Miracle-dz/UniFAR-FTUs huggingface dataset:
TEXT MESSAGE COPY IN PRINT ADS AND CONFIDING IN THE BRAND: A COMMUNICATION TRUST THEORETICAL PERSPECTIVE

Although text messaging in society is rampant and its determinants and effects have been studied in wireless and interactive marketing contexts, outcomes of its application to traditional media (i.e., print advertisements) is lacking. Via its contemporary and hip communicative nature, text messaging may lead receivers to respond favorably toward the text message source. To examine this notion, our research applies communication trust theory, which suggests that trust arises from effective communication, using a text message-embedded print ad in two between-subject experiments. Study 1 reveals that using text message copy compared to traditional language copy (i.e., the text message spelled out) in a print ad leads to heightened levels of consumer trust in the advertised brand. Using a different product category, ad design, and text message, Study 2 shows that a print ad with text message copy compared to traditional language copy leads to heightened levels consumer trust in the advertised brand, and more favorable attitude toward the ad and purchase intention responses.
Redundancy in web-scaled information extraction: probabilistic model and experimental results

Information Extraction (IE) is the task of automatically extracting knowledge from text. The massive body of text now available on the World Wide Web presents an unprecedented opportunity for IE. IE systems promise to encode vast quantities of Web content into machine-processable knowledge bases, presenting a new approach to a fundamental challenge for artificial intelligence: the automatic acquisition of massive bodies of knowledge. Such knowledge bases would dramatically extend the capabilities of Web applications. Future Web search engines, for example, could query the knowledge bases to answer complicated questions that require synthesizing information across multiple Web pages. However, IE on the Web is challenging due to the enormous variety of distinct concepts expressed. All extraction techniques make errors, and the standard error-detection strategy used in previous, small-corpus extraction systems hand-labeling examples of each concept to be extracted, then training a classifier using the labeled examples—is intractable on the Web. How can we automatically identify correct extractions for arbitrary target concepts, without hand-labeled examples? This thesis shows how IE on the Web is made possible through the KnowItAll hypothesis, which states that extractions that occur more frequently in distinct sentences in a corpus are more likely to be correct. The KnowItAll hypothesis holds on the Web, and can be used to identify many correct extractions because the Web is highly redundant: individual facts are often repeated many times, and in many different ways. In this thesis, we show that a probabilistic model of the KnowItAll hypothesis, coupled with the redundancy of the Web, can power effective IE for arbitrary target concepts without hand-labeled data. In experiments with IE on the Web, we show that the probabilities produced by our model are 15 times better, on average, when compared with techniques from previous work. We also prove formally that under the assumptions of the model, "Probably Approximately Correct" IE can be attained from only unlabeled data.
MODELING UNCERTAINTY AND RISK IN INVESTMENT APPRAISAL DECISIONS: A MONTE CARLO SIMULATION APPROACH

The present paper deals with the implementation of risk analysis in engineering economic problems, using Monte Carlo simulation. The paper consists of two parts. After a short introduction about risk analysis methods used in engineering economic problems, the first part discusses the role of simulation, especially Monte Carlo simulation, in risk analysis and presents an extended review of studies, using either statistical techniques in general, or specifically Monte Carlo simulation in risk analysis. In the second part of the paper, we construct a model of a Monte Carlo simulation for the appraisal of a potential investment with uncertain annual revenues and costs, using Excel Spreadsheets and Visual Basic. The implementation and use of the model is demonstrated with a numerical example. The results obtained show that Monte Carlo simulation can prove a valuable technique in the decision making for the evaluation of a potential investment. JEL Classifications: CIS, Gl 1, M10.
A study of the parallelisation of multiobjective evolutionary algorithms in a cluster environment

