Miracle-dz/UniFAR-FTUs huggingface dataset:
Continues wavelet transform for extraction of topographic features from high-resolution digital elevation model

Current progress in computer, geographic information system (GIS) and remote sensing technology has led to the LIDAR (light detection and ranging) data, as a new generation of high-resolution digital elevation model (DEM). Although such a high resolution DEM can be a useful tool for terrain analysis, high amount of noise is also expected to be existent in a mega-data set. To deal with this problem in this study, continues wavelet transform (CWT), as a mathematical filter, was employed to extract slope, specific area and topographic index which are important parameters in hydro-environmental studies. In this way the proposed methodology was applied to 1-m resolution LIDAR DEM of the Elder Creek River watershed where is a subbasin of the South Folk Eel River basin at California. The results were also compared favorably with the results of a classic method. The result indicates that smoothing ability of the wavelet can be a promising DEM processing alternative to cope with the noise and pits of a high resolution DEM.
Evaluation of Self-Sufficient Zero Speed Sensors based on Large Barkhausen Jumps

An alternative system for motion oriented energy-harvesting based on Large Barkhausen Jumps (LBJ) is presented and adapted in detail. The application is used to power decentralized sensing-systems even in aperiodic and sporadic motion. On this issue, the output power is evaluated in terms of a suitable matching circuit and the magnetic behaviour. Therefore, proper model parameters are selected to get an electrical equivalent abstraction of the Vicalloy-sensing coil. An integrated occurrence detection based on non-volatile memory technologies was developed and reviewed with regard to its efficiency.
Deep Learning Based Mandarin Accent Identification for Accent Robust ASR

In this paper, we present an in-depth study on the classification of regional accents in Mandarin speech. Experiments are carried out on Mandarin speech data systematically collected from 15 different geographical regions in China for broad coverage. We explore bidirectional Long Short-Term Memory (bLSTM) networks and i-vectors to model longer-term acoustic context. Starting from the classification of the collected data into the 15 regional accents, we derive a three-class grouping via non-metric dimensional scaling (NMDS), for which 68.4% average recall can be obtained. Furthermore, we evaluate a state-of-the-art ASR system on the accented data and demonstrate that the character error rate (CER) strongly varies among these accent groups, even if i-vector speaker adaptation is used. Finally, we show that model selection based on the prediction of our bLSTM accent classifier can yield up to 7.6 % CER reduction for accented speech.
DroidScraper: A Tool for Android In-Memory Object Recovery and Reconstruction

There is a growing need for post-mortem analysis in forensics investigations involving mobile devices, particularly when application-specific behaviors must be analyzed. This is especially true for architectures such as Android, where traditional kernel-level memory analysis frameworks such as Volatility [9] face serious challenges recovering and providing context for user-space artifacts. In this research work, we developed an app-agnostic userland memory analysis technique that targets the new Android Runtime (ART). Leveraging its latest memory allocation algorithms, called region-based memory management, we develop a system called DroidScraper that recovers vital runtime data structures for applications by enumerating and reconstructing allocated objects from a process memory image. The result of our evaluation shows DroidScraper can recover and decode nearly 90% of all live objects in all allocated memory regions.
Heuristic Projects in Open Learning Environments: Time Phase in Individual and Collective Determinants

Given the inquiry-based nature of research projects, this study aims to investigate how graduate students in business administration majors are being involved in developing, achieving, and completing their master theses. Taking into account of two perspectives including the individual factors and collective environment supported by ICTs, the research purpose is to explore the perception of diverse innovation and to validate its critical determinants while using cloud technologies to collaborate as the research premise. In such an open learning environment (OLE), we adopt a sequential research design to 288 management students of two management colleges in central Taiwan. The research participants were at their three phases, i.e., concept, intermediate, and closure, for their thesis projects. Based on the PLS-SEM analysis, the extent of being in community of practice (CoP) was validated statistically as a partial mediator in the two paths: between self-efficacy (SE) and immerse in knowledge ecology (KE), and between self-determination (SD) and KE. Similarly, the perception of individual self-efficacy (SE) identified as another partial mediator between SD and CoP. The differential effect resultant of the time phase on determinants and consequences is also examined. Regardless of the generalized caveats that result from using a non-randomized, regional sample pool, the contribution of this study remains its practical and academic implications in management education and knowledge ecology enhanced in the OLE.
DICTIONARY-TRANSFORM LEARNING FOR IMAGE RECONSTRUCTION

