Miracle-dz/UniFAR-FTUs huggingface dataset:
A Strategy of Reusing Mode Selection for Device to Device Communication

Interference coordination is the focus for Device-to-Device (D2D)communication. Due to the fact that many works adopt single reusing mode for coordinating interference in D2D communication systems and the efficiency of spectrum is low, a strategy of reusing mode selection is proposed. The aim is let D2D pairs can flexibly select the most suitable reusing mode between one-to-one and multiple-to-one reusing mode according to the signal to interference plus noise power ratio (SINR)performance of D2D pairs and that of the cellular system. The selecting rule of reusing mode is derived, and four radiuses are achieved to determine the reusing mode and select the cellular user to be reused. By using the rule to select a reusing mode, not only the communication quality of D2D communication and cellular communication can be improved, but also more D2D users can successfully establish communication even at the case of insufficient spectrum resources. Simulation results show that the D2D system with the proposed reusing strategy can significantly improve the overall system throughput.
How geography matters: neglected dimensions in contemporary migration research

In Asia, migration is a complex phenomenon, the same as worldwide. The approaches of diaspora as well as transnational migration and transnational social spaces describe contemporary migration processes and are at the centre of this paper. Our major critique about these approaches is their dominantly socio-cultural perspective on migration, the missing link to other existing social theory, and missing consideration of the importance of place and identity, and the multiple ways how people perceive and construct space. To address this critique we present innovative geographical research showing the potential of social geography to contribute to the understanding of increasing mobility worldwide. Abstract In Asia, migration is a complex phenomenon, the same as worldwide. The approaches of diaspora as well as transnational migration and transnational social spaces describe contemporary migration processes and are at the centre of this paper. Our major critique about these approaches is their dominantly socio-cultural perspective on migration, the missing link to other existing social theory, and missing consideration of the importance of place and identity, and the multiple ways how people perceive and construct space. To address this critique we present innovative geographical research showing the potential of social geography to contribute to the understanding of increasing mobility worldwide. the first level of analysis, it requires a consideration of the ways in which men and women of the diaspora are inserted into the social relations of the country of settlement, within their own self-defined ‘diaspora communities’ and within the transnational networks of the diaspora across national borders. […] The other level of analysis, regarding gendering the diaspora notion, relates to an exploration of how gendered relations are constitutive of the positionalities of the groups themselves, paying attention to class and other differences within the group and to different locations and trajectories. (A NTHIAS , 1998:572)
Robust LQR Anti-Swing Control for Quay-Side Crane System with Variable Load

Presently, quay-side cranes are playing more and more important roles in modern port logistics. The cargo-handling efficiency is the most important factor for ports. At present, most of the anti-swing operations of quay-side cranes are still depending on the trolley drivers, which is quite ineffective. It is a great challenge for automatic anti-swing tracking design for quay-side crane owing to mass variation of containers and heavy wind in the port, which bring about many challenges for the development of effective control schemes. In this paper, a dynamic model under generalized coordinate system has been built firstly and the system transfer function and the state space expression are obtained by a reasonable linearization. Aiming at tackling the problem of load mass change, a robust LQR state feedback controller is designed base on the standard LQR control and the Lyapunov theory in a variable load system. The numerical simulation results are included to prove that the presented controller can achieve satisfactory anti-swing and tracking performance with strong robustness against load mass variation.
Human Face Thermal Images Library for Laboratory Studies of the Algorithms Efficiency for Bioinformation Processing

Experimental confirmation of the effectiveness of new developed algorithms for processing the thermal image of a person's face is an actual task at present. The paper considers information aspects of the creation of a specialized thermal images library of a person's face. The developed library structure assumes the presence of both thermal and conventional images of a person's face. For the specified types of images, the library contains both static and dynamic images, which greatly extends its capabilities.
Study on typical metal fatigue failure of circuit breaker operating mechanism unit

Circuit breaker operating mechanism unit endure alternating stress during divide-shut brake operation, and metal components frequently appear fatigue failure. In the paper, the common failure features of energy storage spring facture, shaft sleeve cracking, energy storage connecting bolt cracking were studied. Depending on this situation, macroscopic examination, metallographic examination, scanning electron microscope were applied. The results indicated that the metal components fatigue failure were mainly caused by bad material and inproper installation. Technical supervision suggestion were also proposed to perfect the construction acceptance and operations management process.
Research on Automatic Construction of Sentiment Lexicon Based on Bayesian Framework

