Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Image Face Recognition Using Laplacian of Gaussian and Discrete Cosine Transforms

This paper presents a single image face recognition approach called Laplacian of Gaussian (LOG) and Discrete Cosine Transform (DCT). The proposed concept highlights a major concerned area of face recognition i.e., single image per person problem where the availability of images is limited to one at training side. To address the problem, the paper makes use of filtration and transforms property of LOG and DCT to recognize faces. As opposed to conventional methods, the proposed idea works at pre-processing stage by filtering images up to four levels and then using the filtered image as an input to DCT for feature extraction using mid frequency values of image. Then, covariance matrix is computed from mean of DCT and Principal component analysis is performed. Finally, distinct feature vector of each image is computed using top Eigenvectors in conjunction with two LOG and DCT images. The experimental comparison for LOG (DCT) was conducted on different standard data sets like ORL, Yale, PIE and MSRA which shows that the proposed technique provides better recognition accuracy than the previous conventional methods of single image per person i.e., (PC) 2 A and PCA, 2DPCA, B-2DPCA etc. Hence with over 97% recognition accuracy, the paper contributes a new enriched feature extraction method at pre-processing stage to address the facial system limitations.
Socialising Data with Google Fusion Tables.

Consider a universe of tokens, each of which is associated with a weight, and a database consisting of strings that can be represented as subsets of these tokens. Given a query string, also represented as a set of tokens, a weighted string similarity query identifies all strings in the database whose similarity to the query is larger than a user specified threshold. Weighted string similarity queries are useful in applications like data cleaning and integration for finding approximate matches in the presence of typographical mistakes, multiple formatting conventions, data transformation errors, etc. We show that this problem has semantic properties that can be exploited to design index structures that support very efficient algorithms for query answering.
On the contribution of Aitken mode particles to cloud droplet populations at continental background areas – a parametric sensitivity study

Aitken mode particles are potentially an important source of cloud droplets in continental background areas. In order to find out which physico-chemical properties of Aitken mode particles are most important regarding their cloud-nucleating ability, we calculated the number of cloud droplets formed on Aitken mode particles, CD 2 , with an adiabatic air parcel model. The model output was analyzed using a global sensitivity analysis method that quantifies and ranks the relative importance of the considered input parameters to the total variance of CD 2 . The results show that unless the particle surface tension or the mass accommodation coefficient of water is strongly reduced due to the presence of surface-active organics, the parameters describing the size distribution are generally more important than the particle chemical composition. In the absence of such compounds, the chemical composition may have roughly an equal importance with the size distribution only at low updraft velocities characterized by maximum supersaturations below 0.1%. Furthermore, the largest source of variability is generally the particle number concentration, followed by the particle size. The performed sensitivity analysis revealed that the variability of the particle chemical composition may dominate the total variation of CD 2 if: 1) the value of α varies at least one order of magnitude more than what is expected for pure water surfaces (10 −2 –1), or 2) the particle surface tension varies more than roughly 30% under conditions close to reaching saturation.
Advanced Filter-Bank Multicarrier System for QAM Signal Transmission and Reception: A Survey

— Multicarrier modulations attract a lot of attention among researchers working in the field of telecommunications. One specific form of multicarrier modulation referred to as OFDM has been the dominant technology for broadband multicarrier communications. In spite of their many advantages, OFDM systems have a few, but important drawbacks. Filter bank multicarrier (FBMC) is an evolution with many advantages over the widespread OFDM multicarrier scheme. Filter banks are an evolved form of subband processing based on Fast Fourier Transforms and addressing some of its shortcomings, at the price of a somewhat increased implementation complexity. In this paper, a review of FBMC and its concept is presented, emphasizing its benefits over OFDM in applications such as Cognitive Radio(CR), Multiple access networks, TVWS, PLC and MIMO communication systems.
Long-term weather, hydrometric, and water chemistry datasets in high-temporal resolution at the La Salle River watershed in Manitoba, Canada

