Miracle-dz/UniFAR-FTUs huggingface dataset:
A low complexity simulated annealing approach for training hidden Markov models

An algorithm for the training of Hidden Markov Models (HMMs) by simulated annealing is presented. This algorithm is based on a finite coding of the solution space based on the optimal trajectory of the state. It is applied to both discrete and continuous Gaussian observations. The algorithm needs no specific initialisation of the initial HMM by the user, the cooling schedule being general and applicable to any specific model. The parameters of the algorithm (initial and final temperatures) are derived automatically from theoretical considerations. The objective function evaluations of the algorithm are made independent of the problem size in order to minimise the computation time. A comparative study between the conventional Baumâ€“Welch algorithm, Viterbi based algorithm and our simulated annealing algorithm shows that our algorithm gives better results and overcome the problem of HMM initialisation needed by the others.
Finite automata with continuous input

Euclidean Automata (EA) are finite state computational devices that take continuous parameter vectors as input. We investigate decompositions of EA into simpler Euclidean and classical finite state automata, relate them to better known computational devices such as artificial neural nets and electronic devices, and show that they are plausible Big Mechanism candidates.
Théorie des graphes et dynamique des connexions cérébrales pour la conception d'interfaces cerveau-machines asynchrones

To design brain-computer interfaces, this article aims at using the perturbations of the communication between dierent cortical areas due to a cognitive task. The network of the cerebral connections is built from correlations between cortical areas at specic frequencies and are analyzed using graph theory formalism which provides measures able to describe the topological organisation of the network. This method is applied to an auditive steady-state evoked potential experience and compared to a more classical method based on ltering.
Tradeoffs Among Editorial Goals in Complex Publishing Environments

Editors need to think about their intrinsic rewards, and hence about their motives and goals. Why do you want to serve as an editor? What satisfactions do you hope to receive from the experience? Recognition? Visibility? Methodological change? Establishment of a nascent research domain? Incremental improvement in an established domain? Promote the development of a sub-discipline? This chapter offers an explicit analysis of some central choices that editors can make about their roles. Reflecting on these choices can help editors to use their time and efforts more effectively and to see the advantages and disadvantages of their activities. Some editors achieve greater success than do others. Circulations, subscription revenues, downloads, and citations offer quantitative evidence. The historical statistics include instances in which citations to a specific journal have risen dramatically, or plummeted, during one editor’s term and then stabilized when that editor left the job.
Assessing the vertical distribution of leaf chlorophyll content in a maize crop

The characterization of the vertical distribution in a maize crop was investigated. Chlorophyll content measurements were performed on each leaf of several plants along the crop cycle. Those measurements, as well as the surface measurements and the leaf insertion height measurements give the vertical distribution of chlorophyll pigments within the canopy. The chlorophyll content is higher for the middle leaves than for the top and bottom leaves of the plant. The impact of chlorophyll vertical distribution on the leaf reflectance response was investigated. For this purpose, the PROSPECT model was applied on multiple layers, reproducing the observed chlorophyll profile.
Creating a Minimal Information Vocabulary for a Reproducible Method Description A Case in Column Chromatography

Descriptions of experimental methods in scientific publications are often incomplete or inadequate. In these cases, the experimental work cannot be reproduced or verified due to lack of information. To facilitate the documentation of lab methods, in some domains minimum information guidelines have been developed. If implemented, these guidelines ensure that the information about the method can be easily verified, analysed and clearly interpreted by a wider scientific community. However, there is an evident lack of automated documentation tools to create and edit laboratory reports that follow these guidelines and at the same time do not impose a too rigid framework on the scientist. This paper describes the very first step towards the development of semantically rich but free-text editor for creating descriptions of experimental methods. We created and evaluated the vocabulary for reporting a column chromatography experiment, which is developed using the MIAPE guidelines. Our goal is to check if we can use the MIAPE guidelines in the food chemistry domain.The ultimate use of the vocabulary is in semantically enriched editorial software. An editor should give knowledge-based guidance to the author and semi-automatically add meta-data. The first step in designing such editor is to construct supporting vocabularies and evaluate their use in the domain of interest. Our initial application domain is laboratory of food chemistry. Keywords–MIAPE; vocabulary; material and method sections; HPLC; reproducibility; laboratory experiments.
Automatic Exploration of Hardware/Software Partitioning