Various dictionaries and transforms are known to sparsify images, and have been exploited in algorithms for image reconstruction from limited data. Methods that simultaneously reconstruct the image and learn dictionaries or sparsifying transforms for image patches, called blind compressed sensing methods, have shown promising performance. Motivated by such adaptive algorithms, this paper proposes an approach to train dictionary-transform based methods for image reconstruction by minimizing a minimum absolute reconstruction error criterion. Each “layer” of the algorithm consists of applying (convolving) trained transforms, thresholdings, and dictionaries to images, followed by a simple least squares update of the images. Numerical experiments illustrate the usefulness and speed-ups provided by such trained algorithms compared to related schemes.
Char+CV-CTC: Combining Graphemes and Consonant/Vowel Units for CTC-Based ASR Using Multitask Learning

Previous work has shown that end-to-end neural-based speech recognition systems can be improved by adding auxiliary tasks at intermediate layers. In this paper, we report multitask learning (MTL) experiments in the context of connectionist temporal classification (CTC) based speech recognition at character level. We compare several MTL architectures that jointly learn to predict characters (sometimes called graphemes) and consonant/vowel (CV) binary labels. The best approach, which we call Char+CV-CTC, adds up the character and CV logits to obtain the final character predictions. The idea is to put more weight on the vowel (consonant) characters when the vowel (consonant) symbol ‘V’ (‘C’) is predicted in the auxiliary-task branch of the network. Experiments were carried out on the Wall Street Journal (WSJ) corpus. Char+CV-CTC achieved the best ASR results with a 2.2% Character Error Rate and a 6.1% Word Error Rate (WER) on the Eval92 evaluation subset. This model outperformed its monotask model counterpart by 0.7% absolute in WER and also achieved almost the same performance of 6.0% as a strong baseline phone-based Time Delay Neural Network (“TDNN-Phone+TR2”) model.
Comparative Analysis of Think-Aloud Methods for Everyday Activities in the Context of Cognitive Robotics

We describe our efforts to compare data collection meth-ods using two think-aloud protocols in preparation to be used as a basis for automatic structuring and labeling of a large database of high-dimensional human activities data into a valuable resource for research in cognitive robotics. The envisioned dataset, currently in development, will contain synchronously recorded multimodal data, including audio, video, and biosig-nals (eye-tracking, motion-tracking, muscle and brain activity) from about 100 participants performing everyday activities while describing their task through use of think-aloud protocols. This paper provides details of our pilot recordings in the well-established and scalable “table setting scenario,” describes the concurrent and retrospective think-aloud protocols used, the methods used to analyze them, and compares their potential impact on the data collected as well as the automatic data segmentation and structuring process.
A two-stage authentication scheme for achieving low-latency handoff in wireless network

With the rapid growth of wireless networks, many related topics have been proposed and discussed. Currently, the combination of Fast Handover and Hierarchical Mobile IP can reduce the problems of mobile devices roaming between subnets such as packet loss rate and handover time. During the handover period, a mobile device needs to perform fast handover signaling as well as authentication mechanisms. The total handover latency and packet loss rate of mobile device will increase according to the complexity of the authentication mechanism. This negative impact causes mobile devices unable to transmit its real-time data packets quickly, hence the quality of service will be affected. Several proposed authentication methods such as AAA (Accounting, Authentication, Authorization) and IEEE 802.1x and etc have been proposed. The complexity of each method has different features and influence. We propose a two-stage authentication scheme in this thesis for achieving low-latency handoff. It allows mobile devices to send certain authentication information by fast handover protocol to obtain a temporary pass certificate from new access node before roaming to the new domain. Mobile device can use the temporary pass certificate to receive real-time data packet quickly and then perform re authentication process to complete the total authentication as normal procedure. We evaluate the performance of the proposed method by using ns-2 simulator and it is shown that transient authentication scheme really improve the quality of service during handover process.
Computational Methods for Communication Science| Data Is the New Oil—But How Do We Drill It? Pathways to Access and Acquire Large Data Sets in Communication Science

In the Internet era, many forms of human communication leave massive digital traces that could serve as a revolutionary source of empirical information. However, researchers are often faced with extensive challenges when they try to access these “big communication data. ” These restrictions prevent scientific exploration and the application of new computational methods and, thus, ultimately constrain the development of the field of computational communication science (CCS). As a first step to improve scientific access to large communication data, this article systematizes the data acquisition options and outlines their specific advantages and barriers based on a qualitative interview study with 22 researchers in the field of CCS. Three overarching themes are identified that seem to determine the resulting data quality of all data collection methods. On the basis of these findings, we develop an agenda on how communication science could overcome the challenges of accessing large data sets in the future.
DOMAIN ADAPTATION AND INVESTIGATION OF ROBUSTNESS OF DNN-BASED EMBEDDINGS FOR TEXT-INDEPENDENT SPEAKER VERIFICATION USING DILATED RESIDUAL NETWORKS