Abstract. Lack of long-term datasets in fine temporal resolution hinders environmental studies and modelling efforts; to address this issue in the La Salle River watershed, in Canada, long-term weather (1990–2013), hydrometric (1990–2013 except years with no or poor data), and water chemistry (2009–2013) datasets were developed. The weather variables consisted of temperature, relative humidity, wind speed, solar radiation, and precipitation in an hourly time-step, which is required for physically-based modelling. The only hydrometric variable included in the dataset was stream discharge in a daily time-step, which is the usual time-frame for summarizing the results of long-term studies. The water chemistry data consisted of total nitrogen (TN), total dissolved nitrogen (TDN), total phosphorus (TP) and total dissolved phosphorus (TDP). Samples were collected weekly during the open water season at the same site as they hydrometric gauging station (05OG008) starting in August 2009 until October 2012 with some gaps (i.e. Fall 2011, Spring 2012, September 2012). In 2013 the frequency of sampling was increased to daily or sub-daily during high stream discharge and weekly during low stream discharge. An overview of the data indicates that values and trends are within ranges reported in the literature for the region. Mean annual, winter, and summer temperatures were 3.5 °C–10.7 °C and 17.2 °C, respectively. Annual relative humidity averaged 73.1 % but tended to be higher and more homogenous in cold seasons. Wind speed was very similar over the different seasons with annual average of 4.3 m/s. Solar radiation followed the typical curve reported for western Canada, with peak daily average values around 250 W/m2 in July. The precipitation records were mostly comprised of dry hours and the characteristic precipitation pattern of the Canadian Prairies with high frequency of small precipitation events as observed, with 75.3 % of the hourly precipitation being equal or less than 2 mm/h. The hydrometric characteristics of the dataset were also typical of the Canadian Prairies; the average peak discharge over the entire period was larger in April (2.3 m3/s) due to large amounts of snowmelt runoff. The average concentrations of TN, TDN, TP and TDP of 1.54, 1.35, 0.56, and 0.49 mg/L, respectively, were in agreement with values found in previous studies at the same location. The datasets for weather ( https://doi.org/10.23684/ODI-2017-00957 ), discharge ( https://doi.org/10.23684/ODI-2017-00959 ) and water chemistry ( https://doi.org/10.23684/ODI-2017-00958 ) are accessible through the Government of Canada's Open Data portal ( http://open.canada.ca ).
Wikipedia Revision Toolkit: Efficiently Accessing Wikipediaâ€™s Edit History

We present an open-source toolkit which allows (i) to reconstruct past states of Wikipedia, and (ii) to efficiently access the edit history of Wikipedia articles. Reconstructing past states of Wikipedia is a prerequisite for reproducing previous experimental work based on Wikipedia. Beyond that, the edit history of Wikipedia articles has been shown to be a valuable knowledge source for NLP, but access is severely impeded by the lack of efficient tools for managing the huge amount of provided data. By using a dedicated storage format, our toolkit massively decreases the data volume to less than 2% of the original size, and at the same time provides an easy-to-use interface to access the revision data. The language-independent design allows to process any language represented in Wikipedia. We expect this work to consolidate NLP research using Wikipedia in general, and to foster research making use of the knowledge encoded in Wikipedia's edit history.
Efficient Language Modeling Algorithms with Applications to Statistical Machine Translation

N -gram language models are an essential component in statistical natural language processing systems for tasks such as machine translation, speech recognition, and optical character recognition. They are also responsible for much of the computational costs. This thesis contributes efficient algorithms for three language modeling problems: estimating probabilities from corpora, representing a model in memory, and searching for high-scoring output when log language model probability is part of the score. Most existing language modeling toolkits operate in RAM, effectively limiting model size. This work contributes disk-based streaming algorithms that use a configurable amount of RAM to estimate KneserNey language models 7.13 times as fast as the popular SRILM toolkit. Scaling to 126 billion tokens led to first-place performance in the 2013 Workshop on Machine Translation for all three language pairs where submissions were made. Query speed is critical because a machine translation system makes millions of queries to translate one sentence. Thus, language models are typically queried in RAM, where size is a concern. This work contributes two near-lossless data structures for efficient storage and querying. The first, based on linear probing hash tables, responds to queries 2.42 times as fast as the SRILM toolkit while using 57% of the memory. The second, based on sorted arrays, is faster than all baselines and uses less memory than all lossless baselines. Searching for high-scoring output is difficult because log language model probabilities do not sum when strings are concatenated. This thesis contributes a series of optimizations that culminate in a new approximate search algorithm. The algorithm applies to search spaces expressed as lattices and, more generally, hypergraphs that arise in many natural language tasks. Experiments with syntactic machine translation show that the new algorithm attains various levels of accuracy 3.25 to 10.01 times as fast as the popular cube pruning algorithm with SRILM.
MinBERT and Downstream Tasks

The project’s objective is to enhance the effectiveness of a pretrained BERT model on three distinct tasks, namely sentiment analysis, paraphrase detection, and semantic textual similarity, by employing two different finetuning approaches: contrastive loss and multiple negative ranking loss. We develop the software code for these methods from the ground up and conduct experiments to examine the potential of combining these finetuning techniques to achieve even better outcomes than those reported in existing literature. We find that best results are achieved when combining the finetuning loss with the original loss function from the baseline task with contrastive loss and analyze further steps that can be taken to improve on these findings.
AIS Electronic Library (AISeL)