-Today’s complex applications require an efficient hardware/software co-design. Exploring possible hardware/software partitioning scenarios is a time consuming and challenging job. In this work, we present a flexible and automated workflow “SW2TLM” for generating system level hardware accelerators from software applications and their integration to virtual platform framework. SW2TLM enables efficient exploration of different partitioning scenarios with minimal effort, all complexity of the hardware architecture and software integration is hidden from the user. The analysis results captured by the virtual platform will support the decision making towards a final hardware/software partitioning and thereby improve and accelerate the subsequent development process
Simulation of Adaptive LQ Control of Nonlinear Process

The use of the computer simulation not only in the control engineering grows rapidly nowadays with the increasing speed of the computers and low prices of the hardware. Furthermore, the simulation is very often used at present as it has many advantages over an experiment on a real system, which is not feasible and can be dangerous, time and money demanding. A modelling of the system usually precedes the simulation [1]. The mathematical model is a kind of abstract representation of the process which uses input, state or output variables, relations between these variables collected in the set of mathematical equations [1] and [2]. Some simulation and modelling examples can be found also in [3] and [4].
TOWARDS A METHODOLOGY TO ESTIMATE MINIMUM SAMPLE LENGTH FOR SPEAKING RATE

The aim of the present work is to investigate how long should a speech sample be for the speaking rate derived from it can be considered representative of the whole utterance from which the sample has been taken. Eight Brazilian Portuguese speakers read a 144-word text in three rate levels: slow, normal and fast. Speaking rate was measured cumulatively as the number of phonetic syllables per second from the first to the last syllable in the sample. Two types of rates were measured, speech rate and articulation rate. Change point analysis was used to determine the influence of rate type and level on the amount of time necessary for the cumulative estimate of speech and articulation rates to stabilize around the rate yielded by the whole utterance. Mean stabilization latencies are 9.2 seconds for speech rate and 8.7 s for articulation rate. The slow rate tends to stabilize later than fast and normal rates for both types of rate. Stabilization intervals take up a median number of 41 (speech rate) and 59 (articulation rate) syllables. Mean deviation between the global rate and the rate value at stabilization point are 7.8% (speech rate) and 4.2% (articulation rate).
A Survey on Frequent Pattern Mining Over Data Streams

Frequent pattern mining is one of the important tasks used in data mining domain. Frequent pattern mining is used to find interesting patterns from databases, such as association rules, correlations rules, sequence rules, classifier rules, and cluster rules. The main goal of the association rule is, to analyze the purchased products of a customer in a supermarket transactional data. Association rule is used to describe how frequently items are purchased together. It is mainly used in transactional data base. Data streams (12) are an ordered sequence of items that arrives in timely order. It is impossible to store the data in which item arrives. To apply data mining algorithm directly to streams instead of storing them before in a database. Real time surveillances system, telecommunication system, sensor network, financial applications, transactional data are some of the examples of the data stream systems. These types of streams produced millions or billions of updates every hour. As data stored in a database and data warehouse are processed by using some mining algorithm. Data mining (1) is defined as the process of extracting information or interesting pattern or end product from huge amount of data. In this paper, we have studied the concept of data streams and how the frequent patterns are mined from data streams. We also analyzed the existing research works in the field of frequent pattern mining data streams.
At Least Seven Touches: One Academic Library’s Marketing and Outreach Strategy for Graduate Professional Programs

Column description. The Marketing column features essays about a variety of creative and innovative marketing strategies used to highlight collections or promote services in all types of libraries. Its purpose is to provide real examples of how libraries are using marketing and outreach techniques in interesting ways. The column offers practical insight from libraries engaged with marketing. In addition to marketing, the column also features essays on successful programs and events that promote the library. Readers are invited to suggest topics or projects by contacting the column editor, Katy Kelly. Graduate students in professional programs such as law, journalism, architecture, and business can be challenging for academic librarians to reach because of the nature of their curricula. The University of Oregon Portland Library and Learning Commons meets this challenge by adopting a “seven touches” outreach campaign model for important communications. While there is little evidence that seven means of reaching users is a magic bullet of any kind, the model is supported by the established psychological principle of the mere exposure effect. Setting a goal of creating at least seven communication channels to users helps create an aspirational but achievable framework for outreach. In the example discussed, library staff saw more than a three-fold increase in user responses to a library survey, following a modified “seven touches” campaign.
An Efficient Machine Learning Approach for Identification of Operating System Processes

