Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Security of Automated Teller Machines Using Biometric Authentication: A Case of a Sub-Saharan University

A wide variety of systems need reliable personal recognition systems to either authorize or determine the identity of an individual demanding their services. The goal of such systems is to warrant that the rendered services are accessed only by a genuine user and no one else.In the absence of robust personal recognition schemes, these systems are vulnerable to the deceits of an impostor. The ATM has suffered a lot over the years against PIN theft and other associated ATM frauds. In this research is proposed a fingerprint and PIN based authentication arrangement to enhance the security and safety of the ATM and its users. The proposed system demonstrates a three-tier design structure. The first tier is the verification module, which concentrates on the enrollment phase, enhancement phase, feature extraction and matching of the fingerprints. The second tier is the database end which acts as a storehouse for storing the fingerprints of all ATM users preregistered as templates. The last tier presents a system platform to relate banking transactions such as balance enquiries, mini statement and withdrawal. The system is developed to run on Microsoft windows Xp or higher and all systems with . NET framework employing C# programming language, Microsoft Visio studio 2010 and SQL server 2008. The simulated results showed 96% accuracy, the simulation overlooked the absence of a cash tray. The findings of this research will be meaningful to Banks and other financial institutions. Keywords : SQL Server, ATM, Fraud, . NET framework, financial institutions DOI : 10.7176/IKM/9-7-02 Publication date : August 31 st 2019
Speaker Adaptation of Acoustic Model using a Few Utterances in DNN-based Speech Synthesis Systems

Synthesizing a person’s voice from only a few utterances is a highly desirable feature for personalized text-to-speech systems. This can be achieved by adapting an existing speaker-independent model to a target speaker such that the speaker vari-abilities due to a mismatch between training and testing conditions are minimized. In deep neural network (DNN) based speech synthesis, directly ﬁne-tuning a large number of parameters is susceptible to over-ﬁtting problem, especially when the adaptation set is small. In this paper, we present a novel technique to estimate a speaker-speciﬁc model using a partial copy of the speaker-independent model by creating a separate parallel branch stemmed from the intermediate hidden layer of the base network. This allows the ﬁne-tuning of a speaker-speciﬁc model to take into account the difference between the target speaker and a speaker-independent model output. Experimental results show that the proposed adaptation method achieves improved audio quality and higher speaker similarity compared to another DNN speaker adaptation technique.
UNIFIED APPROACH TO DEPENDENT AND DISPARATE CLUSTERING OF NONHOMOGENOUS DATA

There are many data mining settings that involve a combination of attribute–valued descriptors over entities as well as specified relationships between these entities. We present an approach to cluster such nonhomogeneous datasets by using the relationships to impose either dependent clustering or disparate clustering constraints. Unlike prior work that views constraints as Boolean criteria, we present a formulation that allows constraints to be satisfied or violated in a smooth manner. This enables us to achieve dependent clustering and disparate clustering using the same optimization framework by merely maximizing versus minimizing the objective function. We present results on both synthetic data as well as several real-world datasets. AMS Subject Classification: 62H30, 65H20
Electric Vehicle Routing with Public Charging Stations

We introduce the electric vehicle routing problem with public-private recharging strategy in which vehicles may recharge en route at public charging infrastructure as well as at a privately-owned depot. To hedge against uncertain demand at public charging stations, we design routing policies that anticipate station queue dynamics. We leverage a decomposition to identify good routing policies, including the optimal static policy and fixed-route-based rollout policies that dynamically respond to observed queues. The decomposition also enables us to establish dual bounds, providing a measure of goodness for our routing policies. In computational experiments using real instances from industry, we show the value of our policies to be within 10% of a dual bound. Furthermore, we demonstrate that our policies significantly outperform the industry-standard routing strategy in which vehicle recharging generally occurs at a central depot. Our methods stand to reduce the operating costs associated with electric vehicles, facilitating the transition from internal-combustion engine vehicles.
Target coverage computation protocols in wireless sensor networks: a comprehensive review