The two main issues relating to the use of Multiobjective Evolutionary Algorithms (MOEAs) are the efficiency and effectiveness of the algorithms. As a result of the multiobjective and multi dimensional nature of MOEAs, the overall execution time that is taken to solve real world problems with MOEAs can be significant. Therefore, a few studies have recently been completed to address these performance issues by the use of parallelisation methods. The most widely known parallel Multiobjective Evolutionary Algorithm (pMOEA) models are the Master-slave, the Island, and the Diffusion models. The Master-slave and the Island models are generally implemented using message passing parallelisation mechanisms in a cluster environment while the Diffusion model is implemented using a shared memory parallelisation mechanism. Although any MOEA can be made to execute in parallel based on the above models, there is no known study that identifies which parallelisation model is suitable for problems based on their characteristics. In this study, we compared a serial MOEA and the above-mentioned Master-slave and Island pMOEAs to identify which parallelisation models perform best in a cluster environment in solving problems with certain characteristics. Modality, separability, and bias were the problem characteristics that were studied. The study was conducted using implementations of one serial and the Master-slave and the Island pMOEA versions of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) in a clustered environment. The parallelisation of NSGA-II was achieved by the use of Message Passing Interface (MPI) routines that were executed on multiple processors of a cluster. At the end of the study, the speedup and the quality improvement of the results achieved by the Master-slave and the Island pMOEA models over the serial MOEA was examined. Most problem characteristics had a significant effect on the effectiveness of the Masterslave and the Island pMOEA models. The Master-slave model was marginally more effective than the Island model in solving problems with the multi-modal problem characteristic. However, the Island model with a suitable amount of migration was more effective than the Master-slave model in solving problems with uni-modal, separable, non-separable, and biased problem characteristics. The Master-slave and the Island iii models were equally effective in solving problems with the non-biased problem characteristic. The results of this research can be used by others to determine which parallelisation models should be used when parallelising MOEAs in a cluster environment to solve problems with known characteristics.
Nearest Search Keyword Based On Geometric Properties Using Spatial Inverted Index

Spatial queries such as range search and nearest neighbour retrieval involves the geometric properties on an objects conditions. In these spatial database handles multi dimensional objects. Now-a-days many applications are used for many new queries to find the objects and satisfy both spatial predicate(2) and a predicate on their associated texts . for example, instead of considering all the colleges a nearest neighbour query would ask for the college that is the nearest among those whose department contains " CSE, ECE , EEE, MECH" . In the existing system IR2 trees are used to give the best results of the nearest neighbour it has few flaws like ,it fails to provide real time answers on multiple inputs. So we implement a new algorithm called Spatial Inverted Index(SI-index)(1) which is the extension of the conventional inverted index(6) to manage with multi dimensional data. Thus, the proposed algorithm is easy to find the required objects.
Spam detection using a neural network classifier

The internet has undoubtedly become the linking tool for bringing together customers and business people, countries and regions, continents and islands regardless of their economic, political, cultural and social affiliations. Email service providers are going ahead on making email easy to use, allowing a variety of information to be conveniently and reliably sent through the Internet. The popularity of email has also brought with it challenges for Internet users and Internet Service Providers to the extent that if spamming problem is not dealt with urgently, benefits currently enjoyed by stakeholders would be surpassed by spam concerns. Although spam filtering techniques are now available on the market today, no one can deny that these solutions cannot guarantee 100% effectiveness at eliminating the problems of spam because a variety of these filters have weaknesses and strengths. This paper presents an alternative solution using a neural network classifier on a corpus of email messages received by the three researchers who conducted this investigation. The dataset for our system used descriptive attributes of words, symbols and email messages that are commonly used by email users to correctly identify spam received in email inboxes. The results show that our neural network classifier is able to detect and filter spam with success just like the others already on the market today.
A SURVEY ON WEB RECOMMENDER SYSTEMS

One of the application areas of data mining is the World Wide Web (WWW or Web). The amount of information available on the Web is growing rapidly, as well as the number of Web sites and Web pages per Web site. Consequently, it has become more difficult to find relevant and useful information for Web users. Web usage mining is concerned with guiding the Web users to discover useful knowledge and supporting them for decisionmaking. Web recommendation is a very active research field and new approaches related to its application appear on a regular basis. The Recommender Systems are applied in many areas such as: web-browsing, information filtering, net-news or movie recommender and e-Commerce. In this paper, a survey work is done in the field of web recommendation system.
Tight focusing of higher orders Laguerre-Gaussian modes

The spatial redistribution of the contribution of different electric field components provides a decrease in the size of the central focal spot for higher orders Laguerre-Gaussian modes. It was shown that when sharply focusing laser beams with vortex or special binary phase plate, a sub-wavelength light localization of separate vector field components is possible for any polarization type. This fact should be considered for the interaction of laser radiation with materials selectively sensitive to lateral and longitudinal components of the electromagnetic field.
Medical image visualization based on transfer function design

