Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-end reliability in UMTS: TCP over ARQ

The focus of this paper is end-to-end reliable transmission in UNITS environment where TCP, a reliable transport protocol designed to retransmit information in case of loss, is present at the mobile station as well as the wired portion of the network. The mobile and wireless portion of the network, characterized by significant error figures, relies on ARQ to detect and retransmit corrupted frames. We propose an analytical model for the operation of TCP over ARQ. We investigate several error patterns corresponding to random, slow and fast fading channels. Our results quantify the enhanced performance of TCP over ARQ in terms of throughput as a function of both loss and error.
Evolutionary Computation in Gene Regulatory Network Research

Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
A Survey in Wireless Ad hoc Network Security and Secure Energy Optimization Approaches for Routing

Wireless ad hoc network nodes together establish a network infrastructure without using any access points or base stations for communicates using multi hop schemes. It has significant characteristics like dynamic topologies, constrained in bandwidth and limited resource a high challenge in implementing security with optimized energy resource utilization which is the key aspects while designing modern ad hoc networks architecture. Ad hoc Networks nodes are limited in broadcast range, and also their capabilities of computation and storage are well limited to their energy resources. This limitation of resources in wireless ad hoc creates high challenges in incorporating security mechanism for routing security and privacy maintenance. This paper investigates the various issues and challenges in secure routing and energy optimization during communication in wireless ad hoc network towards security and secure energy utilization improvisation.
Plagiarism Detection in MEDLINE Using Multiple Query Expansion and Approximate Phrase Match Techniques

Plagiarism is using others work and using them off as one’s own without appropriately acknowledging the source. Identifying duplicated and plagiarized passages of text is becoming popular area of research. Plagiarism is an important issue in every academic and research institutes and this situation is becoming worse with easily available online resources. MEDLINE has a dataset of more than 26 million publications from 5639 various publications in the area of medicine and related fields. New publications are getting added continuously which makes difficult to keep track of information contained within it. In this paper we have proposed method for plagiarism detection that will identify most relevant sources of plagiarism from MEDLINE. We have used set of document matching rules based on query expansion techniques and approximate phrase match techniques are used to match relevant documents. Also the system that we are proposing for candidate document selection is built on highly scalable open source technologies. finally, when ranking suspected documents, we carefully rank those documents based on combined weight of various match types. We have evaluated our proposed approach with recently available MEDLINE corpus which gave us very promising results.
Weakly supervised learning of deformable part models and convolutional neural networks for object detection

In this dissertation we address the problem of weakly supervised object detection, wherein the goal is to recognize and localize objects in weakly-labeled images where object-level annotations are incomplete during training. To this end, we propose two methods which learn two different models for the objects of interest. In our first method, we propose a model enhancing the weakly supervised Deformable Part-based Models (DPMs) by emphasizing the importance of location and size of the initial class-specific root filter. We first compute a candidate pool that represents the potential locations of the object as this root filter estimate, by exploring the generic objectness measurement (region proposals) to combine the most salient regions and “good” region proposals. We then propose learning of the latent class label of each candidate window as a binary classification problem, by training category-specific classifiers used to coarsely classify a candidate window into either a target object or a non-target class. Furthermore, we improve detection by incorporating the contextual information from image classification scores. Finally, we design a flexible enlarging-and-shrinking post-processing procedure to modify the DPMs outputs, which can effectively match the approximate object aspect ratios and further improve final accuracy. Second, we investigate how knowledge about object similarities from both visual and semantic domains can be transferred to adapt an image classifier to an object detector in a semi-supervised setting on a large-scale database, where a subset of object categories are annotated with bounding boxes. We propose to transform deep Convolutional Neural Networks (CNN)-based image-level classifiers into object detectors by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We have evaluated both our approaches extensively on several challenging detection benchmarks, e.g. , PASCAL VOC, ImageNet ILSVRC and Microsoft COCO. Both our approaches compare favorably to the state-of-the-art and show significant improvement over several other recent weakly supervised detection methods.
Classroom Arrangement through Computer: A New Approach

