Miracle-dz/UniFAR-FTUs huggingface dataset:
SeMi-supervised adaptive resonance theory (SMART2)

Adaptive resonance theory (ART) algorithms represent a class of neural network architectures which self-organize stable recognition categories in response to arbitrary sequences of input patterns. The authors discuss incorporation of supervision into one of these architectures, ART2. Results of numerical experiments indicate that this new semi-supervised version of ART2 (SMART2) outperformed ART for classification problems. The results and analysis of runs on several data sets by SMART2, ART2, and backpropagation are analyzed. The test accuracy of SMART2 was similar to that of backpropagation. However, SMART2 network structures are easier to interpret than the corresponding structures produced by backpropagation. <<ETX>>
METHODS FOR ANALYSIS OF DATA FROM SOCIAL NETWORKS

This work focuses on the analysis of online social networking services. We examine several formal definitions of various characteristics (numerical and structural) and introduce appropriate concepts, models and methods that could be useful for the analysis of information obtained from social networks. Preference analysis is proposed to be used to study interpersonal relations. Various modifications of Latané’s dynamic social impact theory are proposed. The authors have studied techniques used to carry out psychological operations, and, as a result, have proposed several formal models. The paper briefly describes a software system that we have developed, which allows extracting, processing, analyzing and visualizing data from online social networking services. Our system has a data extraction module that can retrieve information from social networks such as Twitter, Facebook, and VKontakte.
Using Data-Driven Uncertainty Quantification to Support Decision Making

As data collection and analysis methods become increasingly sophisticated, interpretation and use of results by end users becomes increasingly challenging. In this paper, we discuss the role of data-driven uncertainty quantification in supporting and improving decision making. We illustrate our argument with a case study in seismic onset detection, comparing statistically computed distributions over possible signal onset times to the onset times chosen by a set of domain analysts. Importantly, the uncertainty distributions sometimes identify subtle changes in the seismic waveform that are missed by both point estimate calculations and by domain analysts.
Introducing Fatigue Contour Plot in LS-Pre Post LSDYNA Finite Element Crash Simulation Software

In this study, a computer program for calculating fatigue life of component is developed and introduced in LS-PrePost software. The program is written in Fortran programming language and the fatigue life equations used is taken from well-published literature. The materials covered are steel and aluminum. The developed program is able to read stress, strain and element values from d3plot and the keyword file. Having extracted the output from d3plot and keyword file, the fatigue life is then calculated and presented into a separate file called FATIGUE. The integration of output from FATIGUE will is displayed in LS-PrePost. Finally, the results of fatigue life contour are successfully displayed through LS-PrePost.
PENGURANGAN EMISI CO2, N2O, DAN CH4 DARI KENDARAAN BERMOTOR DI UNIVERSITAS NEGERI MALANG

Universitas Negeri Malang (UM) merupakan salah satu perguruan tinggi yang ada di Kota Malang. Memiliki jumlah mahasiswa terbanyak kedua setelah Universitas Brawijaya (UB). Universitas Negeri Malang memiliki luas wilayah 463.992 m 2 , dengan empat gerbang yang menjadi akses utama keluar masuk kendaraan bermotor. Dengan makin banyaknya perguruan tinggi di Indonesia yang menerapkan konsep Green Campus (Kampus Hijau), maka UM mulai berbenah guna mengaplikasikan konsep tersebut. Langkah awal untuk mewujudkan Green Campus yaitu penyiapan lokasi parkir sehingga tidak ada kendaraan bermotor yang parkir di sepanjang jalan dalam kampus UM. Langkah berikutnya yaitu disiapkannya sejumlah sepeda yang bebas digunakan warga UM sebagai sarana transportasi selama berada dalam wilayah kampus UM. Tujuan utama penelitian ini adalah untuk mengetahui jumlah emisi CO 2 , CH 4 , dan N 2 O yang dihasilkan dari kendaraan bermotor yang masuk ke dalam wilayah UM. Jenis kendaaan bermotor dibagi menjadi motor (MC), kendaraan ringan (LV), dan kendaraan berat (HV). Proses analisis jumlah emisi berpedoman pada Intergovermental Panel on Climate Change (IPCC), dimana menggunakan pendekatan konsumsi bahan bakar. Selain itu, penelitian ini menunjukkan pola pergerakan kendaraan bermotor yang ditampilkan dalam Peta Asal Tujuan (desire line map) dari masing-masing gerbang masuk ke tiap area parkir. Hasil penelitian menunjukkan jumlah emisi paling besar dihasilkan pada hari Rabu (weekday) dengan 159,90 Kg CO 2 , 0,0116 Kg CH 4 , dan 0,0014 Kg N 2 O. Sedangkan untuk jumlah emisi pada hari Sabtu (weekend) sebesar 93,71 Kg CO 2 , 0,0068 Kg CH 4 , dan 0,0008 Kg N 2 O.
Anti-herpes Virus Type 2 Activity of Herbal Medicines from Taiwan