The transfer function design is important for medical image visualization. Through the analysis of current transfer function design method, we propose a clustering algorithm with the help of gradation-gradient colored histogram to realize the classification for volume data, which is convenient to highlight the tissues of interest. Additionally, the number of these combined clusterings and the clustering centers can be determined rapidly through the color distribution of the gradation-gradient colored histogram. Of course, the method makes the clustering algorithm more effective and more reasonable. Currently, the transformation of three-dimensional data field from data attributes to optical attributes using the transfer function is carried out in the RGB color space. In this paper, combining the advantages of HSL color mode, we map the identifying information to HSL color space firstly. Through the method, it’s easy and intuitive for us to adjust the visual quality. Based on the method of transfer function design for volume rendering given in this paper, we conduct an experiment with the program of OpenGL/GLSL to realize the medical image visualization using ray-casting volume rendering algorithm. The experimental results show that this method is effective and corrective.
Evaluation of a Peripherally-Located Instrument Landing Display with High-Order Control of a Non-Linear Approach and Landing

We compared the precision of simulated-fixed wing aircraft landing approaches with two different head-up display (HUD) formats: a) the MIL-STD-1787B Instrument Landing System and b) a virtual peripherally-located optical flow ILS HUD. Within each display condition we compared a three second quickened display to a real time non-quickened display. Non-pilot participants used simplified controls to guide a landing simulation under night conditions with extremely poor visibility using Instrument Flight Rules (IFR). Participants followed a non-linear flight and approach path under the two display and quickening conditions. While quickening was found to have no significant effect, the peripheral optical flow ILS afforded greater overall precision in flight path control. The findings suggest an increased ability to extract rate-of-change information from the peripheral optical flow ILS affording improved spatial orientation.
Simulating interactions among multiple characters

In this thesis, we attack a challenging problem in the field of character animation: synthesizing interactions among multiple virtual charact ers in real-time. Although there are heavy demands in the gaming and animation industri es, no systemic solution has been proposed due to the difficulties to model th e complex behaviors of the characters. We represent the continuous interactions among characters as a discrete Markov Decision Process, and design a general objective function t o evaluate the immediate rewards of launching an action. By applying game theory su ch as tree expansion and min-max search, the optimal actions that benefit the character the most in the future are selected. The simulated characters can inter act competitively while achieving the requests from animators cooperatively. Since the interactions between two characters depend on a lo t of criteria, it is difficult to exhaustively precompute the optimal actions fo r all variations of these criteria. We design an off-policy approach that samples and precomputes only meaningful interactions. With the precomputed policy, the optimal movements under different situations can be evaluated in real-time. To simulate the interactions for a large number of character s with minimal computational overhead, we propose a method to precompute s hort durations of interactions between two characters as connectable patche s. T patches are concatenated spatially to generate interactions with multipl e characters, and temporally to generate longer interactions. Based on the optional instructions given by the animators, our system automatically applies concatena tions to create a huge scene of interacting crowd. We demonstrate our system by creating scenes with high quali ty interactions. On one hand, our algorithm can automatically generate artis tic scenes of interactions such as the fighting scenes in movies that involve hundr eds of characters. On the other hand, it can create controllable, intelligent cha racters that interact with the opponents for real-time applications such as 3D compute r games.
Browsing Sports Video

he case for automated video information access systems has been made repeatedly by numerousresearchers since the early 1990s. Finding important episodes in nontext media like video andaudio is made difficult because the same material means different things to different people.Therefore, manually tagging data with textual keywords as a means of facilitating access is notnecessarily reliable for this type of media. This is a crucial point. Information retrieval as a task isreally only defined in the context of the user. This article considers one such context: sports media. In thatcontext, there is a salient structure to the media as well as a series of definable user requirements. Sportbroadcasting is certainly of high commercial importance [1]. All national and international news broadcastscontain specific regular segments devoted to sports. Consumers themselves are increasingly acting as genera-tors of sport media as it becomes easier for parents and coaches to record school sports games, for instance. From a broadcaster’s point of view, efficient archiving of media facilitates later reuse in the creation ofhighlights packages or specialized DVDs. Manual keywording of events is feasible but time consuming and
Simulation of Autonomous Agents using Terrain Reasoning

The representation of a real scenario through a 3D virtual environment populated by autonomous agents has several applications, such as games or animation movies. Such representation can be a difficult and complex task, since natural scenarios are composed by forms with a lot of details that should be reproduced realistically in the virtual world. Moreover, virtual people should understand and consider the virtual world for navigation similarly to real people. In this paper we describe a model to provide motion of groups of characters based on terrain reasoning in 3D virtual worlds. The model starts with the terrain generation process using height maps in a pseudo-infinite world, followed by the simulation of characters movement generated through an space colonization algorithm. Finally, the visualization of characters motion in the 3D terrain can be produced.
SHARP-EDGES METHOD IN ARABIC TEXT STEGANOGRAPHY

