Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Pose Estimation With Deeply Learned Multi-Scale Compositional Models

Compositional models are meant for human pose estimation (HPE) due to their abilities to capture relationships among human body parts. Deeply learned compositional model (DLCM) utilizes deep neural networks to learn compositionality of human body parts and has achieved great improvements in human pose estimation. The DLCM has a hierarchical compositional architecture and bottom-up/top-down inference stages. The previous works have proven that multi-scale deep features are beneficial for computer vision tasks, such as classification and human body keypoints detection. However, learning multi-scale feature pyramids in DLCM has not been well explored. In this paper, we propose a new method to apply the multi-scale feature pyramid module to further improve the performance of the DLCM, which is named as deeply learned multi-scale compositional model (DLMSCM). We design multi-scale residual modules as the basic blocks to learn multi-scale deep features which can capture the scale variations of different body parts. With the multi-scale mechanism in the framework of the DLCM, the model can not only deal with scale variations of body parts but also find joints dependencies, therefore enforce the entire body joints structural constrains. As a result, more precise body keypoints detection can be acquired. Our approach outperforms the other state-of-the-art methods on two standard benchmarks datasets MPII and LSP for human pose estimation.
Design of Sensor Permanent Fail-safe Algorithm based on Deep Learning for 100Nm class Electro-Hydraulic Power Steering System of Medium and Heavy Commercial Vehicle

In this paper, Deep Learning-based sensor Fail-safe for Electro-Hydraulic Steering Systems (EHPS) of 100Nm commercial vehicle is introduced. There are four types of permanent faults, such as shortage, drifted sensitivity, drifted off-set and fixation. When steering wheel angle sensor and motor angular velocity sensor in steering system failed, permanent faults are recovered by using a Deep Learning-based sensor Fail-safe algorithm. The learning variables are given by Electro-hydraulic motor modeling of steering system and are trained by using Tensor Flow. The learning data are extracted through MATLAB/Simulink and TruckSim. The proposed algorithm is verified through Co-simulation of MATLAB/Simulink and TruckSim. Finally, this paper confirmed that the algorithm proposed recovered the faults of steering system sensors.
Collaborative Learning and Learner Autonomy: Beliefs, Practices and Prospects in Pakistani Engineering Universities

Learner autonomy in language classrooms received researchers’ attention quite recently. The research intends to explore the prospects of promoting autonomy in Pakistani learners through collaborative tasks. The sample consists of 16 teachers working in public sector Engineering universities of Punjab. A qualitative approach and interpretive paradigm were selected for the present study. The role of collaborative learning in developing autonomy in learners is explored through gathering teachers’ beliefs using semi-structured interviews and observing their practices in the classrooms. A constructivist’s lens was employed in collecting and analyzing data. The findings show that teachers believe that collaborative learning is inculcating independence, responsibility, confidence, motivation, skills, and positive interdependence which are required for autonomy promotion. They also believe that collaborative tasks, famous among Pakistani learners and few teachers, can be utilized as the right medium in autonomy development. However, class-observations do not depict a promising situation. Teachers are found inclined towards traditional teaching pedagogy, and collaborative tasks are found lacking true spirit.
Robust $\mu$ -Synthesis Control of Dual LCL Type IPT System Considering Load and Mutual Inductance Uncertainty

The output voltage of a dual LCL inductive power transfer (IPT) system varies with the load and mutual inductance, which can affect the system performance, e.g., the charging speed of electrical equipment. This paper proposes a robust controller to maintain constant output voltage against the load and mutual inductance variations. A <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-synthesis method is proposed based on structured singular value to design the dual LCL IPT closed-loop system. The frequency-domain nominal model of the system is established by using the generalized state-space averaging (GSSA) method. A standard M-<inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula> configuration is generalized through separating the nominal and uncertain block by upper linear fractional transformation (LFT). The <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-controller is finally obtained by <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> iteration. The robust performance analysis and time-domain performance analysis are also carried out. The simulation and experimental results verified the effectiveness of the proposed modeling and controller design, and demonstrated the robust stability and robust performance of the dual LCL IPT system.
A Universal SPICE Field-Effect Transistor Model Applied on SiC and GaN Transistors