Robustness of extracted embeddings in cross-database scenarios is one of the main challenges in text-independent speaker verification (SV) systems. In this paper, we investigate this robustness via performing structural cross-database experiments with or without additive noise. This noise can be added from the seen set, where the noise type is similar to the noise which is used in data augmentation for training the SV model, or unseen set, where distribution of additive noise in train and evaluation sets are different. For extracting the robust embeddings, we investigate applying the time dilation in the ResNet architecture, so-called dilated residual network (DRN). Dimension and number of segment level layers are tuned in this architecture. The proposed model with time dilation significantly outperformed the ResNet model and is comparable with the state-of-the-art SV systems on Voxceleb1 dataset. In addition, this architecture showed significant robustness in out of domain scenarios. Language mismatch is part of domain mismatch which recently is one of the main focuses of research in SV systems. Similar to image recognition field, we hypothesize that low-level convolutional neural network (CNN) layers are domain-specific features while high-level CNN layers are domain-independent and have more discriminative power. For adapting these domain-specific units, combination of triplet and intra-class losses are investigated. The adapted model on the evaluation part of the CMN2 dataset, relatively outperformed the DRN and x-vector SV systems without adaptation with 8.0 and 20.5 %, respectively in equal error-rate.
Capital Structure , Solvency Regulation , and Federal Income Taxes for Property-Casualty Insurance Companies

The tax shields from debt financing reduce the cost of operations for firms with low cost of bankruptcy. State regulation prevents insurers from using long-term debt as statutory surplus, to ensure sufficient equity capital to meet policyholder obligations. Constraints on regulatory capital force policyholders to fund high tax costs on insurers and reduce the market forces that support solvency. Banks’ risk-based capital (RBC) standards show how long-term subordinated debt can be used as secondary capital. Revisions in state regulation of capital structure may decrease premiums and give incentives to bondholders to monitor reserve adequacy. Moving to the banking RBC model benefits all parties: policyholders pay lower premiums, insurers have access to wider capital markets, and regulators gain market allies to ensure solvency.
Current Status and Unusual Mechanism of Multi-resistance in Mycobacterium tuberculosis

TB is caused by Mycobacterium tuberculosis and have many related species like M. africanum, M. bovis,, M. chimaera, M. celatum, M. smegmatis, M. branderi, M. cansasii,M. abscessus, M. fortunum, M. orygis, M. mungi, M. kansasii, M. gordonae, M. xenopi, M. marinum and M. pinnipedii. It is a serious disease where lungs’ severely deformation occurs and such patients have short life. M. ovium, M. leprae, M. colombiense, M. indicus pranii and M. interacellulare do not affect lung. Robert Koch’s discovered the tubercle bacillus in 1882 and was a major event in the history of medicine to conquest the deadly disease which had plagued mankind for centuries of speculation as to the possible infectious nature of tuberculosis [1]. In the past (1850s) one person out of seven were died due to tuberculosis before the discovery of tuberculin test by Koch and TB vaccination protocol. The TB bacilli grow very slow but M. abscesseus, M. fortuitum, M. canariasense, M. septicum, M. chelonae, M. boenickel species grow fast [2]. India is a big country and prevalence of TB is also greater than any country. Theobald Smith described Mycobacterium bovis to cause animal TB first and a wide range of animal hosts have been identified and characterized as M. microti, M. pinnipedii, M. mungi, M. caprae, M. orygis and M. suricattae [3,4]. A very long term treatment (6 months) is required for complete eradication of Mycobacterium tuberculosis from body because the bacilli can invade the cells of tissue, muscle, lung, bone so deeply that drug effective concentration hardly reach to kill the bacteria easily (Figure 1). *Corresponding author: Asit Kumar Chakraborty, Associate Professor of Biochemistry, Department of Biotechnology and Biochemistry, Genetic Engineering Laboratory, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore-721102, India, Email: chakraakc@gmail.com; Tel: +917679154141.
Estimating transformation function

In this paper, we propose an estimator for g(x) under the model Yi = g(Zi), i = 1, 2, . , n where Zi, i = 1, 2, . are random variables with known distribution but unknown observed values, Yi, i = 1, 2, . are observed data and g(x) is an unknown strictly monotonically increasing function (we call g(x) transformation function). We prove the almost sure convergence of the estimator and construct confidence intervals and bands when Zi, i = 1, 2, . are i.i. d data based on their asymptotic distribution. Corresponding case when Zi being linear process is handled by resampling method. We also design the hypothesis test regarding whether g(x) equals an expected transformation function or not. The finite sample performance is evaluated by applying the method to simulated data and an urban waste water treatment plant’s dataset. MSC 2010 subject classifications: 62G05, 62G09, 62G10.
A first principles calculation of Ni-16 Cr and Ni-16 Mo alloys

