Miracle-dz/UniFAR-FTUs huggingface dataset:
JQuery UI like approximate autocomplete

Approximate auto complete facilitates and speeds up input writing by offering a list of suggestions which complements the few characters typed by the user. In this paper we present a method based on a trie to do an efficient 1 edit error approximate auto complete in client server architecture, and we discuss different strategies to improve the efficiency of auto completion, depending on different scenarios. We also propose a method that reduces the number of outgoing transition tested in each node-especially in the first levels-of the trie. It uses a hash based index to generate candidate characters to be tested at each branch. We present a library (named appacolib), in fact a set of different language libraries, to be of use either on the server or the client browser or both to rapidly answer approximate requests on an UTF-8 dictionary.
Image Size Variation Influence on Corrupted and Non-viewable BMP Image

Image is one of the evidence component seek in digital forensics. Joint Photographic Experts Group (JPEG) format is most popular used in the Internet because JPEG files are very lossy and easy to compress that can speed up Internet transmitting processes. However, corrupted JPEG images are hard to recover due to the complexities of determining corruption point. Nowadays Bitmap (BMP) images are preferred in image processing compared to another formats because BMP image contain all the image information in a simple format. Therefore, in order to investigate the corruption point in JPEG, the file is required to be converted into BMP format. Nevertheless, there are many things that can influence the corrupting of BMP image such as the changes of image size that make the file non-viewable. In this paper, the experiment indicates that the size of BMP file influences the changes in the image itself through three conditions, deleting, replacing and insertion. From the experiment, we learnt by correcting the file size, it can able to produce a viewable file though partially. Then, it can be investigated further to identify the corruption point.
DEVELOPING A GAME CATEGORIZATION FOR SITUATIONAL GAME PRODUCTION

In earlier work, a reference method for game production was developed. In this research, we start with extending this method with different routes that correspond to different types of games. Therefore, we propose a game categorization scheme, in order to add situationality to the reference method for game production. The categorization scheme consists of four descriptive dimensions and two supportive dimensions.
Enhancement of Conference organization using ontology based information correlation

The world today witnessed an important transmission to the virtual world across the web. After years of interaction and exchanges between people, the web becomes saturated with enormous quantity of data in various fields. Therefore, the (semi-)automatic applications become necessity to find appropriate information in a brief time. In this context, we propose a new approach of ontology based information correlation from various web resources. As validator of this approach, we introduce the conference organizer system that will be useful during the setting up of a conference. Where we benefit from semantic web technologies to extract, correlate, rank and store information, and consequently propose a ranked lists of experts and social events depending on the user requests.
DATA MINING – FUZZY NEURAL GENETIC ALGORITHM IN PREDICTING DIABETES

Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily compressible to humans. Data Mining represents a process developed to examine large amounts of data routinely collected. The term also refers to a collection of tools used to perform the process. One of the useful applications in the field of medicine is the incurable chronic disease diabetes. Data Mining algorithm is used for testing the accuracy in predicting diabetic status. Fuzzy Systems are been used for solving a wide range of problems in different application domain Genetic Algorithm for designing. Fuzzy systems allows in introducing the learning and adaptation capabilities. Neural Networks are efficiently used for learning membership functions. Diabetes occurs throughout the world, but Type 2 is more common in the most developed countries. The greater increase in prevalence is however expected in Asia and Africa where most patients will likely be found by 2030. Implementation of GA is the scope of the paper.
“ A Survey on: A Novel Framework for Tweet Rending in clump mode ''

Considering wide use of Twitter as the source of information, reaching an interesting tweet for a user among a bunch of tweets is challenging. Many private and/or public organizations have been reported to create and monitor targeted Twitter streams to collect and understand users opinions about the organizations. However the complexity and hybrid nature of the tweets are always challenging for the Information retrieval and natural language processing. Targeted Twitter stream is usually constructed by filtering and rending tweets with certain criteria with the help proposed framework. By dividing the tweet into number of parts Targeted tweet is then analyzed to the understand users opinions about the organizations. There is an emerging need for early rending and classify such tweet, and then it get preserved on dual format and used for downstream application. The proposed architecture shows that, by dividing the tweet into number of parts the standard phrases are separated and stored so the topic of this tweet can be better captured in the sub sequent processing of this tweet Our proposed system on large-scale real tweets demonstrate the efficiency and effectiveness of our framework.
Modeling of PM synchronous motors for control and estimation tasks

