Miracle-dz/UniFAR-FTUs huggingface dataset:
Centimeter-Grade Metropolitan Positioning for Lane-Level Intelligent Transportation Systems Based on the Internet of Vehicles

This paper presents a centimeter-grade metropolitan positioning (CGMP) framework for achieving lane-level localization through Internet of Vehicles (IoV) communications. The CGMP framework integrates vehicle-to-roadside (V2R) and vehicle-to-vehicle (V2V) communications with installed roadside/intersection cameras to provide positioning information to vehicles on the road. This framework can be used to estimate the precise position of a vehicle and detect the lane of each vehicle. The estimated positions and detected lanes can be broadcast to nearby vehicles through V2R and V2V communications. According to our review of relevant research, CGMP is the first positioning system to provide the following features: centimeter-grade positioning accuracy through IoV communications that can detect a vehicle's current lane; the employment of existing roadside/intersection cameras, thus keeping additional construction costs low; and an innovative infrastructure for the application of next-generation intelligent transportation systems that can enable lane-level traffic control, collision avoidance, and vehicle navigation. A prototype based on wireless access in vehicular environments/dedicated short-range communications is implemented to verify the feasibility and effectiveness of the proposed framework. The experimental results indicate that fully deployed CGMP can achieve centimeter-grade positioning accuracy with the error around 10 cm, and partially deployed CGMP can significantly reduce the positioning errors of existing localization methods.
Image Registration Based on Regions and Features

Image registration is an issue in imaging processing.Stereopsis is a main way of obtaining depth information for human and animal's vision system.Registration is the main step for stereo metering.In this paper,registration based on area and Barnard method based on edge characteristic point are implemented.Intuitive analysis about experiment results are given.
Information gathering and obfuscation network devices identification for persistent intrusive evaluations

When a security assessment is executed, time and resources are essential, therefore it is important to identify those points that are most likely vulnerable and focus on those. Such points are identi ed in the Information gathering stage turning it in a vital step during the assessment. Emerging the necessity to know if gathered information have been manipulated by a third party either by functionality or by obscuring information goals. This research propose a classi er in order to identify information into obfuscated or integral, as well as the type of obfuscation device, focusing on NAT networks, Protocol Scrubbers and hardened hosts. Through the use of KNN, Naive Bayes, Support Vector Machines and Decision Tree algorithms. Having as a result the correct information classi cation and therefore identi cation and type of obfuscation device. Up to now, there are no researches of this type, there exist researches during an assessment as can be OS Fingerprinting, also in the defensive area, where were proposed security controls to prevent such Fingerprinting, as Protocol Scrubbers, Hardened hosts, and NAT networks, however there are not in the o ensive area, as this research proposed to identify such security controls able to obfuscate information and the obfuscated information. Also did contributions in new techniques to identify Operating Systems through interactions with the target, letting identify it without being in the same collision domain, impacting in new research topics like identifying a Moving Target Defence security control.
Design of Low Complexity SM4 Block Cipher IP Core

A low complexity SM4 IP core is designed aiming at the applications which require medium throughput and very low area and cost.The IP can work at ECB(Electronic Codebook) and another mode at which every block needs a unique key.The IP is synthesized using the Altera's FPGA-Cyclone and under the SMIC 0.18 μm CMOS process.The IP costs 1704 logic elements and 0.113 mm2 respectively.
Image Quality Assessment Based on Dual-scale Edge Structure Similarity

The quality assessment of images is meaningful for most video and images applications.The structure similarity index(SSIM) shows some defects when evaluating the quality of blur images and noise images,the results don’t match the HVS system well.This paper proposes a new image quality assessment algorithm based on dual-scale edge structure similarity(MDESSIM). The edge information is appreciated sufficiently during the structure information in this algorithm.The distortion metric of edge structure is included in the assessment of the image quality.Experimental results demonstrate the MDESSIM algorithm is effective.
A Geographic Ontology Fusion Method Based on Granular Theory

A geographic ontology fusion method based on granular theory is presented.First, Formal concept analysis(FCA) is used to formalize different geographic domain ontologies.Then granular computing is introduced to geo-ontologies fusion to implement concept lattice reduction in different granulations.Finally,experiments are conducted by combing fundamental geographic information data and spatial data in the hydraulic engineering domain.The results show that Geo-ontologies fusion is a progressive process,and the proposed method is valid in multi-source Geo-ontologies fusion.As well as merits and limitations are discussed.
Application of data persistence tier based on ibatis and design pattern

The data persistent layer is the significant point of exploitaion in JavaEE application.The author aimed to build a persistent framework in JavaEE system which could also satisfy new application without rebuilding the whole system but just modified a little part of this framework.And combined to the exploitation of Woss in certain common carrier,made a deep research on framework and design pattern about ibatis,gave the process to realize the framework in detail.Thus,the improved ibatis framework and design pattern was proved to be practicable and effective.
Data Mining Based Approach to Preprocessing TCM Data Set