Wireless sensor network comprises tiny sensor nodes having the ability of sensing to surveil an area of interest. The amount of area sensed by a sensor node is known as coverage. Coverage is a multi-dimensional quality of service (QoS) metric in terms of diversity in its computation algorithms according to the real-time applications. Different applications require a distinctive level of coverage. But every coverage computation algorithm has one common objective, i.e. to maximize the network’s lifespan and coverage quality with minimum number of sensor nodes. Several algorithms have derived optimal or close-to-optimal solutions based on similar or distinct parameters. This paper presents a concise review of various target coverage protocols for both static and mobile targets. Total target coverage provided by the deployed sensor nodes is a measure of sensing quality. Target coverage is bifurcated as static coverage and dynamic coverage. This paper highlights and describes various application specific target coverage protocols and their QoS metrics and parameters. Each protocol has solved multi-dimensional coverage issues and challenges and proposed optimum solutions. The comprehensive review of the target coverage protocol helps in understanding about various research challenges and its existed solutions in target coverage, detection and tracking in a better way.
Multi-Country Tasks Measures: Beyond US-based Data and a Focus on Migration

The US-based O*NET database is commonly used for multi-country studies on labor markets and migration by assuming invariant occupation technology, i.e. the quantitative assignment of tasks to occupations. We claim that the OECD dataset PIAAC (Programme for the International Assessment of Adult Competencies) could provide a valid alternative to obtain country-specific task measures. The US presence in both datasets allows us to compare the consistency of the two data sources along two dimensions. First, we compute the correlation coefficients between aggregate task indexes and they are very high (rarely less than 0.7). Secondly, we use the PIAAC database to replicate the empirical model in Peri and Sparber (2009) on US natives’ task upgrading after a migration shock, and the results are strikingly similar to the original O*NET-based estimates. The multi-country variability of PIAAC-based task indexes for European countries are non-negligible; hence, we recommend these PIAAC-based measures for future multi-country analysis.
Speech Enhancement Based on Teacher–Student Deep Learning Using Improved Speech Presence Probability for Noise-Robust Speech Recognition

In this paper, we propose a novel teacher-student learning framework for the preprocessing of a speech recognizer, leveraging the online noise tracking capabilities of improved minima controlled recursive averaging (IMCRA) and deep learning of nonlinear interactions between speech and noise. First, a teacher model with deep architectures is built to learn the target of ideal ratio masks (IRMs) using simulated training pairs of clean and noisy speech data. Next, a student model is trained to learn an improved speech presence probability by incorporating the estimated IRMs from the teacher model into the IMCRA approach. The student model can be compactly designed in a causal processing mode having no latency with the guidance of a complex and noncausal teacher model. Moreover, the clean speech requirement, which is difficult to meet in real-world adverse environments, can be relaxed for training the student model, implying that noisy speech data can be directly used to adapt the regression-based enhancement model to further improve speech recognition accuracies for noisy speech collected in such conditions. Experiments on the CHiME-4 challenge task show that our best student model with bidirectional gated recurrent units (BGRUs) can achieve a relative word error rate (WER) reduction of 18.85% for the real test set when compared to unprocessed system without acoustic model retraining. However, the traditional teacher model degrades the performance of the unprocessed system in this case. In addition, the student model with a deep neural network (DNN) in causal mode having no latency yields a relative WER reduction of 7.94% over the unprocessed system with 670 times less computing cycles when compared to the BGRU-equipped student model. Finally, the conventional speech enhancement and IRM-based deep learning method destroyed the ASR performance when the recognition system became more powerful. While our proposed approach could still improve the ASR performance even in the more powerful recognition system.
Integrating Participatory Methods and Remote Sensing to Enhance Understanding of Ecosystem Service Dynamics Across Scales

The value of Traditional Ecological Knowledge (TEK) for informing resource management has long been recognized; however, its incorporation into ecosystem services (ES) assessments remains uncommon. Often “top-down” approaches are utilized, depending on “expert knowledge”, that are not relevant to local resource users. Here we propose an approach for combining participatory methods with remote sensing to provide a more holistic understanding of ES change. Participatory mapping in focus group discussions identified TEK regarding what ES were present, where, and their value to communities. TEK was then integrated with satellite imagery to extrapolate to the landscape-scale. We demonstrate our method for Nyangatom communities in the Lower Omo Valley, Ethiopia, showing for the first time the ES impacts of regional environmental change, including the Gibe III dam, for communities in the Omo River basin. Results confirmed the collapse of flood-retreat cultivation associated with the loss of the annual Omo flood. Communities reported declines in many other provisioning ES, and these results were supported by satellite mapping, which showed substantial reductions in land covers with high ES value (shrubland and wetland), leading to consequent ES declines. Our mixed-methods approach has potential to be applied in other regions to generate locally relevant information for evaluating ES dynamics and improving management of natural resources.
Studying Program Effectiveness in Preparing Teachers for English Learners