We describe a general model for permanent magnet synchronous motors in stationary frame. The model is based on linear magnetics and includes secondary effects caused by nonideal motor construction. Possible model transformations are introduced as well, producing various models suitable for different control and estimation tasks. Several applications that illustrate uses of different models are also presented.
Clustering unlabeled data with SOMs improves classification of labeled real-world data

We show the use of a self organizing map to cluster unlabeled data and to infer possible labelings from the clusters. Our inferred labels are presented to a multilayer perceptron along with labeled data, performance is improved over using only the labeled data. Results are presented for a number of popular real-world benchmark problems from domains other than text. This shows one way in which unlabeled data can be used to enhance supervised learning in a general-purpose neural network.
Automated registration of surveillance data for multi-camera fusion

The fusion of tracking and classification information in multi-camera surveillance environments will result in greater robustness, accuracy and temporal extent of interpretation of activity within the monitored scene. Crucial to such fusion is the recovery of the camera calibration which allows such information to be expressed in a common coordinate system. Rather than relying on the traditional time-consuming, labour-intensive and expert-dependent calibration procedures to recover the camera calibration, extensible plug-and-play surveillance components should employ simple learning calibration procedures by merely watching objects entering, passing through and leaving the monitored scene. In this work we present such a two stage calibration procedure. In the first stage, a linear model of the projected height of objects in the scene is used in conjunction with world knowledge about the average person height to recover the image-plane to local-ground-plane transformation of each camera. In the second stage, a Hough transform technique is used to recover the transformations between these local ground planes.
Dynamic stabilization of one wheel mobile robot (OWMR): a soft-computing approach

Abstract This paper presents application of various Soft-computing control strategies for offline control of one wheel mobile robot (OWMR). The techniques considered for controlling were Fuzzy logic reasoning, Adaptive neuro-fuzzy inference system (ANFIS), and Neural networks (NNs). The study compares the performance of proposed techniques in terms of settling time, maximum overshoot, and steady state error. A Matlab-Simulink model of OWMR system has also been developed. The results obtained from simulation of fuzzy controller were used to train ANFIS and NNs controller. The simulation results showed better performance and learning ability of ANFIS controller as compared to other two controllers. The results are shown with the help of graphs and tables to validate the proposed study.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTION

The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naive Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results .
Performance characteristics of Source calibration service

Using optimisers to calibrate hydrological models is a computationally intensive process. Most optimisation algorithms run on desktop machines, with some running on Linux clusters and a couple that run on cloud infrastructure (e.g. cloudPEST). Complex hydrological models require a relatively powerful machine and calibration runtimes vary from an hour or less, to days and sometimes weeks. Increasingly, organisations are looking to outsource provision and management of computationally intensive infrastructures. While virtualisation technology can provide similar performance to high end desktops, there are opportunities to harness parallelisation and reduce calibration times, by hosting the modelling software on the cloud infrastructure and exposing its functionality through web services. This paper investigates the practicality and performance of implementing a calibration wrapper to the eWater Source river modelling package. The Source calibration service allows user to calibrate models, where the modelling software, eWater Source, is running on the cloud and not on end user's premises. The aim of this analysis was to compare the performance characteristics of a simple GR4J model for the Legerwood catchment using eWater Source running as desktop software versus running as a Source calibration service on the cloud. Shuffle Complex Evolution was used as the parameter optimisation algorithm for the GR4J model parameters. The eWater Source product running as desktop software took around 4 minutes to calibrate the model whereas the Source calibration service took around 73 minutes to do the same calibration with similar results. The difference in run times can be attributed either to: 1) the chatty nature of communication between the machines running the eWater Source and the optimization algorithm; and/or 2) time inefficient implementation of SCEoptim routine from the hydromad package; and/or 3) performance bottle necks in Source's external interface which exposes eWater Source modelling capability through command prompt. Given the long simulation runtimes, the current Source calibration service fails to meet expectations of hydrological model builders for improved performance. For software implementers, we would recommend careful attention to the software architecture and performance characteristics of proposed cloud-based software implementations early in development. In this case, we anticipate future improvements to the infrastructure, or renewed effort improving the implementation would lead to a faster implementation.
An Authenticated Privacy-preserving Attribute Matchmaking Protocol for Mobile Social Networks

