Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive control of two-wheeled mobile balance robot capable to adapt different surfaces using a novel artificial neural network–based real-time switching dynamic controller

In this article, a novel real-time artificial neural network–based adaptable switching dynamic controller is developed and practically implemented. It will be used for real-time control of two-wheeled balance robot which can balance itself upright position on different surfaces. In order to examine the efficiency of the proposed controller, a two-wheeled mobile balance robot is designed and a test platform for experimental setup is made for balance problem on different surfaces. In a developed adaptive controller algorithm which is capable to adapt different surfaces, mean absolute target angle deviation error, mean absolute target displacement deviation error and mean absolute controller output data are employed for surface estimation by using artificial neural network. In a designed two-wheeled mobile balance robot system, robot tilt angle is estimated via Kalman filter from accelerometer and gyroscope sensor signals. Furthermore, a visual robot control interface is developed in C++ software development environment so that robot controller parameters can be changed as desired. In addition, robot balance angle, linear displacement and controller output can be observed online on personal computer. According to the real-time experimental results, the proposed novel type controller gives more effective results than the classic ones.
Does Resource Ownership Matter? Oil and Gas Royalties and the Income Effect of Extraction

We study how subsurface ownership shapes the income effects of oil and gas extraction. For the average US county with growth in extraction from 2000 to 2014, we find that royalty income and its multiplier effect accounted for 70% of the total income gain, with each royalty dollar generating an additional 49 cents of local income. A county where residents own the subsurface captured 28 cents more of each dollar in production than one with absentee ownership. Nationally, oil and gas production increased US personal income in 2014 by $67 billion (0.5%) more than if all royalties accrued abroad. Areas with the same resource abundance can therefore experience contrasting economic outcomes because of differences in ownership.
OER use in intermediate language instruction: a case study

This paper reports on a case study in the experimental use of Open Educational Resources (OERs) in intermediate level language instruction. The resources come from three sources: the instructor, the students, and open content repositories. The objective of this action research project was to provide student-centered learning materials, enhance student motivation, and encourage learner autonomy. The content modules, designed to complement and supplement materials from the commercial textbook used, represent a variety of disciplines and genres. Grammar tutorials cover the structures typically introduced in intermediate language study. Also included are modules on language self-study, which feature annotated guides to online language resources. To accommodate further student personal or professional interest, students were asked to find and curate additional reading or multimedia content.
CONTEMPORARY APPROACHES FOR CONSUMER SEGMENTATION ACCORDING TO THE LEVEL OF ECOLOGICAL RESPONSIBILITY: THE CASE OF REPUBLIC OF MACEDONIA

The management and protection of the environment is a problem and companion to the development of human civilization, but in the recent decades is becoming more popular. The European Union supports the development of ecologically adapted and competitive economy, which aims to achieve the objectives of the concept of sustainable development. The ecological awareness today is a planetary phenomenon because it comes as an expression of knowledge about the danger for humanity from pollution of soil, water and air due to the carelessness of the consequences of certain activities of the environment. Consumers are increasingly more aware of the consequences of global pollution and the constant disorder of the environment. People around the world show concern for the environment and change their behavior. The ecological awareness and environmentally responsible behavior of the consumers is a necessity and obligation of the modern world and is part of the social responsibility. The green consumers as one of the most important features of the modern ecological marketing are environmentally responsible consumers who require producers to express their ecological responsibility.
Towards interpreting informal place descriptions

Informal place descriptions are human-generated descriptions of locations, expressed by the means of natural language in an arbitrary fashion. The aim we pursued in this thesis is finding methods for better automatic interpretation of situated informal place descriptions. This work presents a framework within which we attempt to automatically classify informal place descriptions for the accuracy of the location information they contain. Having an available corpus of informal place descriptions, we identified placenames contained therein and manually annotated them for properties such as geospatial granularity and identifiability. First, we make use of the annotations and a machine learning method to conduct the classification task, and then report the accuracy scores reaching 84%. Next, we classify the descriptions again, but instead of using the manual annotations we identify the properties of placenames automatically.
COMPARATIVE ANALYSIS OF NAIVE BAYS CLASSIFIER AND DECISION TREE C4.5 ON CREDIT PAYMENT DATA SET

