Miracle-dz/UniFAR-FTUs huggingface dataset:
Significance of group delay based acoustic features in the linguistic search space for robust speech recognition

In this paper we discuss the complementarity of the group delay features with respect to other conventional acoustic features and also propose the use of such diverse information in the linguistic search space for robust speech recognition. A discriminability analysis is carried out on various classes of phonetic units. A class based phonetic unit analysis is conducted to compare the suitability of using different acoustic feature streams for recognition of different phonetic unit classes. The results of recognition for isolated phonemic or syllabic units, give the appropriate feature for each unit. We then turn to describe the signiﬁcance of this diversity of information present in the various acoustic features and their integration into the linguistic search space for syllable based continuous speech recognition. A weighted average likelihood method is used here, which appropriately weights the relevant acoustic feature for each syllable in question during the Viterbi decoding process. This technique of integrating the complementarity of acoustic features into the linguistic search space gives reasonably reduced word error rates (WER) compared to conventional single or multi-stream acoustic features for experiments conducted on the TIMIT and the DBIL databases.
Non Identical Duplicate Video Detection using SIFT Method

Non-Identical Duplicate video detection is a challenging research problem. Non-Identical Duplicate video are a pair of videos that are not exactly identical but are almost similar.In this paper, we evaluate two methods - Keyframe -based and Tomography-based methods to determine the Non-Identical Duplicate videos. These two methods make use of the existing scale based shift invariant (SIFT) method to find the match between the key frames in first method, and the cross-sections through the temporal axis of the videos in second method.We provide extensive experimental results and the analysis of accuracy and efficiency of the above two methods on a data set of Non- Identical Duplicate video-pair.
Usage Context-based Object Recommendation

Recommender systems are taking on a steadily growing importance in an expanding number of domains (movies, music, books, etc.) to filter relevant items for users. The most well-known approaches are collaborative filtering (i.e. neighbourhood-based and matrix factorization techniques) and contentbased filtering (Adomavicius and Tuzhilin (2005), Koren et al. (2009)). With the evolution of recommender systems, the expectations regarding the recommendations also rise, e.g. in terms of accuracy, diversity, and novelty and several systems have been created that deal with these expectations (Adamopoulos and Tuzhilin (2011), Adomavicius and Kwon (2012), Vargas and Castells (2011)).
On the detection of the level of attention in an orchestra through head movements

Results from a study of non-verbal social signals in an orchestra are presented. Music is chosen as an example of interactive and social activity, where non-verbal communication plays a fundamental role. The orchestra is adopted as a social group with a clear leader (the conductor) of two groups of musicians (the first and second violin sections). It is shown how a reduced set of simple movement features – head movements – can be used to measure the levels of attention of the musicians with respect to the conductor and the music stand under various conditions (different conductors/pieces/sections of the same piece).
Lifestyle Oriented Small Tourism [Lost] Firms and Tourism Destination Development

Small tourism firms in attractive tourism destinations are often generically described as 'lifestyle' oriented. This paper finds that a range of the positive and negative connotations have been ascribed to that term in the tourism literature. A discourse on what the prefix 'lifestyle' means, when attached to small tourism firms in different economic and social contexts has been developing for a decade now, reaching a point of complexity and contradiction. In order to explore this ubiquitous, yet nebulous term, the relevant literature and associated discourse is reviewed with the aim of understanding the motives, meanings, values and contexts of these lifestyle small tourism firms. Key research questions and areas for further research are then presented and a number of possible research questions arising from the relevant literature are proposed. It is concluded that there are many deficiencies in both the conceptualisation as well as the content of current research in this increasingly important area. A multi-disciplinary and systemic approach is required to address these gaps in understanding of 'lifestyle' and small tourism firms.
Pushing the limits of stereo using variational stereo estimation

We examine high accuracy stereo estimation for binocular sequences that where obtained from a mobile platform. The ultimate goal is to improve the range of stereo systems without altering the setup. Based on a well-known variational optical flow model, we introduce a novel stereo model that features a second-order regularization, which both allows sub-pixel accurate solutions and piecewise planar disparity maps. The model incorporates a robust fidelity term to account for adverse illumination conditions that frequently arise in real-world scenes. Using several sequences that were taken from a mobile platform we show the robustness and accuracy of the proposed model.
Efficiently Learning Trends in User Preferences

