Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel wireless visual sensor network protocol based on LoRa modulation

At present, aiming at complexity of topology and limited network coverage for wireless visual sensor networks so that partial application cannot be implemented. Considering this situation, a novel low-power wide area network protocol is proposed with the purpose of overcoming this disadvantage, which combines LoRa modulation technique with embedded microprocessor technology. This protocol has the advantages of low power consumption, wide coverage, good extensibility, and simple topological structure. This protocol applies to the related fields such as intrusion detection for video intelligent monitoring, which has certain reference significance to research in the wireless visual sensor network field.
A framework for spatiotemporal prediction with small and heterogeneous data - and an application to consumer price indexes -

This text considers the prediction of consumer price indexes which allow to compare the consumer price level across time and space. For specificity, the discussion is in terms of German counties and the years 1993–2014, but all methods apply more generally. The first part of this text outlines the application, and the second part presents some corresponding theory for estimation and prediction.
Regression model focused on query for multi documents summarization based on significance of the sentence position

Document summarization is needed to get the information effectively and efficiently. One method used to obtain the document summarization by applying machine learning techniques. This paper proposes the application of regression models to query-focused multi-document summarization based on the significance of the sentence position. The method used is the Support Vector Regression (SVR) which estimates the weight of the sentence on a set of documents to be made as a summary based on sentence feature which has been defined previously. A series of evaluations performed on a data set of DUC 2005. From the test results obtained summary which has an average precision and recall values of 0.0580 and 0.0590 for measurements using ROUGE-2, ROUGE 0.0997 and 0.1019 for measurements using the proposed regression-SU4. Model can perform measurements of the significance of the position of the sentence in the document well.
Novel Hybrid Extraction Systems for Fetal Heart Rate Variability Monitoring Based on Non-Invasive Fetal Electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.
A Survey on Biomedical Named Entity Recognition and Normalization

With a rapidly-growing amount of biomedical information available only in textual form, there is considerable interest in applying NLP techniques to extract such information from the biomedical literature. Much of the research has paid special attention to extracting information about biomedical named entities. In this paper, we conducted a survey on biomedical named entity recognition and normalization, focusing on gene mention recognition and normalization. We believe this can help researchers to find work of their interest and interpret their own research.
A Review on Different Opinion and Aspect Mining Techniques

With the rising popularity of internet, online drug reviews have been proved to be extremely helpful for patients suffering from chronic diseases. Most of the patients search upon online reviews before taking any medicine. Online reviews, blogs, and discussion forums such as WebMD on chronic diseases and medicines are becoming important supporting resources for patients. Extracting useful information from these reviews is very difﬁcult and challenging. Opinion mining or aspect mining involves the extraction of useful information (e.g. positive or negative sentiments of a product) from a large quantity of text opinions or reviews given by Internet users. Various algorithms had been proposed to extract information from the opinion of web users. Some of the algorithms are LDA, sLDA, NMF, SSNMF, DiscLDA and PAAM. A detailed review of the most important opinion mining algorithms is presented and a comparison among the discussed techniques is given.
Using a Scalable Feature Selection Approach For Big Data Regressions

Logistic regression is a widely used statistical method in data analysis and machine learning. When the capacity of data is large, it is time-consuming and even infeasible to perform big data machine learning using the traditional approach. Therefore, it is crucial to come up with an efficient way to evaluate feature combinations and update learning models. With the approach proposed by Yang, Wang, Xu, and Zhang (2018) a system can be represented using small enough matrices, which can be hosted in memory. These working sufficient statistics matrices can be applied in updating models in logistic regression. This study applies the working sufficient statistics approach in logistic regression machine learning to examine how this new method improves the performance. This study investigated the difference between the performance of this new working sufficient statistics approach and performance of the traditional approach on Spark\rq s machine learning package. The experiments showed that the working sufficient statistics method could improve the performance of training the logistic regression models when the input size was large.
Markov Processes in Image Processing

