Miracle-dz/UniFAR-FTUs huggingface dataset:
International Congress on Acoustics Madrid, 2-7 September 2007 Synchronization of Acoustic Speech Data for Machine Learning Based Audio to Visual Conversion

The present paper studies the role of synchronisation in lip-readable speech to animation conversion for hearing impaired users. Formerly, we presented a neural network based speaker dependent audio to visual conversion system, trained on the data of a professional lip-speaker. Our current effort aims to decrease the speaker dependency of the system. Our proposed solution is to train the system on the acoustic data of numerous everyday speakers coupled with the visual data of professional lip-speakers. Their articulation is much more lip-readable. This method needs time synchronization between data files of lip-speakers and other speakers. This paper details the acoustic data synchronization procedure applied on our database. Dynamic Time Warping (DTW) was used on the acoustic feature vector sets to match them with the reference acoustic vectors and the same time warping function was applied to the visual feature vectors. The quality of the matching of audiovisual feature sets was evaluated by subjective tests. The simple DTW gave acceptable solution. Our iterative DTW gave even better matching. The listeners were not able to differentiate whether the original speech and video pairs or the warped video and speech from different speakers were presented. INTRODUCTION Our goal is to create an application which attends to convert voice to facial animation [1-3]. This would help deaf people to understand voice-only communication channels as telephone systems [4-6]. Recent research projects on conversion of speech audio signal to facial animation have concentrated on development of feature extraction methods, database construction and system training [7,8].
Analysis of Prostate-Specific Antigen Transcripts in Chimpanzees, Cynomolgus Monkeys, Baboons, and African Green Monkeys

The function of prostate-specific antigen (PSA) is to liquefy the semen coagulum so that the released sperm can fuse with the ovum. Fifteen spliced variants of the PSA gene have been reported in humans, but little is known about alternative splicing in nonhuman primates. Positive selection has been reported in sex- and reproductive-related genes from sea urchins to Drosophila to humans; however, there are few studies of adaptive evolution of the PSA gene. Here, using polymerase chain reaction (PCR) product cloning and sequencing, we study PSA transcript variant heterogeneity in the prostates of chimpanzees (Pan troglodytes), cynomolgus monkeys (Macaca fascicularis), baboons (Papio hamadryas anubis), and African green monkeys (Chlorocebus aethiops). Six PSA variants were identified in the chimpanzee prostate, but only two variants were found in cynomolgus monkeys, baboons, and African green monkeys. In the chimpanzee the full-length transcript is expressed at the same magnitude as the transcripts that retain intron 3. We have found previously unidentified splice variants of the PSA gene, some of which might be linked to disease conditions. Selection on the PSA gene was studied in 11 primate species by computational methods using the sequences reported here for African green monkey, cynomolgus monkey, baboon, and chimpanzee and other sequences available in public databases. A codon-based analysis (dN/dS) of the PSA gene identified potential adaptive evolution at five residue sites (Arg45, Lys70, Gln144, Pro189, and Thr203).
Determination of Cluster Distances from Chandra Imaging Spectroscopy and Sunyaev-Zeldovich Effect Measurements. I; Analysis Methods and Initial Results

X-ray and Sunyaev-Zeldovich Effect data can be combined to determine the distance to galaxy clusters. High-resolution X-ray data are now available from the Chandra Observatory, which provides both spatial and spectral information, and interferometric radio measurements of the Sunyaev-Zeldovich Effect are available from the BIMA and OVRO arrays. We introduce a Monte Carlo Markov chain procedure for the joint analysis of X-ray and Sunyaev-Zeldovich Effect data. The advantages of this method are the high computational efficiency and the ability to measure the full probability distribution of all parameters of interest, such as the spatial and spectral properties of the cluster gas and the cluster distance. We apply this technique to the Chandra X-ray data and the OVRO radio data for the galaxy cluster Abell~611. Comparisons with traditional likelihood-ratio methods reveal the robustness of the method. This method will be used in a follow-up paper to determine the distance of a large sample of galaxy clusters for which high-resolution Chandra X-ray and BIMA/OVRO radio data are available.
Competitive generalized auctions

We describe mechanisms for auctions that are simultaneously truthful (alternately known as strategy-proof or incentive compatible) and guarantee high "net" profit. We make use of appropriate variants of competitive analysis of algorithms in designing and analyzing our mechanisms. Thus, we do not require any probabilistic assumptions on bids.We present two new concepts regarding auctions, that of a cancellable auction and that of a generalized auction. We use cancellable auctions in the design of generalized auctions, but they are of independent interest as well. Cancellable auctions have the property that if the revenue collected does not meet certain predetermined criteria, then the auction can be cancelled and the resulting auction is still truthful. The trivial approach (run a truthful auction and cancel if needed) yields an auction that is not necessarily truthfu.Generalized auctions can be used to model many problems previously considered in the literature, as well as numerous new problems. In particular, we give the first truthful profit-maximizing auctions for problems such as conditional financing and multicast.
Sentiment classification using principal component analysis based neural network model

