Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology Alignment Optimization Method Based on NSGA-II

In order to solve the problem of optimizing the mapping result integration of three distinct similarity measurement technologies (grammar based similarity measurement, linguistic based similarity measurement and taxonomy based similarity measurement), a method based on NSGA-II is proposed. Compared with the traditional genetic algorithm, this method can achieve the maximum mapping recall ratio, precision ratio and f-measure value at the same time. The ontology mapping results obtained by this method can solve the problem of preference for recall or precision. The experimental results indicate that the put forward method is feasible.
Variable Speed Limit Control at SAG Curves Through Connected Vehicles: Implications of Alternative Communications and Sensing Technologies

VARIABLE SPEED LIMIT CONTROL AT SAG CURVES THROUGH CONNECTED VEHICLES: IMPLICATIONS OF ALTERNATIVE COMMUNICATIONS AND SENSING TECHNOLOGIES Reza Vatani Nezafat Old Dominion University, 2019 Director: Dr. Mecit Cetin Connected vehicles (CVs) will enable new applications to improve traffic flow. This study’s focus is to investigate how potential implementation of variable speed limit (VSL) through different types of communication and sensing technologies on CVs may improve traffic flow at a sag curve. At sag curves, the gradient changes from negative to positive values which causes a reduction in the roadway capacity and congestion. A VSL algorithm is developed and implemented in a simulation environment for controlling the inflow of vehicles to a sag curve on a freeway to minimize delays and increase throughput. Both vehicle-to-vehicle (V2V) and infrastructure-tovehicle (I2V) options for CVs are investigated while implementing the VSL control strategy in a simulation environment. Through a feedback control algorithm, the speed of CVs are manipulated in the upstream of the sag curve to avoid the formation of bottlenecks caused by the change in longitudinal driver behavior. A modified version of the intelligent driver model (IDM) is used to simulate driving behavior on the sag curve. Depending on the traffic density at a sag curve, the feedback control algorithm adjusts the approach speeds of CVs so that the throughput of the sag curve is maximized. A meta-heuristic algorithm is employed to determine the critical control parameters. Various market penetration rates for CVs are considered in the simulations for three alternative communications and sensing technologies. It is demonstrated that for higher Market Penetration Rates (MPR) the performance is the same for all three scenarios which means there is no need for infrastructure-based sensing when the MPR is high enough. The results demonstrate that not only the MPR of CVs but also how CVs are distributed in the traffic stream is critical for system performance. While MPR could be high, uneven distribution of CVs and lack of CVs at the critical time periods as congestion is building up may cause a deterioration in system performance.
The Effect of Using Mobile apps on the Acquisition of Conditional Sentences among Iranian Intermediate EFL Learners

Nowadays, there has been an increasing interest in the integration of technology in pedagogical purposes. This study was an attempt to delve in to the impact of a mobile application (Cushy Grammar) on the learning of conditional sentences (type1, 2 and 3) among Iranian intermediate EFL learners in Rooyesh institute in Isfahan. To this end, a group of 75 intermediate EFL learners were non-randomly placed in three groups. The participants were assigned in two experimental groups, namely Application learning (N=25), Blended learning (N=25), and one Control (N=25). The participants of the Application learning received materials and instructions merely through the app. In the second group, the Blended-based, the participants received traditional teaching methods of grammar (conditional sentences) plus learning via the app. Participants of the control group, however, were taught based on the traditional teaching methods of grammar and received the materials, instructions, and feedback through traditional methods. Data were analyzed using a one-way ANOVA and paired sample t-test to examine the impact of the App learning, Blended learning, and traditional method on the learning of conditional sentences. The results unraveled that the participants of the Application group outperformed two other groups in their learning of conditional sentences. Having a more meticulous look at the results, it was observed that participants of the Control group had a lower performance compared to the experimental groups in the post-test. The results revealed that employing an application learning method could create an appropriate condition to enhance the EFL learners’ acquisition of conditional sentences.
The impact of augmented reality on curriculum and training design

Augmented reality (AR) is a relatively emerging technology that is being applied in a somewhat exploratory stage, for training design and curriculum development. In this review, 30 articles were selected and analyzed to identify some current uses of augmented reality in training and curriculum design. Four major themes were identified: 1) a brief introduction about AR; 2) the current uses of AR in curriculum and training development, 3) a review of the effects AR has on student engagement, and 4) the future implications of augmented reality in curriculum and training development. Recommendations for the future are also discussed.
Method based on Separation Confidence Computation and Scale Synthesis Optimization for Real-Time Target Detection in Streetscape Videos