ABSTRACT To investigate how competent preservice candidates were prepared to teach English learners (ELs), a case study was conducted to examine 30 candidates use of effective practices for ELs during preservice preparation and the first years of teaching. Data included: (1) preservice assignments related to planning and teaching; (2) student teaching supervisors’ records; (3) observations during the first two years of teaching; and (4) English language proficiency results from the first year of teaching in grades 3 and above. Results indicate that some practices which participants were exposed to in their preservice preparation were found with greater frequency than others when comparing preservice artifacts and observations with the first years of teaching. Contextual factors including certification choice, grade level, and content area correlated with the use of some practices. Findings are discussed in relation to existing research on preparing candidates to meet the academic and linguistic needs of ELs.
Examination of Adolescent Physical Activity and Overweight Levels

EXAMINATION OF ADOLESCENT PHYSICAL ACTIVITY AND OVERWEIGHT LEVELS Maika E. Nelson Department of Physical Education Master of Science Research suggests declines in physical activity may occur after age 12. The purpose of this study was to determine the relationships between adolescent physical activity, BMI, and nutrition. Adolescents, ages 12-17 (N = 223), wore pedometers for 11 consecutive days and completed a nutrition survey, the Youth Adolescent Questionnaire. Activity levels did not differ among the ages of 12-17 year old adolescents, and boys were more active than girls. There was a low, but significant relationship between BMI and physical activity levels for both boys and girls. Energy intake was not related to BMI or physical activity levels in boys or girls.
The INTERSPEECH 2019 Computational Paralinguistics Challenge: Styrian Dialects, Continuous Sleepiness, Baby Sounds & Orca Activity

The INTERSPEECH 2019 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Styrian Dialects Sub-Challenge, three types of Austrian-German dialects have to be classified; in the Continuous Sleepiness SubChallenge, the sleepiness of a speaker has to be assessed as regression problem; in the Baby Sound Sub-Challenge, five types of infant sounds have to be classified; and in the Orca Activity Sub-Challenge, orca sounds have to be detected. We describe the Sub-Challenges and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by the ‘usual’ ComParE and BoAW features, and deep unsupervised representation learning using the AUDEEP toolkit.
Learning Association Rules and Tracking the Changing Concepts on Webpages: An Effective Pornographic Websites Filtering Approach

We applied the decision tree algorithm to learn association rules between webpage’s category (pornographic or normal) and the critical features. Based on these rules, we proposed an efficient method of filtering pornographic webpages with the following major advantages: 1) a weighted window-based technique was proposed to estimate for the condition of concept drift for the keywords found recently in pornographic webpages; 2) checking only contexts of webpages without scanning pictures; 3) an incremental learning mechanism was designed to incrementally update the pornographic keyword database.
Modeling CDC in a fuzzy description logic A comparison of different approaches

Relative information about locations of places can be described using the cardinal direction calculus (CDC). Some extensions of the calculus allow to express vague information and others include nearness/closeness. The development of description logics for qualitative spatial calculi has also been done. In this paper, we propose several knowledge bases for fuzzy description logics to manage information on relative positions of points of interest (POI) with respect to cardinal directions and distance. The theory of vague description logics is used and applied on the cardinal direction calculus. The paper compares the diﬀerent approaches and aims to objectify the comparison by generating ar-tiﬁcial datasets that form the basis for the evaluation. The evaluation is performed in diﬀerent scenarios with diﬀerent parameters and the results are discussed. The advantages and disadvantages of each model are related to potential application scenarios.
Characterization of neuromuscular properties, functional mobility and fatigue in persons with Type 2 diabetes mellitus

Persons with Type 2 diabetes (T2D) report fatigue, compromised strength and physical performance, yet studies utilizing objective measures are sparse. This study assessed isometric strength, contractile properties and performance in the tibialis anterior in 6 women (50 ±5 years) and 8 men (54 ±6 years) with T2D, and 6 gender-, ageand activitymatched women (50 ±7 years) and 4 men (55 ±4 years). Muscle fatigue was examined using a 75 % intermittent protocol. HbA1c and blood glucose were higher in T2D, but strength, contractile properties and physical performance did not differ. Fatigue and recovery were parallel between groups. When non-neuropathic individuals with T2D are age, gender and activity matched to controls, they are not weaker nor fatigue faster. This presents good news for persons who control glucose fairly well and suggests that neuropathy, age, gender and physical activity should be considered when assessing persons with strength and fatigue in T2D.
Deep Learning for Natural Language Parsing

