Miracle-dz/UniFAR-FTUs huggingface dataset:
The Application of Artificial Neural Networks with the Multilayer Perceptron Classification for Discovering Webpages

This experimental study aims to apply the method of artificial neural networks with the multilayer perceptron for machine learning in pattern recognition and producing better retrieval results of unstructured data sets. The researchers used the Waikato Environment for Knowledge Analysis (WEKA) software as a toolbox to generate machine learning algorithms for data mining tasks. The main datasets were divided into five sub-datasets. The multilayer perceptron classification was applied to every group, but there were differences in word frequencies and sequences among them. After the experiment, it is found that the first sub-dataset provided the best overall results because of its learning performance of recognition. It implies that the application of artificial neural networks with the multilayer perceptron classification significantly improves the pattern of recognizing word meanings. This potential technique also reinforces an effort to develop a novel system of web information retrieval.
Efficient and Scalable Algorithms for Network Motifs Discovery

Networks are a powerful representation for a multitude of natural and artificial systems. They are ubiquitous in real-world systems, presenting substantial non-trivial topological features. These are called complex networks and have received increasing attention in recent years. In order to understand their design principles, the concept of network motifs emerged. These are recurrent over-represented patterns of interconnections, conjectured to have some significance, that can be seen as basic building blocks of networks. Algorithmically, discovering network motifs is a hard problem related to graph isomorphism. The needed execution time grows exponentially as the size of networks or motifs increases, thus limiting their applicability. Since motifs are a fundamental concept, increasing the efficiency in its detection can lead to new insights in several areas of knowledge. To develop efficient and scalable algorithms for motifs discovery is precisely the main aim of this thesis. We provide a thorough survey of existing methods, complete with an associated chronology, taxonomy, algorithmic description and empirical evaluation and comparison. We propose a novel data-structure, g-tries, designed to represent a collection of graphs. Akin to a prefix tree, it takes advantage of common substructures to both reduce the memory needed to store the graphs, and to produce a new more efficient sequential algorithm to compute their frequency as subgraphs of another larger graph. We also introduce a sampling methodology for g-tries that successfully trades accuracy for faster execution times. We identify opportunities for parallelism in motif discovery, creating an associated taxonomy. We expose the whole motif computation as a tree based search and devise a general methodology for parallel execution with dynamic load balancing, including a novel strategy capable of efficiently stopping and dividing computation on the fly. In particular we provide parallel algorithms for ESU and g-tries. Finally, we extensively evaluate our algorithms on a set of diversified complex networks. We show that we are able to outperform all existing sequential algorithms, and are able to scale our parallel algorithms up to 128 processors almost linearly. By combining the power of g-tries and parallelism, we speedup motif discovery by several orders of magnitude, thus effectively pushing the limits in its applicability.
Interactions between TCP and RLP protocols in a cellular system

We investigate the interactions between the transmission control protocol (TCP) and radio link protocols (RLP) of various reliability, when used in a spectrally efficient microcellular system similar to the EDGE cellular system. The study is done through detailed downlink simulations of a large cellular system with numerous simultaneous users. Either fixed-load or WWW-like packet traffic is generated for each link, and RENO TCP is run. The results show that the choice of RLP retransmission parameters impacts the throughput loss and application packet delay significantly, and that the range of 3-10 RLP block retransmissions (60-200ms persistency) offers the smallest performance degradation caused by TCP/RLP interactions. We conclude that variable RLP persistency based on the knowledge of radio conditions for individual users, is highly desirable.
Implementation and Assessment of Undergraduate Experiences in SOAP: An Atmospheric Science Research and Education Program

ABSTRACT The Student Operational Aggie Doppler Radar Project (SOAP) involved 95 undergraduates in a research and education program to better understand the climatology of storms in southeast Texas from 2006–2010. This paper describes the structure, components, and implementation of the 1-credit-hour research course, comparing first-year participants' experiences and career outcomes with students who were engaged in SOAP for multiple years. Groups of five or six students, led by a senior-level undergraduate and mentored by a graduate student and faculty advisor, performed several daily research tasks, including producing precipitation forecasts, archiving observations, and operating and analyzing data from an S-band Doppler radar for precipitation events on their assigned day. Anonymous surveys given to SOAP students at the end of each semester indicated that student confidence in performing most SOAP tasks exhibited statistically significant positive correlations with their interest and experience in doing them. In addition, students participating in SOAP for multiple years were significantly more confident in performing program tasks than single-year participants (with correlations increasing an average of 19%) and were more likely to obtain meteorology or science-related employment upon graduation (94% versus 69%). First-year participants were significantly more likely to indicate that their interactions with undergraduate student leaders or peers were most beneficial, whereas interactions with the faculty advisor or graduate student mentors were equally or more important to returning students. Students were also more likely to consider research careers and matriculate to graduate programs as they participated longer in SOAP, suggesting research and education programs have a strong influence on students' career outcomes in addition to fostering positive self-efficacy.
Applying Different Neural Network's Topologies to the Forecasting Task