As an effort to explore new antiviral agents from natural products, 14 herbal medicines from Taiwan, which are traditionally used for the treatment of various diseases such as infection, gastric ulcer, edema, and rheumatism, etc., were tested for its anti-HSV-2 activity. Among them, only Cinnamomum insularimontanum Hay. , Pinus massoniana Lamb. , Plantago major Linn. , and Uranaia crinita Desvaux. exhibited significant antiviral activity. The 50% inhibition concentration (IC 50) of C. insularimontanum, P. massoniana, U. crinita, and P. major was 180 ± 30, 250 ± 40, 350 ± 50, and 852 ± 50µg/ml, respectively, whereas the 50% cell cytotoxic concentration (CC 50) was 2210 ± 270, 1510 ± 180, 590 ± 70, and 1809 ± 200µg/ml, respectively. The selectivity index was in the range of 1.18-12.28. Thus, C. insularimontanum, P. major, P. massoniana, and U. crinita were concluded to possess anti-HSV-2 activity. Keywords: Herbal medicines, Cinnamomum insularimontanum Hay. , Plantago major Linn. , Pinus massoniana Lamb. , Uranaia crinita Desvaux. , anti-HSV-2 activity.
Safety of Moxifloxacin following repeated intramuscular administration in Wistar rats

Moxifloxacin is a novel fourth generation fluoroquinolone with broad spectrum of antibacterial activity. The study was conducted to evaluate the safety of Moxifloxacin (5.0 mg/kg) after repeated intramuscular administration at 24 h interval for 14 days in male and female wistar rats. Hematological (Haemoglobin, RBC, WBC, MCV, MCH, MCHC, HCT and DLC), blood biochemical parameters (AST, ALT, ALP, Total Bilirubin, Total Serum Protein, Serum Albumin, Globulin, Serum Creatinine, Urea, Uric acid and Blood glucose) and histopathological examination of various tissues were carried out in the present study. Male and female animals of any group did not reveal any clinical symptoms and mortality attributable to the 14 days intramuscular administration of Moxifloxacin. The data were compared by unpaired two tail t` test using Graph Pad Prism (Version 4.00). All above hematological and blood biochemical parameters were found to fluctuate within normal range during treatment period and the mean values were not significantly differ (p < 0.05) from corresponding control values. Moreover, no gross or microscopic changes were found in the liver, kidney, heart, spleen, stomach, intestine and joint cartilages of the treated wistar rats. Results indicate that daily administration of Moxifloxacin for 14 days seems to be safe and well tolerated in rats.
Perceptual content and tone adaptation for HDR display technologies