Digital images are used as an information carrier in different sciences and technologies. The aspiration to increase the number of bits in the image pixels for the purpose of obtaining more information is observed. In the paper, some methods of compression and contour detection on the basis of two-dimensional Markov chain are offered. Increasing the number of bits on the image pixels will allow one to allocate fine object details more precisely, but it significantly complicates image processing. The methods of image processing do not concede by the efficiency to well-known analogues, but surpass them in processing speed. An image is separated into binary images, and processing is carried out in parallel with each without an increase in speed, when increasing the number of bits on the image pixels. One more advantage of methods is the low consumption of energy resources. Only logical procedures are used and there are no computing operations. The methods can be useful in processing images of any class and assignment in processing systems with a limited time and energy resources.
Integration of traffic and network simulators for vehicular ad-hoc networks

Abstract Simulation is the replication of the process of the real-world process. Research tool selection is important for generating the realistic scenario. VanetMobiSim is used to generate the realistic scenario for vehicular mobility model and that traffic file simulated by network simulator NS-2.34. With the help of this step, one can generate the simulation results and run simulations for different scenarios to evaluate the performance of the on-demand routing protocols for VANET in terms of different performance parameters those are average throughput, PDF, NRL, average end-to-end delay and packet loss. In this paper, discuss the Integration of traffic and network simulators i.e. VanetMobiSim and NS-2 for vehicular adhoc network simulation. That Integration is useful for calculate the throughput for VANETs.
A Survey on Comparisons of Cryptographic Algorithms Using Certain Parameters in WSN

The Wireless Sensor Networks (WSNs) have spread its roots in almost every application. Owing to their scattered nature of sensor nodes, they are more prone to attacks. There are certain applications e.g. military, where sensor data’s confidentiality requirement during transmission is essential. Cryptography has a vital role for achieving security in WSNs.WSN has resource constraints like memory size, processing speed and energy consumption which bounds the applicability of existing cryptographic algorithms for WSN. Any good security algorithms has higher energy consumption by the nodes, so it’s a need to choose most energy-efficient cryptographic encryption algorithms for WSNs. This paper surveys different asymmetric algorithms such as RSA, Diffie-Hellman, DSA, ECC, hybrid and DNA cryptography. These algorithms are compared based on their key size, strength, weakness, attacks and possible countermeasures in the form of table.
Design of Rice Lesion Identification System based Embedded

Research the overlap of rice lesion segmentation.The traditional rice lesion segmentation algorithm directly processes the lesion pixels,which easily leads to the loss of pixel information,a bad segmentation,and low recognition rate.For this problem,we propose a probabilistic model based on pixel rice lesion segmentation.By adaptive lesions segmentation algorithm,Gaussian mixture model is used to describe the color distribution of each pixel,and the maximum fitness value is as the current distribution model for sub-model to describe the characteristics of each lesion pixel,avoiding the pixel direct operation.Experiments show that this method can effectively separate most of the characteristics of overlapping lesion of rice and achieved better results
Efficient Feature Extraction in Sentiment Classification for Contrastive Sentences

Sentiment Classification is a special task of Sentiments Analysis in which a text document is assigned into some category like positive, negative, and neutral on the basis of some subjective information contained in documents. This subjective information called as sentiment features are highly responsible for efficient sentiment classification. Thus, Feature extraction is essentially an important task for sentiment classification at any level. This study explores most relevant and crucial features for sentiment classification and groups them into seven categories, named as, Basic features, Seed word features, TF-IDF, Punctuation based features, Sentence based features, N-grams, and POS lexicons. This paper proposes two new sentence based features which are helpful in assigning the overall sentiment of contrastive sentences and on the basis of proposed features; two algorithms are developed to find the sentiment of contrastive sentences. The dataset of TripAdvisor is used to evaluate our proposed features. Obtained results are compared with several state-of-theart studies using various features on the same dataset and achieve superior performance.
Relative visual localization (RVL) for UAV navigation

Most of today's UAVs make use of multi-sensor GNSS/INS fusion for localization during navigation. In such a context GNSS systems are used as a compact and cost-effective way to constrain the unbounded error induced by the INS sensors on the localization. Unfortunately, GNSS systems have been proven to be unreliable in multiple contexts. The drawback of such an approach resides in the radio communications necessary to acquire the localization data. Radio communication systems are prone to availability problems in some environments, to signal alteration and to interference. The root cause of the problem resides in the use of global information to solve a local problem. In this work, we propose the use of local visual information to perform relative localization in an unknown outdoor environment. The algorithm uses feature point methods to extract salient points from a set of images pertaining to possible matches during the navigation. The extracted features are matched with available visual data stored during previous navigation or from an aerial view map. Different feature extraction techniques were analyzed, and ORB was the one that gave the best mean absolute error. The estimated distance between the best match and ground-truth localization was within 70 meters on average at an altitude of 150 meters. Experimental tests were conducted on outdoor videos captured using a quadcopter. The obtained results are promising and show the possibility of using relative visual data in GPS/GNSS-denied environments to improve the robustness of UAVs navigation.
Evaluation of Undergraduate Teaching Quality Base on Cloud Mode