The abstract gives brief information about a contents of the paper. The abstract should not be shorter than 80 words and should not exceed 250 words. The abstract introduces a reader into a field of your research, describes the problem and it's solution. It also introduces results reached in experiments. This paper is a demonstration of usage of LaTeX for IWIM 2007 camera ready paper. We also provide a template for MS Word but usage of LaTeX is highly recommended for production of high quality paper. It also eases a process of editing the proceedings if IWIM 2007.
Continuous flow routing (CFR) : a wireless ad hoc network routing protocol for supporting multimedia flows

CONTINUOUS FLOW ROUTING (CFR): A WIRELESS AD HOC NETWORK ROUTING PROTOCOL FOR SUPPORTING MULTIMEDIA FLOWS Ahmet KARA M.S. in Computer Engineering Supervisor: Assist. Prof. Dr. Ibrahim Korpeoglu July, 2006 Multimedia flows require special routing layer support in ad hoc networks due their unique characteristics and certain requirements on packet delay, jitter, loss rate and bandwidth. In this thesis we propose a wireless ad hoc routing protocol (called CFR) with route discovery and maintenance mechanisms, that is specifically designed for better supporting multimedia flows in wireless ad hoc networks. Since multimedia flows are long durational, it is important to route them through stable routes in order to minimize route failures and disturbance on the flows. We propose to improve the stability by considering the energy drain rates and estimated remaining lifetimes of nodes while selecting the best routes. Additionally we provide a maintenance scheme that acts pro-actively and re-routes the traffic if a node starts getting very low energy. For this we define two thresholds on remaining lifetime of a node. After the first threshold is reached, the node re-routes some of its flows so that the traffic load on the node is reduced. This helps to an even distribution of traffic to nodes of an ad hoc network. After the second threshold is reached, the node has very few energy and therefore redirects all its traffic to some other routes so that the flows are not disturbed by the route failure when the node depletes all of its energy and can not route anymore. We implemented the protocol as part of an ns2-based simulator and proved that it works correctly. Additionally, we compared the protocol against some other similar protocols. The results show that CFR can indeed help supporting multimedia flows better.
Benchmarking parallel simulation algorithms

Parallel simulation has been an active research area for more than a decade. The parallel simulation community needs a common benchmark suite for performance evaluation of parallel simulation environments. Performance evaluation of a parallel simulation environment is harder than evaluating a parallel processing system, since the underlying system is nor only composed of architecture and operating system, but also of simulation kernel. Thus, simulation kernel designers often confront a twofold task: (i) to evaluate how efficiently their simulation kernel runs on certain architectures; and (ii) to evaluate how simulation problems scale using this kernel In this paper we advocate an incremental benchmarking methodology that focuses on the evaluation of a parallel simulation system which is based on Time Warp. We start from a reduced set of ping models that can effectively estimate the various overheads, contention and latencies of Time Warp running on a multiprocessor. The benchmark suite has been used to locate several sources of overhead in an existing Time Warp implementation. Using this benchmark suite we also compare the performance of the improved version of the Time Warp implementation with the original one.
Random Term Generation for Compiler Testing in Spoofax

Testing is the most commonly used technique for raising confidence in the correctness of a piece of software, but constructing an effective test suite is expensive and prone to error. Property-based testing partly automates this process by testing whether a property holds for all randomly generated inputs, but its effectiveness depends upon the ability to automatically generate random test inputs. When using property-based testing to test a compiler backend, the problem becomes that of generating random programs that pass the parsing and analysis phase. We present SPG (SPoofax Generator), a language-parametric generator of random well-formed terms. We describe three experiments in which we evaluate the effectiveness of SPG at discovering different kinds of compiler bugs. Furthermore, we analyze why the generator fails to detect certain compiler bugs and provide several ideas for future work. The results show that random testing can be a cost-effective technique to find bugs in small programming languages (such as DSLs), but its application to practical programming languages requires further research.
RANCANG BANGUN APLIKASI PEMBELAJARAN DASAR PEMROGRAMAN BERBASIS MOBILE PHONE