The rapid growth of online social media acts as a medium where people contribute their opinion and emotions as text messages. The messages include reviews and opinions on certain topics such as movie, book, product, politics and so on. Opinion mining refers to the application of natural language processing, computational linguistics, and text mining to identify or classify whether the opinion expressed in text message is positive or negative. Back Propagation Neural Networks is supervised machine learning methods that analyze data and recognize the patterns that are used for classification. This work focuses on binary classification to classify the text sentiment into positive and negative reviews. In this study Principal Component Analysis (PCA) is used to extract the principal components, to be used as predictors and back propagation neural network (BPN) have been employed as a classifier. The performance of PCA+ BPN and BPN without PCA has been compared using Receiver Operating Characteristics (ROC) analysis. The classifier is validated using 10-Fold cross validation. The result shows the effectiveness of BPN with PCA used as a feature reduction method for text sentiment classification.
Real-life escape rooms as a new recreational attraction: the case of Turkey

Abstract The purpose of this paper is to theoretically and empirically investigate experiences of real-life escape rooms, a phenomenon that has gained prominence in recent years. An exploratory empirical study of visitors’ (escapers) experiences of 33 escape rooms in Turkey was conducted using content analysis. A total of 1079 reviews were extracted and used in the analysis. Data were collected from TripAdvisor – a well-known website – that allows guests to share their experiences from all around the world. The findings demonstrate that escape room attractions provide authenticity and flow experiences through the challenging activities and social component of the games. Furthermore, the findings show that visitors enjoy experiencing novelty-seeking (authenticity) and hedonistic experience-seeking (flow) and a feeling of being outside time and place.
EPPDR: An Efficient Privacy-Preserving Demand Response Scheme with Adaptive Key Evolution in Smart Grid

Smart grid has recently emerged as the next generation of power grid due to its distinguished features, such as distributed energy control, robust to load fluctuations, and close user-grid interactions. As a vital component of smart grid, demand response can maintain supply-demand balance and reduce users' electricity bills. Furthermore, it is also critical to preserve user privacy and cyber security in smart grid. In this paper, we propose an efficient privacy-preserving demand response (EPPDR) scheme which employs a homomorphic encryption to achieve privacy-preserving demand aggregation and efficient response. In addition, an adaptive key evolution technique is further investigated to ensure the users' session keys to be forward secure. Security analysis indicates that EPPDR can achieve privacy-preservation of electricity demand, forward secrecy of users' session keys, and evolution of users' private keys. In comparison with an existing scheme which also achieves forward secrecy, EPPDR has better efficiency in terms of computation and communication overheads and can adaptively control the key evolution to balance the trade-off between the communication efficiency and security level.
Molecular Surface Mesh Generation by Filtering Electron Density Map

Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface.
Contextual Image Compression from Adaptive Sparse Data Representations

—Natural images contain crucial information in sharp geometrical boundaries between objects. Therefore, their descrip-tion by smooth isotropic function spaces (e.g. Sobolev or Besov spaces) is not sufﬁciently accurate. Moreover, methods known to be optimal for such isotropic spaces (tensor product wavelet de- compositions) do not provide optimal nonlinear approximations for piecewise smooth bivariate functions. Among the geometry- based alternatives that were proposed during the last few years, adaptive thinning methods work with continuous piecewise afﬁne functions on anisotropic triangulations to construct sparse representations for piecewise smooth bivariate functions. In this article, a customized compression method for coding the sparse data information, as output by adaptive thinning, is proposed. The compression method is based on contextual encoding of both the sparse data positions and their attached luminance values. To this end, the structural properties of the sparse data representation are essentially exploited. The resulting contextual image compression method of this article outperforms our previous methods (all relying on adaptive thinning) quite signiﬁcantly. Moreover, our proposed compression method also outperforms JPEG2000 for selected natural images, at both low and middle bitrates, as this is supported by numerical examples in this article.
Time and the Fulfillment of Election Pledges

