Miracle-dz/UniFAR-FTUs huggingface dataset:
Spam Mail Detection Using Artificial Neural Network

Today e-mail is the most popular and financially cheapest way of communication for internet users.e-mail is going to be misused due to its popularity. One such misuse is the posting of money offering message, unwanted e-mails known as spam or junk e-mails. E-mail spam has various consequences like productivity is reduced, takes extra memory space in mail boxes, extra time for suffering, software damaging viruses, and materials that have potentially harmful information for Internet users, destroy stability of mail on servers, resulting users to spend lots of time for sorting incoming mail and deleting unwanted correspondence mails. So there is a need of spam detection system so that its consequences can be reduced. In this project our prime aim is to detect text as well as image based spam to achieve the objective we applied ANN algorithm , Pre-processing of email text before executing the algorithms is used to make them predict better . We uses Enron corpus's dataset of spam emails.
Analysis of a MaxSAT approach of the B2B Scheduling Optimization Problem

Business-to-business (B2B) are events where brief meetings between participants with similar interests are celebrated. These participants can be companies, investors, research groups, etc. That kind of events come up in several fields like sports, social life, research, etc. A close example can be found in the "Parc cientific i tecnologic de la UdG" where every year two of these events were hold. They were the "Forum del Parc cientific I tecnologic de la Universitat de Girona" and the "Jornades R+D+I en Tic i Salut". In this project we deal with the problem of scheduling the meetings of a B2B event
Stem-Based Query Expansion for Arabic Corpus

Abstract This paper provides an improvement to Arabic Information Retrieval Systems. The proposed system relies on the stem-based query expansion method, which adds different morphological variations to each index term used in the query. This method is applied on Arabic corpus. Roots of the query terms are derived, then for each derived root from the query words, all words in the corpus descendant from the same root are collected and classified in a distinct class. Afterward, each class is reformulated by co-occurrence analysis (for each pair of terms) to guarantee a good relationship between terms. In addition, we used the whole word indexing technique to index the corpus. Each term added to the query must be correct and correlated to the original query terms. We show how this technique improves recall (by 20%), and how it affects precision. Keywords : Arabic query expansion; Query expansion; Arabic stemming. Introduction Experiments show that the number of terms used in the queries of the end users will not exceed two in most cases, while the number of terms used by information experts exceeds this number by many folds. That means there is a substantial difference in the formulation of the queries between the end users and information experts. Short queries lead to the retrieval of a large set of inadequate documents [1]. Many techniques used in the field of information retrieval (IR) to enhance retrieval performance. This paper emphasizes on query expansion techniques applied to Arabic text. Query expansion usually leads to a reduction in the number of retrieved documents, and to an increment in the recall measure. The IR researchers divided over the issue of using stemming in information retrieval systems (IRS) and search engines (SE). Some of those reveals in their studies that stemming has a negative effects on the retrieval of
Yale Climate Opinion Maps

The Yale Climate Opinion Maps (YCOM) allow users to visualize and explore differences in public opinion about global warming in the United States in unprecedented geographic detail. The Yale Program on Climate Change Communication regularly updates YCOM using national survey data in order to accurately reflect current public opinion. The interactive and up-to-date nature of this tool makes it a perfect model for students to manipulate in order to explore beliefs around climate change both locally and nationally.
Extracting Gray Level Profiles of Human Chromosomes by Curve Fitting

In this paper, a unified algorithm for extracting gray level profiles of Human chromosomes is presented. It is a unified approach since we do not discriminate chromosomes as straight and bended. This is a very helpful procedure which extends the domain of success of most of the previously reported algorithms to highly curved chromosomes. The gray image of the chromosome is thresholded at the gray level 0.9, and the matrix of gray image is transformed into a list of pixel coordinates whose gray level is less than 0.9. To the list of two dimensional points, the most appropriate smooth curve is fitted. Then this smooth curve subdivided into n arcs of equal lengths, and straight lines are drawn that are normal to the curve at the end points of the subdivision. The points of the list are classified into n bins according to their distance to these n straight lines. The average of gray levels of each bin gives the gray levels at the points of the gray level profile of the chromosome. It is seen that the gray level profiles of the bended chromosomes have a high similarity with the straight counterparts.
Optical Microscanner Technique and Realization

Microscanning imaging is a new technique to improve imaging system resolution.It utilizes multiple low resolution picture sequence which have sub-pixel shift between each other to rebuild high resolution picture.Microscanner is the most important component of Microscanning imaging system.Firstly,the paper briefly presented the imaging system’s aliasing effect as a result of subsampling and the theory of reducing aliasing effect by increasing sampling frequency.Then the microscanning working mode and the optical microscanning imaging process was analyzed.Finally several realization of optical microscanner was introduced in detail.
Cooperative Caching: Using Remote Client Memory

