Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian approach to integrate molecular data into genetic evaluations

Integration of molecular data into breeding value estimation is an important issue. Recently about 60.000 SNPs (Single Nucleotide Polymorphisms) became available for cattle (Van Tassell et al., 2007) and simulation showed that genomic selection based on these SNPs could improve greatly the selection of young bulls (Meuwissen et al., 2001). However, even if a selection based on molecular information is in theory very promising, it has proven to be very difficult to implement in real life situations, especially if genetic evaluations are needed for extremely large populations as for dairy cattle, and if very few animals are genotyped. Most current ideas are based on methods where first phenotypic information is resumed and then integrated as daughter yield deviations or deregressed proofs in simplified mixed models. However, very few contributions addressed the issue of reintegration of these molecular data into large breeding value estimation systems. Also molecular data are not used to estimate breeding values for all evaluated animals. In the present study we propose a Bayesian approach to address both issues. We present an illustration for a single gene; however this approach could be easily extended to many SNPs effects and can thus be considered as a first step leading to the integration of genomic selection into national breeding value estimation systems.
AN OPTIMIZED ENERGY AWARE CHAINING TECHNIQUE FOR DATA GATHERING IN WIRELESS SENSOR NETWORKS

Wireless Sensor Network (WSN) is a collection of small sensor nodes with aptitude to sense, compute and transmit data that are deployed to observe a physical environment. The sensor node has limited capabilities, especially its energy reserve, its processing ability and its memory storage. Data dissemination and gathering protocols design for WSN are crucial challenges since those protocols should be easy, energyefficient, and robust to deal with a very large number of nodes. Also, they should be selfconfigurable to node failures and dynamic changes of the network topology. In this paper, we present a new algorithm for gathering sensor reading based on chain forming using Ant Colony Optimization (ACO) technique. To allow network lifetime extension, the ACO provides the shortest network nodes chaining instead of starting from the furthest node and using Greedy algorithm as PEGASIS do. The leader role duration is defined for each node based on its required energy to do this role in the established chain. Which avoids fast node’s energy depletion and then, the network lifetime would be extended. Through simulation, it is proved that the proposed algorithm allows network stability extension compared to the most known chaining algorithm.
Classification and improvements of Adaptive Random Testing methods

Adaptive Random Testing (ART) is an effective enhancement of RT, wh ich is based on the observations that failure-causing inputs are normally clustered in one or a few contiguous regions in the input domain. Hence, it has been proposed that test case g eneration should refer to the locations of successful test cases (those that have been e xecuted but not revealed a failure) to achieve an even spread of test cases throughout the input domain. Recently, several ART methods (algorithms) have been proposed, which have their own advantages and disadvantages. Since no proper classification of existing ART methods in previous studies, the relationships among ART methods were unclear. These are the ma in focuses of this thesis. Firstly, this thesis examines existing ART methods and establishes a taxonomy for them, which is a framework for further studies of ART methods. Existing ART method s are classified into 2 categories based on different interpretations of the basis of ART. They are “far apart” ART methods and Proportional Sampling Strategy (PSS) ART meth ods. Based on this classification, characteristics for each category of ART methods ca n be summarized; improvements can be proposed for each category of ART methods rather tha n individual methods; new ART methods can also be inspired. Secondly, this thesis focuses on the shortcomings of these ART methods. Fo r “far apart” ART methods, two major shortcomings are extensive computational overhead and boundary effect (test cases prefer to be near the boundary of the input domain). For PSS ART methods, a common problem is that test cases still have chances to be clustered togethe r, which has adverse impact on the fault-detection capability. This thesis investigates the re asons behind and proposes improvements of the original methods. Finally, with regard to the practicality of ART methods in high dimensional input do mains, this thesis proposes a new ART method, ART by balancing, based on a n distinctive interpretation of an even spread of test cases, which requires that the c entroid of test cases in a partition should be close to the centroid of that partition. This method has a go od fault-detection capability, especially in high dimensional input domains.
Mobile system for the estimation of internal parameters of distributed cameras

Estimation of internal parameters of a camera is a very important task for photo-and videogrammetric applications. This operation is time-consuming for multiple cameras. The method based on the mobile backlight illuminated calibration board is proposed. Wireless link for the calibration result transmission with the pattern features detection results is applied in the system. The OpenCV library for calibration process and libdc library for real-time image acquisition are applied
Realization of a dynamically reconfigurable preprocessor