For providing security to computer systems various approaches like firewalls, anti-virus tool, network security tools, malware removal tools, monitoring tools and many more are being used in present scenario. Computer security tools available in present era need regular updating and monitoring. If any computer users do not regularly update the security tools, then the system may be infected by any virus or any other attack. In this paper a learning system is proposed to identify the operating system process as self and non-self using the concepts of Machine Learning. Three concepts of machine learning have been used to provide the efficient learning system. As a first concept the approach of Concept Learning and the general-to-specific ordering of hypotheses has been used in which Version Space has been generated using the Candidate-Elimination algorithm to provide the learning. As second concept Decision Tree Learning has been used in which ID3 algorithm has been used to construct a decision tree. As a third concept an Artificial Neural Network (ANN) has been used and this concept uses the Gradient Descent Algorithm. Finally, it has been observed that the Decision Tree and Artificial Neural Network learning are the best suited learning approach for identifying self and non-self process.
MultiCAD MOGA. A system for conceptual design of buildings.

The synthesis of the three-dimensional morphology of a building is one of the most important tasks in architecture. Space planning and morphology are of the most interesting and complex of architectural design problems. Architectural design is guided by the constraints on the spatial composition and the morphology of the final building. During the conceptual phase problems are characterised by fuzziness and complexity. Building requirements are ill-defined and contradictory. The designer should explore the solution space for alternative building solutions while refining requirements and style preferences. In this paper we present the development and implementation of an Evolutionary Declarative Design system prototype for the aid of conceptual style design of buildings. The system is a specific MultiCAD prototype system including architectural knowledge, architectural style and a multi-objective genetic algorithm. Two design cases are presented for two different architectural styles. The applicability and efficiency of the system prototype are discussed.
Evaluating, Comparing, and Best Practice in Electronic Portfolio System Use

Electronic portfolios (e-portfolios) are commonly positioned to show evidence of student learning with formative and summative assessment benefits. At the University of Auckland teacher education program, two e-portfolio systems were adopted to document preservice teacher's course work and to attest to the Graduating Teacher Standards. This article seeks to (a) evaluate e-portfolio systems, (b) compare the MyPortfolio (Mahara) and Google Sites systems, and (c) provide recommendations for best practice in system use. The software system comparison between the MyPortfolio (Mahara) and Google Sites system was based on the essential technology features for system use. User satisfaction and usability data were obtained through self-reported surveys (N = 192). Open-ended questions (N = 192) and semistructured interviews with a sample of course participants (N = 12) ascertained the best practice approach for system use. Mean scores were higher for the MyPortfolio (Mahara) system. One-way analysis of variance indicated that the differences were statistically significant.
On the Application of the Process-genre Approach to Teaching English Writing

The process-genre approach to teaching writing has been forward by Badger and White (2000), but how to apply this approach is a great challenge we meet. Based on the previous research on the nature of writing, this thesis explicitly explores the theoretical basis of this approach. And then the writer puts forward a series of teaching procedures based on Badger and White’s (2000) teaching model. Furthermore, this thesis presents the theoretical foundation and the strengths of these procedures.
Community Detection by Fuzzy Relations

The increasing demand for knowledge from network data poses significant challenges in many tasks. Discovering community structure from a network is one of the classic and significant problems faced in network analysis. In this paper, we study the network structure from the perspective of the composition of fuzzy relations, and a novel algorithm based on fuzzy relations, i.e., CDFR (Community Detection by Fuzzy Relations), is proposed for non-overlapping community detection. The key idea of CDFR is to find the NGC node (Nearest node with Greater Centrality) for each node and compute the fuzzy relation between them. Then, the community to which a node belongs depends on its NGC node. In addition, the decision graph will be constructed to guide community detection. Experimental results on artificial and real-world networks verify the effectiveness and superiority of our CDFR algorithm.
Detecting salt body using texture classification