This paper presents a universal SPICE model for field-effect transistors, which is independent from technology and semiconductor material. The created behavioral simulation model is based on a set of collected measurement data. The temperature-dependent output characteristics are modeled using a hybrid approach consisting of lookup tables and analytical equations. This leads to fast simulation times and very high accuracy. The validity for the static temperature-dependent behavior of this approach is verified for one SiC and one GaN transistor using the respective datasheet curves. The transistors nonlinear capacitances are modeled in dependence of their inter-electrode voltages. In order to verify the validity in the dynamic range, the universal model is applied to a GaN high-electron-mobility transistor. Double pulse measurements are used for the dynamic validation at a characterized measurement test bench regarding its parasitic elements. Therewith a proper validation of the simulation model at switching transients as low as 5 ns is achieved.
Procedural Composition of Traditional Persian Music Using Deep Neural Networks

In recent years the explosion in the amount of digital content used in various forms in virtual reality environments such as videogames requires generating a massive amount of new artistic materials. Procedural content generation using artificial intelligence algorithms helps producing this artistic materials with significant variety and based on various traditional source materials and avoiding virtual environments to be derivative. These systems can also lead to multi-cultural variety and use of traditional art in digital environments. In this paper, a recurrent deep neural network based framework for procedural composition of the traditional Persian music is proposed. The proposed system learns from the classical Persian musical modal systems and then produces new music. The proposed system is evaluated and the results show that the deep neural networks are capable of producing traditional Persian music with acceptable quality.
CO2 control of Trichodesmium N2 fixation, photosynthesis, growth rates, and elemental ratios: Implications for past, present, and future ocean biogeochemistry

Diazotrophic marine cyanobacteria in the genus Trichodesmium contribute a large fraction of the new nitrogen entering the oligotrophic oceans, but little is known about how they respond to shifts in global change variables such as carbon dioxide (CO2) and temperature. We compared Trichodesmium dinitrogen (N2) and CO2 fixation rates during steady‐state growth under past, current, and future CO2 scenarios, and at two relevant temperatures. At projected CO2 levels of year 2100 (76 Pa, 750 ppm), N2 fixation rates of Pacific and Atlantic isolates increased 35–100%, and CO2 fixation rates increased 15‐128% relative to present day CO2 conditions (39 Pa, 380 ppm). CO2‐mediated rate increases were of similar relative magnitude in both phosphorus (P)‐replete and P‐limited cultures, suggesting that this effect may be independent of resource limitation. Neither isolate could grow at 15 Pa (150 ppm) CO2, but N2 and CO2 fixation rates, growth rates, and nitrogen : phosophorus (N : P) ratios all increased significantly between 39 Pa and 152 Pa (1500 ppm). In contrast, these parameters were affected only minimally or not at all by a 4°C temperature change. Photosynthesis versus irradiance parameters, however, responded to both CO2 and temperature but in different ways for each isolate. These results suggest that by the end of this century, elevated CO2 could substantially increase global Trichodesmium N2 and CO2 fixation, fundamentally altering the current marine N and C cycles and potentially driving some oceanic regimes towards P limitation. CO2 limitation of Trichodesmium diazotrophy during past glacial periods could also have contributed to setting minimum atmospheric CO2 levels through downregulation of the biological pump. The relationship between marine N2 fixation and atmospheric CO2 concentration appears to be more complex than previously realized and needs to be considered in the context of the rapidly changing oligotrophic oceans.
A Study on Mobile Robot Control by Hand gesture Detection

Recently, method of controlling machine with natural control called Natural User Interface has attracted attention. particularly, human hand is considered to be communication media that can communicate information most naturally. In this paper, user hand is detected by following acquired skin color of the user's face, through face detection. After detecting the user hand, hand gesture is determined. Finally, a robot is controlled to have multi movements according to detected several hand gesture patterns. We succeeded in acquiring the skin color information from user's face and detecting both user's hand and hand gesture following that. In this paper, we developed a mobile robot to conduct experiments. We also confirmed that robot can be controlled by human hand gesture.
Correlations between Sensory Processing Patterns and Pain Catastrophizing Levels in Well Older Adults