Our research demonstrates the feasibility of employing field programmable gate arrays (FPGAs) to realize high-speed algorithm-specific processing architectures for avionic signal processing applications. Architectures composed of FPGAs provide a low-cost and flexible alternative to custom hard-wired preprocessors and a lower-cost, physically smaller alternative to massively parallel processors (both SIMD and MIMD machines). Algorithm segments which require processing hundreds of millions of operations per second have been mapped into a single FPGA device. This technology may ultimately fill a range of processing requirements in the areas of radar and communication processing as well as image enhancement applications. The application of configurable logic devices allows realization of processing architectures to efficiently compute low-level algorithmic functions, or segments. Reconfiguration of FPGAs to implement several algorithm segments is analogous to selecting subroutines to form a software algorithm suite in a conventional processor, since it can be accomplished without hardware modification. <<ETX>>
Questions and Answers in a Chinese as a Foreign Language Speech Event

This paper reports the analysis of a stretch of classroom discourse using a Conversation Analysis (CA) approach to uncover the nature of how participants produce and respond to a particular question type yes/no interrogatives (YNI) within a student-led presentation. In this speech event, the analysis reveals how YNI, as proposed by Raymond (2003), produce constraints as well as expectations on the answers they structure. Using the basic CA unit of analysis, the turn-taking structure (where first pair parts make conditionally relevant elements within second pair parts), the data reveal that when non-type conforming responses are produced they are done so for cause generally as a signal of some type of troublesome condition set by the YNI formation. In addition, non-type conforming responses are fashioned so as to provide accounts. Data support Raymond's view of type-conformity set by YNI as shown within a classroom speech event.
Towards automatic debugging of computer programs

Programmers spend considerable time debugging code. Symbolic debuggers provide some help but the task still remains complex and difficult. Other than breakpoints and tracing, these tools provide little high level help. Programmers must perform many tasks manually that the tools could perform automatically, such as finding which statements in the program affect the value of an output variable under a given testcase, what was the value of a given variable when the control last reached a given program location, and what does the program do differently under one testcase that it does not do under another. If the debugging tools provided explicit support for such tasks, the whole debugging process would be automated to a large extent. In this dissertation, we propose a new debugging paradigm that easily lends itself to automation. Two tasks in this paradigm translate into techniques called dynamic program slicing and execution backtracking. We discuss what these techniques are and how they can be automated. We present ways to obtain accurate dynamic slices of programs that may involve unconstrained pointers and composite variables. Dynamic slicing algorithms spanning a range of time-space-accuracy trade-offs are presented. We also propose ways in which multiple dynamic slices may be combined to provide further fault localization information. A new space-efficient approach to execution backtracking called "structured backtracking" is also proposed. Our experiment with the above techniques has also resulted in development of a prototype tool, S scPYDER, that explicitely supports them.
Blind multi-user detection by fast fixed point algorithm without prior knowledge of symbol-level timing

We consider the estimation of the source process of the desired user an the downlink of a code-division multiple access (CDMA) communication system. In downlink signal processing, only the code of the mobile telephone user is known, while the codes of the interfering users are unknown. Blind source separation or independent component analysis is an approach offering the solution to this problem. In this work we apply the fast fixed point algorithm to the separation problem. The algorithm is based on fourth-order statistics optimization. Knowledge about the symbol level timing has to be known only coarsely.
Abstraction-based deductive-algorithmic verification of reactive systems

This thesis presents a framework that combines deductive and algorithmic methods for verifying temporal properties of reactive systems, to allow more automatic verification of general infinite-state systems and the verification of larger finite-state ones. Underlying these deductive-algorithmic methods is the theory of property-preserving assertion-based abstractions, where a finite-state abstraction of the system is deductively justified and algorithmically model checked. After presenting an abstraction framework that accounts for fairness, we describe a method to automatically generate finite-state abstractions. We then show how a number of other methods, including deductive rules, (Generalized) Verification Diagrams, and Deductive Model Checking, can also be understood as constructing finite-state versions of the system that are model checked. Our analysis leads to a better classification and understanding of these verification methods. Furthermore, it shows how the different abstractions that they construct can be combined. For this, we present an algorithmic extended abstract model checking procedure, which uses all the information produced by these methods, expressed in a finite-state format that can be easily and incrementally combined. Besides a standard safety component, the combined abstractions include extra bounds on fair transitions, well-founded orders, and constrained transition relations for the generation of counterexamples. Thus, our approach minimizes the need for user interaction and maximizes the impact of the available automated deduction and model checking tools. Once proved, verification conditions are reused as much as possible, leaving the temporal and combinatorial reasoning to automatic tools.
Environment Classification and Semantic Grid Map Building Based on Laser Range Finder Data