Abstract Matchmaking on mobile social networks has evolved overthe years since its advent. On MSN, important, personal,private and sensitive information are shared. This hasnecessitated the need for ecient and privacy-preservingmatchmaking protocols so as to prevent unintended per-sons from accessing such information. In most of the ex-isting matchmaking protocols the inputs are private andpersonal. Hence malicious participants may choose theirsets arbitrarily so as to learn more information about theinput of an honest individual. As an improvement onthe existing matchmaking protocols, we propose an au-thenticated hybrid matchmaking protocol that will helpmatch-pair seekers nd the most appropriate pair withoutleaking private and sensitive information to unintendedpersons. In our protocol, attributes used are certi ed by acerti cation authority. Also, the initiator sets a thresholdnumber of common attributes that an individual shouldhave to qualify as a match-pair. With the use of certi- cation of attributes to ensure that the inputs are notarbitrary and a preset threshold number of common at-tributes de ned, an initiator can adequately nd the bestpair(s) without leaking any private and sensitive informa-tion. Furthermore, our protocol has the ability to resistsemi-honest and malicious attacks.Keywords: authentication, certi cation, matchmaking,nonspoofability, user-pro ling
Bayesian Hierarchical Clustering with a Gaussian Conjugate Prior

A standard tool in studying gene expression profiling data is clustering analysis. Bayesian hierarchical clustering (BHC) (Heller and Ghahramani, 2005) employing a multinomial prior has been shown to produce biologically meaningful results (Savage et al., 2009) and the algorithm automatically learns the optimal number of clusters in the data. We show in this paper that the standard BHC algorithm is vulnerable to noise. In this paper, we present an extension of the BHC algorithm for non-time-series microarray data. We incorporate BHC with a Gaussian model assumption together with a normalgamma conjugate prior to capture intrinsic structure of the data. We demonstrate our algorithm on four cancer gene expression datasets. The results show that our algorithm consistently produces biologically plausible results than other commonly used algorithms
Towards Unsupervised Deep Learning Based Anomaly Detection

—Novelty or anomaly detection is a challenging problem in many research disciplines without a general solution. In machine learning, inputs unlike the training data need to be identiﬁed. In areas where research involves taking measurements, identifying errant measurements is often necessary and occasionally vital. When monitoring the status of a system, some observations may indicate a potential system failure is occurring or may occur in the near future. The challenge is to identify the anomalous measurements that are usually sparse in comparison to the valid measurements. This paper presents a land-water classiﬁcation problem as an anomaly detection problem to demonstrate the inability of a classiﬁer to detect anomalies. A second problem requiring the identiﬁcation of anomalous data uses a deep neural network (DNN) to perform a nonlinear regression as a method for the estimation of the probability that a given input is valid and not anomalous. A discussion of autoencoders is then proposed as an alternative to the supervised classiﬁcation and regression approaches in an effort to remove the necessity of representing the anomalies in the training dataset.
A Tele-rehabilitation System with an Automated Pegboard Utilizing Radio Frequency Identification

Due to the expense of health care and the need to contain costs, many stroke patients are discharged from hospitals while still in an impaired condition. Using Tele-rehabilitation, these patients can receive rehabilitation services remotely. A pegboard is a conventional rehabilitation therapeutic device that integrates cognition, sensation and hand motor function. This study proposes a Tele-rehabilitation content with automated pegboard and shows its functional feasibility. The evaluation of the pegboard session was automated with RFID (radio frequency identification), and a 16-hole pegboard was rapid-prototyped. After a pegboard session is completed, the session result is uploaded to a server automatically for viewing on a web browser by a remote therapist. The therapist can also send messages to remote patients to encourage them or to manage the rehabilitation process.
Implementasi PHP Web Service Sebagai Penyedia Data Aplikasi Mobile

This paper demonstrates how PHP web service technology can be used in mobile application. Web service testing can be done manually by creating php client file that call the SOAP web service. Testing can also be done using SoapUI, an open source software that can be used to do functional testing on web service. This paper is proof of concept that PHP Web Service can be used as data provider for application that need dynamic data. This paper proposed PHP Client Layer as an additional layer to simplify the web service calling from mobile application. Index Terms - web service, php, json, SOAP, SoapUI
Effective Verification and Encryption of Data for Users Delegation Privacy