"Objective – In the classroom, computers can be used for its better learning arrangements. Computers can be used either by the teacher or the learner but whatever way it is used, it is an important tool in the teaching-learning environment. Without proper class room arrangement and management, it becomes difficult to create a proper environment for learning. It can also affect the learning behaviors of students. Student may learn more effectively and efficiently in a cool environment. Different educationalists from the education field put in a lot of effort in this respect, suggesting many different methods for solving learning problems. In this paper an attempt has been made to demonstrate how computers may assist in the arrangement of classes for better learning environments. Methodology/Technique – Different perspectives are investigated by collecting data from both students and teachers with different levels of familiarity with the use of computers in the learning environment. Findings – Based on the analysed results, different techniques and strategies are proposed for use in the classroom to achieve better learning environments. Novelty – The study suggests an approach for classroom arrangement though the use of computers. "
CAR: A MATLAB Package to Compute Correspondence Analysis with Rotations

Correspondence analysis (CA) is a popular method that can be used to analyse relationships between categorical variables. Like principal component analysis, CA solutions can be rotated both orthogonally and obliquely to simple structure without affecting the total amount of explained inertia. We describe a MATLAB package for computing CA. The package includes orthogonal and oblique rotation of axes. It is designed not only for advanced users of MATLAB but also for beginners. Analysis can be done using a user-friendly interface, or by using command lines. We illustrate the use of CAR with one example.
E-Learning Based on Cloud Computing

According to some limitations that are being about E-learning application in educational institutions, such as costly and inflexible solutions and some similar problems so, we should search the way to reduce costs and leveraging the flexibility of common electronic learning system. Therefore, in this research, at first we have tried to consider Electronic Learning and the solutions are provided for the discussed problems. In follow cloud computing is introduced as a general solution for these problems. Therefore this research focuses on the ways of applying cloud computing to transfer current e-Learning to a cloud based e-Learning. In the final step of the research we consider a hypothetical case study and try to get an overview of the cloud-based E-learning in Hypothetical case. Finally this research presents a formula for showing cost-effectiveness of cloud based e-Learning. To evaluate the correctness of the formula, a questionnaires based statistics is performed and performing a survey among 50 IT professionals (with different academic degrees).
Scholarly publishing for the network generation

The increasing momentum towards opening up various dimensions of society is discussed in this article, and the authors consider whether ‘open’ is now an unstoppable force for change in the world. Various topics within research communication, such as open access (OA) and post-publication peer review (PPPR), are considered from the perspective of the authors as participants in the scholarly communication community of more than 20 years’ standing, with both for- and non-profit credentials. The authors explore how harnessing the wisdom of the crowd in rating everyday services manifests itself by improving our ability to make choices in our daily lives. They explain how this network effect can be applied to scholarly communication and how it provided some of the inspiration behind the launch of ScienceOpen, the research and OA publishing network, in May 2014. This publishing platform is then described as an example of democratizing publishing. The increasing importance of software development in publishing and the need for stand-alone expertise in this space (as opposed to a publisher-centric approach) is also discussed. Finally, the authors consider the role that the impact factor and the promotion/tenure system play in holding back progress in scholarly communication and they highlight the efforts of early career researchers to break the stalemate by taking ‘open’ pledges.
Morpheme Extraction in Tamil using Finite State Machines (FIRE-2013 - Morpheme Extraction Task)

In this work we present an efficient morphological analysis system for Tamil where we extract the individual morphemes using Finite State Automaton (FSA). The aim is to perform morpheme analysis by modeling the regular inflectional pattern exhibited by Tamil as an FSA. We handle the compound and agglutinated words using CRF based word boundary identifier. On linguistic evaluation the system achieved an encouraging accuracy of 86.17%
Impact evaluation of clustering-based k-anonymity for recommendations

in English, 250 words or less): SaNGreeA is a greedy and deterministic clustering algorithm for achieving kanonymous clusters on a labeled, undirected graph. It is nowadays a classic and the leading work in clustering based k-anonymity algorithms. It has a quadratic time complexity which makes it really slow for a reasonably big network (1 M nodes). Our project adapts SaNGreeA to make it scalable for a real world big network and specialises it to make sure the generated clusters are useful for a recommender system. SaNGreeA always returns the same solution, but it is never the best solution as the underlying problem is NP-complete. It generates a solution that is a local optima. Our adaptation generates hundreds of similar solutions with a multistart biased randomization procedure and selects the best one. We have developed new metrics and evaluation framework that consider the quality of the clusters from a recommender system point of view. We were able to run our algorithm in a real world dataset with around 1M nodes and 75M relationships. The results are encouraging for recommendations as the most important quasi-identifier attributes are not heavily damaged. The algorithm has been implemented in Spark, and can be run in up to a hundred of computing processors in parallel to reduce the computing time of hundreds of different clusterizations to a couple of hours.
An Automatic Mobile Cell Counting System for the Analysis of Biological Image