Most of the current sentiment lexicons only mark positive and negative polarities, and rarely considering the ambiguity of words that may lead to changes in sentimental polarity and intensity. This paper proposes a method on the construction of sentiment lexicon based on Bayesian formula. Considering the different parts of speech distribution factors, the sentimental polarity and intensity calculations of the words are converted into the positive and negative probability calculations of the <word, part of speech> under the Bayesian formula. Then, in order to synthesize prior sentiment knowledge and corpus sentiment knowledge based on Bayesian formula, this paper will construct a unified sentiment lexicon based on Bayesian framework and evaluate the lexicon through text sentiment classification tasks. The final experimental results show that compared to the existing sentiment lexicons, the effect of the sentiment lexicon constructed in this paper has been significantly improved, especially for the long text corpus, and the Fl-measure reaches 77%. (Abstract)
Classifier Independent Subbands Selection based on Information-Theoretic

In this research, a consolidated framework is proposed for the automatic selection of the most discriminant subbands for the problem of face recognition. The subbnds selection method is independent of classifier. Essentially, a Local Binary Pattern (LBP) is used for transforming the face image into texturized face image undergo to wavelet packet decomposition which produces the several detail and approximation images. An information-theoretic method is used to select the most important subbands which are independent of classifiers. Two classifiers chi-square and support vector machine are used to verify that selected subbands are independent of classifiers. The proposed algorithm are evaluated on several databases and are shown to pick the most significant subbands which produce the better performance.
Advanced Signal Processing for Communication Networks and Industrial IoT Machines Using Low-Cost Fixed-Point Digital Signal Processor

In this paper, the uses and functions of an existing, widely available digital signal processor (DSP) is extended to include using it to generate needed waveforms that could be used for computing, filtering, modulation and noise removal applications at the edges of communication networks that supports Industrial Internet of Things (IIoT) and Cyber Physical Systems (CPS). Such waveforms could also be used to optimize performances of IIoT communication networks such as wireless and powerline communication networks. The C28x digital signal processor (DSP), manufactured by Texas Instruments is used as a case study, and it is deployed in this paper as a mother wavelet generator by programming it to generate needed wavelets using embedded C programming language. Our implementation is the first known application of the C28x as a basic mother wavelet generator. Advanced signal processing method which include signal clipping, sequencing and concatenation are used with embedded C to program the C28x DSP. Wavelets generated with the C28x DSP are found to satisfy the wavelet admissibility condition. The open-loop voltage signal of an IIoT machine, generated at an IIoT network edge, and sent across a powerline communication (PLC) channel is used to evaluate the performance of the constructed Mexican Hat wavelet. The Mexican Hat wavelet constructed using the C28x DSP is used to remove noise from the machine signal corrupted with noise when the signal is transmitted over a PLC channel. Denoised signal using the DSP based Mexican Hat wavelet shows high correlation when compared to the original transmitted signal, while communication channel noise is successfully removed from the original signal.
Comparison of the dietary intake amongst women in the late postoperative period after Roux-en-Y gastric bypass with the bariatric food pyramid

Michelle T. Frota Reichmann. Programa de Pós Graduação em Alimentação e Nutrição. Universidade Federal do Paraná (UFPR). Av. Lothário Meissner, 632. 80210-170 Jardim Botânico, Curitiba (PR). Brazil e-mail: mitfreichmann@gmail.com Reichmann MTF, Todeschini S, Setter N, Vilela RM, Radominski R. Comparison of the dietary intake amongst women in the late postoperative period after Roux-en-Y gastric bypass with the bariatric food pyramid. Nutr Hosp 2019;36(3):599-603
Ontology-Driven System for Monitoring Global Processes on Basis of Internet News

An approach to development of ontology-driven system for monitoring global processes is presented. Ontology-driven architecture (ODA) intends using a few ontology types for different purposes. Ontologies are used to search Internet news with information retrieval tools. The domain ontology describes research interests of the experts needing analysis of world events and creation of global processes models including events of specified types. The sources ontology defines information sources (news feeds for example) used by experts for analysis. Also, domain ontologies are used to clarify results at facts extracting for forming event logs and to cluster objects in logs. Event logs formats are described with logs ontology. It allows storing logs instances with different settings too. Analysts can use these logs to compare results of global processes monitoring during time periods given by experts. Global process models are constructed with process mining tools on the basis of event logs. An advanced multilevel ontology is the kernel of system. The general system architecture and ontology representation are described. Some examples are included in the paper.
Outdoor Navigation of a Mobile Robot by Following GPS Waypoints and Local Pedestrian Lane