In this paper, we contend that due to the particularities and widespread use of new ICT, traditional ways of explaining IT adoption in companies -rational decision making, technology diffusion models, and the psychology of the decision maker-are insufficient to explain some observed cases of ICT diffusion. We prove that there is a new role played by the user community in the technology adoption process, brought about by factors like the existence of a strong development community in the case of OSS, or widespread access to Internet technology. A mixed two-phase method was used for this study. In the first phase, through a qualitative analysis of some significant cases we depicted under which conditions a significant adoption of a particular technology (OSS) might unfold, showing that in some circumstances user groups could play a significant role. In the second phase an exploratory quantitative analysis was carried out to verify the new dimensions of the technology adoption process. The results showed a high influence of social factors like peer group adoption and user’s pressure. Due to reasons of geographical proximity the research was carried out in Spanish companies, but we believe that, like with similar adoption studies, the results are of general nature as there are no culturally specific dimensions that we could identify. Conclusions and further research are outlined.
AIS Electronic Library (AISeL)

The development of smartphone applications is prevailing globally, including the underserved communities consisting of a huge group of novice users. In spite of the growing number of novice users, we hardly consider usability for users with varying expertise level when we evaluate performance and satisfaction with usage of mobile applications. In this study, we argue that it is not suitable to design one interface for all users of progressively varying communities. Based on theories in design science research, we propose a user-accustomed approach to adapt mobile applications that integrate three types of interaction elements, namely localization, structural navigation and illustration. In an investigation of the proposed approach on mobile application, we empirically proved the effects of user-accustomed interaction techniques on performance and satisfaction between novice and expert users. The findings provide significant theoretical and practical implications for design and implementation of user interfaces on mobile applications.
Performance Portable Optimizations for Loops Containing Communication Operations

As high end computing systems continue to scale in CPU computational power and overall node count, optimization techniques that can reduce communication overhead have proven important. We present a loop optimization framework designed to achieve both efficient communication/computation overlap and performance portability. The framework has been implemented in the Berkeley UPC compiler and uses a combination of compile time analysis and runtime mechanisms. We extend the compiler to perform message vectorization and message strip mining optimizations. At compile time loop nests are analyzed, their communication requirements are determined, and the computation overhead is estimated. The compiler passes analysis information to the runtime and performance portability is achieved by decoupling data movement from local computation. We generate template code that uses the transferred data without making any assumptions about the communication mechanism.
Journal of Geophysical Research: Solid Earth Relocation of River Storage From Global Hydrological Models to Georeferenced River Channels for Improved Load-Induced Surface Displacements

Variations in terrestrial surface water storage cause elastic crustal displacements of several millimeters in the vertical direction on daily to seasonal time scales. Locally, strong signals with exceptionally high amplitudes can be observed along the major river channels. As the horizontal resolution of global hydrological models is typically limited to 0.5 ∘ × 0.5 ∘ , the generalized model drainage network leads to simulated river mass distributions that are not necessarily collocated with the real courses of the rivers. Moreover, the spreading of locally concentrated surface water on the coarse model grids leads to substantially underestimated amplitudes of simulated hydrologically induced surface displacements. We therefore develop a relocation procedure to improve such hydrological loading estimates, especially at locations near the river banks. We separate the water masses stored in the modeled river network from the total water storage and relocate them on a georeferenced river map with higher resolution. Applying the relocated river masses, simulated hydrological loading amplitudes increase by 0.2–5 mm for the vertical load-induced surface displacement along the major rivers, at the Amazon even up to additional 15 mm. The amplitudes for the horizontal load-induced surface displacements are slightly increased, up to maximum 3 mm. Besides changes in the distance to the mass loads, the horizontal displacement ﬁelds is also heavily aﬀected by signiﬁcant changes in the directions to the relocated river mass load. The comparison of modeled hydrologically induced surface displacement time series with Global Positioning System observations shows a signiﬁcantly improved ﬁt for stations in close proximity to larger rivers when applying the relocation procedure.
Cluster-Based Epidemic Control through Smartphone-Based Body Area Networks

Increasing population density, closer social contact and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be quarantined to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphone-based body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective clusterbased quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks.
Acoustic emission technology for engineering health monitoring of a wave energy converter