Salt body detection from 3D seismic data is a very complex process and unfortunately it is a tedious labor for the interpreter due to they traditionally use a manually process. In this paper we present a methodology for detecting salt body from 3D seismic data with minimum user intervention. Thus, we can reduce interpretation time significantly. The main goal of our methodology is to present a novel texture classification workflow using seismic attributes, clustering techniques and segmentation by thresholds. Also, we use mathematical morphological and basic operations between volumes to improve the detection. We have used 3D real seismic data from offshore Brazil to test our methodology and the preliminary detection obtained shows encouraging results as potential solution to automatically detect high percentage of geobody.
Facilitating High Performance Code Parallelization

With the surge of social media on one hand and the ease of obtaining information due to cheap sensing devices and open source APIs on the other hand, the amount of data that can be processed is as well vastly increasing. In addition, the world of computing has recently been witnessing a growing shift towards massively parallel distributed systems due to the increasing importance of transforming data into knowledge in today’s data-driven world. At the core of data analysis for all sorts of applications lies pattern matching. Therefore, parallelizing pattern matching algorithms should be made efficient in order to cater to this ever-increasing abundance of data. We propose a method that automatically detects a user’s single threaded function call to search for a pattern using Java’s standard regular expression library, and replaces it with our own data parallel implementation using Java bytecode injection. Our approach facilitates parallel processing on different platforms consisting of shared memory systems (using multithreading and NVIDIA GPUs) and distributed systems (using MPI and Hadoop). The major contributions of our implementation consist of reducing the execution time while at the same time being transparent to the user. In addition to that, and in the same spirit of facilitating high performance code parallelization, we present a tool that automatically generates Spark Java code from minimal user-supplied inputs. Spark has emerged as the tool of choice for efficient big data analysis. However, users still have to learn the complicated Spark API in order to write even a simple application. Our tool is easy to use, interactive and offers Spark’s native Java API performance. To the best of our knowledge and until the time of this writing, such a tool has not been yet implemented. Facilitating High Performance Code Parallelization
An Incremental Rapid DBSCAN Clustering Algorithm for Detecting Software Vulnerabilities

Many current methods for detecting software vulnerabilities couldn’t use the collected vulnerability features to describe the unknown vulnerability, so the unknown vulnerabilities couldn’t be detected efficiently. In this paper, an incremental rapid DBSCAN clustering algorithm for detecting software vulnerabilities is proposed. Sequence pattern library is built by rapid DBSCAN, and the analyzing software vulnerability sequence is clustered based on incremental rapid DBSCAN algorithm. Firstly, the affected set and seeds set of analyzing sequence are calculated. Then the cluster is extended from seed set. For the analyzing sequence, the insert operation is completed. It doesn’t need to re-cluster vulnerabilities on the entire sequence set, but implement incremental cluster by using the previous cluster results. Lastly, the vulnerability type is similar to the sequence with the maximum similarity in its cluster. Experimental analysis shows that the efficiency of detection unknown vulnerability is greatly improved by using this method.
Graph-Based Social Media Analysis

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies. The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution. Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more. This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social networks.
The Semi-automatic Construction of the Polish Cyc Lexicon

In this paper we discuss the problem of building the Polish lexicon for the Cyc ontology. As the ontology is very large and complex we describe semi- automatic translation of part of it, which might be useful for tasks lying on the border between the fields of Semantic Web and Natural Languag e Processing. We concentrate on precise identification of lexemes, which i s crucial for tasks such as natural language generation in massively inflected l anguages like Polish, and we also concentrate on multi-word entries, since in Cyc for every 10 concepts, 9 of them is mapped to expressions containing more than one word.
A priority queueing model for the cognitive radio paradigm on a single channel

The goal of cognitive radio networking technologies is to recuperate the unused bandwidth in existing wireless telecommunication infrastructures. We consider a single channel on which licensed users (primary users, PU) can freely transmit packets, while the ‘spectrum gaps’ in between such transmissions can be used by packets from unlicensed users (secondary users, SU). Assuming Poisson arrivals, we derive the SU transmission and queueing delay distribution. In case of saturation with fixed-length SU packets, we also compute their throughput and probability of preemption by a PU transmission. Numerical investigations focus on the optimal SU packet length to maximise the throughput.