Les technologies de capture et d'affichage d'images ont fait un grand pas durant la derniere decennie en termes de capacites de reproduction de la couleur, de gamme de dynamique et de details des scenes reelles. Les cameras et les ecrans a large gamme de dynamique (HDR: high dynamic range) sont d'ores et deja disponibles, offrant ainsi plus de flexibilite pour les createurs de contenus afin de produire des scenes plus realistes.Dans le meme temps, a cause des limitations des appareils conventionnels, il existe un gap important en terme de reproduction de contenu et d'apparence coloree entre les deux technologies. Cela a accentue le besoin pour des algorithmes prenant en compte ces considerations et assurant aux createurs de contenus une reproduction cross-media fidele.Dans cette these, nous focalisons sur l'adaptation et la reproduction des contenus a gamme de dynamique standard sur des dispositifs HDR. Tout d'abord, les modeles d'apparence coloree ainsi que les operateurs de mappage tonal inverse ont ete etudies subjectivement quant a leur fidelite couleur lors de l'expansion de contenus anterieurs. Par la suite, les attributs perceptuels de clarte, chroma et saturation ont ete analyses pour des stimuli emissifs ayant des niveaux de luminance de plus hautes gammes et ce en menant une etude psychophysique basee sur la methode de mise a l'echelle de partitions. Les resultats experimentaux ont permis de mener a la definition d'un nouvel espace couleur HDR efficace et precis, optimise pour les applications d'adaptation de la gamme de dynamique. Enfin, dans l'optique de recuperer les details perdus lors de captures d'images standard et d'ameliorer la qualite percue du contenu anterieur avant d'etre visualise sur un dispositif HDR, deux approches de correction des zones surexposees ou ayant subi un ecretage couleur ont ete proposees. Les modeles et algorithmes proposes ont ete evalues en comparaison avec une verite terrain HDR, menant a la conclusion que les resultats obtenus sont plus proches des scenes reelles que les autres approches de la litterature.
An Effective Conversation-Based Botnet Detection Method

A botnet is one of the most grievous threats to network security since it can evolve into many attacks, such as Denial-of-Service (DoS), spam, and phishing. However, current detection methods are inefficient to identify unknown botnet. The high-speed network environment makes botnet detection more difficult. To solve these problems, we improve the progress of packet processing technologies such as New Application Programming Interface (NAPI) and zero copy and propose an efficient quasi-real-time intrusion detection system. Our work detects botnet using supervised machine learning approach under the high-speed network environment. Our contributions are summarized as follows: (1) Build a detection framework using PF_RING for sniffing and processing network traces to extract flow features dynamically. (2) Use random forest model to extract promising conversation features. (3) Analyze the performance of different classification algorithms. The proposed method is demonstrated by well-known CTU13 dataset and nonmalicious applications. The experimental results show our conversation-based detection approach can identify botnet with higher accuracy and lower false positive rate than flow-based approach.
Credit Scoring Using Support Vector Machine: A Comparative Analysis

Credit Scoring is the use of statistical/intelligent models to transform relevant data into numerical measures that guide the management and decision makers to make decisions such as accept/reject, pricing, pay/no pay and collections. This study focuses on predicting whether a credit applicant can be categorized as good or bad from the supplied data. Many researchers have recently worked on an ensemble of classifiers for such problems. It is observed from the literature that feature selection reduces the complexity of the system and improves the accuracy as well. Efficiency of SVM for feature selection and as a classifier in tandem and its application to credit scoring is analyzed in this paper. During the first step, SVM-RFE (Recursive Feature Elimination) is employed for feature selection and during the second step various architectures of SVM viz., Standard SVM, PSO-SVM and EVO-SVM are employed for classification purpose. The effectiveness of various approaches tested are evaluated using UK credit data and German credit data. It is observed that feature selection using SVM-RFE not only simplifies the process of credit scoring but also improves the accuracy of the system.
Key technology for multi-virtual machine collaborative computing oriented to path search tasks

To provide a flexible and effective computing environment for solving the path search problems in Computer Aided Engineering(CAE),a Collaborative Computing System over Multiple Virtual Machines(CCSMVM) was studied.Based on the uncertainty of the task scale and the search path,a self-adaptive architecture and collaborative workflow in virtualized platform were constructed,and the key technologies were also proposed.Aiming at the specific requirements of path search engine,a heuristic algorithm for exploratory tasks decomposition to mine the parallelism in data and functions was designed.By scheduling parallel execution of multiple subtasks on multiple virtual machines,utilization rate of resources was improved greatest so as to exchange resources for a shorter execution time and the optimal solutions or satisfactory solutions were obtained quickly.A heuristic algorithm for solution space optimization was designed to optimize the computing process and result data management.By monitoring status of the tasks,the waste of resources and the duplication of execution paths were avoided,therefore the execution time was further reduced.A prototype system with path search engine module was developed.A path search problem of sheet cutting in CAE field was taken as an experiment appraisal example to verify the algorithms,and compared the performances of CCSMVM with four other systems further.The experimental results showed that the CCSMVM could provide stable execution status with high performance and efficiency,and the prototype realized good solution environment for problems oriented to path search tasks.
Taste Perception and Caffeine Consumption: An fMRI Study