Marine renewable energy has the potential to produce up to 20% of the UK’s current electricity demand, leading to extensive research and development activities in this area. As the industry of wave energy progresses toward commercialisation, a number of barriers still limit its potential, including the cost of energy. A significant portion of this is the cost of operation and maintenance activity due to the challenging environments in which wave energy devices reside. Maintenance activity as it stands can include vessel activity and skilled workers including divers. Condition monitoring techniques designed for the wave energy industry and utilising the water that surrounds the devices could reduce maintenance costs by decreasing human intervention and the early detection of faults and degradation. This thesis looks to explore the feasibility of underwater Acoustic Emission (AE) monitoring as a method of condition-based maintenance for wave energy converters and other marine energy devices. This is achieved through three strands of work: sea trials, component testing and propagation modelling. Eighteen months of acoustic data from the Lifesaver wave energy converter, deployed at Falmouth Bay Test Facility in Falmouth Bay, UK, are re-assessed for evidence of component AE signatures. Signatures are identified and presented from the power-take-off and generator of the device. Novel testing is conducted through submerged marine component testing adapted to include AE monitoring. Three 12-strand double braided synthetic mooring ropes undergo cyclic fatigue testing and AE signals detected can be classified by amplitude and frequency range, with increasing severity as the component loading is increased before sample failure. Finally, propagation modelling is used to understand the effect of AE from engineering components on the local soundscape. To enable a comparison between devices, a new efficiency ratio is presented that compares the underwater acoustic energy emitted through the life cycle of the wave energy converter to the useful electrical energy produced by the device. This thesis presents the first steps toward a novel method of condition monitoring in an ocean environment, potentially adaptable for arrays of devices.
Mixing expressiveness and e(cid:141) ciency in a manufacturing planner

The application of arti® cial intelligence planning techniques to real world problems is a very di(cid:141) cult task which usually implies overcoming expressiveness and e(cid:141) ciency handicaps. Expressiveness in order to cope with the great amount of knowledge involved in these problems and also to obtain results directly, or almost directly, applicable in the ® eld of study. E(cid:141) ciency to achieve a good course through the potentially enormous search spaces and, therefore, a satisfactory response capability. This work describes M a c h i n e , a manufacturing planning architecture which is able to obtain sequential control programs for manufacturing systems, the steps given to overcome these expressiveness and e(cid:141) ciency handicaps and how it is able to solve some real size problems.
FIU Digital Commons FIU Digital Commons

: Magneto-elasto-electric (ME) coupling heterostructures, consisting of piezoelectric layers bonded to magnetostrictive ones, provide for a new class of electromagnetic emitter materials on which a portable (area ~ 16 cm 2 ) very low frequency (VLF) transmitter technology could be developed. The proposed ME transmitter functions as follows: (a) a piezoelectric layer is ﬁrst driven by alternating current AC electric voltage at its electromechanical resonance (EMR) frequency, (b) subsequently, this EMR excites the magnetostrictive layers, giving rise to magnetization change, (c) in turn, the magnetization oscillations result in oscillating magnetic ﬁelds. By Maxwell’s equations, a corresponding electric ﬁeld, is also generated, leading to electromagnetic ﬁeld propagation. Our hybrid piezoelectric-magnetostrictive transformer can take an input electric voltage that may include modulation-signal over a carrier frequency and transmit via oscillating magnetic ﬁeld or ﬂux change. The prototype measurements reveal a magnetic dipole like near ﬁeld, demonstrating its transmission capabilities. Furthermore, the developed prototype showed a 10 4 times higher efﬁciency over a small-circular loop of the same area, exhibiting its superiority over the class of traditional small antennas.
The E(cid:11)ects of Training Set Size on Decision Tree Complexity

This paper presents experiments with 19 datasets and 5 decision tree pruning algorithms that show that increasing training set size often results in a linear increase in tree size, even when that additional complexity results in no signi(cid:12)cant increase in classi(cid:12)cation accuracy. Said di(cid:11)erently, removing randomly selected training instances often results in trees that are substantially smaller and just as accurate as those built on all available training instances. This implies that decreases in tree size obtained by more sophisticated data reduction techniques should be decomposed into two parts: that which is due to reduction of training set size, and the remainder, which is due to how the method selects instances to discard. We perform this decomposition for one recent data reduction technique, John’s robust-c4.5 (John 1995), and show that a large percentage of its e(cid:11)ect on tree size is attributable to the fact that it simply reduces the size of the training set. We conclude that random data reduction is a baseline against which more sophisticated data reduction techniques should be compared. Finally, we examine one possible cause of the pathological relationship between tree size and training set size.