본 논문에서는 모바일 환경에서 미세세포 영상으로부터 셀을 자동 검출하고 계수하는 자동화 방법을 제시하였다. 셀 카운팅은 생물학 또는 병리학적 영상분석에 있어서 매우 중요한 과정이다. 과거에는 셀 카운팅은 수동적인 방법으로 진행되어 매우 지루하고 많은 시간을 필요로 하는 작업이었다. 이에 더하여 수동 계수 방법은 정확한 카운팅 결과를 도출하는데 어려움이 있었다. 따라서, 정확하고 일관된 셀 검출과 카운팅 결과를 생물학적인 영상으로부터 획득하기 위해서는 자동화방법이 필요하다. 제안된 다단계 셀 계수방법은 배양된 세포영상으로부터 셀을 자동으로 분할하고 분할된 셀의 위상학적 분석을 통하여 셀을 라벨링 한다. 셀 카운팅의 정확도를 높이기 위하여 워터쉐드 알고리듬에 의하여 서로 덩어리로 뭉쳐진 셀을 서로 분리하고 모폴로지 연산을 통하여 영상으로부터 획득한 개별 셀의 형태를 개선한다. 제안된 시스템은 모바일 환경에서 사용될 수 있도록 개발되었다. 따라서 셀 영상은 모바일 폰의 카메라로 획득하며 미세세포의 통계학적인 분석 데이터는 유비쿼터스 환경의 모바일 장치에 의해 전송 된다. 실험을 통하여 수동으로 계수한 셀의 숫자와 제안된 방법에 의해 자동 카운팅 된 셀의 수를 비교한 결과 제안된 방법이 매우 효과적이고 정확한 결과를 제시한다는 사실을 입증하였다.
Associating Semantic Sensor Web with Domain Ontology: The Way to Obtain Meaningful Sensor Data

To realize the sharing and reuse of sensor data and improve interoperability, semantic sensor web(SSW)is proposed to add semantics information to existing sensor networksby utilizing domain, spatial and temporal anthologies and other related semantic technology.However these is seldom research on how to fully utilize the sensor data through a semantic way such as domain ontology based inference. This paperpresents stateofthe art of SSWin various aspects,and proposesthe method to associate data from semantic sensor web with domain ontology to realize the communication between different domain ontologies and SSW. In addition, this paperalso proposes a new calculation method of semantic similarity amongdifferent entities in different ontology. Experiments show that this method can effectively find the similar entitiesandrealize the knowledge sharing and ontologies reuse.
International Journal of Advanced Robotic Systems Visual Appearance-based Unmanned Vehicle Sequential Localization Regular Paper

Localizationis of vital importance for an unmanned vehicle to drive on the road. Most of the existing algorithms are based on laser range finders, inertial equipment, artificial landmarks, distributing sensors or global positioning system(GPS) information. Currently, the problem of localization with vision information is most concerned. However, vision‐based localization techniquesare still unavailable for practical applications. In this paper, we present a vision‐based sequential probability localization method. This method uses the surface information of the roadside to locate the vehicle, especially in the situation where GPS information is unavailable. It is composed of two step, first, in a recording stage, we construct a ground truthmap with the appearance of the roadside environment. Then in an on‐ line stage, we use a sequential matching approach to localize the vehicle. In the experiment, we use two independent cameras to observe the environment, one is left‐orientated and the other is right. SIFT features and Daisy features are used to represent for the visual appearance of the environment. The experiment results show that the proposed method could locate the vehicle in a complicated, large environment with high reliability.
Real-time Bayesian Search Control for MDPs

