Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Cognitive Simulation Models in the Development of Advanced Training and Testing Systems.

Abstract : To be most efficient, an intelligent tutoring system must be based on a thorough analysis of the task that is being trained, and the knowledge, both declarative and procedural, that the learner must acquire in order to perform the task. This paper explores the thesis that constructing a cognitive simulation model of the task is an efficient approach to characterizing this content. Because the simulation model must be explicitly stated in order to carry out the task properly, the model acts as a specification of (1) the exact nature of the user's task in terms of goals and subgoals; (2) the exact procedural knowledge required to accomplish the goals in the task setting; (3) the exact declarative knowledge required to support the procedural knowledge in carrying out the task. Such specifications could be used to both specify the content of instruction and the content of diagnostic tests. Two examples of the applications of this approach are presented, both assuming that the procedural knowledge is represented in the form of production rules. In the first example, by determining what production rules were actually stated by the training materials for a word processing system, it became clear that certain procedures were not correctly stated. The second example concerned the training and testing materials used in experiments in which subjects learned the fictitious inner workings of a simple control panel system, and then had to infer how to operate the control panel to accomplish a simple goal under different situations.
The MVDR guided constant modulus adaptive array (MVDR-GCMARY) for signal separation in fading channels

The constant modulus array (CMARY) is a blind adaptive beamformer, which is capable of recovering a narrowband signal that is received along with other cochannel signals without using a pilot or training signal. It is based on the constant modulus algorithm (CMA) The CMARY typically captures the signal that has the greatest power among the cochannel signals received by the array. In order to help the beamformer select-the desired signal, the capture process should be controlled, or guided, by controlling the initial weights of the array. This can be done by using a suitable method for direction of arrival (DOA) estimation, such as subspace-based methods, e.g. the MUSIC algorithm, to estimate the angle of arrival of the desired signal. The initial weights of the array are then computed using a suitable beamforming technique such as MVDR to recover the desired signal. By this way the desired signal will have a dominant power at the array output and the CMARY array will capture the desired signal. The resulting configuration is named the MVDR guided constant modulus array (MVDR-GCMARY) and is described in detail in this paper. Computer simulations show the high efficiency of the proposed GCMARY in separating cochannel signals received from different sources and directions for different scenarios of signal constellations, array geometries and channel fading and multipath characteristics.
MPEG-7 e a recuperação da informação de objetos multimídia

The description of the multimedia objects content is currently a great challenge which falls directly upon the result of the information retrieval. MPEG-7, interface for description of multimedia contents, is a complex standard based on metadata templates that operates on the description of this sort of content. This article is intended to describe the MPEG-7 structure, establish its relations with the problems of the multimedia objects information retrieval and briefly present some initiatives of information management and retrieval systems development, based on MPEG-7.
Turbo Codes Performance Optimization Over Block Fading Channels

In this paper, the best achievable performance of a turbo coded system on a block fading channel is obtained. The block fading channel model is motivated by the fact that in many wireless systems the coherence time of the channel is much longer than one symbol interval, resulting in adjacent symbols being affected by the same fading value. The fading blocks will experience independent fades, assuming a sufficient separation in time, in frequency, or both in time and in frequency. This channel model is suitable for analyzing, for instance, wireless communication systems employing techniques such as slow frequency-hopping, as is done in the Global System for Mobile communications (GSM). In such systems, coded information is transmitted over a small number of fading channels in order to achieve diversity. The best coded information allocations over a certain number of fading channels are evaluated, using the Eades-McKay algorithm to generate distinct permutations of a multiset. Bounds on the achievable performance due to coding are derived using information-theoretic techniques. In particular, in the paper an analytical method is proposed, based on the sphere-packing bounding technique, to assess the achievable performance. Moreover, simulation results are obtained and compared with the theoretical ones
eCall – MINIMUM SET OF DATA (MSD)

The introduction and use of in-vehicle eCall for deployment of emergency assistance will save lives and reduce social burden by improving the notification of road accidents and speeding up emergency service response.This articles describes the specifies of an emergency message (minimum set of data - MSD) that is sent to to the most appropriate emergency response center – Public Safety Answering Point (PSAP), as defined by the relevant public authorities. The mobile network operator forwards the call to the PSAP where the operator can analyze the data on a display so that he will be able to determine which emergency services are needed at the scene.
Computational methods and graphical models for integrative proteogenomics