In this article, we highlight the importance of accounting for time in the study of pledge fulfillment, effectively adding a significant element to the ongoing academic discussions of the factors that influence the fulfillment of party promises. Unlike previous analyses in which pledge fulfillment is assumed to be a uniform process occurring over time, we analyze party pledge fulfillment using a discrete time approach: doing so highlights yet unobserved dynamics. More precisely, we find that if the government does not enact pledges within the first half of its mandate, the probability of these pledges ever being fulfilled drops drastically. The discrete time modeling approach also allows us to investigate the relationships existing between the budget balance and pledge fulfillment more thoroughly. Our research also extends the study of pledge fulfillment to a new case, the province of Quebec, for the period of 1994–2014 encompassing six governments. Finally, we also conduct similar analyses on Canadian pledge fulfillment data spanning seven successive governments from 1993 to 2015. This study analyzes a total of 1431 manually coded election pledges.
Five level single phase inverter scheme with fault tolerance for islanded photovoltaic applications

A fault tolerant single phase five level inverter is proposed in this paper for islanded photovoltaic (PV) generation system. The topology has the capability of maintaining same output voltage magnitude in case of switch open circuit fault and/or source open or short circuit fault with slightly reduced number of voltage levels. This helps in supplying uninterruptable power to essential loads even under fault condition. The topology also has the major advantage of energy balancing between two batteries using redundant switching states. This helps in reducing difference in state of charge (SOC) of batteries during partial shading or hotspots on PV panels. The fault tolerant single phase five-level inverter is simulated using MATLAB/SIMULINK and results are verified with laboratory prototype.
Melody Extraction on Vocal Segments Using Multi-Column Deep Neural Networks

Singing melody extraction is a task that tracks pitch contour of singing voice in polyphonic music. While the majority of melody extraction algorithms are based on computing a saliency function of pitch candidates or sepa-rating the melody source from the mixture, data-driven approaches based on classiﬁcation have been rarely explored. In this paper, we present a classiﬁcation-based approach for melody extraction on vocal segments us-ing multi-column deep neural networks. In the proposed model, each of neural networks is trained to predict a pitch label of singing voice from spectrogram, but their outputs have different pitch resolutions. The ﬁnal melody contour is inferred by combining the outputs of the networks and post-processing it with a hidden Markov model. In order to take advantage of the data-driven approach, we also augment training data by pitch-shifting the audio content and modifying the pitch label accordingly. We use the RWC dataset and vocal tracks of the MedleyDB dataset for training the model and evaluate it on the ADC 2004, MIREX 2005 and MIR-1k datasets. Through several settings of experiments, we show incremental improvements of the melody prediction. Lastly, we compare our best result to those of previous state-of-the-arts.
Probability and conspiratorial thinking

Conspiracy theories as alternative explanations for events and states of affairs enjoy widespread popularity. We test one possible explanation for why people are prone to conspiratorial thinking: We hypothesize that conspiratorial thinking as an explanation for events increases as the probability of those events decreases. In order to test this hypothesis, we have conducted five experiments in which participants were exposed to different information about probabilities of fictional events. The results of all experiments support the hypothesis: The lower the probability of an event, the stronger participants embrace conspiratorial explanations. Conspiratorial thinking, we conclude, potentially represents a cognitive heuristic: A coping mechanism for uncertainty.
Sensitivity Analysis in Multi-objective Evolutionary Design

In real world engineering design problems we have to search for solutions that simultaneously optimize a wide range of different criteria. Furthermore, the optimal solutions also have to be robust. Therefore, this chapter describes a method where a multi-objective genetic algorithm is combined with response surface methods in order to assess the robustness of a set of identified optimal solutions. The multi-objective genetic algorithm is used in order to optimize two different concepts of hydraulic actuation systems. The different concepts have been modeled in a simulation environment to which the optimization strategy has been coupled. The outcome from the optimization is a set of Pareto optimal solutions that elucidate the tradeoff between the energy consumption and the control error for each actuation system. Based on these Pareto fronts, promising regions could be identified for each concept. In these regions sensitivity analyses are performed with the help of response surface methods. It can then be determined how different design parameters affect the system for different optimal solutions.
Redefining employability: student voices mapping their dance journeys and futures

Abstract This paper explores dance students’ understanding of employability and their views about their dance futures in order to inform higher education curricula and workforce development. The investigation focuses on the student experience on a Bachelor of Arts Honours degree course in Dance at an English university. First and final year undergraduate voices were sought, transcribed and analysed using grounded theory methodology. A key theme, explicit in the student data was that of journeying. Students referred to metaphors that related to journeys, travel, routes, roads and paths. The undergraduate students interviewed conceived of employability in dance as a journey from the beginning of the dance experience towards gaining and sustaining employment. The beginning stage of the journey was getting to university and in the liminal space of the first year, students focused solely on their dance experiences. By the final year, the students perceive their dance futures as diverse journeys of continuous development. The onward journey out of university for final year students is fuelled by a passion for dance, self-reliance and continuous learning. The student voice revealed a determination to forge a future in dance whilst recognising the obstacles; financial, emotional and physical that lay in their way.
New particle formation events in semi-clean South African savannah

