Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Biometrics at Feature Level Fusion using Texture Features

In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
Parsing , Paninian Gram mar , Dependency Grammar , semantics , syntax , kaaraka roles , thematic roles 1 Introduction Last

Last decade has seen introduction of several parsers for English ranging from rule based to statistical based. In recent years there is also a growing trend towards producing dependency output in addition to the constituency trees. The dependency format is pre­ ferred over the constituency not only from evaluation point of view but also because of its suitability for a wide range of NLP tasks. However there is no consensus among the dependency parser developers on the number of dependency relations and names of these relations. Paninian Grammar (PG), the first de­ pendency formalism, though is devel­ oped necessarily for Sanskrit, has po­ tential to provide guidelines for pro­ ducing the dependency output for English. We first summarize the is­ sues involved with reference to En­ glish language parsing based on the dependency format output of the cur­ rent English parsers. Next we high­ light the Information theoretic point of view of Paninian Grammar. Fourth section contains guidelines for pro­ ducing the dependency output for En­ glish, in the light of PG. We conclude with some suggestions for evaluation of existing parsers. Key Words: Parsing, Paninian Gram­ mar, Dependency Grammar, semantics, syntax, kaaraka roles, thematic roles
1 Approximate Normalization for Gradual Dependent Types

Dependent types help programmers write highly reliable code. However, this reliability comes at a cost: it can be challenging to write new prototypes in (or migrate old code to) dependently-typed programming languages. Gradual typing makes static type disciplines more exible, so an appropriate notion of gradual dependent types could fruitfully lower this cost. However, dependent types raise unique challenges for gradual typing. Dependent typechecking involves the execution of program code, but gradually-typed code can signal runtime type errors or diverge. ese runtime errors threaten the soundness guarantees that make dependent types so aractive, while divergence spoils the type-driven programming experience. is paper presents GDTL, a gradual dependently-typed language, with an emphasis on pragmatic dependently-typed programming. GDTL fully embeds both an untyped and dependently-typed language, and allows for smooth transitions between the two. In addition to gradual types we introduce gradual terms, which allow the user to be imprecise in type indices and to omit proof terms; runtime checks ensure type safety. To account for nontermination and failure, we distinguish between compile-time normalization and run-time execution: compile-time normalization is approximate but total, while runtime execution is exact, but may fail or diverge. We prove that GDTL has decidable typechecking and satises all the expected properties of gradual languages. In particular, GDTL satises the static and dynamic gradual guarantees: reducing type precision preserves typedness, and altering type precision does not change program behavior outside of dynamic type failures. To prove these properties, we were led to establish a novel normalization gradual guarantee that captures the monotonicity of approximate normalization with respect to imprecision.
Chapter 3 Molecular Diagnostic Tools for Nematodes

The phylum of Nematoda is a species‐rich taxonomic group in abundant numbers across a wide range of habitats, including plant and animal pathogens, as well as good environ‐ mental health indicators. Morphological observations are of low throughput and more importantly have problems with their discriminatory capacity, particularly at the spe‐ cies level. For these reasons, diagnostic tools are of paramount importance for all fields of human, animal and plant nematology as well as for environmental studies in water and soil. Accurate, fast and low‐cost methodologies are required in order to identify and quantify the population of nematodes in samples from various sources. Scientists in basic research as well as in routine application fields need to have tools for resolving these identification obstacles. Their decisions can be human‐, animal‐ or plant‐health related, while many times legally committing. As a result, applicable and accredited methods are required and should be readily available in a common routine lab or in the field of battle or at border control agencies. This chapter aims to inform with the most current informa‐ tion on the available tools for nematode diagnostics, their positives and negatives and hints about the trends in the field and suggestions for those who would like to pursue further this field of biotechnology as researchers or simple users.
Gauss : The Last Entry by Frans Oort

We present one of the shortest examples of a statement with a visionary impact: we discuss an expectation by Gauss. His idea preludes developments only started more than a century later. Several proofs were given for the prediction by Gauss. We show where this statements fits into modern mathematics. We give a short proof, using methods, developed by Hasse, Weil and many others. Of course this is history upside down: instead of seeing the Last Entry as a prelude to modern developments, we give a 20-th century proof of this 19-th century statement. I thank Norbert Schappacher for discussions and suggestions on this topic.
Texture Transfer Based on Texture Descriptor Variations