Caffeine is ubiquitous, yet its impact on central taste processing is not well understood. Although there has been considerable research on caffeine’s physiological and cognitive effects, there is a paucity of research investigating the effects of caffeine on taste. Here we used functional magnetic resonance imaging (fMRI) to investigate group differences between caffeine consumers and non-consumers in blood-oxygenation-level-dependent (BOLD) activation during hedonic evaluation of taste. We scanned 14 caffeine consumers and 14 caffeine non-consumers at 3 Tesla, while they rated three tastes: caffeine (bitter), sucrose (sweet), and saccharin (sweet with bitter after taste), in aqueous solutions. Differences in BOLD activation were analyzed using voxel wise independent samples t-tests within Analysis of Functional Neuroimage (AFNI). Results indicated that during the hedonic evaluation of caffeine or sucrose, caffeine non-consumers had significantly greater activation in neuronal areas associated with memory and reward. During the hedonic evaluation of saccharin, caffeine consumers had significantly greater activation in areas associated with memory and information processing. The findings suggest caffeine consumption is associated with differential activation in neuronal areas involved in reward, memory, and information processing. Further research on intensity and hedonics of bitter and sweet stimuli in caffeine consumers and non-consumers will be of great interest to better understand the nature of differences in taste perception between caffeine consumers and non-consumers.
A comparison between features for a residential security prototype based on speaker identification with a model of artificial neural network

This paper presents an implementation of a security system for elevators using speaker identification. The system uses a model of an artificial neural network called a multi-layer perceptron as a classifier. In this work, some features, such as pitch, formants, perceptual linear prediction coefficients, mel-cepstral coefficients and cepstral coefficients, are used to obtain the best results in the classification process.
Frequency-Domain Characterization and Performance Bounds of ALMS Loop for RF Self-Interference Cancellation

Analog least mean square (ALMS) loop is a promising method to cancel self-interference (SI) in in-band full-duplex (IBFD) systems. In this paper, the steady state analyses of the residual SI powers in both analog and digital domains are firstly derived. The eigenvalue decomposition is then utilized to investigate the frequency domain characteristics of the ALMS loop. Our frequency domain analyses prove that the ALMS loop has an effect of amplifying the frequency components of the residual SI at the edges of the signal spectrum in the analog domain. However, the matched filter in the receiver chain will reduce this effect, resulting in a significant improvement of the interference suppression ratio (ISR). It means that the SI will be significantly suppressed in the digital domain before information data detection. This paper also derives the lower bounds of ISRs given by the ALMS loop in both analog and digital domains. These lower bounds are joint effects of the loop gain, tap delay, number of taps, and transmitted signal properties. The discovered relationship among these parameters allows the flexibility in choosing appropriate parameters when designing the IBFD systems under given constraints.
Survival analysis in plant pathology Análisis de supervivencia en fitopatología

ABSTRACT Survival analysis is applied when the time until the occurrence of an event is the object of interest. Such data are routinely co-llected in plant pathology whereas application of methods of survival analysis is uncommon. Basic concepts in survival analysis for use in plant pathology such as Kaplan-Meier methods, log-rank test and Cox proportional hazards models are introduced and applied estimating the effects of cultivars on the survival times of brown rot symptoms and on the instantaneous risk of expressing symptoms in a hypothetical study. Key words: time-occurrence, Kaplan-Meier, Cox regression. RESUMEN El analisis de supervivencia se aplica cuando el tiempo hasta la ocurrencia de un evento es el objeto de interes. En enfermedades vegetales, tales datos se recogen habitualmente, aunque aplicacion no es comun. El objetivo de este trabajo fue introducir conceptos basicos de analisis de supervivencia para su uso en patologia vegetal. Fueron utilizados metodos de Kaplan-Meier, prueba log-rank y riesgos proporcionales de Cox para estimar el efecto de cultivares en la expresion de los sintomas de la pudricion parda y sobre el riesgo instantaneo de expresar en un estudio hipotetico.
Multi-Objective Optimization of Pilots’ FFS Recurrent Training Problem