Combining global GPS-based navigation and local pedestrian lane following is an promising way to autonomous navigation of mobile robots in unknown outdoor environments. By taking advantage of open SDK online satellite maps, the navigation route which contains a sequence of intersection waypoints could be autonomously planned. Through moving toward the GPS coordinates of the waypoints sequentially, the robot could be piloted to the destination in a global and coarse-grained navigation manner. Meanwhile, a simple vision-based lane following method is proposed to control the robot to move safely on the pedestrian lane in a local and fine-grained navigation manner. The two types of controllers are implemented as two behaviors which are managed by a Finite State Automaton (FSA)and coordinated with an obstacle avoiding behavior. The overall navigation system is implemented on the HunterBot platform with a cloud based human-robot interaction system. Elementary blind test results on the campus environments have shown that the proposed method is potentially effective to address the autonomous navigation problem in unknown outdoor environments.
Monocular Wide Baseline Stereo Measurement Using High-Speed Catadioptric System

We propose a novel monocular wide baseline stereo vision system based on an ultrafast mirror-drive pan-tilt active vision device that can simultaneously switch hundreds of different views in one second. By combining high-speed video-shooting and multithread ultrafast gaze control processing, one camera can be performed as two virtual cameras with different views. Stereo image pairs can be captured by two virtual cameras frame by frame at arbitrary viewpoints by adding auxiliary catadioptric equipment and designing suitable way of pan-tilt mirror device switching. In this study, two additional plane mirrors are used to make up a wide stereo system in a limited space such as indoor environment. Frame interpolation is used to eliminate the unsynchronization problem for moving object. By switching 125 different views in a second with realtime image recording at 125 fps, the proposed system can function virtually as two cameras operating at 62.5 fps that can capture stereo pairs of 8-bit 512 × 512 color images. 3-D measurement experiment and analysis are demonstrated to verify the effectiveness of the proposed monocular wide baseline stereo system.
An Evaluation of the Belief in Science Scale

The Belief in Science Scale (BISS) is a unidimensional measure that assesses the degree to which science is valued as a source of superior knowledge. Due to increased academic interest in the concept of belief in science, the BISS has emerged as an important measurement instrument. Noting an absence of validation evidence, the present paper, via two studies, evaluated the scale’s factorial structure. Both studies drew on data collected from previous research. Study 1 (N = 686), using parallel analysis and exploratory factor analysis, identified a unidimensional solution accounting for 56.43% of the observed variance. Study 2 (N = 535), using an independent sample, tested the unidimensional solution using confirmatory factor analysis (CFA). Data-model fit was good (marginal for RMSEA): CFI = 0.93, TLI = 0.91, RMSEA = 0.09 (90% CI of 0.08 to 0.10), SRMR = 0.04. Invariance testing across gender supported invariance of form, factor structure, and item intercepts for this one-factor model. BISS at the overall level correlated negatively with the reality testing dimension of the Inventory of Personality Organization (IPO-RT), demonstrating convergent validity. Researchers often use the IPO-RT as an indirect index of preference for experiential processing (intuitive thinking). In this context, only BISS scores above the median (second quartile) produced a reduction in experiential-based thinking. The authors discuss these findings in the context of belief in science as a psychometric construct.
Late Reverberation Cancellation Using Bayesian Estimation of Multi-Channel Linear Predictors and Student's t-Source Prior

Multi-channel linear prediction (MCLP) can model the late reverberation in the short-time Fourier transform domain using a delayed linear predictor and the prediction residual is taken as the desired early reflection component. Traditionally, a Gaussian source model with time-dependent precision (inverse of variance) is considered for the desired signal. In this paper, we propose a Student's t-distribution model for the desired signal, which is realized as a Gaussian source with a Gamma distributed precision. Further, since the choice of a proper MCLP order is critical, we also incorporate a Gaussian distribution prior for the prediction coefficients and a higher order. We consider a batch estimation scenario and develop variational Bayes expectation maximization (VBEM) algorithm for joint posterior inference and hyper-parameter estimation. This has lead to more accurate and robust estimation of the late reverb component and hence its cancellation, benefitting the desired residual signal estimation. Along with these stochastic models, we formulate single channel output (MISO) and multi channel output (MIMO) schemes using shared priors for the desired signal precision and the estimated MCLP coefficients at each microphone. Experiments using real room impulse responses show improved late reverberation suppression with the proposed VBEM approach over the traditional methods, for different room conditions. Additionally, we achieve a sparse coefficient vector for the MCLP avoiding the criticality of manually choosing the model order. The MIMO formulation is easily extended to include spatial filtering of the enhanced signals, which further improves the estimation of the desired signal.
Applications of Unmanned Ariel Vehicles (UAV) in Agriculture : A Study