In this report, we tackle the problem of image-space texture transfer which aims to modify an object or surface material by replacing its input texture by another reference texture. The main challenge of texture transfer is to successfully reproduce the reference texture patterns while preserving the input texture variations due to its environment such as illumination or shape variations. We propose to use a texture descriptor composed of local luminance and local gradients orientation and magnitude to characterize the input texture variations. We then introduce a guided texture synthesis algorithm to synthesize a texture resembling the reference texture with the input texture variations. The main contribution of our algorithm is its ability to locally deform the reference texture according to local texture descriptors in order to better reproduce the input texture variations. We show that our approach is able to produce results comparable with current state-of-the-art approaches but with fewer user inputs.
A spatiotemporal database framework for infrastructure systems analytics and modelling

The size, scale, variety and complexity of spatial datasets required for the analysis of critical infrastructure is ever increasing. Decision makers require knowledge at fine temporal and spatial scales, such as at the household scale, resulting in demand for significantly larger datasets then previously used. Data storage and management mechanisms for such datasets require novel solutions. A heterogeneous federated database solution is presented where buildings are considered as the primary spatial entity, and where multiple optimal SQL/NoSQL databases are employed to store the wide range of datasets required in the analysis of infrastructure systems. A pilot study is presented for the city of Newcastle-Upon-Tyne where local building level electricity distribution networks are coupled with buildings for which household characteristics are associated.
WATER QUALITY STATUS AND TRENDS IN THE CLARK FORK-PEND OREILLE WATERSHED Trends Analysis from 1984-2002

This report summarizes water quality data collected in the Clark Fork-Pend Oreille Basin from 1984-2002. Data were collected by Montana Department of Environmental Quality (formerly MDHES) and more recently the Tri-State Water Quality Council. Analyses presented in this study describe the temporal and spatial variability in concentrations of algal nutrients, heavy metals and periphyton (attached algae) in the Clark Fork-Pend Oreille watershed.
Semantic Annotation of Mobile Phone Data Using Machine Learning Algorithms

Cell phone call location data has been utilized for the study of travel patterns, but the underlying activities that originate the movement are still at a less explored stage. Resulted from routine and automated features of decision-making processes, human activity and travel behaviour exhibit a high level of spatial-temporal periodicities as well as a certain order of the activities. In this chapter, a method has been developed based on these regularities, which predicts activities being conducted at call locations. The method includes four steps: a set of comprehensive variables is defined; feature selection techniques are applied; a group of state-of-the-art machine learning algorithms and an ensemble of the above algorithms are employed; an additional enhancement algorithm is designed. Using data gathered from natural communication of 80 users over a period of 1 year, the proposed method is evaluated. Based on the ensemble of the models, prediction accuracy of 69.7% was achieved. Using the enhancement algorithm, the performance obtained 7.6% improvement. The experimental results demonstrate the potential to annotate call locations based on the integration between machine learning algorithms and the characteristics of underlying activity and travel behaviour, contributing towards the semantic interpretation and application of the massive data.
Intelligent control for a quadrotor using reinforcement learning with proximal policy optimization

Aerial platforms, such as quadrotors, are inherently unstable systems. In several prior works, the task of stabilizing the flight of a quadrotor was approached by different techniques, generally based on classic control algorithms. However, recently, model-free reinforcement learning algorithms have been successfully used for controlling these platforms. In this work, we show the feasibility of applying a reinforcement learning method to optimize a stochastic control policy (during training), to perform the position control of the quadrotor. This process maintains a good sampling efficiency while allowing fast convergence even when using computationally expensive off-the-shelf simulators for robotics and without the necessity of any additional exploration strategy. We used the Proximal Policy Optimization (PPO) algorithm to make the agent learn a reliable control policy. Then, we presented the results of the response of the obtained intelligent controller in several conditions. Additionally, we investigated reward signals based on the Proportional-Integrative-Derivative controller and the possibility of reducing the steady state error of the controller. The experiments for the resultant intelligent controller were performed using the V-REP simulator and the Vortex physics engine and results show that it is possible to train such algorithms to control quadrotors.
Stochastic Behavior of Phase Synchronization Mapped with Seizure-Free 256-Channel Scalp EEG