Proteogenomics describes the integration of genomic, transcriptomic, and proteomic data. The combination of this multi-omics information offers unprecedented possibilities for more accurate and sample-specific gene and protein identification. Further, the advent of high-throughput technologies has led to a wealth of studies aiming at a deeper understanding of protein function and interaction. Hence, methods analyzing proteogenomic data, and particularly integrating various data types, are strongly demanded. In this thesis, we present new proteogenomic approaches for the integration of next-generation sequencing and mass spectrometry data in form of DNA and RNASeq and tandem mass spectra. These contributions can be divided into three main projects: First, we developed the method GIIRA (Gene Identification Incorporating RNA-Seq data and Ambiguous reads) for the construction of gene models and transcript prediction based on RNA-Seq data. GIIRA analyzes RNA-Seq mappings on prokaryotic and eukaryotic reference genomes in order to identify expressed genes on the reference. Unlike other RNA-Seq analysis methods, it does not exclude ambiguously mapping reads, but rather explicitly includes all mappings to perform a more comprehensive prediction. It first extracts candidate regions based on the complete RNA-Seq mapping and represents all connections of reads and candidates in a network. This network is optimized in a maximum-flow approach to resolve ambiguous mappings and identify the most likely origin of each read. The optimization is realized by an integer linear program formulation. In several experiments we show that GIIRA is well suited for RNA-Seq-based gene identification and improves the accuracy of existing methods. For instance, on an Escherichia coli data set GIIRA showed up to 15% improved identification accuracy in comparison to other prediction methods. The second main project builds on the output of GIIRA and post-processes gene prediction results in order to improve prediction accuracy. We developed IPred (Integrative gene Prediction), a computational approach that explicitly combines the results of ab initio gene finders and evidence-based methods. Ab initio approaches employ machine learning techniques and predict genes exclusively based on a given reference sequence. Hence, their results are accurate for standard gene structures, but they are not sample-specific. Thus, IPred provides an automated simplistic framework to integrate the results of varying evidence-based predictions to ab initio identifications. Thereby, it excludes false positives and allows support
Virksomhedens sociale kapital – en ny indfaldsvinkel til det psykiske arbejdsmiljø

Siden 1990 har der været en omfattende forskning og teoriudvikling vedrørende begrebet social kapital inden for områderne økonomi, udviklingsbistand og folkesundhed. Imidlertid har begrebet kun i meget begrænset omfang været anvendt inden for arbejdsmiljøforskningen. I denne artikel introducerer vi begrebet i en arbejdsmiljøkontekst. Vi foreslår en defi nition af virksomhedernes sociale kapital, der omfatter tre nøglefaktorer: samarbejdsevne, tillid og retfærdighed. Endvidere illustrerer vi denne model ved hjælp af to empiriske undersøgelser: den nationale undersøgelse af psykisk arbejdsmiljø (Tre-dækker undersøgelsen) samt VIPS-projektet. I begge undersøgelser er der klare sammenhænge mellem social kapital på den ene side og de ansattes psykiske arbejdsmiljø og helbred på den anden. Vi konkluderer, at en teori om virksomhedernes sociale kapital potentielt kan tjene som en ramme for forskning og handling, der omfatter psykisk arbejdsmiljø, produktivitet og de ansattes helbred og velbefi ndende.
Thai: An Essential Grammar

Introduction 1. Pronunciation 2. The writing system 3. Nouns, classifiers and noun phrases 4. Pronouns 5. Verbs 6. Adjectives (stative verbs) and adjectival constructions 7. Adverbs and adverbial constructions 8. Location markers and prepositions 9. Clauses and sentences 10. Sentence particles 11. Negation 12. Questions 13. Numbers, measurement and quantification 14. Time 15. Thai speech conventions Appendix 1-Romanisation systems. Appendix 2-The verbs hai, dai, bpen: a summary. Glossary. Bibliography and further reading. Index
REPORT RAPPORT

Term rewriting is frequently used as implementation technique for algebraic speci(cid:12)cations. In this paper we present the abstract term rewriting machine (ARM), which has an extremely compact instruction set and imposes no restrictions on the implemented TRSs. Apart from standard conditional term rewriting, associative lists are supported. ARM code is translated to (ANSI) C; the resulting execution speeds are good (on a sun4, an average of 80000 rewriting steps per second and a maximum of 416000 r/s were measured). Several benchmarks are shown, and related work is discussed in depth.
A Quantitative Study of a Software Tool that Supports a Part-Complete Solution Method on Learning Outcomes