Many websites encounter the problem of recommending content to users based on what they have shown interest in in the past. They must find correlation in what the user in question and other users have purchased or looked at to make these recommendations. This is one common example of a collaborative filtering problem. The goal of this project is to develop a very efficient method for solving this sort of problem. Researchers have applied many different techniques to the collaborative filtering problem and have interpreted the problem in different ways. It can be interpreted as a classification problem, a clustering problem, a regression problem, or a searching problem. It is a common problem for online stores, such as amazon.com, but it has also been solved for other purposes, such as in the categorization of Usenet news. The problem is not limited to user systems; it deals with general prediction of missing data scenarios. [7][5][4]
Flood-Filling Networks

State-of-the-art image segmentation algorithms generally consist of at least two successive and distinct computations: a boundary detection process that uses local image information to classify image locations as boundaries between objects, followed by a pixel grouping step such as watershed or connected components that clusters pixels into segments. Prior work has varied the complexity and approach employed in these two steps, including the incorporation of multi-layer neural networks to perform boundary prediction, and the use of global optimizations during pixel clustering. We propose a unified and end-to-end trainable machine learning approach, flood-filling networks, in which a recurrent 3d convolutional network directly produces individual segments from a raw image. The proposed approach robustly segments images with an unknown and variable number of objects as well as highly variable object sizes. We demonstrate the approach on a challenging 3d image segmentation task, connectomic reconstruction from volume electron microscopy data, on which flood-filling neural networks substantially improve accuracy over other state-of-the-art methods. The proposed approach can replace complex multi-step segmentation pipelines with a single neural network that is learned end-to-end.
Non-invasive Knee Osteoarthritis Diagnosis via Vibroarthrographic Signal Analysis

Crepitus is a prominent sign during the knee joint undergoing a range of motion which can be detected to form the vibroarthrographic (VAG) signals. These signals can be used as useful indicators of osteoarthritis (OA) status for supplementing conventional X-ray imaging in the diagnosis of knee OA. In this study, a non-invasive knee OA diagnosis system was conducted via VAG signal analysis.This system included a goniometer to provide an analog reference signal for positioning, an electronic stethoscope to detect knee VAG signals and an FPGA signal processing system as a system kernel. Power spectra of the signals provided by the Fourier transform were obtained and partition indices calculated. Discriminant functions were built with distribution parameters constructed from the partition indices. The processed VAG signals were compared with X-ray images in OA diagnosis to explore the differences between predicted and observed results. System model performance was evaluated using measures for discriminative ability, including the area under the receiver operating characteristic curve (AUC). The negative predictive value (NPV) was checked for the rate of correctly predicting the absence of OA. The positive predictive value (PPV) is the proportion of patients with OA who are correctly diagnosed.System performance was validated using the receiver operating characteristic curve. The classification result illustrated a sensitivity of 89.52% and a specificity of 67.50%, with a total accuracy rate of 81.52%. The AUC values obtained were 0.68 (95% CI 0.61-0.74). The rates of correctly predicting the lack of OA were approximately 78.6% (NPV) and the proportions of patients with OA who are correctly diagnosed were approximately 82.8% (PPV), respectively.VAG analysis in OA diagnosis provides an economic alternative to X-ray examination in osteoarthritic patients.
Intelligent tagging of online texts using fuzzy logic

We propose four fuzzy-logic based models of tag recommendation, which are based on the interpretation of word frequency as a fuzzy membership function, and provide experimental results of tag recommendation for a variety of text datasets using different fuzzy logic operators. The novelty of the proposed models is the use of fuzzy logic modeling concepts to define a set of tags, the use of an existing set of tags for the selection of tags to strengthen the selection of most relevant (i.e., commonly used) tags, and the possibility to use an ontology to select semantically generalized tags. A system developed using the proposed models is adaptive (adapts the recommended tags to the existing set of tags), has a feedback (after each tagging, the set of tags and the dictionary are updated), is personalized (each user develops its own set of tags), and is semantics-aware (uses an ontology to refine tags). The models are validated using five sets of texts with different topics (technology, cooking, carrier, scientific, nature) and different length.
Retail Gasoline Price Cycles: Evidence from Guelph, Ontario Using Bi-Hourly, Station-Specific Retail Price Data