A whole world is revolving around data. Current techno-world is generating huge amount of data. For research and analysis purpose data mining is essential. Data is collected from various resources, it needs preprocessing. Z-Normalization technique is one of the transformation technique which is used in preprocessing. Accuracy of Naïve Bays and Decision tree algorithm is increased after applying Z-score normalization on UCI - default of credit card clients data set
Importance of Shellfish in the Diet of Two Ray Species (Rhinoptera steindachneri and Hypanus dipterurus) in the Upper Gulf of California

ABSTRACT The upper Gulf of California, Mexico is a marine protected area (MPA), considered important for the conservation and protection of species. The present study assessed the resource–consumer interactions of two ray species (Rhinoptera steindachneri and Hypanus dipterurus), to know the importance of shellfish in the food-web dynamics of two predators present in this MPA. A total of 33 prey species for R. steindachneri and 48 prey species for H. dipterurus were recorded. The bivalve Tellina subtrigona (%IRI = 99.5) was the most important species in the diet of R. steindachneri; whereas the gastropod Mitrella dorma (%IRI = 32.7) was the primary prey of H. dipterurus. The ray R. steindachneri had a lower Shannon's index of diversity (H' = 0.49) than H. dipterurus (H' = 1.41). Based on Amundsen's graphical method, R. steindachneri have a specialist feeding strategy; whereas H. dipterurus presented a mixed strategy that includes both specialist and generalist organisms. However, the trophic specialization evidenced by these rays may reflect the grouping behavior of their prey, which tend to form large aggregations. Both ray species are secondary consumers (TLR. steindachneri = 3.10 and TL H. dipterurus = 3.12) of shellfish. Nevertheless, there is a low similarity in the prey species composition between them (ANOSIM: R = 0.47), indicating that these two ray species have different diets, and therefore, each ray species has specific resource–consumer interactions. This information allows to understand the importance of shellfish in the food-web dynamics in the ecosystem and MPA effectiveness in the upper Gulf of California for species at risk, like ray species.
COMPARING GRU AND LSTM FOR AUTOMATIC SPEECH RECOGNITION

This paper proposes to compare Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) for speech recognition acoustic models. While these recurrent models were mainly proposed for simple read speech tasks, we experiment on a large vocabulary continuous speech recognition task: transcription of TED talks. In addition to be simpler compared to LSTM, GRU networks outperform LSTM for all network depths experimented. We also propose a new model termed as DNN-BGRU-DNN. This model uses Deep Neural Network (DNN) followed by a Bidirectional GRU and another DNN. First DNN acts as a feature processor, BGRU is used to store temporal contextual information and final DNN introduces additional non-linearity. Our best model achieved 13.35% WER on TEDLIUM dataset which is a 16.66% & 17.84% relative improvement on baseline HMM-DNN and HMM-SGMM models respectively.
Inertial measurement unit–aided dual-frequency radio frequency identification localization in line-of-sight and non-line-of-sight hybrid environment

The mitigation of non-line-of-sight propagation conditions is one of main challenges in wireless signal–based indoor localization. When radio frequency identification localization technology is applied in applications, the received signal strength fluctuates frequently due to the shade and multipath effect of radio frequency signal, which could result in localization inaccuracy. In particular, when tag carriers are walking in line-of-sight and non-line-of-sight hybrid environment, great attenuation of received signal strength will happen, which would result in great positioning deviation. The article puts forward a dual-frequency radio frequency identification–based indoor localization approach in line-of-sight–non-line-of-sight hybrid environment with the help of inertial measurement unit. Dual-frequency radio frequency identification includes passive radio frequency identification and active radio frequency identification. Passive radio frequency identification is used to assist in determining the tag initial location with passive reader. Active radio frequency identification is used to locate the tag and send the sensor information to active radio frequency identification readers. The proposed method includes three improvements over previous received signal strength–based positioning methods: inertial measurement unit–aided received signal strength filtering, inertial measurement unit–aided line-of-sight/non-line-of-sight distinguishing, and inertial measurement unit–aided line-of-sight/non-line-of-sight environment switching. Also, Cramér–Rao low bound is calculated to prove theoretically that indoor positioning accuracy for the proposed method in line-of-sight and non-line-of-sight mixed environment is higher than position precision using only received signal strength information. Experiments are conducted to show that the proposed method can reduce the mean positioning error to around 3 m without site survey.
Beginning WAP: WML and WMLScript