Today the Unmanned Aerial Vehicle (UAV) or also known as drones are used for many operations in agriculture for enhancing the crop yield and reducing the cost of production. UAV application in agriculture leverages the economy of farmers. The UAV powered by solar or wind mounted with imaging processing capabilities and other devices will enable efficient operations like crop monitoring, spraying of fertilizers and chemical. UAV are extensively used in precision farming in developed counties. The authors have carried out the review of literature on the applications of UAV in agricultural across the world.
Persistence of Pseudomonas fluorescens LBUM677 in the rhizosphere of corn gromwell (Buglossoides arvensis) under field conditions and its impact on seed oil and stearidonic acid bioaccumulation

The aim of this study was to evaluate the persistence of Pseudomonas fluorescens LBUM677 in the rhizosphere of Buglossoides arvensis under agricultural field conditions and determine if B. arvensis intraspecific genetic variations affect the capacity of LBUM677 to colonize its rhizosphere and increase seed oil and stearidonic acid (SDA) accumulation.
Optimization of water allocation system at the river basins

Most of river basins around the world are experiencing water crises because of the increasing intensity of human activity and global climate change. Thus, how to equitably and effectively allocate the limited water resources becomes an urgent research topic. In this study a new multi-objective optimization model is developed to solve the water allocation problems. In the new model, the equity and effectivity principles of water allocation are firstly quantified as harmony functions of multiple indicators. Then, the total equity and effectivity harmony functions are taken as the objective functions which represent the harmonious degree of the water allocation system. Furthermore, a multiobjective fast harmony search algorithm (MOFHS) is adopted to find the Pareto optimal solutions (trade-off curve). Finally, the applicability and efficiency of the newly developed multiobjective optimization model is demonstrated by the Beijiang river basin in southern China. The optimization results certify that the proposed methodology can get a series of Pareto optimal solutions that are useful for decision makers to get the optimal water allocation strategy by trading off between equity and effectivity principles, and may provide decision makers with a new idea to optimally design a water allocation system at river basins.
The Right Direction Needed to Develop White-Box Deep Learning in Radiology, Pathology, and Ophthalmology: A Short Review

The popularity of deep learning (DL) in the machine learning community has been dramatically increasing since 2012. The theoretical foundations of DL are well-rooted in the classical neural network (NN). Rule extraction is not a new concept, but was originally devised for a shallow NN. For about the past 30 years, extensive efforts have been made by many researchers to resolve the “black box” problem of trained shallow NNs using rule extraction technology. A rule extraction technology that is well-balanced between accuracy and interpretability has recently been proposed for shallow NNs as a promising means to address this black box problem. Recently, we have been confronting a “new black box” problem caused by highly complex deep NNs (DNNs) generated by DL. In this paper, we first review four rule extraction approaches to resolve the black box problem of DNNs trained by DL in computer vision. Next, we discuss the fundamental limitations and criticisms of current DL approaches in radiology, pathology, and ophthalmology from the black box point of view. We also review the conversion methods from DNNs to decision trees and point out their limitations. Furthermore, we describe a transparent approach for resolving the black box problem of DNNs trained by a deep belief network. Finally, we provide a brief description to realize the transparency of DNNs generated by a convolutional NN and discuss a practical way to realize the transparency of DL in radiology, pathology, and ophthalmology.
Dynamic Bayesian Game Based Power Allocation in Mobile Edge Computing with Users’ Behaviors

In this paper, we investigate the power allocation problem based on users’ behaviors in mobile edge computing (MEC) networks. Due to the information limitation about the behavior types of mobile users, the power allocation is quite challenging. To deal with this problem, we propose a dynamic Bayesian game based power allocation algorithm to maximize the utility function of MEC network. In Bayesian game model, the MEC server imposes a price per unit power on mobile users. And the MEC server modified the price based on the Bayesian probability information about the behaviors of the users. The mobile users dynamically adapt the behavior types and the required power according to their own performance and cost. We investigate the existence of Bayesian Nash equilibrium and simulation studies are carried out to demonstrate the effectiveness of the proposed algorithm.
Purinergic Signaling in the Vertebrate Olfactory System

Adenosine 5′-triphosphate (ATP) is an ubiquitous co-transmitter in the vertebrate brain. ATP itself, as well as its breakdown products ADP and adenosine are involved in synaptic transmission and plasticity, neuron-glia communication and neural development. Although purinoceptors have been demonstrated in the vertebrate olfactory system by means of histological techniques for many years, detailed insights into physiological properties and functional significance of purinergic signaling in olfaction have been published only recently. We review the current literature on purinergic neuromodulation, neuron-glia interactions and neurogenesis in the vertebrate olfactory system.