Evaluation of undergraduate teaching quality is an important task in education management.It have very important influence on improving undergraduate teaching quality.This paper brings forth a method undergraduate teaching quality evaluation based on cloud model by applying cloud model,Delphi and multifactor synthetic judgment model.Last,this paper experiments with a typical example,and analyzes the feasibility of the application.
Spark Distributed Medical Image Segmentation Method Based on KFCM-CV Model

Medical image segmentation is great significance for the study of medical diagnosis and pathology. In order to process medical images effectively and quickly, a medical image processing method based on Spark is proposed in this paper. Firstly, a model for medical image segment based on improved fuzzy kernel clustering and CV model is built, then the medical images are processed in batches by using this model on Spark. Simulation experiments show that the method proposed in this paper achieves efficient and accurate segmentation.
IN VITRO ANTIOXIDANT CAPACITY AND TOTAL PHENOLIC CONTENT OF TOMATO INDUSTRIAL WASTE

Antioxidants are bioactive compounds important for human health. Tomato pomace is a residue generated during tomato processing, composed of tomato skin, pulp and seeds. This paper evaluated the antioxidant capacity and total phenolic content of tomato pomace. Samples were collected from a processing plant located in Goiânia, Goiás, Brazil in 6 consecutive work days and freeze-dried. In vitro antioxidant capacity was evaluated trough DPPH, TEAC and FRAP assays, and total phenolic content trough the Folin-Ciocalteu assay. Average results for the 6 batches, in dry weight, were: DPPH 36.02 ± 4.13 % inhibition of DPPH radical/g, TEAC 1308.41 ± 54.26 μmol trolox equivalent/100g, FRAP 1123.41 ± 63.98 μmol ferrous sulfate equivalent/100g, and total phenolic content 15.77 ± 1.69 mg gallic acid equivalent/100g. Tomato pomace presented a high antioxidant activity, and a low total phenolic content. It could be used as source of antioxidant bioactive compounds.
Passive amplitude and hybrid parasitic beam steerable array antennas.

Highly directive antennas with beam steering capabilities are needed to overcome high path-loss, mitigate against interference from unwanted signals and to ensure network connectivity for the next generation 5G millimetre wave communication systems. Phased arrays using phase shifters are the conventional approach of achieving beam steerable antennas. However, due to the insertion loss incurred by phase shifters and the high cost of acquiring phase shifters, alternative solutions that offers lower loss and cost are being researched. This work proposes two beam steerable antenna solutions that offer reduced insertion loss and cost. The first beam steering solution is a novel passive amplitude steering antenna that makes use of a reconfigurable ratio power divider instead of the conventional variable gain amplifiers for amplitude control. The designed antenna is a 2X1 antenna array which achieves continuous steering over the range 0 degree to 21 degrees in one direction. The second beam steering solution combines the parasitic steering technique with the phased array approach to form a hybrid parasitic antenna. The hybrid parasitic antenna was formed from a 3X1 phased antenna array surrounded by eight parasitic elements. The parasitic elements are operated by switches and the antenna achieved a fine steering resolution of 1 degree. Computer simulations were performed to assess the effect of using real switches and phase shifters within the antenna. To this effect, 3-bit phase shifters were incorporated into the hybrid parasitic antenna and its performance compared with that of a conventional 3X1 phased array incorporating 5-bits phase shifters. The realised gain of the hybrid parasitic antenna was 4 dB better than that of the conventional phased array. This suggests that the novel antenna offers significant improvement in antenna performance. This work also proposed a novel approach that improves the isolation bandwidth of a power divider which will be vital for phased arrays and MIMO applications. The compensated power divider achieves a 15 dB isolation bandwidth of 114% which is more than 40% of the isolation bandwidth of conventional Wilkinson power divider. This power divider solution is suitable for ultra-wideband frequency applications.
Collaborative Power Management Through Knowledge Sharing Among Multiple Devices