In many Canadian cities, retail gasoline prices appear to move in cycles resembling those predicted in the Edgeworth cycle literature. In the alternating-moves pricesetting oligopoly model developed by Maskin and Tirole (1988) and extended by others, an equilibrium exists in which each firm slowly undercuts its rival’s price until prices fall near marginal costs. At this point, one of the firms will raise its price, initiating a new cycle. Unfortunately, tests of the basic theoretical predictions ideally require high-frequency price data for every station in a market, and these data are not publicly available. To fill this gap in the literature, retail price data for 27 out of 28 stations in Guelph, Ontario were collected bi-hourly from 8:00AM-10:00PM, every day from August 14 November 24, 2005. It has been found that while price movements in Guelph are consistent with the alternating-moves theory in many respects, certain patterns in the data suggest that the theory is incomplete. This paper also describes the retailers’ reactions to Hurricanes Katrina, Rita and Wilma and considers “gouging” accusations.
Thermal Noise Canceling in LNAs: A Review

Most wide-band amplifiers suffer from a fundamental trade-off between noise figure NF and source impedance matching, which limits NF to values typically above 3dB. Recently, a feed-forward noise canceling technique has been proposed to break this trade-off. This paper reviews the principle of the technique and its key properties. Although the technique has been applied to wideband CMOS LNAs, it can just as well be implemented exploiting transconductance elements realized with other types of transistors.
All they know in turn

This paper studies the reasoning of non-knowledge for multiple agents. In a multi-agent environment, agents may derive knowledge from the observation that other agents do not know a fact. That means normally there does not exist a vector of maximum relations that reflect the minimum knowledge (i.e. only knowing) of all agents. In this paper, we introduce a causal relation to characterize the appropriate order in which we determine the minimum knowledge of each agent. We base our formalism on relational calculus. The technique will be applied to solve the puzzle of Unfaithful Wives. We reveal an assumption about the puzzle that is implicitly used but unnoticed in previous research. It is shown that determining only-knowing in accordance with the causal relation will avoid incorrect reasoning against the assumption.
A Two-step Feature Selection Algorithm Adapting to Intrusion Detection

In intrusion detection data set is high dimensional, which leads to low processing speed for intrusion detection algorithms, but it holds many features affecting little for detection. To address the above issue, a two-step feature selection algorithm is proposed in this paper. Depending on the definition of relevant feature and redundant feature and using mutual information as criterion, it firstly eliminates the irrelevant features and then eliminates the redundant features. With low time complexity, the feature selection algorithm independent of detection algorithm could easily balance the detection accuracy and the number of features through threshold. Experiments over networks connection records from authoritative data set KDD CUP 1999 were implemented for several detection algorithms to evaluate the proposed method. The results show the algorithm could effectively select features, ensure detection accuracy and improve processing speed.
Income Distribution and Structural Transformation: Empirical Evidence from Developed and Developing Countries

The process of structural transformation forms the very basis of economic growth and development. This paper analyses the implications of alternate patterns of structural change for changes in the overall distribution of income within an economy. An empirical analysis is carried out based on evidence from seventy-eight developing and transition economies and developed countries. The estimated results from a fixed effects panel data model, indicate the existence of substantial differences as well as important similarities between developed and developing country experience with respect to structural change and associated changes in income inequality.
Shape averaging under Time Warping

Dynamic Time Warping (DTW) distance measure has increasingly been used as a similarity measurement for various data mining tasks in place of traditional Euclidean distance metric due to its superiority in sequence-alignment flexibility. However, in some tasks where shape averaging is required, e.g., in template matching and k-means clustering problems, current averaging methods are inaccurate in that they produce undesired templates and cluster representatives. In this work, we emphasize the importance of the correctness of this averaging subroutine and propose a novel shape averaging method, called Prioritized Shape Averaging (PSA), using hierarchical clustering approach. In experimental evaluation, our proposed method, PSA, achieves a lower discrepancy distance between an averaged sequence and every original sequence than existing method on various domains.
An interest point based illumination condition matching approach to photometric registration within augmented reality worlds