Abstract. This study is based on 18 months (20 July 2006–5 February 2008) of continuous measurements of aerosol particle size distributions, air ion size distributions, trace gas concentrations and basic meteorology in a semi-clean savannah environment in Republic of South Africa. New particle formation and growth was observed on 69% of the days and bursts of non-growing ions/sub-10 nm particles on additional 14% of the days. This new particle formation frequency is the highest reported from boundary layer so far. Also the new particle formation and growth rates were among the highest reported in the literature for continental boundary layer locations; median 10 nm formation rate was 2.2 cm−3 s−1 and median 10–30 nm growth rate 8.9 nm h−1. The median 2 nm ion formation rate was 0.5 cm−3 s−1 and the median ion growth rates were 6.2, 8.0 and 8.1 nm h−1 for size ranges 1.5–3 nm, 3–7 nm and 7–20 nm, respectively. The growth rates had a clear seasonal dependency with minimum during winter and maxima in spring and late summer. The relative contribution of estimated sulphuric acid to the growth rate was decreasing with increasing particle size and could explain more than 20% of the observed growth rate only for the 1.5–3 nm size range. Also the air mass history analysis indicated the highest formation and growth rates to be associated with the area of highest VOC (Volatile Organic Compounds) emissions following from biological activity rather than the highest estimated sulphuric acid concentrations. The frequency of new particle formation, however, increased nearly monotonously with the estimated sulphuric acid reaching 100% at H2SO4 concentration of 6 · 107 cm−3, which suggests the formation and growth to be independent of each other.
Multi-versions data and epsilon-serializability for QoS enhancement in distributed RTDBMS

Distributed Real-Time Database Management Systems (DRTDBMS) are characterized by the large amount of distributed data, the unpredictable transactions and the unbalanced workload between different nodes. A lot of work dealing with Quality of Service (QoS) has been done to control the behaviour of DRTDBMS. They are based on feedback control scheduling theory. In this paper, we propose to apply both multi-versions data technique (MVD) and epsilon-serializability criterion (ESR) in distributed feedback control loop by using three data replication policies. The proposed architecture is called MVD-ESR-DFCS architecture. We also show, by a set of simulations, that our proposed architecture can significantly improve the QoS in DRTDBMS by increasing the number of transactions that meet their deadlines.
WiNoN - Plugging the Leaky Boat of Web Anonymity

Despite the attempts of well-designed anonymous communication tools to protect users from being tracked or identified, other artifacts, such as a user's environment and behavior, may leak a user's identity. Plugging this leaky boat of web anonymity requires a " top-to-bottom " whole-system approach, rather than focusing on specific protocols or layers. As an initial step, we present WiNoN, a general purpose anonymity-centric system architecture that plugs identity leaks from the network layer up. The core concept powering WiNoN– nym-browse or pseudonym browsing mode – gives the user a web-browsing environment in which each of the user's contextually independent web activities run in unique, yet homogenous containers. This enforced isolation limits accidental leakage of private information regardless of misconfigured anonymity tools, trojan applications, or bugged files. This paper explores the WiNoN design space and presents an early prototype supporting Tor, Dissent, and SWEET communication tools as well as an incognito mode.
Network coding to combat packet loss in underwater networks

Channel variability and a high level of ambient noise lead to significant probability of packet loss in many underwater networks. Techniques based on acknowledgements and re-transmissions (such as ARQ) can be used to build robust networks over the unreliable links between underwater nodes. An alternative solution based on erasure codes can also be used to combat the packet loss. However, both solutions rely on a node re-transmitting information originating at that node. We propose an alternative solution based on network coding, where nodes transmit packets which are composed partially from information originating at that node, and partially from information received by that node from other nodes. The intuition behind this solution is to effectively route the information over good paths in the network rather than to simply rely on re-transmission of the information by the originating nodes. In this paper, we show that our proposed solution indeed performs better than the acknowledgment and erasure coding based solutions, and has the potential to effectively combat the high packet loss experienced by many underwater networks.
Non-determinism in Functional Languages

The introduction of a non-deterministic operator in even a very simple functional programming language gives rise to a plethora of semantic questions. These questions are not only concerned with the choice operator itself. A surprisingly large number of different parameter passing mechanisms are made possible by the introduction of bounded nondeterminism. The diversity of semantic possibilities is examined systematically using denotational definitions based on mathematical structures called power domains. This results in an improved understanding of the different kinds of nondeterminism and the properties of different kinds of non-deterministic languages.