This study proposes a method for the real-time detection and recognition of targets in streetscape videos. The proposed method is based on separation confidence computation and scale synthesis optimization. First, on the basis of generalization in transfer learning, we combine a fine-tuning method suitable for non-convex optimization and adaptive moment estimation in high-dimensional space. Then, we dynamically adjust the learning rates of parameters on the basis of first and second gradient moment estimations. We establish the framework and implementation steps of the proposed method by organically combining regular term super-parameter generalization and hard-example mining technology. We use the proposed method to detect and recognize targets in streetscape videos with high frame rates and high definition. Furthermore, we experimentally demonstrate that the accuracy and robustness of our proposed method are superior to those of conventional methods.
Scientific Tractability and Relevance Theory

There is a widespread view that communication and language use more generally are too complex to study systematically and scientifically1 (e.g. Putnam, 1978; Fodor, 1983; Chomsky, 1992b; Chomsky, 1992a; and perhaps Davidson, 1986). Here I suggest that three distinct arguments have been given for the view. The first, due to Fodor (1983), is that utterance interpretation is unencapsulated and holistic: any information may be relevant to working out what a speaker meant. We have, Fodor claims, no good theories of unencapsulated cognitive processes, and the prospects of developing any are dim. The second argument comes from Chomsky: language use is a mystery because it is a species of human intentional action. He claims that we cannot develop naturalistic theories of such phenomena. In his view, human action is free, in the strong sense that it is not causally determined, and it is a mystery how this is compatible with its evident coherence and appropriateness to circumstances (Chomsky, 1991: 40–41). The third argument, which can be found in Putnam, Chomsky, and perhaps Davidson, is that because many or even all of our mental faculties are simultaneously involved in utterance interpretation, it is too complex to study scientifically. As the claim is sometimes hyperbolically put, a theory of language use and communication would require, per impossibile, “study of everything” (Chomsky, 1992b: 103). This paper aims to examine and defuse these arguments, at least insofar as they pertain to theories of utterance interpretation and production seen as cognitive abilities. Of these, the account developed within Sperber and Wilson’s relevance theory is the most fully articulated. From the outset, relevance theory has been in dialogue with scepticism about pragmatics, particularly that of Chomsky and Fodor, thinkers with whom it shares key assumptions. It adopts from Chomsky the aim of providing a fully explicit account of a cognitive capacity (Sperber & Wilson, 1986: 94) and from Fodor the Computational/Representational Theory of Cognition (Sperber & Wilson, 1986: 71–2). 2 Sperber and
Multi-Breath: Separate Respiration Monitoring for Multiple Persons with UWB Radar

Human respiration state is an important indicator to reflect health conditions. Recent advances in wireless human sensing have enabled device-free respiration monitoring using narrow-band wireless signals, which, however, fail to map the estimated respiration states to multiple persons. In this paper, we present Multi-Breath, a UWB-based system to achieve separate respiration monitoring for multiple persons. The UWB radar can accurately measure the travelling distance of the signals, which helps to separate the signals affected by different persons and map the detected respiration patterns to the corresponding persons with the location information. However, the radar signal time series of each person are quite noisy due to the multi-path effects caused by the respiration movements of other persons, making it difficult to accurately estimate the respiration state. To overcome this challenge, we propose to transform the UWB radar signal matrices of different persons as separate RGB images to reveal the respiration pattern of each individual. Then, the image processing operations, including image smoothing, edge detection, dilation and erosion, are applied to identify the breathing cycles. Finally, the respiration state, including the respiration rate and the presence of apnea, is estimated via blob detection and calibration. Extensive experiments show that the mean absolute error on respiration rate estimation is 0.3 - 0.6 bpm, and the percentage of missed and false detected apnea is 3% - 7%.
Research and Improvement of Single Image Super-Resolution Based on Generative Adversarial Network

This paper proposes a new image super-resolution method based on Generative Adversarial Network (GAN). Firstly, the algorithm model includes generating model and discriminant model, generating model to generate high-resolution image, discriminating model to distinguish the image true or false, the original image is true, and the generated image is false. Using alternate training method, the generated model and discriminant model achieve Nash equilibrium, and finally generate high-quality image. Compared with previous super-resolution method based on generative adversarial network (SRGAN), the following changes have been made: modifying the network structure, removing the unnecessary batch normalization layer in the standard residual block, deepening the network layer number and improving the loss function. The experimental results show that compared with the traditional bicubic interpolation method and compared with SRGAN, the proposed algorithm improves the actual image effect, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in varying degrees.
Artificial Intelligence's Role in Global Camera Trap Data Management and Analytics via Wildlife Insights