Natural language processing problems (such as speech recognition, text-based data mining, and text or speech generation) are becoming increasingly important. Before effectively approaching many of these problems, it is necessary to process the syntactic structures of the sentences. Syntactic parsing is the task of constructing a syntactic parse tree over a sentence which describes the structure of the sentence. Parse trees are used as part of many language processing applications. In this paper, we present a multi-lingual dependency parser. Using advanced deep learning techniques, our parser architecture tackles common issues with parsing such as long-distance head attachment, while using ‘architecture engineering’ to adapt to each target language in order to reduce the feature engineering often required for parsing tasks. We implement a parser based on this architecture to utilize transfer learning techniques to address important issues related with limited-resourced language. We exceed the accuracy of state-of-the-art parsers on languages with limited training resources by a considerable margin. We present promising results for solving core problems in natural language parsing, while also performing at state-of-the-art accuracy on general parsing tasks.
Unsupervised Phonetic and Word Level Discovery for Speech to Speech Translation for Unwritten Languages

We experiment with unsupervised methods for deriving and clustering symbolic representations of speech, working towards speech-to-speech translation for languages without regular (or any) written representations. We consider five low-resource African languages, and we produce three different segmental representations of text data for comparisons against four different segmental representations derived solely from acoustic data for each language. The text and speech data for each language comes from the CMU Wilderness dataset introduced in [1], where speakers read a version of the New Testament in their language. Our goal is to evaluate the translation performance not only of acoustically derived units but also of discovered sequences or “words” made from these units, with the intuition that such representations will encode more meaning than phones alone. We train statistical machine translation models for each representation and evaluate their outputs on the basis of BLEU-1 scores to determine their efficacy. Our experiments produce encouraging results: as we cluster our atomic phonetic representations into more word-like units, the amount information retained generally approaches that of the actual words themselves.
All Together Now: The Living Audio Dataset

The ongoing focus in speech technology research on machine learning based approaches leaves the community hungry for data. However, datasets tend to be recorded once and then released, sometimes behind registration requirements or paywalls. In this paper we describe our Living Audio Dataset. The aim is to provide audio data that is in the public domain, multilingual, and expandable by communities. We discuss the role of linguistic resources, given the success of systems such as Tacotron which use direct text-to-speech mappings, and consider how data provenance could be built into such resources. So far the data has been collected for TTS purposes, however, it is also suitable for ASR. At the time of publication audio resources already exist for Dutch, R.P. English, Irish, and Russian
A Spectral-Spatial Domain-Specific Convolutional Deep Extreme Learning Machine for Supervised Hyperspectral Image Classification

Spectral-spatial feature extraction is of great importance to hyperspectral image (HSI) classification. Different from the traditional feature extraction methods, deep learning models such as convolutional neural network (CNN) can learn the spectral-spatial discriminative feature automatically. However, deep learning models usually need to construct a large and complicated network and the training is time-consuming. To deal with these issues, in this paper, a spectral-spatial domain-specific convolutional deep extreme learning machine (ELM), named S2CDELM, is proposed for HSI classification. At first, by using the conception of local receptive filed (LRF), a spectral-spatial convolutional learning module with two branches is constructed for spectral and spatial feature extraction respectively. Specifically, the convolutional learning module is constructed by using random convolutional nodes but without back propagation, in which a spectral branch and a spatial branch are designed respectively. Then the extracted features are concatenated and fed to a fully connected stacked ELM network to further exploit spectral-spatial information for classification. As the convolutional filters and input weights of ELM are randomly generated, the whole framework is compact, simple and fast to construct. Experimental results on popular HSI benchmark data sets demonstrate that S2CDELM can provide satisfactory classification performance and a fast learning speed in comparison with several state-of-the-art classifiers.
Measuring the impact of sentiment for hate speech detection on Twitter

—While social media platforms, such as Twitter offer users the opportunity to express their opinions and insights freely, there is a signiﬁcant risk of users silencing each other based on prejudice by means of hateful Tweets. Since Twitter’s public nature makes these messages more widely disseminated, it is important to aid in the detection of such messages, which may cause harm to targeted (groups of) users. Following current state of the art, we assume the usefulness of sentiment features for the detection of hate speech messages, which tend to exhibit a higher degree of negative polarity. Therefore, we investigate the impact of these sentiment features as well as Twitter-speciﬁc and hate speech features on the performance of a supervised classiﬁcation method with Support Vector Machines (SVMs). The Twitter-speciﬁc features offer the best performance increase over our strong token n-gram baseline.