Two multi-objective programming models are built to describe Pilots’ full flight simulator (FFS) recurrent training (PFRT) problem. There are two objectives for them. One is the best matching of captains and copilots in the same aircraft type. The other is that pilots could attend his training courses at proper month. Usually the two objectives are conflicting because there are copilots who will promote to captains or transfer to other aircraft type and new trainees will enter the company every year. The main theme in the research is to find the final non-inferior solutions of PFRT problem. Graph models are built to help to analyze the problem and we convert the original problem into a longest-route problem with weighted paths. An algorithm is designed with which we can obtain all the non-inferior solutions by a graphic method. A case study is present to demonstrate the effectiveness of the algorithm as well.
Performance Analysis of Full-Duplex Decode-and-Forward Relaying With Media-Based Modulation

In this paper, we introduce and analyze the performance of a two-hop three-node full-duplex (FD) relay network, where the source and relay nodes transmit using media-based modulation (MBM). An MBM is a promising new modulation scheme that conveys information bits by digitally controlling the parasitic elements (known as radio frequency mirrors) placed near the transmit antenna. The relay uses decode-and-forward relaying protocol. We refer to this system as FD relaying system with MBM (FDR-MBM). We first derive an upper bound on the end-to-end average bit error probability of FDR-MBM with maximum-likelihood detection at the relay and destination nodes. The bound is increasingly tight with increasing signal-to-noise ratios. Numerical results show that, for the same spectral efficiency, FDR-MBM can perform better than FD relaying using conventional modulation schemes. We then derive the diversity order achieved in FDR-MBM. The analytically predicted diversity orders are validated through simulations. Finally, we derive lower and upper bounds on the achievable rate in FDR-MBM.
Algorithms for manipulating large geometric data

This thesis deals with manipulating huge geometric data in the field of computer graphics. The proposed approach uses a data stream technique to allow processing gigantic datasets that by far exceed the size of the main memory. The amount of data is hierarchically reduced by clustering and replacing each cluster by a representative. The input data is organised into a hierarchical structure which is stored on the hard disk. Particular clusters from various levels of the hierarchy can be loaded on demand. Such a multiresolution model provides an efficient access to the data in various resolutions. The Delaunay triangulation (either 2D or 3D) can be constructed to introduce additional structure into the data. The triangulation of the top level of the hierarchy (the lowest resolution) constitutes a coarse model of the whole dataset. The level of detail can be locally increased in selected parts by loading data from lower levels of the hierarchy. This can be done interactively in real time. Such a dynamic triangulation is a versatile tool for visualisation and maintenance of large geometric models. Further applications include local computations, such as height field interpolation, gradient estimation, and mesh smoothing. The algorithms can take advantage of a high local detail and a coarse context around. The method was tested on large digital elevation maps (digital terrain models) and on large laser scanned 3D objects, up to half a billion points. The data stream clustering processes roughly 4 million points per minute, which is rather slow, but it is done only once as a preprocessing. The dynamic triangulation works interactively in real time. Title image: The statue of David by Michelangelo, various stages of digital processing. Laser scanned data kindly provided by the Digital Michelangelo project, Stanford computer graphics laboratory [132]. This dissertation was supported by the following projects: Czech science foundation (GAČR), project 201/09/0097 Ministry of education, youth and sports, project 2C 06002 (VIRTUAL) Ministry of education, youth and sports, project KONTAKT 5/2005-06 Ministry of education, youth and sports, project LC 06008 (CPG) Copyright c ⃝ 2012 University of West Bohemia, Czech Republic
Total Physical Response Storytelling And The Teaching Of Grammar Rules In Second Language Instruction

This project examines methodologies of second language instruction such as The Natural Approach, Total Physical Response, Total Physical Response Storytelling, The Grammar Translation Approach, and Communicative Language teaching. Research is presented on brain research and how it relates to second language acquisition. The author of the project presents a detailed explanation of Total Physical Response Storytelling, and explains why it is an effective method of second language instruction when combined with the teaching of grammar rules.