This study investigated the relationships between sensory processing patterns and pain catastrophizing levels in well older adults to provide basic information for rehabilitation intervention. The participants of this study were 216 older adults aged over 65. The Adolescent/Adults Sensory Profile(AASP) was used to measure sensory processing patterns and the Pain Catastrophizing Scale(PCS) was employed to evaluate psychological variables of the pain. Self perception of the pain was measured with the Numerical Rating Scale(NRS). Gender and age differences were analyzed with t-test and relationships among sensory processing patterns, pain catastrophizing and pain symptom levels were analyzed with Pearson product-moment coefficients of correlation. Women had higher `sensory sensitivity` and `sensory avoiding. ` The group over 75 years old had higher `sensory avoiding,` `low registration` and `sensory seeking. ` Women had higher pain catastrophizing and pain symptom levels. Negative correlations between `rumination` and `low registration` and `helplessness` and `sensory seeking` were found. Positive correlations were found between pain catastrophizing and pain symptom levels when they were `sensory sensitivity` and `sensory avoiding. ` The results infer that sensory processing patterns are associated with psychological variables of the pain and there are differences based on gender and age. Further studies are warranted to support the results.
University of Birmingham Challenging the knowledge transfer orthodoxy: knowledge co-construction in technology enhanced learning for children with autism

Experimental intervention studies constitute the current dominant research designs in the autism education field. Such designs are based on a ‘knowledge-transfer’ model of evidence-based practice in which research is conducted by researchers, and is then ‘transferred’ to practitioners to enable them to implement evidence-based interventions. While these research designs contribute important knowledge, they lead to a gap between what the research evidence may prescribe and what happens in practice, with a concomitant disparity between the priorities of researchers and practitioners. This paper discusses findings from the ESRC-funded ‘SHAPE’ project, which adopted a different model of evidence-based practice, focusing on knowledge co-construction. Pupils ( N = 8), teachers ( N = 10), a speech and language therapist and a parent in three different school communities investigated creative ways in which children’s social communication skills could be enhanced through technology use. Through a participatory methodology, digital stories were used as a method to enable engagement with the practical realities of the classroom and empower practitioners to construct and share their own authentic narratives. Participants articulated precise knowledge about the learning opportunities afforded to them and their pupils through quality interactions that were mediated by the technologies, as evidenced through digital stories. The SHAPE project shows that it is feasible to develop methodologies that enable genuine knowledge co-construction with school practitioners, parents and pupils. Such co-construction could offer realistic opportunities for pedagogical emancipation and innovation in evidence-based practice as an alternative to the currently dominant and narrow model of knowledge transfer.
Fast S-Parameter TAN Model of n-Port Lumped Structures

This paper deals with the fast S-parameter modeling of multi-port lumped structures. The developed model is based on unfamiliar formalism using the tensorial analysis of networks (TAN). The modeling methodology is described with the general abstract topology and different application cases. The methodology consists, first, in elaborating the equivalent graph topology of the considered problem. Then, it is followed by the TAN mathematical abstraction, including, successively, the branch and mesh space analyses. The problem metric can be written with the tensorial Ohm’s law expressed in function of the covariant voltage, contravariant current, and the twice covariant impedance in the mesh spaces. The equivalent Z-matrix of the considered multi-port structure is established from an innovative reduction method of the mesh impedance. The S-parameter model is extracted from the Z-to-S matrix transform. The effectiveness of the established fast S-parameter TAN modeling is validated with three cases of proof of concept constituted by TT-cell, the TTT-cell two-port circuit, and four-port structure inspired from the 3D coaxial shielded cable. As expected, an excellent agreement between the S-parameters calculated from the TAN model and simulated from the commercial tools from DC to some hundred’s megahertz is obtained. In the future, the developed model is outstandingly beneficial for fast and accurate applications notably for the conduced shielded cable electromagnetic compatibility analysis.
An Efficient Hierarchical Distributed SDN Controller Model

Software defined network (SDN) is a new architecture for network in which the control plane is separated from data plane and implemented centrally. There are two general models for the control plane, single and distributed controller. A single SDN controller faces with some problems such as scalability and single point of failure. Distributed model is introduced to solve these problems. The classical hierarchical model is known as one of the main kinds of distributed SDN controllers. In this paper, we introduce an efficient hierarchical SDN model to reduce the load of the root controllers in comparison with classical hierarchical model.
Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features Perspective

Peer-to-Peer lending is developing quickly around the world as a new E-finance industry, especially in China. Yet fraudulence and business ceasing of Peer-to-Peer Lending Intermediaries (P2P-INTs) occur frequently, making P2P investors facing serious risk. This paper attempts to explore a bridge connecting managerial research with some most advanced natural language processing (NLP) technologies, and examines the risk assessing power of automatic learning text classifiers based on data of hazard status and top management team profile texts of the P2P-INTs. A risk evaluation model named MULTIPLE NLP Integrated Learning Text Classifier (MUN-LETCLA) based on five NLP techniques and meta-learning is proposed. Then risk classification power of the MUN-LETCLA and the single NLP models is assessed. The results show that the proposed model is effective in classifying low-risk and high-risk P2P-INTs. The NLP models can automatically detect the P2P-INTs risk from Top Management Team (TMT) members’ working experience, educational background, and TMT composition with a precision level of more than 75%.
Multidimensional Reputation Evaluation Model for Crowdsourcing Participants Based on Big Data