Region’s differences of TCM’s culture lead to much uncertainty in TCM,to solve the data problem of decision support system for new medicine development based on data mining,a series of processing methods to standardize the original TCM data are proposed.Data reduction technology,clustering analysis and fuzzy set theory are applied to improve the quality of TCM data,getting important rules from the preprocessed TCM database,and providing powerful decision support for exploring new medicine.
The Relationship Between Future Career Self Images and English Achievement Test Scores of Japanese STEM Students

Background: College and university science, technology, engineering, and mathematics (STEM) students in Japan, who hope for careers in science and technology fields, lack the motivation to learn English as a second language (L2), impairing their current capacities to learn the L2 and their future abilities to communicate globally once employed. Literature review: Although these students’ motivation to learn English seems strongly linked to the external pressure to do well on a standardized English test, gain employment, and progress up the career ladder, this extrinsic motivation may not be as beneficial for their L2 learning as positive images of themselves using English in future situations. Three types of future career-related self images—an Ideal L2 Self, a Probable L2 Self, and an Ought-to L2 Self—are hypothesized to promote L2 achievement. Research methodology: Data from questionnaires examining psycholinguistic variables for 1013 Japanese STEM students of English were subjected to ANOVA and multiple regression analysis with three L2 Self variables as predictor variables and scores from the TOEIC standardized English exam as the outcome variable. Results/discussion: ANOVA results showed that students had a strong image of themselves as needing English for future career goals, as measured by the Ought-to L2 Self, but had lower levels of Ideal L2 Self, the variable measuring a future image as a fluent user of English. In the regression analysis, the Ought-to L2 Self predicted lower TOEIC exam scores; conversely, the Ideal L2 Self predicted greater TOEIC scores. These paradoxical results indicate that Japanese STEM students struggle motivationally to improve English skills needed for future STEM job-related communication, despite feeling pressured to do so. Conclusions: To encourage the formation of students’ images of Ideal L2 selves or stronger Probable L2 selves, STEM teachers and language teachers of Japanese STEM students could introduce motivational interventions. For example, positive role models of English language learners could visit classes and demonstrate how they have applied their English as a foreign language (EFL) learning experiences to future careers.
Application of Urban Population Prediction Based on Modified Logistic Model

The population system is a very complicated nonlinear system and an accurate prediction of urban population will provide the important basis for economy sustainable development.Two modified Logistic models were first applied to predict urban population in this paper based on historical population data of Fuzhou City from 1961 to 2005,the population was predicted and compared to several traditional models,parameters were evaluated by modified simplex method.The results showed that the forecasting result of modified Logistic models was more precise and effective than that of traditional models.
Trading ETFs with Deep Q-Learning Algorithm

This paper reports the use of double deep Q-learning (DDQN) for trading exchange-traded funds (ETFs). When compared with the buy-and-hold strategy, the proposed approach has better return in a downtrend market and comparable return in a sideways market. The buy-and-hold has some advantage in an uptrend market.
On EDA Solutions for Reconfigurable Memory-Centric AI Edge Applications

Memory-centric designs deploy computation to storage and enable efficient in-memory computation while avoiding massive amount of data movement. The in-memory-computing schemes have shown distinct advantages and concerns when applying to different types of memory technologies, from conventional SRAM, DRAM to emerging ReRAM. Moreover, the next-generation smart edge systems are expected to support various intelligent applications by employing multi-task machine learning models which would be dynamically activated. To attain an efficient design within short design cycle, it is imperative to have an integrated design framework with automated tools to support hybrid memory systems and perform effective optimization across design stages. This work will introduce a unified framework which integrates EDA solutions to address the design and optimization challenges at different aspects of next-generation memory-centric designs, including fast reconfiguring in-memory/near-memory computing designs to provide optimized solutions (behavioral models and APR cell layouts) for designers to choose the best suitable architectures for their applications.
Practical Implementation of Computational Algorithms for Efficient Power Conversion in Photovoltaic Energy Generation Systems

Photovoltaic energy generation systems are characterized by the dependence of their output power on environmental conditions and load matching. The power conversion stage serves as an intermediate adaptation interface that matches the operating point of the source to an optimal operating point (Maximum Power Point MPP). In this paper, different versions of computational intelligence based Maximum Power Point Tracking (MPPT) algorithms illustrated using the Particle Swarm Optimization algorithm are implemented in a DSP-based experimental setup to evaluate their performance for tracking the MPP in various operating conditions. Experimental results show the enhanced power conversion efficiency obtained by using computational intelligence techniques in photovoltaic energy generation systems.
Video quality assessment in underwater acoustic networks