Camera traps have existed since the 1890s (Kucera and Barrett 2011), but they weren’t widely used until the introduction of commercial infrared-triggered cameras in the early 1990s (Meek et al. 2014). Since then, millions, perhaps billions of camera trap images have been collected for many reasons, biodiversity monitoring being one of the key applications. Unfortunately, although there are camera trap deployments all over the world, these operations occur in isolation, limiting the impact they could have on a global understanding of biodiversity health. Even within individual institutions, managing and analyzing multiple camera trap deployments in aggregate can be challenging. In fact, managing a single deployment of camera traps is non-trivial and important data are frequently cast aside as bycatch, left unanalyzed on decaying hard drives. Wildlife Insights attempts to overcome these hurdles by providing camera trap data upload, management, and analysis services. It provides the world’s largest database of camera trap images by bringing together the camera trapping efforts of several the world’s largest conservation and research organizations, and it is open to future contributors. Artificial Intelligence-driven services sit at the heart of the platform. New camera trap data uploads are automatically analyzed to differentiate between images with people, non-human animals, and no animals. The images with non-human animals are further analyzed to detect specific species. The proposed labels are sent back to the submitter for review and then uploaded to the database. All uploaded images, unless specifically embargoed, are immediately available for analysis by all users of the system. A selection of tools are provided to support analyses of global biodiversity. This presentation will describe Wildlife Insights and its AI implementation in detail, contextualized by case studies using analyses of the data currently stored on the platform. Challenges around integrating camera trap data within the platform and with other external services that work with the platform will also be discussed. The talk will end with some thoughts about future directions for the AI services, especially with regards to integration with related platforms.
Subsynchronous Resonance and FACTS-Novel Control Strategy for Its Mitigation

The subsynchronous resonance (SSR) is an important problem in the power system, and especially the series compensated transmission lines may cause SSR in the turbine generators, such that it leads to the electrical instability at subsynchronous frequencies and potential turbine-generator shaft failures. Taking the Vietnamese Vungang thermal plants as an example, a shaft failure of Vungang I thermal power unit has occurred on November 24, 2015, due to SSR. The main cause for this failure is a resonance caused by the series capacitors on the 500 kV grid. This paper analyzes the SSR based on the location of shaft cracks and turbine generator mode shape for Vungang I and II thermal power plants. On the basis of that, it develops a novel control strategy for each Flexible AC Transmission system (FACTS) device as the thyristor controlled series compensator (TCSC), static VAR compensator (SVC), and static synchronous compensator (STATCOM). Then they are comparable to one another in order to choose a feasible solution for mitigating the SSR. The effectiveness of the proposed control strategy is verified via time domain simulation of the Vietnamese 500/220 kV transmission system using EMTP-RV and PSS/E programs. The obtained results show that the proposed strategy for SVC can be applied to immediately solve the difficulties encountering in the Vietnamese power system.
From BPMN2 to Event B: A Specification and Verification Approach of Workflow Applications

The BPMN2 language suffers from the absence of a precise formal semantics of the various notations used, which often leads to ambiguities. In addition, this language does not have a proof system that validates a BPMN2 specification. Consequently, the use of a formal method, such as Event B, is a solution for dealing with the shortcomings found in the BPMN2 language. We propose in this paper a model-driven approach based on meta-model and meta-model transformation implemented in KerMeta to specify and formally verify workflows.
Acoustic Evaluation of Simplifying Hypotheses Used in Articulatory Synthesis

Articulatory synthesis allows the link between the temporal evolution of the Vocal Tract (VT) shape and the acoustic cues of speech sounds to be investigated unlike other approaches of speech synthesis. A number of simplifying assumptions have to be made to enable the speech signal to be generated in a reasonable time. They mainly consist of approximating the propagation of the sound in the vocal tract as a plane wave, approximating the 3D VT shape from the mid-sagittal shape, and simplifying the vocal tract topology by removing small cavities. This work is dedicated to the evaluation of these assumptions. For this purpose 3D MRI VT shapes for sustained vowels and fricatives were acquired together with the speech sound recorded via an optical microphone. Vocal tract resonances were evaluated from the 3D acoustic simulation computed with the K-wave Matlab package from the complete 3D VT shape and compared to those of real speech, those provided by simplified 3D VT shapes (near epiglottis), and those provided by either the electric simulation from the mid-sagittal shape, or the 2D K-wave package from a mid-sagittal VT shape. We will exploit these results to improve the simplified acoustic simulations used in articulatory synthesis.
Toward Secure Microfluidic Fully Programmable Valve Array Biochips