One of the issues that arise in the text steganography is the capacity of hiding secret bit. Focusing in Arabic text steganography we propose a sharp-edges method to encounter the issue. This new method will hide the secret bits in the sharp-edges for each character in the Arabic text document. The main processes involved are identifying sharp-edges in the cover-text, secret message preparation to be hidden as a binary string and lastly, the bit hiding process. The experiments show that the capacity percentage used to hide the secret bit was increased up to 37.8%, resolving the capacity issue. The stego-text for this method has high invisibility, and therefore it is possible to be published publicly. We introduce keys to determine the possition of the secret bit randomly. This method utilizes the advantages of the Arabic text for steganography and can also be implemented to the same text scripting of languages such as Jawi, Persian or Urdu.
Multiple Pronunciation Generation using Grapheme-to-Phoneme Conversion based on Conditional Random Fields

We propose an approach to grapheme-to-phoneme conversion with multiple pronunciations based on a probabilistic method: Conditional Random Fields (CRF). CRF give a long term prediction and assume relaxed state independence condition compared to HMMs. Moreover, we propose an algorithm to one-to-one letter to phoneme alignment needed for CRF training. This alignment is based on discrete HMM. This paper investigated the impact of the training set size and the multiple pronunciation generation. Validated on BDLex French dictionary, our approach compares favorably with the performance of the state-of-the-art Joint-Multigram Models in term of the quality of the pronunciations and in term of recall and precision measures for multiple pronunciation variants generation.
Global Model Of Distributed Data Mining Is Not Summing Of Local Models

Discovering meaningful information from data is what is known data mining. By meaningful information we mean sequential patterns, rules and other indexes embedded in the data. So far, attention in the data mining process has always focused on extracting information from data physically located at one central site. However, most real life applications rely on data distributed in several locations. As a result, a feasible model for mining distributed data is required. The paper presents a model for mining distributed sequential patterns. The model is further verified by a simulator that can work with different mining tasks.
Professional Attributes: Teaching the Fine Arts of Being a Professional Accountant

One day during a junior-level cost accounting course, one of my colleagues tried an experiment. The topic was cost-volume-profit (CVP) analysis with a sales mix. Prior to class the students were told to read the chapter and complete a homework assignment. The class session included discussion of the homework assignment. At the end of class, the professor asked students to write a paragraph on the following question: ―When using CVP analysis for planning, how is it possible that managers might not know for certain what the sales mix will be? ‖ After collecting and reading student responses, my colleague was surprised to learn that a large proportion of students did not know the meaning of the term ―sales mix. ‖ These students had completed a homework assignment involving calculations and discussed the calculations during class, but they had not stopped to think about what the various terms used in the formula mean—or about how uncertainty in the data used for CVP analysis might affect an organization’s planning. In other words, the students had learned little, if anything, about CVP analysis and its use— other than to replicate computations shown in the textbook.
Question classification in social media

Question classification is an important part in modern Question Answering systems. Most approaches to question classification are based on handcrafted rules. Recent studies classify simple questions using machine learning techniques and recommends SVM as on of the best performing classifiers. This study applies a hierarchical classifier based on the SVM machine learning algorithm on questions posed by users, drawn from Yahoo! Answers. The significance of this study is that we attempted to directly classify complex questions with multiple sentence questions posed by real users. We report the accuracy achieved using both a coarse-grained classifier and fine-grained classifier to illustrate the effectiveness of our approach on complex questions. We also present a confusion matrix to analyze the results made by our classifier.
Active Equalization for Lithium Batteries Combined Cell Voltage with State of Charge

The equalization strategy simply based on cell voltage or state of charge is used in pure electric racing car. However, there is deviation in estimating state of charge or detecting cell voltage, which causes switches to open and close frequently or improperly, lowing equalization efficiency. Taking energy utilization and equalization efficiency into account, according to the actual operating characteristics of lithium batteries, we studied one new equalization strategy combined cell voltage and state of charge. First of all, one kind of lithium battery model was built and then the active equalization circuit based on the battery model was also built. Secondly, the equalization strategy was simulated. At last, one discharge experiment on the equalization module was conducted. The simulation and experiment results show that our strategy can improve energy utilization, reduce opening and closing times of switches and obtain better equalization effects. Keywords-pure electric racing car; battery consistency; battery model; equalization strategy