This paper reports on the findings from a quantitative research study into the use of a software tool that was built to support a part-complete solution method (PCSM) for the learning of computer programming. The use of part-complete solutions to programming problems is one of the methods that can be used to reduce the cognitive load that students experience during their learning. The tool that was built is a code restructuring tool known as CORT. It permits the display of a part-complete solution to a programming task together with a set of possible lines of code that can be used to "fill-in" the solution. Students can then easily manipulate the programming lines within CORT and then test their solutions within a traditional development environment such as Microsoft's Visual Studio. An inquiry into CORT's effect on student learning outcomes took place over a period of 14 weeks at an Australian university. Two student groups participated in an introductory programming unit, one of which utilized the CORT system whilst the other acted as a control group. Data were collected on student background, time taken to complete programming tasks, the amount of help that students required, and student learning outcomes. The data were analysed using a statistical package, and it was found that there was no significant difference in the relative levels of achievement between students in the two groups. However the results did indicate that students in the CORT group spent significantly less time and required significantly less help than the students in the control group. This suggests that if the CORT students had spent extra time on further programming tasks such that their overall time had been equal to that of the non-CORT students, then they may have achieved higher learning outcomes. Also the fact that the CORT students required less help than the control group suggests that the use of CORT might be beneficial to students studying programming in external mode where access to tutor help is more problematic.
The Switched Reluctance Electric Machine with Constructive Asymetry

Results of researches of forces of a unilateral attraction of a rotor to a stator of switched reluctance electric machines taking into account unevenness of an air gap are given. It is offered for configurations of magnetic systems with weak magnetic communication between the coils making a phase to carry out a supply of coils in parallel or independently from each other for reduction of not compensated forces of a unilateral attraction of a rotor to a stator.
Mobile Robot Path Planning In Dynamic Environments

This paper presents our approach to the problem of, controlling a mobile robot which must travel in dynamic environments. A dynamic environment is one which has obstacles, both stationary and moving, of which the robot is unaware at the time the original path plan is developed. There exists a natural decomposition of the path planning problem which provides the basis for dealing with such environments. The decomposition consists of (1) planning a path which avoids all the known static obstacles prior to initiating any motion, and (2) as the path motion is executed, the motion control is updated in real-time to avoid collisions with any unforseen obstacles either stationary or moving. The algorithms for both the static and the dynamic avoidance of obstacles is presented.
Evolution of Coalition Structures under Uncertainty

In Hart and Kurz (1983), stability and formation of coalition structures has been investigated in a noncooperative framework in which the strategy of each player is the coalition he wishes to join. However, given a strategy profile, the coalition structure formed is not unequivocally determined. In order to solve this problem, they proposed two rules of coalition structure formation: the $\gamma$ and the $\delta$ models. \par In this paper we look at evolutionary games arising from the $\gamma$ model for situations in which each player can choose mixed strategies and has vague expectations about the formation rule of the coalitions in which is not involved; players determine at every instant their strategies and we study how, for every player, subjective beliefs on the set of coalition structures evolve coherently to the strategic choices. Coherency is regarded as a viability constraint for the differential inclusions describing the evolutionary game. Therefore, we investigate viability properties of the constraints and characterize velocities of pairs belief/strategies which guarantee that coherency of beliefs is always satisfied. Finally, among many coherent belief revisions (evolutions), we investigate those characterized by minimal change and provide existence results.
Big Data Issues: Performance, Scalability, Availability

Nowadays, Big Data is probably one of the most discussed topics not only in the area of data analysis, but, I believe, in the whole realm of information technology. The simple typing of the words „big data” on an online search engine like Google will retrieve approximately 1,660,000,000 results. Having such a buzz gathered around this term, I could not help but wonder what this phenomenon means. The ever greater portion that the combination of Internet, Cloud Computing and mobile devices has been occupying in our lives, lead to an ever increasing amount of data that must be captured, communicated, aggregated, stored, and analyzed. These sets of data that we are generating are called Big Data.
Study on Recent Approaches for Human Action Recognition in Real Time

—The important area in computer vision is human understanding and recognizing actions. The main aim of action recognition is an automatic analysis of various actions from video data. Major video analysis system includes action detection and classification, action tracking, recognizing actions and behavior understanding. Even though, traditional methods have achieved greater success on several human actions. But, still it is a challenging problem to recognize human action. The challenge is to recognize human actions with more accuracy and efficiency in recognition time. The action recognition application includes CCTV, video indexing, patient monitoring systems and HCI systems. In this paper, we focus our attention to various modern approaches to human action recognition in real time.
Reducing Application Runtime Variability on Jaguar XT5