Emerging high-speed networks will allow machines to access remote data nearly as quickly as they can access local data. This trend motivates the use of cooperative caching: coordinating the file caches of many machines distributed on a LAN to form a more effective overall file cache. In this paper we examine four cooperative caching algorithms using a trace-driven simulation study. These simulations indicate that for the systems studied cooperative caching can halve the number of disk accesses, improving file system read response time by as much as 73%. Based on these simulations we conclude that cooperative caching can significantly improve file system read response time and that relatively simple cooperative caching algorithms are sufficient to realize most of the potential performance gain.
Research of Medical Information Text Categorization Based on KNN Algorithm

Designs and implements a system of medical information text categorization based on KNN algorithm.This system uses the vector space model to represent a text,uses the fast KNN algorithm to classify a text,and uses the reverse maximum match to segment the words.Therefore,it improves the accuracy of medical information classification and the efficiency of information processing.In addition,constructs a dataset of medical information including 582 medical documents,which is randomly divides into a training set including 433 documents and 149 documents.The system of medical information text classification is tested on our dataset and a F1 score of 74.83% is obtained.The result shows the better classification performance on medical information.
Trajectories of adolescent depression and gender/racial disparity

LIN WANG: Trajectories of Adolescent Depression and Gender/Racial Disparity (Under the direction of Kenneth Bollen) This paper attempts to understand the trajectories of adolescent depression and gender/racial disparities. I conduct data analysis using data from the National Longitudinal Survey of Adolescent Health. I use growth curve models that trace individual depression trajectories over different ages employing a measurement that is comparable across gender/race. I find that depression increases at early adolescence and decreases at late adolescence. This pattern is consistently found among both males and females. Females are found to be significantly disadvantaged in depression when compared to males for all races. Racial disparity, unclear in previous literature, is clarified in this study. Minority groups do encounter greater levels of depression, ad compared to their white counterparts, female minorities face greater disadvantages compared to male minorities. However, in spite of gender and racial gaps, after the respondents enter their 20s, the disparities are greatly reduced, as the depression overall has decreased for all groups.
Wikipedia Talk Labels: Aggression

This data set includes over 100k labeled discussion comments from English Wikipedia. Each comment was labeled by multiple annotators via Crowdflower on whether it has aggressive tone. See our wiki for documentation of the schema of each file and our research paper for documentation on the data collection and modeling methodology. For a quick demo of how to use the data for model building and analysis, check out this ipython notebook.
Application of Neural PID Controller to Two-wheeled Robot Control

To the problem that the parameters of classical PID controller two-wheeled robots cannot be easily adjusted,a PID controller based on neural network is presented.By making use of the self-learning and self-adaptive ability of neuron,the parameters of the controller can be easily adjusted on-line in real-time.The nonlinear model of two-wheeled robot is built,and the controll algorithm and the parameter algorithm learn are discussed.The controller is applied to the balancing control of two-wheeled robot and also a comparison is made,the simulation result show that the controller is correct and effective.The controller to the two-wheeled robot platform is applied and achieved good effect.
An Evening with... EM

The EM algorithm (Dempster/Laird/Rubin, 1977) is one of the most widely used unsupervised learning methods in NLP. It is important to have a solid understanding of its properties and limitations in order to use it well. However, it is quite complex and a bit tricky, and can easily be confusing for the beginner. There are a lot of terms, a lot of implementations, and a lot of Greek letters to add to the confusion. This tutorial tries to avoid the confusion by providing the step-by-step implementation of a concrete case (with pseudocode and a Python implementation), highlighting only the necessary terms and providing layman’s explanations of the underlying concepts.
Centralized authentication and authorization based on Kerberos

Because of lacking support for centralized authorization,widely-applied Kerberos couldn't realize cross-domain access control in group enterprises. Therefore,a Kerberos-based Centralized Authentication Authorization (KCAA) scheme is proposed. By integrating centralized authorization into Kerberos,KCAA achieves centralized authentication and authorization in headquarter for crossdomain access. KCAA uses abstract users to reduce information synchronization in different domains,and identity agents to simplify deployment of Kerberos. KCAA shows its application value in the informatization construction of a large group enterprise which proves that it could achieve centralized cross-domain access control without mass transformation of legacy systems.
A Novel Algorithm to Optimize the Hidden Layer of Neural Networks

This paper proposes a novel algorithm to Optimize the number of Hidden nodes based on Agent(OHA). This approach is completed by two cooperating agents,the RL agent and the NN agent.The RL agent searches better number of hidden nodes according to the reinforcement learning method,and the NN agent optimizes the weights of network with the number by using the separate learning algorithm.After much running,the best solution(weights and hidden nodes)is located.The optimization algorithms and tests are discussed.The test results obtained by using the Iris data set and the risk evaluation data set show the algorithm is better than those by the most commonly used optimization techniques.
Software/Hardware Solutions for Information Processing in All Programmable Systems-on-Chip