From the Publisher: This book is aimed at several audiences. First, it's for those with an interest in the rapid development of the communications industry, but little or no previous experience of computer programming. Second, it's for those with some 'traditional' programming skills, and a desire to transfer their knowledge to this brand new area. Third, the thorough coverage given to the behavior of WAP applications on real consumer devices makes it a valuable resource for existing WAP developers. What does this book cover? Provides an introduction to wireless technologies: the what, why, and how of WAP Explains how markup languages work, and charts the evolution and purpose of XML Teaches WML and WMLScript from the ground up, assuming no prior programming knowledge Examines different ways of presenting and prompting for information, and gives recommendations Explores the behavior of WAP applications on real consumer devices, not just simulators Introduces ASP and XSLT as ways of customizing applications for individual clients All code and examples compatible with version 1.2 of the WAP Specification
A Cooperative Cache Management Scheme for IEEE802.15.4 based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) based on the IEEE 802.15. 4 MAC and PHY layer standards is a recent trend in the market. It has gained tremendous attention due to its low energy consumption characteristics and low data rates. However, for larger networks minimizing energy consumption is still an issue because of the dissemination of large overheads throughout the network. This consumption of energy can be reduced by incorporating a novel cooperative caching scheme to minimize overheads and to serve data with minimal latency and thereby reduce the energy consumption. This paper explores the possibilities to enhance the energy efficiency by incorporating a cooperative caching strategy.
MORPHOMUSEUM: AN ONLINE PLATFORM FOR PUBLICATION AND STORAGE OF VIRTUAL SPECIMENS

Since the early 1990s, methods for the acquisition of three-dimensional (3-D) data and computer-assisted techniques for the visualization of such data have grown increasingly popular among biologists, paleontologists, and paleoanthropologists. However, thus far no standardized repository for complex virtual models based on 3-D digital data of specimens has emerged, whereas the need for researchers to provide access to 3-D models of specimens as well as the pressure imposed on authors by scientific journals to make original 3-D morphological data publicly available have increased. MorphoMuseuM (M3) aims to fill this gap. M3 is both a peer-reviewed scientific journal (M3 Journal) and a virtual specimen repository (M3 Repository). All scientific articles and their associated 3-D models deposited in M3 go through a formal review process. Each published model is given a DOI and a unique identifier code, which should be cited by researchers using this model in their scientific publications. In this paper, we describe the place of M3 among other online repositories for 3-D data, and explain how the growing community of biologists working with 3-D data can benefit from using M3.
Learning sparse graphical models for data restoration and multi-label classification

Sparse probabilistic graphical models play an important role in structured prediction when the dependency structure is unknown. By inducing sparsity over edge parameters, a typical sparse graphical model can combine structure learning and parameter estimation under a unified optimization framework. In this thesis, we propose three specific sparse graphical models accompanied by their applications in data restoration and multi-label classification respectively. For the data restoration task, we propose random mixed field (RMF) model to explore mixed-attribute correlations among data. The RMF model is capable of handling mixed-attribute data denoising and imputation simultaneously. Meanwhile, RMF employs a structured meanfield variational approach to decouple continuous-discrete interactions to achieve approximate inference. The effectiveness of this model is evaluated on both synthetic and real-world data. For the multi-label classification task, we propose correlated logistic model (CorrLog) and conditional graphical lasso (CGL), to learn conditional label correlations. (1) The CorrLog model characterizes pairwise label correlations via scalar parameters, thus effects in an explicit (or direct) fashion. More specifically, CorrLog extends conventional logistic regression by jointly modelling label correlations. In addition, elastic-net regularization is employed to induce sparsity over the scalar parameters that define label correlations. CorrLog can be efficiently learned by regularized maximum pseudo likelihood estimation which enjoys a satisfying generalization bound. Besides, message passing algorithm is applied to solve the multi-label prediction problem. (2) The CGL model further leverages features in modelling pairwise label correlations in terms of parametric functions of the input features, which effects in an implicit (or indirect) fashion. In general, CGL provides a unified Bayesian framework for structure and parameter learning conditioned on input features. We formulate the multi-label prediction as CGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient after applying the maximum a posterior (MAP) methodology and solved by a proximal gradient procedure. The effectiveness of CorrLog and CGL are evaluated on several benchmark multi-label classification datasets.
Analysis of the Performance of the New Generation of 32-bit Microcontrollers for IoT and Big Data Application