Abstract. We examined the spatiotemporal stochastic behavior of the phase synchronization index (SI) derived from seizure-free scalp EEG data of an adult male subject for a three minute period. It was found that in the epileptogenic area the stochastic fluctuations in theta (3-7 Hz) and low gamma (30-50 Hz) bands were higher as compared with the surrounding areas and also exhibited complex spatio-temporal patterns. This has a potential to nonivasively localize the epileptogenic areas from the seizure-free scalp EEG data. The EEG data of three minute duration was filtered in theta or low Gamma band. A detrended fluctuation analysis (DFA) was used to find the averaged stochastic fluctuations in the SI. Contour plots were constructed with 20 sec time-frames using a montage of the layout of 256 electrode positions. The phase synchronization was computed after taking Hilbert transform of the EEG data. The SI between a pair of channel was inferred from a statistical tendency to maintain a nearly constant phase difference over a given period of time even though the analytic phase of each channel may change markedly during that time frame.
Performance Comparison of Image Retrieval Using Fractional Coefficients of Transformed Image Using DCT , Walsh , Haar and Kekre ’ s Transform

The thirst of better and faster retrieval techniques has always fuelled to the research in content based image retrieval (CBIR). The paper presents innovative content based image retrieval (CBIR) techniques based on feature vectors as fractional coefficients of transformed images using Discrete Cosine, Walsh, Haar and Kekre’s transforms. Here the advantage of energy compaction of transforms in higher coefficients is taken to greatly reduce the feature vector size per image by taking fractional coefficients of transformed image. The feature vectors are extracted in fourteen different ways from the transformed image, with the first being considering all the coefficients of transformed image and then fourteen reduced coefficients sets (as 50%, 25%, 12.5%, 6.25%, 3.125%, 1.5625% ,0.7813%, 0.39%, 0.195%, 0.097%, 0.048%, 0.024%, 0.012% and 0.06% of complete transformed image) are considered as feature vectors. The four transforms are applied on gray image equivalents and the colour components of images to extract Gray and RGB feature sets respectively. Instead of using all coefficients of transformed images as feature vector for image retrieval, these fourteen reduced coefficients sets for gray as well as RGB feature vectors are used, resulting into better performance and lower computations. The proposed CBIR techniques are implemented on a database having 1000 images spread across 11 categories. For each proposed CBIR technique 55 queries (5 per category) are fired on the database and net average precision and recall are computed for all feature sets per transform. The results have shown performance improvement (higher precision and recall values) with fractional coefficients compared to complete transform of image at reduced computations resulting in faster retrieval. Finally Kekre’s transform surpasses all other discussed transforms in performance with highest precision and recall values for fractional coefficients (6.25% and 3.125% of all coefficients) and computation are lowered by 94.08% as compared to DCT.
Stepping Stones or Stumbling Blocks : Psychological Capital and Overcoming New Venture Failure

It is common for entrepreneurs to face failure as a part of the entrepreneurial process. In fact over half of all new businesses fail within the first five years. The ability to emerge from failure in a positive manner is central for repeat entrepreneurs. This paper examines new venture failure through the lens of Psychological Capital to develop a better understanding of how entrepreneurs emerge from a failed business leading to future entrepreneurial activity. I explore how and why some entrepreneurs deal with a failed venture so that the failure becomes a springboard for beginning the entrepreneurial process again
Spoken Language Parsing with Robustness and Incrementality