Algorithms in computer engineering and applied informatics often require extraction of data with some desired properties from large sets. Such tasks appear within different clustering algorithms in the scope of data mining, in classification of objects in accordance with given criteria, in knowledge acquisition obtained from controlled environments, in statistical analysis and in other areas. Many of these tasks involve widely used data processing techniques that are sort and search and for numerous practical applications, especially in real-time and high-performance systems, speeding-up is important. The paper suggests effective portable solutions that enable fast parallel information processing to be implemented in recently appeared all-programmable systems-on-chip that combine multi-core computations with programmable logic interacting through multiple high-performance interfaces. Acceleration is achieved with parallel networks for data sort created in programmable logic and enabling software running in multi-core processing units to be speeded-up which is demonstrated in numerous practical examples fully implemented and tested in commercial microchips.
Research on embedded image coding methods based on EZW

EZW (embedded zerotree wavelet) based on wavelet transform,is a kind of image coding. It can perform both lossy and lossless compression,and progressive codec,by providing high compression rate and image reconstruction quality. The process of al-gorithm application is described,and the concrete realization mentality is elaborate. Finally unifies the multi-groups image data to carry on the test to the algorithm process,and carry on the appraisal to its performance.
Practical image filtering algorithm

A practical image filter algorithm named Windows Median Filter(WMF) was proposed based on the analysis of classical median filter algorithm,and the principles and implement steps were given.Comparison experiments between WMF and neighborhood averaging filter,median filter,Butterworth low-pass filter,Wiener filter were made.Experimental results indicated the excellent filtering performance of WMF from subjective impression and objective parameters.
A Content-Based Approach to Recommend TV Programs Enhanced with Delayering Tagging

In response to explore how to extract the recommended items' features, a method is put forward called a Content-based TV Program Recommendation Approach Enhanced with Delayering Tagging. The Content-based approach is optimized to recommend TV programs and improved the way to extract the recommended items' features. Besides, the existing way of using supervised method to build user modeling is replaced with an unsupervised method using delayering tagging to show recommended TV program's content features and set up user preference model. After compared with Latent Factor Model and Collaborative Filtering recommendation algorithm with the same experimental data, the proposed algorithm in this paper increased the accuracy of 2.67\%, coverage rate of 3.02\% and 3.2\% of the Feature 1 value and achieved good recommendation results compared to the Latent Factor Model which revealed the best effect of recommendation.
Nüfuzumu Kullanırken Ben: Türkiye’deki Moda Instabloggerlarının Ürün Yerleştirme Uygulamaları Üzerine bir İçerik Analizi

Farkindalik ve urunlere yonelik talep yaratma konusunda giderek artan nufuzlari, bloggerlari markalar icin vazgecilmez birer pazarlama oyuncusuna d.nu s turmu s tur. Zaman icinde olu s turduklari takipci tabanlarina yonelik surekli icerik payla s iminda bulunan bu dijital elitler, Nufuz Pazarlamasi’nin yeni otoriteleri haline gelmi s tir. Markalar ise ticari de g eri olan bu iceri g in parcasi olmanin yolu olarak genellikle urun yerle s tirme cali s malarini secmektedir. Bu cali s manin amaci, Turkiye’deki moda Instabloggerlari’nin yaptiklari Instagram payla s imlarina icerik analizi yontemi uygulayarak urun yerle s tirme pratiklerinin belirleyici unsurlarini ortaya koymaktir. Yapilan analiz sonucunda, hesabin buyuklu g u (Mega, Orta Gu., Mikro) ve turunun (Sokak Modasi/Yuksek Moda) yapilan urun yerle s tirmelerin belirginlik, interaktivite, anlatisal uyum ve etkile s im duzeyinde istatistiksel olarak anlamli farklar yaratti g i bulgulanmi s tir.
Determinants of User’s Intention to Use MMS: A Pilot Study

This report is the result of my summer project at academic year 2001. The focus of this project is to investigate the individual’s acceptance of Multimedia Message Service (MMS). In order to have an insight understanding of MMS adoption, I proposed an integrated conceptual model to describe and predict individual’s attitude and intention towards using multimedia message service. Technology Acceptance Model is used and I sought to extend it by investigating the impact of perceived playfulness on the user’s acceptance of this 3Gfeatured wireless telecommunication innovation. Also, because MMS can be seen as a brandnew communication channel, media richness theory is adopted to investigate the influence of perceived media richness on individual’s perceptions on MMS. This modified model is preliminary tested by the pilot study using a small sample-60 of research students and undergraduate students in a local university. Study finding indicates that the instrument used in this research is good enough to test the fit of the model. This project can been seen as one exploratory work in finding if the famous TAM model is applicable in this new context and if other distinct factors can also been added to increase the model’s explaining power. The limitation of this project and future direction are also discussed.
Agent Reasoning Transparency’s Effect on Operator Workload

