the PCI bus twice, but also to be processed in software by the operating system, strongly reducing the achievable forwarding rate. It is therefore interesting to explore the performance of customizable NICs based on field-programmable gate array (FPGA) logic devices we developed and assess how well they can overcome the limitations of today's commercially-available NICs
Stagger: A modern POS tagger for Swedish

The field of Part of Speech (POS) tagging has made slow but steady progress during the last decade, though many of the new methods developed have not previously been applied to Swedish. I present a new system, based on the Averaged Perceptron algorithm and semi-supervised learning, that is more accurate than previous Swedish POS taggers. Furthermore, a new version of the Stockholm-Umea Corpus is presented, whose more consistent annotation leads to significantly lower error rates for the POS tagger. Finally, a new, freely available annotated corpus of Swedish blog posts is presented and used to evaluate the tagger’s accuracy on this increasingly important genre. Details of the evaluation are presented throughout, to ensure easy comparison with future results.
Humanoid navigation with dynamic footstep plans

Humanoid robots possess the capability of stepping over or onto objects, which distinguishes them from wheeled robots. When planning paths for humanoids, one therefore should consider an intelligent placement of footsteps instead of choosing detours around obstacles. In this paper, we present an approach to optimal footstep planning for humanoid robots. Since changes in the environment may appear and a humanoid may deviate from its originally planned path due to imprecise motion execution or slippage on the ground, the robot might be forced to dynamically revise its plans. Thus, efficient methods for planning and replanning are needed to quickly adapt the footstep paths to new situations. We formulate the problem of footstep planning so that it can be solved with the incremental heuristic search method D* Lite and present our extensions, including continuous footstep locations and efficient collision checking for footsteps. In experiments in simulation and with a real Nao humanoid, we demonstrate the effectiveness of the footstep plans computed and revised by our method. Additionally, we evaluate different footstep sets and heuristics to identify the ones leading to the best performance in terms of path quality and planning time. Our D* Lite algorithm for footstep planning is available as open source implementation.
ERNA and Friedman's Reverse Mathematics

Abstract Elementary Recursive Nonstandard Analysis, in short ERNA, is a constructive system of nonstandard analysis with a PRA consistency proof, proposed around 1995 by Patrick Suppes and Richard Sommer. Recently, the author showed the consistency of ERNA with several transfer principles and proved results of nonstandard analysis in the resulting theories (see [12] and [13]). Here, we show that Weak König's lemma (WKL) and many of its equivalent formulations over RCA0 from Reverse Mathematics (see [21] and [22]) can be ‘pushed down’ into the weak theory ERNA, while preserving the equivalences, but at the price of replacing equality with equality ‘up to infinitesimals’. It turns out that ERNA plays the role of RCA0 and that transfer for universal formulas corresponds to WKL.
Performance Improvement of Authentication of Fingerprints Using Enhancement and Matching Algorithms

Fingerprint is one of the most extensively used biometric systems for authentication in areas where security is of high importance. This is due to their permanence, accuracy and reliability. However, extracting features out of degraded fingerprints is the most challenging in order to obtain high fingerprint matching performance. This paper intends to enhance the clarity of fingerprint minutiae, removing false minutiae and improve the matching performance using a Gabor filtering technique and artificial neural network. The experiments showed improved results as compared with results of fingerprints that were not enhanced.
It doesn't compare to being there : in-situ vs. remote exploration of museum collections

The digitization of museum collections has the potential not only to make them accessible remotely but also to augment their in-situ exploration at the gallery. We are interested in how mobile visual interfaces to museum collections can enhance explorations and experiences of the collection insitu, and how these in-situ explorations compare to remote browsing of collections, e.g., through web-based interfaces. We discuss findings from a study where potential visitors explored a museum collection using a tablet-based visualization remotely, at first, and then as part of their museum visit. Our findings show that the entry points to the collection differ in the two different contexts: while overview visualizations linked with individual artifacts triggered curiosity and promoted exploration in the remote setting, it was the individual (physical) artifacts that drove explorations of the mobile visualization in-situ. While remote explorations of the visualization can be characterized as highly open-ended, in-situ, people approached the interface in a more targeted way. These findings suggest an item-centric approach to the design of mobile visualizations that bridges the physical and digital information space to assist in-situ explorations of museums collections.
A Contextual Modeling Approach to Context-Aware Recommender Systems

for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper proposes a novel type of contextual modeling (CM) based on the contextual neighbors approach and introduces four specific contextual neighbors methods. It compares these four types of contextual neighbors techniques to determine the best-performing alternative among them. Then it compares this best-of-breed method with the contextual pre-filtering, post-filtering and un-contextual methods to determine how well the CM approach compares with other context-aware recommendation techniques.
Asset-based approaches: their rise, role and reality

Asset-based Approaches is the 20th book in the Policy and Practice in Health and Social Care Series. In its attempt to capture and reflect the permeation of asset-based approaches within public health policy in Scotland, this book provides a critical overview of the evidence which would be of wider interest to an international audience of those working and studying in the fields of health, social services and community development.
Dimensionality Reduction and Prior Knowledge in E-Set Recognition

It is well known that when an automatic learning algorithm is applied to a fixed corpus of data, the size of the corpus places an upper bound on the number of degrees of freedom that the model can contain if it is to generalize well. Because the amount of hardware in a neural network typically increases with the dimensionality of its inputs, it can be challenging to build a high-performance network for classifying large input patterns. In this paper, several techniques for addressing this problem are discussed in the context of an isolated word recognition task.
Exemplar-based speech enhancement for deep neural network based automatic speech recognition