Pembelajaran pemrograman dasar meraupakan suatu upaya untuk memfasilitasi, mendorong dan mendukung pengguna dalam belajar pemrograman. Namun banyak pengguna khususnya pemula tidak memiliki pengetahuan dasar tentang pemrograman sehingga mereka menganggap pemrograman merupakan pelajaran yang sulit. Anggapan ini membuat mereka malas untuk belajar pemrograman. Sehingga dibutuhkanlah aplikasi pembelajaran dasar pemrograman berbasis mobile phone yang menampilkan materi dan simulasi soal untuk dapat memudahkan dan meningkatkan minat pengguna dalam memahami pemrograman. Aplikasi ini akan menampilkan materi mengenai konsep dasar, percabangan, perulangan, array dan terdapat simulasi soal pada setiap materi serta terdapat evaluasi soal seluruh materi yang berjumlah 20 soal dengan waktu 15 menit. Dengan fitur ini diharapkan agar dapat membantu membantu orang - orang dalam pembelajaran mengenai pemrograman. Kata Kunci : Aplikasi, Dasar Pemrograman, Mobile Phone
Reference Algorithm for Computing Peak Signal to Noise Ratio (PSNR) of a Video Sequence with a Constant Delay

Peak Signal to Noise Ratio (PSNR) has been used as a benchmark to evaluate new objective perceptual video quality metrics. For example, PSNR has been used as a benchmark for both the Multimedia (MM) and Reduced Reference Television (RRTV) test programs recently completed by the Video Quality Experts Group (VQEG). However, there is not currently an international Recommendation specifying exactly how to perform this critical measurement. Since the calculation of PSNR is highly dependent upon proper calculation of spatial alignment, temporal alignment, gain, and level offset between the processed video sequence and the original video sequence, one must also specify the method of performing these calibration procedures. The past two validation tests (MM and RRTV) performed by VQEG utilized the exhaustive search PSNR algorithm that is the subject of this contribution. Members of VQEG agreed to use this PSNR method as a benchmark for assessing the effectiveness of perceptual video quality metrics after extensive discussions. This PSNR calculation method has the advantage of automatically determining the highest possible PSNR value for a given video sequence over the range of spatial and temporal shifts. Only one temporal shift is allowed for all frames in the entire processed video sequence (i.e., constant delay).
Improvement the Wireless Network Security by Requiring the News Cryptographic Primitives

Wireless network is an efficient solution involving a robust security equivalent to wired network when strong encrypton and mutual authentications are used. It requires lightweight, dynamic and robust cryptographical primitives to improve best security and performances of a weakely protcted environnement. Unfortinitly, the weak physical protection makes it more exposed to various types of attacks. In this paper, we focuse on data confidentiality and integrity to enhance the wireless network security. To event the CRC32 weakness, we introduce a dynamic DCRCn(M,G) that calculates a chechsum of primitive polynomials generator. Then, we evoluate the data confidentiality by a symmetric key system founded on quadratic fields. This synchronous stream cipher generator proves its robustness, of a weak input secret-key, by its aptitude to regenerated unpredictable primitive signals under the minimal perturbations.
Multimedia edukien ulerpen semantikorako ekarpen metodologikoak: irudien behe-mailako analisitik bideoen ekintzen sailkapenera

In the last years, the amount of digital content that is produced worldwide has grown exponentially. Concretely, digital images and videos are the main representatives of this growth. They have become the core unit of all the digital communications and the experts in the field foretell that this trend will continue in the next few years. Therefore, there is a real need of effective and stable methods for the storage, management and analysis of this huge volume of content. The presented dissertation work focuses on the research and development of analysis methods for images and videos. The main objective is to establish the pillars for the semantic understanding of images and videos. Remark that understanding the analysed content permits the automatic labelling of it, which incurs in benefits from different perspectives. On the one hand, content producers could automatically label all their information while they are generating it. This fact permits the adoption of more effective storage strategies and content retrieval systems. On the other hand, content consumers gain the possibility of searching content using semantic concepts, ergo, natural language terms. In consequence, Human-Computer Interaction (HCI) becomes more natural. Internet is nowadays the biggest warehouse of the digital content. Most of the content that is everyday produced in the world is housed there and the main characteristic of this content is its heterogeneity. Content can proceed from very different fields; in other words, they don’t belong to a unique domain. Hence, domain specific strategies can’t be applied and this hinders the analysis of the content. In order to tackle this, lot of research has been done in effective methods for the domain recognition. However, these methods are commonly based on the analysis of the content itself which ends in a Vicious circle. Computer Vision drives all the research results presented in this dissertation work. Main contributions rely on the fact that designed and developed methods are domain specific; even though, some domain-agnostic methods has also been studied. Main contributions can be divided into three principal research lines. First research line includes the analysis and development of methods based on low-level descriptors
ASCHEA: new results using adaptive segregational constraint handling