The existing reputation evaluation mechanism of crowdsourcing platform in China has the problems of single evaluation index, simple evaluation model and poor differentiation ability, which can’t fully reflect the real reputation status of crowdsourcing participants. Under the big data environment, from the perspective of incentive and punishment, collecting of crowdsourcing participant evaluation indexes extensively, this paper construct the multidimensional reputation evaluation model (MREM) of crowdsourcing participants from four dimensions: initial reputation dimension, transaction dimension, evaluation dimension and fraud penalty dimension. Empirical analysis based on the data of participants from “epwk” platform, the validity of the model is verified from two aspects: incentive trading and fraud prevention, the results show that MREM model is more comprehensive, dynamic and accurate than the existing model of crowdsourcing platform, and has stronger practicability.
Text Summarization for Tamil Online Sports News Using NLP

Text summarization plays an important problem in natural language understanding and information retrieval. Automatic text summarization get much more attention by people presently because it is efficiently and effectively serve time in decision making process even for day to day life. Presently deep learning models get more attention than the traditional approaches. The primary objective of this research work is to propose a methodology to address the problem of summarization for Tamil sports news which can automatically create extractive summary for the news data with the use of Natural Language Processing (NLP) and a generic stochastic artificial neural network. Features such as sentence position, sentence position related to paragraph, number of named entities, term frequency and inverse document frequency and Number of numerals are employed to construct the feature matrix for each sentence and Restricted Boltzmann Machine is used to improve those features while enhancing the accuracy without loosing the main idea of the text. Experimentation is carried out using Online Tamil sports news and ROUGE tool kit is used to evaluate the recall, precision and F-measure for the summary generated by both the human experts and the system.
A 2.8-mW Sub-Harmonic Injection-Locked Frequency Multiplier in 130-nm CMOS for 2.4-GHz WLAN Applications

A low-power ring-oscillator-based injection-locked frequency multiplier with a continuous frequency-tracking loop (FTL) that generates 2.4 GHz frequency is proposed. This clock multiplier is designed for wireless local area network (WLAN) applications. A low-power delay cell is used to enhance the efficiency of the system. The FTL is used for continuous calibration of the ring voltage-controlled oscillator (VCO) frequency drift across process, supply, and temperature variations. The proposed architecture is designed using 130-nm CMOS process. The integrated RMS-jitter achieved is 272 fsec in the range from 12 kHz to 20 MHz of the 2.4-GHz output signal with total power consumption of 2.8 mW.
Do corporate taxes reduce investments? Evidence from Italian firm-level panel data

Abstract This paper uses an Italian firm-level panel data-set over the period 1994–2006 to investigate the nexus between corporate taxation and investment. Studying the effects of corporate taxation on investment at the micro level has two advantages. Firstly, investment is free of aggregation biases and secondly, the firm-level dimension allows asking whether the effects of corporate taxation differ across firms with different characteristics. In the empirical analysis, we employ a Generalized Method of Moments estimator, which permits us to handle not only the dynamic structure of the model and of the predetermined or endogenous explanatory variables, but also firm-specific factors, heteroskedasticity, and autocorrelation of individual observations. We find that corporate taxes distort investment decisions. The results are robust to the inclusion of many controls.
A Rule-based Lemmatizing Approach for Sinhala Language

Speech recognition, natural language processing, language translation and deep learning researches are bridging the communication gap between humans as well as between humans and machines. Sinhala is a native language in Sri Lanka which is being used by 19 million people approximately. The growth of Sinhala natural language processing tools is less when compared to European and other Asian Languages. A lemmatizer for Sinhala can be used for the morphological analysis and is an essential module in Sinhala language processing mechanisms. Lemmatizing is a complex process in morphological analyzing where base/root of words are derived. There is not much work published focusing on lemmatizer approaches for Sinhala. This paper presents a rule based lemmatizing approach which can be used to determine the base form of Sinhala words with an accuracy of 77.3%. It differs from similar works because the data used in the research are extracted from social media.