Deep neural network (DNN) based acoustic modelling has been successfully used for a variety of automatic speech recognition (ASR) tasks, thanks to its ability to learn higher-level information using multiple hidden layers. This paper investigates the recently proposed exemplar-based speech enhancement technique using coupled dictionaries as a pre-processing stage for DNN-based systems. In this setting, the noisy speech is decomposed as a weighted sum of atoms in an input dictionary containing exemplars sampled from a domain of choice, and the resulting weights are applied to a coupled output dictionary containing exemplars sampled in the short-time Fourier transform (STFT) domain to directly obtain the speech and noise estimates for speech enhancement. In this work, settings using input dictionary of exemplars sampled from the STFT, Mel-integrated magnitude STFT and modulation envelope spectra are evaluated. Experiments performed on the AURORA-4 database revealed that these pre-processing stages can improve the performance of the DNN-HMM-based ASR systems with both clean and multi-condition training.
Geosocial Graph-Based Community Detection

We apply spectral clustering and multislice modularity optimization to a Los Angeles Police Department field interview card data set. To detect communities (i.e., cohesive groups of vertices), we use both geographic and social information about stops involving street gang members in the LAPD district of Hollenbeck. We then compare the algorithmically detected communities with known gang identifications and argue that discrepancies are due to sparsity of social connections in the data as well as complex underlying sociological factors that blur distinctions between communities.
Predicting plot basal area and tree density in mixed-conifer forest from lidar and Advanced Land Imager (ALI) data

Multispectral satellite imagery are appealing for their relatively low cost, and have demonstrated utility at the landscape level, but are typically limited at the stand level by coarse resolution and insensitivity to variation in vertical canopy structure. In contrast, lidar data are less affected by these difficulties, and provide high structural detail, but are less available due to their comparatively high cost. Two forest structure attributes measured at the plot level, basal area and trees per hectare, were predicted using stepwise multiple regression on 40 predictor variables derived from discrete-return lidar data (2 m post spacing), Advanced Land Imager (ALI) multispectral (30 m resolution) and panchromatic (10 m resolution) images, and geographic X,Y,Z location. Square root and natural logarithm transforms were applied to normalize the positively skewed response variables. Stepwise variable selection used the AIC statistic to guard against overfitting. Models predicting the transformed variables explained 80-93% of variance, based on 20-22 predictor variables. Lidar-derived variables had the most explanatory power; especially height and intensity variables for predicting plot basal area, and cover and intensity variables for predicting tree density. The ALI variables were less useful for predicting these attributes of forest structure, but could prove more helpful for predicting attributes of forest composition.
Image feature selection using modified ICM method

This paper discusses a version of the ICM method in which the contextual information is modeled by Markov random fields (MRF). To select the feature, a new local MRF model with a fitting block neighborhood is introduced. This model extracts contextual information not only from the relative intensity levels but also from the geometrically directional position of neighboring cliques. Feature selection depends on each block's contribution to the local variance. They discriminates it into binary regions, context and background. Boundary between two regions is also distinctive. The proposed algorithm performs segmentation using directional block fitting procedure which confines merging to spatially adjacent elements and generates a partition such that pixels in unified cluster have a homogeneous intensity level. From experiment with ink rubbed copy images, this method is determined to be quite effective for feature identification. In particular, the new algorithm preserves the details of the images well, without over-and under-smoothing problem occurring in general iterated conditional modes (ICM). It should be noted that the smoothing effect is not serious in this approach.
Discriminating Native from Non-Native Speech Using Fusion of Visual Cues

The task of classifying accent, as belonging to a native language speaker or a foreign language speaker, has been so far addressed by means of the audio modality only. However, features extracted from the visual modality have been successfully used to extend or substitute audio-only approaches developed for speech or language recognition. This paper presents a fully automated approach to discriminating native from non-native speech in English, based exclusively on visual appearance features from speech. Long Short-Term Memory Neural Networks (LSTMs) are employed to model accent-related speech dynamics and yield accent-class predictions. Subject-independent experiments are conducted on speech episodes captured by mobile phones from the challenging MOBIO Database. We establish a text-dependent scenario, using only those recordings in which all subjects read the same paragraph. Our results show that decision level fusion of networks trained with complementary appearance descriptors consistently leads to performance improvement over single-feature systems, with the highest gain in accuracy reaching 7.3%. The best feature combinations achieve classification accuracy of 75%, rendering the proposed method a useful accent classification tool in cases of missing or noisy audio stream.
Public Higher Education Institutions' Investment in Performing Arts Centers

ABSTRACT Although commonplace in academic settings, performing arts centers are multifaceted and remain unexplored. Framed by resource dependency theory, this research sheds light on the motivations of higher education institutions in a resource-scarce environment. Specifically, why do these institutions—with limited resources—prioritize the co-curricular arts? This study employs process research to understand the origination, funding sources, and purposes of performing arts centers at higher education institutions in Virginia. Evidence suggests that these institutions invest in the co-curricular arts to not only fulfill their public service missions, but also to capitalize on and acquire resources.
Recognition for the Banknotes Grade Based on CPN

The counter propagation networks (CPN) is used to improve the accuracy of the grade of banknotes recognition. First, self-organizing map (SOM) is used to cluster banknotes data into regions based on the different feature of banknotes; second, the principal component analysis (PCA) is performed in each region to extract the main principal features of banknotes data; finally, the CPN is employed as the main classifier to identify the banknotes of different grades. The recognition effects of CPN and BP are compared in this paper. The results show that the reliability and speed of CPN are greatly better than that of the BP. The experimentation shows that the CPN can primly solve the recognition problem of the grade of banknotes.