This paper describes a method of incremental parsing for grammatically ill-formed sentences. The method consists of incremental chart parsing and error correcting, which are executed synchronously. The error correction at an early stage enables the parser to construct a syntactic structure of an initial fragment of a grammatically ill-formed sentence whenever a word is inputed. The parser has two features: One is to utilize the syntactic constraints such as reachability and connectability, and the other is to assign a cost, which represents degrees of error corrections, to every syntactic structure. Therefore, the parser, at any time, adopts the syntactic structure of the well-formed sentence which is most similar to the input sentence as a parsing result.
Learning to generate HTML code from images with no supervisory data

Transforming graphical designs into HTML code is a typical task of front-end web development which requires an expert programmer to spend a considerable amount of time, while this time can be dedicated to more creative problem-solving tasks where the machine intelligence is still far from the human intelligence. Automating HTML code generation from a design mock-up can serve as a solution to this problem. More recently, there have been several attempts on using machine learning techniques, especially deep neural networks, for automatic program synthesis. Among those, some works are devoted to the structured markup languages (Deng et al., 2016). However, there is still a long way to go before reaching to a satisfactory code generator. Here we introduce a new approach for generating HTML codes from a given web-page image which uses a reinforcement learning technique.
Distribution and conservation status of Emys orbicularis in Morocco

The distribution and conservation status of Emys orbicularis occidentalis is reviewed for Morocco. Populations are highly fragmented and vulnerable. The species is largely restricted to mountainous areas in the western Rif and Middle Atlas, with scattered observations of isolated individuals in the Gharb plain and the eastern Rif. Our literature survey and fieldwork indicate an accelerated regression of Emys populations along the Moroccan Atlantic coast due to habitat loss. At the same time, we found European pond turtles having a wider distribution in the Rif Mountains than previously known. However, in the central and western Rif, the species is increasingly threatened by expanding cannabis plantations. Other country-wide menaces include water pollution, catching of turtles for pets, desertification, and possibly competition with another terrapin species (Mauremys leprosa).
Electroencephalographic signal processing and classification techniques for noninvasive motor imagery based brain computer interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reducedmean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy. Index Words: Brain computer interface, Common spatial pattern, Empirical mode decomposition, Independent component analysis, Support vector machine ELECTROENCEPHALOGRAPHIC SIGNAL PROCESSING AND CLASSIFICATION TECHNIQUES FOR NONINVASIVE MOTOR IMAGERY BASED BRAIN COMPUTER INTERFACE by MD ERFANUL ALAM B.S., Bangladesh University of Engineering and Technology, Bangladesh, 2014 M.S., Georgia Southern University, 2017 A Thesis Submitted to the Graduate Faculty of Georgia Southern University in Partial Fulfillment of the Requirements for the Degree
Comparing the Impact of Children ’ s Participation on Policy Making in Wales and Tamil Nadu

Key search terms: Disciplines: political science, social policy, sociology. Topics / themes: citizenship (civic and political participation, human rights, non-governmental-organisations); governance (devolution); policy (policy development, policy implementation); socio-demographic processes (childhood). Units of comparison: cultural units (citizenship groups); political institutions (policy actors, policy networks). Concepts: politics (civic and political participation and engagement, participation, rights); socio-demographic processes (childhood); values (social status). Funding: national research agencies (Economic and Social Research Council, UK). Theoretical underpinnings: critical realism, social constructionism. Methodological approaches: case studies; documentary searches (literature review); qualitative approaches (focus groups, participant observation, policy analysis, semi-structured interviews). Methodological issues: bias (cultural, interviewer); case selection; contextualisation; interpretation; reflexivity; representativeness; sampling; translation and back translation; validity.
Open Source Security Token for Linux

The project investigates and implements a two-factor authentication system utilizing the RSA cryptography scheme. The system consists of an FPGA security token and a PAM module for Linux. Two similar solutions were made, one air-gapped with a shorter key (version A), whereas the other communicated over USB (version B). The cryptography module in the FPGA supports no more than 512-bit RSA and is the greatest area of improvement since a longer key would provide more security and still be supported by the rest of the system. Additionally, interesting follow-up projects could be to explore quantum safe cryptography schemes especially if to be used for decades to come. Altogether, the prototype created is a basic, yet fully functional, two-factor system with no obvious security flaws if deployed correctly. The project is released as open source under the BSD license.