Cloud computing provides storage facility and services in which users can store their data remotely and avail the on demand high quality applications, services. It is all done through the shared pool of configurable computing resources. But the important thing that need to be understood is, users no longer have physical possession of the out sourced data, which is the tougher part in the cloud computing and it is a formidable task for the users. So the cloud storage should be used as local storage without worrying about the verification of integrity. Thus enabling the method-Effective verification for cloud storage. It is of important so that clients or users can go for TPA, to check the integrity of the outsourced data. To introduce the secure method verification of data, Auditing process should be done, in order not to bring any new problems, or no new vulnerabilities towards the user delegation data. In this paper, we propose a secure cloud storage system supporting Effective Verification and Encryption of Data by Auditing using several techniques such as Index-hash table, Fragment structure, and Random sampling. We further extend our results to enable TPA to audit the data simultaneously and effectively for the multiple users by using auditing Protocol.
Hybridisation Of Process-based Ecosystem Models WithEvolutionary Algorithms: Multi-objective OptimisationOf Process And Parameter Representations Of The LakeSimulation Library SALMO-OO

Over the past three decades, numerous lake ecosystem models incorporating phytoand zooplankton population dynamics as well as nutrient cycles have been developed and published [1–8], which were represented by complex ordinary differential equations (ODE). Those models usually consist of numerous state and driving variables, process equations as well as parameters, which are rigidly networked by the ODE. The process-based lake simulation library SALMO-OO [9–12] is an attempt to overcome rigidity of ecosystem models by hierarchically modularising complex ODE and replacing rigid process representations by optional arrays of alternative process representations implemented by object-oriented programming [13]. The present study demonstrates by means of SALMO-OO that an object-oriented implemented simulation library also gains fl exibility with regard to hybridisation with evolutionary algorithms (EA). SALMO-OO as documented by Recknagel and Cao (Chapter 9) is based on more than 100 process equations and constant parameters. Even though the processes are causally represented in some cases, the knowledge is limited and the representations are shallow and vague. Therefore, fi rst SALMO-OO was embodied with EA to redefi ne or extend vague process representations in its complex ODE. EA utilised merged data of the same category lakes to discover process functions being generic for the lake category refl ected by the merged data. The numerous constant parameters estimated from experimental and literature data signifi cantly determine process dynamics and simulation results [14]. Therefore, secondly, EA were applied to merged data of the same category lakes to optimise and adapt crucial model parameters to distinct conditions of specifi c lake categories. Whilst traditional optimisation techniques allowed fi netuning constant parameter values within their observed variance [15, 16], the use of
Secure Fuzzy keyword Search using an Advanced Technique over Encrypted Cloud Data

Cloud computing is a technology that uses the internet and central remote servers to maintain data and applications. As the data produced by individuals and enterprises that need to be stored and utilized are increasing rapidly, data owners are motivated to outsource their local complex data management systems into the cloud for its great flexibility and economic savings. To ensure that the data can be stored in the cloud securely, data owners encrypt their data before outsourcing to the cloud, which makes searching scheme on a large amount of encrypted data a demanding task. The traditional searchable encryption schemes provide a number of approaches to search on encrypted data, but they all support only exact keyword search. Exact keyword search is unsuitable for cloud storage systems, because it doesn't allow users to make any spelling errors or format inconsistencies, and thereby reduces the system usability. The proposed system uses a wild-card based technique with edit distance as the similarity metric to obtain a fuzzy keyword sets.This solves the problems of the cloud user who search the encrypted cloud data with the help of fuzzy keyword, thereby maintaining keyword privacy.
A Review on Facial Expression Recognition: Feature Extraction and Classification

ABSTRACT Facial expression recognition (FER) is currently a very active research topic in the fields of computer vision, pattern recognition, artificial intelligence, and has drawn extensive attentions owing to its potential applications to natural human–computer interaction (HCI), human emotion analysis, interactive video, image indexing and retrieval, etc. This paper is a survey of FER addressing the most two important aspects of designing an FER system. The first one is facial feature extraction for static images and dynamic image sequences. The second one is facial expression classification. Conclusions and future work are finally discussed in the last section of this survey.
Improvement of Radon Transform-Based Perceptual Hashing using Image Normalization

Abstract This paper proposes an improvement method using image normalization for Radon transform (RT)-based perceptual image hashing in which a normalized image is generated before the image hashing estimation doing it invariant to a wide range of geometric distortions. Proposed scheme improves the robustness of previously proposed RT-based image hashing schemes against geometric distortions. The proposed improvement is evaluated using two efficient RT-based image hashing algorithms, which are Wu’s and Seo’s algorithms. The evaluation results demonstrate that the proposed method, significantly improves the efficiency of Wu’s and Seo’s image hashing algorithms when the images to be analysed are distorted by several geometric attacks, such as rotation, scaling and shearing.