Operating system (OS) noise is defined as interference generated by the OS that prevents a compute core from performing “useful” work. Compute node kernel daemons, network interfaces, and other OS related services are major sources of such interference. This interference on individual compute cores can vary in duration and frequency, and can cause de-synchronization (jitter) in collective communication tasks and thus results in variable (degraded) overall parallel application performance. This behavior is more observable in large-scale applications using certain types of collective communication primitives, such as MPI Allreduce. This paper presents our effort towards reducing the overall effect of OS noise on our large-scale parallel applications. Our tests were performed on the quad-core Jaguar, the Cray XT5 at the Oak Ridge National Laboratory Leadership Computing Facility (OLCF). At the time of these tests, Jaguar was a 1.4 PFLOPS supercomputer with 149,504 compute cores and 8 cores per node. We aggregated OS noise sources onto a single core for each node. The scientific application was then run on six of the remaining cores in each node. Our results show that we were able to improve the MPI Allreduce performance by two orders of magnitude. We demonstrated up to a 30% boost in the performance of the Parallel Ocean Program (POP) using this technique.
Lexical intuitions and collocation patterns in corpora

Language teachers are often called upon by their students to provide examples of vocabulary usage in the classroom. Drawing on their experience of language, these teachers model lexical combinations and collocations, not only in their classes, but also in materials writing. However, corpus linguists have claimed that native speaker intuitions about the typical collocates of words are not reliable, because they do not align with the patterns observed in large corpora. These claims are critically evaluated, and an alternative explanation for the mismatch, the possibility that the corpora might not be representative of actual language in use, is also examined. Various linguistic and psycholinguistic explanations for the disparity between corpus data and elicited data are examined, and theories dealing with the mental representation of collocations are also discussed. Data from word frequency estimate research, and word association research are also analysed for relevant information on the subject. Five experiments are then reported, investigating the ability of native speakers (students and EFL teachers) and non- native speakers (Arab university teachers) to rank, recognize and spontaneously produce frequent adjective-noun collocations. The results indicate that a key factor affecting the 'quality' of lexical intuitions may be the employment of an 'availability heuristic' in judgements of frequency. It is argued that some collocates of words may be more hidden from memory searches than others, and that there may be a systematic bias in the respondants' lexical intuitions based on how words are stored in the mental lexicon. Conclusions are drawn that reflect the many facets of research relevant to the questions under discussion: corpus linguistics, frequency theory, word association research, learning theory and theories of lexical storage. The thesis ends in applying some of the key findings to language teachers.
When Smart People Fail: An Analysis of the Transaction Log of an Online Public Access Catalog.

A vehicle-type thermostat having a housing provided with a valve seat that is controlled by a movable valve member which is interconnected to a temperature responsive device which is carried by the housing, the housing having a positioning arrangement thereon for rotationally orienting the housing in a vehicle engine cooling system whereby the structure of the thermostat can be disposed in a desired rotational position within the engine cooling system.
Detection statistics for a packet-based CDMA system

We consider a wireless CDMA channel which operates in a true packet mode. Multiple users share the same spreading code, and may collide at the receiver. As each packet user is not strictly time synchronized, the receiver is essentially in a continuous acquisition mode testing a window of delays for the presence of a packet. We develop expressions for probability of detection and false alarm for Gaussian, flat Rayleigh fading, and multipath Rayleigh fading. Most of these expressions can be written in closed form.
Analysis of a two-layer cellular mobile communication system

Teletraffic aspects of hierarchical cellular systems with a focus on a reversible system are presented. An analytical model based on product forms is proposed. For the same QoS objective, carried traffic in the reversible system is superior to the non reversible scheme. The impact of the resource partition between layers is studied in a hierarchical network with independent layers. The mean number of handovers per call and the base station density are used to optimize the partition.
Practical Analysis for Refactoring

One of the important ways to make software soft, (i.e., easy to change, reuse, and develop), is to automate the various program transformations that occur as software evolves. Automating refactorings is hard because a refactoring tool must be both fast and reliable, but program analysis is often undecidable. This dissertation describes several ways to make a refactoring tool that is both fast enough and reliable enough to be useful. First, it gives a new definition of refactoring that focuses on preconditions and postconditions of the refactorings, rather than on the program transformation itself. Preconditions are assertions that a program must satisfy for the refactoring to be applied, and postconditions specify how the assertions are transformed by the refactoring. The postconditions can be used for several purposes: to reduce the amount of analysis that later refactorings must perform, to derive preconditions of composite refactorings, and to calculate dependencies between refactorings. These techniques can be used in a refactoring tool to support undo, user-defined composite refactorings, and multi-user refactoring. This dissertation also examines techniques for using runtime analysis to assist refactoring, presents the design of the Refactoring Browser, a refactoring tool for Smalltalk that is used by commercial software developers, and identifies the criteria that are necessary for any refactoring tool to succeed.