Las imagenes subacuaticas reciben una atencion cada vez mayor por parte de la comunidad cientifica dado que las fotografias y los videos son herramientas de gran valor en el estudio del entorno oceanico que cubre el 90% de la biosfera de nuestro planeta. Sin embargo, las Redes de Sensores Submarinas deben enfrentarse al canal hostil que el agua de mar constituye. Las comunicaciones de medio rango son solo posibles con modems acusticos de capacidades muy limitadas con tasas binarias de pico de unas decenas de kbps. En transmision de video, estas reducidas tasas binarias fuerzan una compresion elevada que produce niveles de distorsion mucho mayores que en otros entornos. Ademas, los usuarios de video submarino son oceanografos u otros especialistas con una percepcion de la calidad diferente a la de un grupo generico de usuarios. Las peculiaridades descritas exigen un estudio dedicado de la evaluacion de calidad de video para redes submarinas. Esta tesis doctoral aborda el problema de la evaluacion de calidad de video y presenta contribuciones en las dos areas principales de esta disciplina: evaluacion subjetiva y evaluacion objetiva. La referencia para la percepcion de calidad en cualquier servicio es la opinion de los usuarios y, por tanto, un analisis de la calidad subjetiva es el primer paso en este trabajo. Se presentan el diseno experimental y los resultados de un test de acuerdo a metodos psicometricos estandares. Los participantes del test fueron cientificos del oceano y las secuencias de video utilizadas fueron grabadas en campanas de exploracion y procesadas para simular las condiciones de las comunicaciones submarinas. Los resultados experimentales muestran como los videos son utiles para tareas cientificas incluso en condiciones de muy baja tasa binaria. Los metodos de evaluacion de la calidad objetiva son algoritmos disenados para calcular puntuaciones de calidad. Son una herramienta esencial en las fases de planificacion y operacion de redes dado que los experimentos de calidad subjetiva son caros e inviables para algunas tareas. Esta tesis presenta tres modelos para calidad de video objetiva basados en tecnicas de aprendizaje maquina y disenados especificamente para los requisitos de las redes submarinas. El primer metodo se centra en la simplicidad y calcula una estimacion de calidad con dos parametros de aplicacion. El segundo utiliza regresion logistica ordinal para estimar la distribucion de puntuaciones completa a partir de una combinacion de parametros de aplicacion y de metricas de imagen. El tercer modelo se basa en el mecanismo de percepcion del sistema visual humano y estima la calidad a partir de un analisis de la distorsion. El analisis de rendimiento muestra como los metodos propuestos superan a otros existentes que no han sido disenados para video submarino.
Research of algorithm for automatic opinion leader detection in BBS

Detection of"opinion leaders"automatically in BBS is significant for us to grasp and guide the public opinion in time.Consequently,we proposed an algorithm which can detect the"opinion leaders"in BBS automatically.After analyzing the essential attributes of"opinion leaders",a X-means iteration clustering filter model based on the Bayesian information gain maximization formula was proposed to find out the opinion leader feature points in the feature space.The experiment shows that the algorithm based on the model has excellent performance on precision and recall,hence the method can monitor the public opinion on the network effectively.
Super-Resolution Imaging Test of Novel Mitochondrial Probe

Fluorescence microscopy (FM) can selectively and specifically detect fluorescence molecules with high signal-to-noise ratio, but unfortunately its resolution cannot exceed half wavelength due to the limitation of optical diffraction. Stimulated emission depletion (STED) microscopy provides sub-diffraction resolution and super-resolution nano-microstructures while preserving useful aspects of fluorescence microscopy. However, widespread use of STED microscopy especially in living cell bioimaging is limited due to high illumination intensity. In this paper, a new type of fluorescent dye that is designed, which is resistant to high-intensity illumination, provides accurate targeting and is well positioned on the mitochondria of cells. The imaging result shows that super resolution imaging with the mitochondrial dye can obtain a clear super resolution image with high signal-to-noise ratio.
Personality as a metric for topic models on social networks

As user-generated content suffers from severe problems of data sparseness, many topic models designed for social network are proposed. Without a unified metric, the methods to weigh topics are mixed. Since topic models like Dirichlet Allocation (LDA) can summarize the main information of news articles, topics of short messages on social networking websites should reveal key features of the authors of messages. Personality is the natural characteristic of human. Past work on personality identification has shown that the words people say on social network can reveal people's personality but none of them compare the effects of different topic model tactics on personality identifying. We run LDA and one of its variant (Twitter-LDA) on real social network data (Facebook status messages) then use the topics distribution as features to identify pre-labelled Big-Five personality traits. The results demonstrate that the likelihood of personality as a metric can discover more features of topic models than the model designer said. Furthermore, our research add values to personality identification.
Normal/Abnormal Magnetic Resonant Brain Images Detection Based on Supervised Classifiers