The present paper describes the structural stability and mechanical properties of the phase for Ni-16Cr and Ni-16Mo alloys using first principles ensity functional theory (DFT) within generalized gradient approximation (GGA). The equilibrium lattice constant values of these alloys are in ood agreement with the experimental data obtained by X-ray diffraction technique. The formation energy per atom values suggests that the hase of both alloys is stable. The Ni-16Mo alloy is energetically more stable than Ni-16Cr. Both alloys satisfy the Born stability criteria in terms f elastic constants and are associated with ductile behaviour based on shear to bulk modulus ratios. Both alloys show an anisotropic behaviour. he Ni-16Mo alloy exhibits higher anisotropy than the Ni-16Cr one. 2016 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico. This is an open access article under he CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Winning Isn ’ t Everything : Training Agents to Playtest Modern Games

Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. We propose an approach that instead addresses how the player experiences the game, which we consider to be a more challenging problem. In this paper, we present an alternative context for developing AI that plays games. Specifically, we study the problem of creating intelligent game agents in service of the development processes of the game developers that design, build, and operate modern games. We highlight some of the ways in which we think intelligent agents can assist game developers to understand their games, and even to build them. Our main contribution is to propose a learning and planning framework that is uniquely tuned to the environment and needs of modern game engines, developers and players. Our game agent framework takes a few steps towards addressing the unique challenges that game developers face. We discuss some early results from an initial implementation of our framework. Artificial intelligence; artificial game agent; reinforcement learning; imitation learning; deep learning; model-based learning; game design; game playtesting; non-player character (NPC).
DURATIONAL VARIATION IN POLISH FRICATIVES PROVIDES EVIDENCE FOR HYBRID MODELS OF PHONOLOGY

The neighborhood density of a word is the number of words that sound similar to it. Phonotactic probability is a measure of how typical (for a given language) the phoneme sequences in a word are. These two factors are known to affect speech perception in opposing directions: high neighborhood density slows down processing while high phonotactic probability speeds it up [30]. This finding supports hybrid models of phonological representation [24], as neighborhood density effects operate on lexical, and phonotactic probability effects on sublexical representations. The present paper, investigating word-initial double clusters retrieved from the Greater Poland Spoken Corpus [14], tests the predictions for durational variation in fricatives following from Vitevitch and Luce [30]. It has been found that high neighborhood density is associated with longer while high phonotactic probability with shorter fricative durations. Thus, further support for hybrid models of phonological storage is provided.
Pedestrian Virtual Space Based Abnormal Behavior Detection

It is a crucial issue to efficiently detect anomaly from surveillance videos. Abnormal behavior detection is developed in an unsupervised way based on spatio-temporal motion analysis in pedestrian virtual space. The pedestrian virtual plane is constructed which consists of both the ground plane and the pedestrian head one. The abnormal behavior is discriminated by a circular variance of pedestrian trajectories around the 3D virtual region instead of traditional 2D protected one. The protected region can be assigned as different shapes and sizes. Experiments show that the proposed method is efficient for distinguishing the anomaly in a protected region without any hypothesis for the scenario contents in advance. Comparisons with state-of-the-arts highlight the superior performance of the proposed method.
Creating Competence from Chaos: A Comprehensive Guide to Home-Based Services

In Pennsylvania, a commitment has been made, on a statewide basis, to serve these children and strengthen their vulnerable families through a home-based approach grounded in ecosystemic thinking and practice. This book tells the story of Pennsylvania's evolving treatment program, providing a model for other professionals who believe that a family's needs are best met through individually tailored, family-centered, community-based, culturally competent, and outcome-oriented services. This is a complete, comprehensive guide, covering everything from planning and development of home-based services through supervision and training of home-based practitioners and evaluation of treatment outcomes. Particular attention is given to the clinical challenges faced by home-based therapists working with families where children are depressed and perhaps suicidal, oppositional and defiant, out-of-control and aggressive, or hyperactive/impulsive. These families commonly have multiple problems, complex histories, and a negative view of outside "helpers. " Delivered in the family's home and involving parents as partners, the services described here work to improve child and family functioning through family therapy, creation of collaborative links between appropriate community and family resources, and provision of family support funds for concrete services such as transportation, respite care, and emergencies. Home-based treatment serves both children at risk for out-of-home placement due to a diagnosis of severe mental illness or behavioral disorders and children being discharged from inpatient hospitals and psychiatric residential placements. The authors, active at every level of program conceptualization and implementation, share their wealth of experience with readers. Their advice and case studies move from the big picture to the small details of where to sit in a family's home, what to say, and how to think about a problematic situation. Several appendices of forms used for assessment, evaluation, and training add to the book's practical value. Theoretically sound and fully practical, this guide to home-based services will encourage all professionals serving children to involve their families and communities-and to meet them where they live.