This paper explores the relationship between performance of different generations of MCU solutions for classical computing processing, as well as FPU/DSP processing of input information. Finally, it questions how fast software-controlled I/O ports (GPIO) are. By using the battery of standardized computer tests, and custom GPIO, GPU / DSP tests, the report gives clear and measurable results. The results of measuring carried out in the study clearly show that it is possible to unload the network resources necessary for transmitting large quantities of information if they represent a part of a complex pretreatment data for Big Data processing systems on the MCU platforms themselves with IoT sensor devices.
MPLS Multicast Traffic Engineering

In this paper, we present multicast traffic engineering and we compare it to unicast traffic engineering. We study the advantages given by the integration of multicasting and MPLS. We present current proposals for multicast traffic engineering and using MPLS network. We describe our approach, the MPLS multicast tree (MMT) protocol. In order to reduce forwarding states and enhance scalability, MMT utilizes MPLS LSPs between branching routers of the multicast tree. We present a simulator for MMT and finally we discuss some simulation results.
UDP Pervasive Protocol Implementation for Smart Home Environment on MyRIO using LabVIEW

Smart Home concept is to connect several devices to perform control and the device to be controlled to help any human needs. So it should have one device being Host and several devices being Client. The environment in this case is a home that there is too much technology that planted technologies that has much influence at human activities is called pervasive computing. The device communication should be has a protocol, at the research used UDP protocol because it doesn't need to negotiate before performing communication like a TCP. With UDP behavior did not use to validate at every send or deliver data because at Smart Home environment doesn't need a large or big data being processed. LabView used at this research based on previous research, but MyRIO has used this research to perform Client and Host performed by PC. So it can measure how usable the design that created. This research proves that LabView and MyRIO successfully implemented with the design that created to perform Pervasive Computing for Smart Home environment. Functional testing scenario conducted by every condition that on system environment, based on design all testing scenario successfully and working as well as expected.
Topology preservation and control approach for interference aware non-overlapping channel assignment in wireless mesh networks

The Wireless Mesh Networks (WMN) has attracted significant interests due to their fast and inexpensive deployment and the ability to provide flexible and ubiquitous internet access. A key challenge to deploy the WMN is the interference problem between the links. The interference results in three problems of limited throughput, capacity and fairness of the WMN. The topology preservation strategy is used in this research to improve the throughput and address the problems of link failure and partitioning of the WMN. However, the existing channel assignment algorithms, based on the topology preservation strategy, result in high interference. Thus, there is a need to improve the network throughput by using the topology preservation strategy while the network connectivity is maintained. The problems of fairness and network capacity in the dense networks are due to limited available resources in WMN. Hence, efficient exploitation of the available resources increases the concurrent transmission between the links and improves the network performance. Firstly, the thesis proposes a Topology Preservation for Low Interference Channel Assignment (TLCA) algorithm to mitigate the impact of interference based on the topology preservation strategy. Secondly, it proposes the Max-flow based on Topology Control Channel Assignment (MTCA) algorithm to improve the network capacity by removing useless links from the original topology. Thirdly, the proposed Fairness Distribution of the Non-Overlapping Channels (FNOC) algorithm improves the fairness of the WMN through an equitable distribution of the non-overlapping channels between the wireless links. The F-NOC is based on the Differential Evolution optimization algorithm. The numerical and simulation results indicate that the proposed algorithms perform better compared to Connected Low Interference Channel Assignment algorithm (CLICA) in terms of network capacity (19%), fractional network interference (80%) and network throughput (28.6%). In conclusion, the proposed algorithms achieved higher throughput, better network capacity and lower interference compared to previous algorithms.
Different marches of the machines: Institutional change and identity work of professionals.

This study examines how the introduction of new technology had a differential effect of the work of a matched set of two workgroups of professionals. One group of internal auditors, at a major multinational corporation based in an emerging market, embraced the new methods of working, prompting new identity creation that reinforced their adherence to one of the conflicting institutional logics, and increase their professionalism. The other group, also internal auditors but working at a major domestic company in the same industry and country, resisted the change, allowing management to exploit the opportunity to undermine the professionalism of the workgroup, while engaging in symbolic management to reap the external benefits of what looked like good corporate governance. The study demonstrates, theoretically, the role that identity formation plays (or may not play) in embedding institutional change, and how a professional logic can be reconciled with organization identification. It also shows, practically, how in certain conditions resistance to technology can disrupt professional integrity.