Automatic classification of brain images as normal or abnormal is imperative for medical analysis and to save hospital resources. Computational methods can provide more information which cannot be attained by visual interpretation. Due to this, a lot of research has been targeted using computer vision to do medical image analysis. In this research, seven brain diseases are used to consider as abnormal brain condition, which include Glioma, Pick's disease, Meningioma, Huntington, Alzheimer, Sarcoma and Alzheimer's disease plus visual agnosia respectively. The dataset has been taken from the website of Harvard Medical School which contains 160 images. A novel combination of supervised classifiers has been used to segregate the MRI brain images. The proposed research will assist the doctors in the detection process of brain image as normal or abnormal for the aforementioned brain diseases.
Design of Reconfigurable Processor Based on the Loop Mapping

A reconfigurable processor is presented to execute the loop automatically in reconfigurable cell array.Data distribution and asymmetric first in first out buffer(FIFO) can speedup the data transfer with 8 times.The hardware architecture is verified on the platform of field-programmable gate array(FPGA) with some kernel algorithms of multimedia applications such as integer invert discrete cosine transform(IDCT) and motion estimation of advanced video coding of moving pictures experts group-4(H.264) and IDCT of moving pictures experts group-2(MPEG-2). With a same scale of reconfigurable array,the performance will be 3.5 times higher than the similar researches.
Multi‐authority attribute‐based encryption scheme with constant‐size ciphertexts and user revocation

Ciphertext‐policy attribute‐based encryption (CP‐ABE) is regarded as one of the most suitable technologies for data access control in cloud storage system. It gives data owners direct and flexible control on access policies. However, there still exists practicality concerns in CP‐ABE applications, for example, the key escrow problem, user revocability, and large ciphertext size. Considering these problems, we propose a multi‐authority attribute‐based encryption scheme with constant‐size ciphertexts and user revocation for threshold access policy in this paper. The security proof shows that the proposed scheme is selectively secure under the augmented multi‐sequence of exponents decisional Diffie‐Hellman assumption, and it also achieves forward security, backward security, and collusion‐resistance.
Software Defect Prediction Using Ensemble Learning: An ANP Based Evaluation Method

Software defect prediction (SDP) is the process of predicting defects in software modules, it identifies the modules that are defective and require extensive testing. Classification algorithms that help to predict software defects play a major role in software engineering process. Some studies have depicted that the use of ensembles is often more accurate than using single classifiers. However, variations exist from studies, which posited that the efficiency of learning algorithms might vary using different performance measures. This is because most studies on SDP consider the accuracy of the model or classifier above other performance metrics. This paper evaluated the performance of single classifiers (SMO, MLP, kNN and Decision Tree) and ensembles (Bagging, Boosting, Stacking and Voting) in SDP considering major performance metrics using Analytic Network Process (ANP) multi-criteria decision method. The experiment was based on 11 performance metrics over 11 software defect datasets. Boosted SMO, Voting and Stacking Ensemble methods ranked highest with a priority level of 0.0493, 0.0493 and 0.0445 respectively. Decision tree ranked highest in single classifiers with 0.0410. These clearly show that ensemble methods can give better classification results in SDP and Boosting method gave the best result. In essence, it is valid to say that before deciding which model or classifier is better for software defect prediction, all performance metrics should be considered.Keywords— Data mining, Machine Learning, Multi Criteria Decision Making, Software Defect Prediction
Single and Multi-Label Environmental Sound Classification Using Convolutional Neural Networks

Artificial neural networks are computational systems made up of simple processing units that have a natural propensity for storing experiential knowledge and making it available for use. In the recent years this technology has seen an exponential growth in the fields of image recognition, natural language processing or speech recognition. However, there is a dearth of research on environmental sound analysis. In combination with IoT and wireless sensor networks, artificial neural networks could help to characterize and therefore better address noise issues present in urban environments. This master thesis investigates the theory and construction of artificial neural net-works for single-label and multi-label multiclass classification of environmental sounds like dog bark, street music or jackhammer. Evaluation to di˙erent cor-ruptions of the sounds are studied, as well as methods to increase robustness to these variations. A convolutional neural network arquitecture is proposed for both tasks. The in-puts to the networks are time-frequency patches extracted from the computed mel-spectrogram of the signals. Dropout and weight decay regularization methods are applied and the cross-entropy loss is optimized using Adam algorithm. Results show that these systems are very sensitive to noise and level corruptions of the inputs. Techniques like data augmentation and amplitude scaling are needed to avoid these issues. Results to the multi-label classification task show that it is still possible for a neural network to learn in a complicated mixed environment. However there is still room for improvement regarding prediction accuracy. Since no previous benchmarks are available for comparison, this study sets the stage for the multi-label classification task using UrbanSound8K dataset.