The fully programmable valve array (FPVA) is a general-purpose programmable flow-based microfluidic platform, akin to the VLSI field-programmable gate array (FPGA). FPVAs are dynamically reconfigurable and, hence, are suitable in a broad spectrum of applications involving immunoassays and cell analysis. Since these applications are safety critical, addressing security concerns is vital for the success and adoption of FPVAs. This study evaluates the security of FPVA biochips. We show that FPVAs are vulnerable to malicious operations similar to digital and flow-based microfluidic biochips. FPVAs are further prone to new classes of attacks—tunneling and deliberate aging. This study establishes security metrics and describes possible attacks on real-life bioassays. Furthermore, we study the use of machine learning (ML) techniques to detect and classify attacks based on the golden and real-time biochip state. In order to boost the classifier’s performance, we propose a smart checkpointing mechanism. Experimental results are presented to showcase: 1) best-fit ML model classifier; 2) performance of different tradeoffs in checkpointing; and 3) effectiveness of the proposed smart checkpointing scheme.
Classification of Pulmonary Nodules by using Improved Convolutional Neural Networks

Nowadays, malignant tumors, especially lung cancer, have become the number one killer of people's lives. The early clinical manifestations of lung cancer are mostly isolated pulmonary nodules, and the CT images show a round white area. In this paper, we studied about the image segmentation and lung nodule judgment in the diagnosis of CT image. Firstly, a segmentation method of lung region of interest was used to separate the ROI region, and then based on the the classical convolutional neural network VGGNet network model structure, the last three layers of the full connection layer are improved, and the convolution layer is used instead of the full connection layer to maintain the accuracy of the model and accelerate the training speed of the network.
Probabilistic Distribution Modeling of Crosstalk in Multi-Conductor Transmission Lines via Maximum Entropy Method

A novel statistical method, referred to as the maximum entropy (MaxEnt) method is proposed in this paper for effective probabilistic modeling of crosstalk in multi-conductor transmission lines (MTLs). The principle of the MaxEnt method states that for the given information constraints, the probability distribution that has the MaxEnt is considered to be the most unbiased and reasonable one. As for low- and high-dimensional spaces composed of the input random variables in MTL, the statistical moments of crosstalk required by the MaxEnt method as its information constraints are obtained through full-factorial numerical integration and sparse grid numerical integration, respectively. Then, a probability distribution model of crosstalk in MTL is established based on the MaxEnt method. Compared with the conventional Monte Carlo method, the proposed method can not only accurately predict the probability distribution and statistics of crosstalk in MTL but also considerably increases the computational efficiency. Therefore, the MaxEnt method is effective in modeling the probability distribution of crosstalk in MTL.
The Role Software Project Scope Verification in Software Development

This research aims to shed light on one of the important factors that play a prominent role in the success of the software project, namely the process of verifying the scope of the software product and its compatibility with the requirements of the end users. This paper has reached important results through the field study and the survey conducted That the verification of the scope of the project plays an important and influential role in the completion of the software product within the cost and time specified and if there is any defect in this leads to negative results and failure of the software project
Is the Threat of Reemployment Services More Effective than the Services Themselves? Evidence from Random Assignment in the UI System *

We examine the effect of the Worker Profiling and Reemployment Services system. This program "profiles" Unemployment Insurance (UI) claimants to determine their probability of benefit exhaustion and then provides mandatory employment and training services to claimants with high predicted probabilities. Using a unique experimental design, we estimate that the program reduces mean weeks of UI benefit receipt by about 2.2 weeks, reduces mean UI benefits received by about $143, and increases subsequent earnings by over $1,050. Most of the effect results from a sharp increase in early UI exits in the treatment group relative to the control group. (JEL J650)
SDN‐based wireless body area network routing algorithm for healthcare architecture

The use of wireless body area networks (WBANs) in healthcare applications has made it convenient to monitor both health personnel and patient status continuously in real time through wearable wireless sensor nodes. However, the heterogeneous and complex network structure of WBANs has some disadvantages in terms of control and management. The software‐defined network (SDN) approach is a promising technology that defines a new design and management approach for network communications. In order to create more flexible and dynamic network structures in WBANs, this study uses the SDN approach. For this, a WBAN architecture based on the SDN approach with a new energy‐aware routing algorithm for healthcare architecture is proposed. To develop a more flexible architecture, a controller that manages all HUBs is designed. The proposed architecture is modeled using the Riverbed Modeler software for performance analysis. The simulation results show that the SDN‐based structure meets the service quality requirements and shows superior performance in terms of energy consumption, throughput, successful transmission rate, and delay parameters according to the traditional routing approach.