We examined how varying the transparency of agent reasoning affected operator workload in a route selection task, and how the differing measures of workload compared in assessing and understanding cognitive workload. Participants guided a three-vehicle convoy safely through a simulated environment of which they had a limited amount of information, while maintaining communication with command and monitoring their surroundings for threats. The intelligent route-planning agent assessed potential threats and suggested changes to the convoy route as needed. Each participant was assigned to one of three agent reasoning transparency conditions. Contrary to our hypothesis, NASA-TLX Global workload measures indicated that workload decreased slightly as access to agent reasoning increased. However, psychophysical measures of workload disagreed with NASA-TLX global results. Comparison of individual NASA-TLX workload factors with the psychophysical measures indicated that performance satisfaction was highest in the intermediary transparency condition, and the addition of ambiguous information in the highest transparency condition increased effort and resulted in increased complacent behavior. Recommendations for future workload analysis are offered.
Trellis-Coded Modulation With Bit Interleaving and Iterative Decoding

This paper considers bit-leaved coded modulation (BICM) for bandwidth-efficient transmission over AWGN channels. A simple iterative decoding (ID) method with hard-decision feedback is suggested to achieve better performance. The paper shows that convolutional codes with good Hamming-distance property can provide large free Euclidean distance for BICM-ID. BICM-ID allows an efficient combination of punctured convolutional codes and multiphase/level modulation, and therefore provides a simple mechanism for variable-rate transmission.
Utilizing mobile devices to enrich the learning style of students

ABSTRACT As digital technologies evolve in education, business faculty have increased access to an extensive range of mobile devices and online applications to help them inspire students' passion for learning. Adopting new digital approaches to teaching can also enhance the learning style of students who are immersed in the use of digital devices. How can the use of these devices and associated applications (apps) help students broaden the opportunities for a more effective learning experience? In this exploratory article, the authors examine the impact of two mobile apps—Flipboard and Stitcher—on students' learning and engagement with class-assigned marketing materials. Students enjoyed Flipboard more than Stitcher in contributing to the requirements of class work.
Fast CFD for Shape and Flow Parameterization with Meta-models Built on High-order Derivatives. Applications to Fast Design

The design of new geometries for one aerodynamic conception leads to difficulties of CPU costs when discrete methods are used (one run per configuration). Strategies of simplification must be chosen either to restrict the number of calculations, or to limit the CPU time associated with each run. The approach proposed here consists in associating two strategies: using a meta-model and adjusting the limits of the parameters domain. A continuous database is so built of which size varies according to the number of parameters. The meta-model is based on high-order derivatives of flow variables (Turb'Opty TM solver). These are obtained by automatic differentiation of the discretized averaged Navier-Stokes 3D equations around a reference solution. The equations are therefore like those of conventional solvers (like SC/Tetra, Fluent, Star CD), but the unknown factors are here the derivatives of order 1, 2, . , N of the field with respect to the parameters of operation or shape. These successive derivatives are stored in a database and their exploitation makes it possible to instantaneously obtain the new solution fields corresponding to the configurations sought by the user. The limits of the exploration domain depend on the derivatives order, the possible evaluation of the coupled terms, imposed constraints and physical high non-linearities. A continuous database can then be explored directly or coupled with other physics solvers, as well as with optimization tools. Some simplified applications show the validity of this approach. As an illustration, the database is coupled with a multi-objective Genetic Algorithm (GA) in order to solve large scale global optimization problems. It was found that high-order reconstruction leads to a drastic reduction in the number of design iterations, shortens the design cycle, lowers the cost and improves the quality.
A GIS Spatial Indexing Approach Based on Hilbert Ordering Code

Raster grid based indexing takes important role in spatial querying. Since spatial ordering based on one dimensional mapping for multi dimensional data has its own merits, the spatial clustering characteristics of Morton code,Gray code,Hilbert code and Sierpinsky code are analyzed and compared. A conclusion is drawn that Hilbert code is the most efficient mapping scheme for spatial querying. Taking into consideration the characteristics of data models of feature based geographical information system and giving major attention to memory indexing, the authors set forward a dynamic indexing structure for point features based on Hilbert spatial ordering code with binary balanced ordering tree, and an indexing structure for line features based on vertex retrospection. Application of the indexing structures for spatial querying in geographical information system is illustrated.