Many oversubscribed planning problems with multiple (potentially jointly unachievable) goals can be cast in the Markov decision process (MDP) framework when appropriate reward functions and objective criteria can be defined. Recent realtime, search-based solutions to MDPs maintain upper and lower bounds on the value function at all stages of execution prior to convergence. While many algorithms already use this information to detect convergence and to prioritize state exploration, we propose a novel Bayesian interpretation of these bounds to evaluate the myopic value of perfect information (VPI) of exactly knowing a state’s value. This VPI analysis permits two general Bayesian search control modifications to existing algorithms: (1) we can use it to dynamically adapt the backup depth in real-time, search-based bounded dynamic programming approaches, and (2) we can use it for real-time policy evaluation with a bounded value function produced by an anytime algorithm. We empirically evaluate both of these modifications to existing algorithms and analyze their performance. While real-time Bayesian search control may reduce the number of backups until convergence for (1), it offers more substantive improvements for (2), making it an attractive real-time policy evaluation approach for MDPs.
An Improved Bilateral Remote User Authentication Scheme that Preserves User Anonymity using Symmetric Cryptology

In recent 2012, Wen and Li. 's has presented a dynamic ID-based remote user authentication with key agreement scheme. They claimed that their scheme can resisted insider attack and provide anonymity for the users. However, Juan Qu and Li-min Zou. , described that Wen and Li. 's scheme could not withstand insider attack, does not provide anonymity for the users, and inefficiency for error password login. A different approach to symmetric cryptology is taken in this study to resolve the fore was made Compared to Juan Qu and Li-min Zou scheme, a different approach was made with symmetric cryptology in this study to supplement the forementioned weak points. We propose an enhanced authentication scheme, which covers all the identified weakness of Wen and Li. 's scheme and an efficient user authentication scheme that preserve perfect anonymity to both the outsider and remote server.
Eigenvalue-based estimators for optimal dimensionality reduction in information retrieval

Latent Semantic Indexing (LSI) extends Salton's vector space model (VSM) of information retrieval, using dimensionality reduction to construct a statistical model of the relationships among the terms in a document collection. Though empirical studies have shown that such statistical models can improve retrieval over traditional key word-based approaches, dimensionality reduction raises an important question: if we are to reduce model dimensionality, how aggressively should we do so? Or conversely, what is the optimal value for k, the number of dimensions in an LSI system? In the unsupervised learning environment native to information retrieval, notions of model optimality and goodness of fit are difficult to define. This dissertation pursues the viability of five statistical methods for estimating the optimal dimensionality of LSI systems. Though the five pursued methods entail different theoretical assumptions, they are all predicated on an analysis of the eigenvalues that arise naturally during LSI. This thesis contends that LSI's relation to principal component analysis makes an analysis of the eigenvalues the natural vehicle for dimensionality estimation. To judge the utility of eigenvalue-based estimators for dimensionality estimation under LSI, two groups of experiments were performed. Both rounds of experimentation tested the accuracy of five dimensionality estimation criteria: eigenvalue-one, parallel analysis, percent-of-variance, Bartlett's test of isotropy, and a novel method dubbed amended parallel analysis. During the first experiments, these criteria were applied to six standard information retrieval test collections and evaluated with regard to their accuracy. The second round of experiments applied each dimensionality estimator to simulated data. The first round of experiments suggested that the family of estimation techniques based on the notion that dimensionality reduction is merited to the extent that the indexing features violate the VSM's assumption of statistical independence were especially accurate. Also during this round, amended parallel analysis yielded statistically significant improvements over traditional parallel analysis. The data simulations supported these findings. Applying parallel analysis and amended parallel analysis to simulated data of known intrinsic dimensionality yielded categorically superior estimates to all other tested estimation techniques.
Development of double-octagon tactile sensor for grasping control

An intelligent robot hand must be able to grasp a variety of objects, such as a fragile egg, a heavy iron and so on. It must be also able to have the ability to finish a job on the basis of tactile information detected by tactile sensors. In order to realize the sensor for an intelligent hand, the conflicting problem between strength and sensitivity of the tactile sensor must be resolved. To resolve this requirements, we propose a tactile sensor called "double-octagon tactile sensor (DOTS)" for the intelligent robot hand. This sensor has high sensitivity and strength, and can precisely sense the grasping force and shearing force between a grasped object and the finger. The paper describes the structure, design and sensing principle of the DOTS, and shows application to prove the effectiveness of DOTS.
Distinction of Wet Road Surface Condition at Night Using Texture Features

SUMMARY The necessity of distinguishing the road surface condition at night time is increasing because most of the previously proposed methods correspond only to daytime. In this paper, we propose a method of distinguishing road surface condition using only video information acquired from a visible surveillance video camera. The feature of this method is that it simply uses automobile headlights as the light source. Thus it becomes possible to distinguish between road surface conditions, such as dry and wet, with high accuracy using this method.