ASCHEA is an adaptive algorithm for constrained optimization problem based on a population level adaptive penalty function to handle constraints, a constraint-driven mate selection for recombination, and a segregational selection that favors a given number of feasible individuals. In this paper, we present some new results obtained using ASCHEA after extending the penalty function and introducing a niching technique with adaptive radius to handle multimodal functions. Furthermore, we propose a new equality constraint handling strategy. The idea is to start, for each equality, with a large feasible domain and to reduce it progressively along generations, in order to bring it as close as possible to null measure domain. Two approaches are proposed and experimented, the first based on dynamic adjustment and the second based on adaptive adjustment.
Entity recognition for multi-modal socio-technical systems

Entity Recognition (ER) can be used as a method for extracting information about sociotechnical systems from unstructured, natural language text data. This process is limited by the set of entity classes considered in many current ER solutions. In this thesis, we report on the development of an ER classifier that supports a wide range of entity classes that are relevant for analyzing multi-modal, socio-technical systems. Another limitation with current entity extractors is that they mainly support the detection of named entities, typically in the form of proper nouns. The presented solution also detects entities not referred to by a name, such as general references to places (e.g. forest) or natural resources (e.g. timber). We use supervised machine learning for this project. To overcome data sparseness issues that results from considering a large number of entity classes, we built two separate classifiers for predicting labels for entity boundary and class. We herein investigate rules for merging both labels while minimizing the loss of accuracy due to this step. The accuracy of our classifier for the largest model with 94 classes achieves 75.9%. We compare the performance of our solution to other standard systems on several datasets, finding that with the same number of classes, the accuracy of our classifier is comparable to other state-of-the-art ER packages.
Sure Staf: A Computerized Staff Scheduling System for Telephone Business Offices

Scheduling of staff between answering incoming calls and processing paper-work has been a continual problem in telephone company Business Offices. A staff scheduling system has been developed and implemented in Bell Canada on a trial basis. Incoming call load data are recorded automatically and are entered daily into a time share computer terminal. The next day's forecast, by half hour, is made by a cyclic exponential smoothing routine and the number of servers required to take calls is calculated using a queueing model of the Business Office. A schedule of call answering periods for the available staff to match the requirements is made using a heuristic algorithm. Managers are able to choose their objective on the accessibility of the office to incoming customer calls and the staff can organize their work day without unplanned interruptions.
Feature Selection and Data Sampling Methods for Learning Reputation Dimensions

We report on our participation in the reputation dimension task of the CLEF RepLab 2014 evaluation initiative, i.e., to classify social media updates into eight predefined categories. We address the task by using corpus-based meth- ods to extract textual features from the labeled training data to train two classifiers in a supervised way. We explore three sampling strategies for selecting training examples, and probe their effect on classification performance. We find that all our submitted runs outperform the baseline, and that elaborate feature selection methods coupled with balanced datasets help improve classification accuracy.
WARP : speeding up the software development process

Nowadays business operations happen very fast and they are really complex, so almost every organization supports its activities using information systems and other technological instruments. Because sometimes there are no final products that fit their needs, it is common that companies are enrolled in software development activities. The complexity and abstract character of software implies new challenges for these enterprises. The success of software development process is determined by several factors, for example the value created by the product, technical difficulties, team skills or communication between stakeholders. If the organization adopts an organized process, choosing the correct methodologies and tools to support it, we can speed up it and, at the same time improve its quality. This project is all about improving the software development process, looking for three activities that improve communication: business modeling, object-relational mapping and prototyping user interfaces. Business modeling is a complex activity, involving the knowledge of problem domain and some technological background. However their common outputs, texts and visual diagrams, are usually informal and ambiguous. WARP solution proposes developing formal and executable business models, using an object-oriented programming language. These models are testable and can be reused in following development stages. Most of business entities have a long life, requiring that their knowledge and data must be safely saved. Object-relational mapping software helps the developer in the implementation of persistency concerns, decreasing the effort to connect relational schemas and objects. Prototyping user interfaces provides earlier discussion with clients about concrete things, which would detect fails on requirements or on system design. Moreover, nowadays there are methods for automatic generation of user interfaces from models, which increase significantly the developer’s productivity. The WARP project studies current techniques and tools associated with these three activities, choosing an approach to be integrated in current software development processes. Finally this approach is applied in the development of Equipment Database, a product that solves a Qimonda’s specific problem, to evaluate its applicability in real projects.