Rapidly evolving embedded applications continuously demand more functionalities and better performance under tight energy and thermal budgets, and maintaining high energy efficiency has become a significant design challenge for mobile devices. Learning-based methods are adaptive to dynamic conditions and show great potential for runtime power management. However, with the ever-increasing complexity of both hardware and software, it is a challenging issue for a learning agent to explore the state-action space sufficiently and quickly find an efficient management policy. In this paper, we propose a reinforcement learning-based multi-device collaborative power management approach to address this issue. Multiple devices with different runtime conditions can acquire related knowledge during the learning process. Efficient knowledge sharing among these devices can potentially accelerate the learning process and improve the quality of the learned policies. We integrate the proposed method with dynamic voltage and frequency scaling on the multicore processors in mobile devices. Experimental results on realistic applications show that the collaborative power management can achieve up to a $7 \times$ speedup and 10% energy reduction compared with state-of-the-art learning-based approaches.
An equivalent strut and tie model for steel plate shear-wall systems

Steel Plate Shear Walls (SPSWs) have been accepted widely as an effective lateral load resisting system. For seismic performance evaluation of a multi-story building with SPSWs, detailed finite element models or a strip model can be used to represent the SPSW components. However, analysing such models often require significant effort for tall or medium height buildings. Using equivalent bracing to replace the infill plate can significantly simplify the analysis process. The focus of this article is to develop such a model to represent SPSWs in a building frame. This would facilitate a simplification to the structural modelling of large buildings with SPSWs in order to evaluate the seismic performance using regular structural analysis tools. The study shows that proposed model can capture the global behaviour of the structures quite accurately and potentially aid in performance-based seismic design of SPSW buildings.
Using Decomposition to Solve Facility Location/Fleet Managment Problems

Using Decomposition to Solve Facility Location/Fleet Management Problems Mohammad Mehdi Fazel Zarandi Master of Applied Science Graduate Department of Mechanical and Industrial Engineering University of Toronto 2010 The central thesis of this dissertation is that logic-based Benders decomposition can be effective in solving deterministic and stochastic facility location and fleet management problems. We demonstrate this thesis by developing logic-based Benders decomposition models for a facility location/fleet management problem from the literature and for a stochastic extension of the problem. We experimentally show the effectiveness of logic-based Benders in solving such problems. To our knowledge, this is the first work on solving deterministic and stochastic facility location/fleet management problems using logic-based Benders decomposition.
Dynamic Inversion with Neural Network For Aircraft Attitude Control

Classical dynamic inversion has been applied for the most types of aircrafts. It is a feedback linearization designed method and it is employed to linearize the dynamic behaviour that is separated into slow and fast dynamics. However, classical dynamic inversion controllers need an accurate dynamic model in order to satisfy the objective desired command and it has been shown previously that it is a sensitive to the unknown uncertainties. In this study, we combine the Radial Basis Function neural network for controller with dynamic inversion through the outer loop to obtain the desired dynamics that can improve the system robust stability. In addition, with different training parameters, the same network structure is used for the system model identification in order to obtain the sensitivity function. This function is necessary to support the neural network in the outer loop through the training algorithm so that the input command tracking can be achieved. Finally simulation results for the aircraft attitude control motion were presented to assure the robustness of this design approach when the input time delay is considered. Keywords—Dynamic Inversion, Aircraft Control, Neural Network, Nonlinear Control, System Identification.
UCSB-Math-2009-27 Mapping 6 D N = 1 supergravities to F-theory

We develop a systematic framework for realizing general anomaly-free chiral 6D supergravity theories in F-theory. We focus on 6D (1, 0) models with one tensor multiplet whose gauge group is a product of simple factors (modulo a finite abelian group) with matter in arbitrary representations. Such theories can be decomposed into blocks associated with the simple factors in the gauge group; each block depends only on the group factor and the matter charged under it. All 6D chiral supergravity models can be constructed by gluing such blocks together in accordance with constraints from anomalies. Associating a geometric structure to each block gives a dictionary for translating a supergravity model into a set of topological data for an F-theory construction. We construct the dictionary of F-theory divisors explicitly for some simple gauge group factors and associated matter representations. Using these building blocks we analyze a variety of models. We identify some 6D supergravity models which do not map to integral F-theory divisors, possibly indicating quantum inconsistency of these 6D theories.