With recent and continued increases in computing power, and advances in the field of computer graphics, realistic augmented reality environments can now offer inexpensive and powerful solutions in a whole range of training, simulation and leisure applications. One key challenge to maintaining convincing augmentation, and therefore user immersion, is ensuring consistent illumination conditions between virtual and real environments, so that objects appear to be lit by the same light sources. This research demonstrates how real world lighting conditions can be determined from the two-dimensional view of the user. Virtual objects can then be illuminated and virtual shadows cast using these conditions. This new technique uses pairs of interest points from real objects and the shadows that they cast, viewed from a binocular perspective, to determine the position of the illuminant. This research has been initially focused on single point light sources in order to show the potential of the technique and has investigated the relationships between the many parameters of the vision system. Optimal conditions have been discovered by mapping the results of experimentally varying parameters such as FoV, camera angle and pose, image resolution, aspect ratio and illuminant distance. The technique is able to provide increased robustness where greater resolution imagery is used. Under optimal conditions it is possible to derive the position of a real world light source with low average error. An investigation of available literature has revealed that other techniques can be inflexible, slow, or disrupt scene realism. This technique is able to locate and track a moving illuminant within an unconstrained, dynamic world without the use of artificial calibration objects that would disrupt scene realism. The technique operates in real-time as the new algorithms are of low computational complexity. This allows high framerates to be maintained within augmented reality applications. Illuminant updates occur several times a second on an average to high end desktop computer. Future work will investigate the automatic identification and selection of pairs of interest points and the exploration of global illuminant conditions. The latter will include an analysis of more complex scenes and the consideration of multiple and varied light sources.
Replication initiation complex formation in the absence of nuclear function in Xenopus

In this article, we study how intercalation-induced changes in chromatin and DNA topology affect chromosomal DNA replication using Xenopus egg extracts. Unexpectedly, intercalation by ethidium or doxorubicin prevents formation of a functional nucleus: although nucleosome formation occurs, DNA decondensation is arrested, membranous vesicles accumulate around DNA but do not fuse to form a nuclear membrane, active transport is abolished and lamins are found on chromatin, but do not assemble into a lamina. DNA replication is inhibited at the stage of initiation complex activation, as shown by molecular combing of DNA and by the absence of checkpoint activation. Replication of single-stranded DNA is not prevented. Surprisingly, in spite of the absence of nuclear function, DNA-replication proteins of pre-replication and initiation complexes are loaded onto chromatin. This is a general phenomenon as initiation complexes could also be seen without ethidium in membrane-depleted extracts which do not form nuclei. These results suggest that DNA or chromatin topology is required for generation of a functional nucleus, and activation, but not formation, of initiation complexes.
Short-term freeway traffic flow prediction : Bayesian combined neural network approach

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. On the basis of many existing prediction models, each having good performance only in a particular period, an improved approach is to combine these single predictors together for prediction in a span of periods. In this paper, a neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes' rule. Two single predictors, i.e., the back propagation and the radial basis function neural networks are designed and combined linearly into a Bayesian combined neural network model. The credit value for each predictor in the combined model is calculated according to the proposed credit assignment algorithm and largely depends on the accumulative prediction perfor- mance of these predictors during the previous prediction intervals. For experimental test, two data sets comprising traffic flow rates in 15-min time intervals have been collected from Singapore's Ayer Rajah Expressway. One data set is used to train the two single neural networks and the other to test and compare the performances between the combined and singular models. Three indices, i.e., the mean absolute percentage error, the variance of absolute percentage error, and the probability of percentage error, are employed to compare the forecasting performance. It is found that most of the time, the combined model outperforms the singular predictors. More importantly, for a given time period, it is the role of this newly proposed model to track the predictors' performance online, so as to always select and combine the best-performing predictors for prediction.
Printed in Great Britain The Adaptive Kernel Neural Network

A neural network architecture for clustering and classification is described. The Adaptive Kernel Neural Network (AKNN) is a density estimation technique closely related to kernel estimation. The accompanying learning scheme adjusts the connection weights, activation functions, and the number of nodes in the network. The network, as described here, is made up of three layers of nodes: the input layer, a kernel layer and the output, or classification layer. The AKNN retains the inherent parallelism that is common in neural network models. Its relationship to the kernel estimator allows the network to be understood statistically, and meaningful analysis of the internal representations and the outputs is possible. Kevwords, Neural networks; Density estimation; Kernel estimator; Mixture model.
A Compact Diversity Antenna for Handheld Terminals

The handheld terminals antenna should have a small size, sufficient gain and big bandwidth. In this paper, a compact planar inverted-L diversity antenna for handheld terminals is proposed. Three diversity antennas operating at 2.15 GHz are designed and the effect of important parameters of the proposed antenna is measured. The isolation is found to be better than 13 dB, the usable bandwidth is about 13%. Moreover, the measured radiation patterns are also obtained that the backward radiation is decreased.