Human robot interaction has become an important area of research in the robotics community. High level abstractions, which are commonly used by humans, can be learnt by robots to effectively communicate with humans. In this paper, we propose a Semantic Grid Map (SGM) to represent an environment. SGM is similar to an Occupancy Grid (OG) map, however with high level information as environment type labels. We use a robot-mounted laser range finder (LRF) data to learn and classify an environment into various area types. Then the classification results are combined probabilistically to update the semantic grid map. The classification accuracy is further improved by outlier rejection and topological correction. Finally we present a labeling strategy while a robot is exploring an unknown environment. Experimental results of a robot exploring in a university environment are presented to assess the performance of the algorithm.
Interactions between Learning and Evolution : The Outstanding Strategy Generated by the Baldwin E ect

The Baldwin e ect is known as interactions between learning and evolution, which suggests that individual lifetime learning can in uence the course of evolution without the Lamarckian mechanism. Our concern is to consider the Baldwin e ect in dynamic environments, especially when there is no explicit optimal solution through generations and it depends only on interactions among agents. We adopted the iterated Prisoner's Dilemma as a dynamic environment, introduced phenotypic plasticity into strategies, and conducted the computational experiments, in which phenotypic plasticity is allowed to evolve. The Baldwin e ect was observed in the experiments as follows: First, strategies with enough plasticity spread, which caused a shift from defective population to cooperative population. Second, these strategies were replaced by a strategy with a modest amount of plasticity generated by interactions between learning and evolution. By making three kinds of analysis, we have shown that this strategy provides the outstanding performance. Further experiments towards open-ended evolution have also been conducted so as to generalize our results.
VERT : a method for automatic evaluation of video summaries

Video Summarization has become an important tool for Multimedia Information processing, but the automatic evaluation of a video summarization system remains a challenge. A major issue is that an ideal “best” summary does not exist, although people can easily distinguish “good” from “bad” summaries. A similar situation arise in machine translation and text summarization, where specific automatic procedures, respectively BLEU and ROUGE, evaluate the quality of a candidate by comparing its local similarities with several human-generated references. These procedures are now routinely used in various benchmarks. In this paper, we extend this idea to the video domain and propose the VERT (Video Evaluation by Relevant Threshold) algorithm to automatically evaluate the quality video summaries. VERT mimics the theories of BLEU and ROUGE, and counts the weighted number of overlapping selected units between the computer-generated video summary and several human-made references. Several variants of VERT are suggested and compared, and the best variant is selected through experimentation. Index Items Summaries Evaluation, VERT, Rouge, Video Summarization
Why numbers, invites and visits are not enough: Evaluating the user experience in Social Eco-Systems

Social eco-systems are often evaluated through quantitative data that is automatically logged and analysed. However, where the user’s experience of social eco-systems is evaluated, more explicit intervention approaches are typical, with questionnaires, focus groups and user testing widely used, directly asking the user about their experience. User experience evaluation thus ruptures the social eco-system, occurring as a separate, discrete activity outside of that system. In this paper, we propose that evaluation should be part of the social eco-system adding value to the user experience. We outline an evaluation approach that has been applied within games-based learning environments where the evaluation is seamlessly embedded. We briefly outline our approach to generating and analyzing data highlighting its potential for social eco-system evaluation.
A Preliminary Study on the Validity of English Passage Dictation Test

This paper tries to demonstrate that passage dictation test is a valid testing method to test learners’ comprehensive English ability via a correlation study with the supplement of a questionnaire survey and interviews. Due to the fact that TEM (Test for English Majors) tests have been proved to be reasonably reliable and valid in China and are commonly acknowledged as well-established English proficiency language tests, we therefore attempted to conduct a study in which we compared the learners’ passage dictation test scores with their total scores and scores of each parts of TEM4 (TEM-Band Four) with the help of a statistical software (SPSS (Statistical Program for Social Sciences)) to see if they were correlated. The results of the correlation study (the correlation coefficient between passage dictation test scores and the total scores is 0.756 at the 0.01 level, which means highly significant) together with the questionnaire and interview results provided fairly solid evidence that passage dictation test is indeed a valid and reliable testing method to assess test takers’ comprehensive English competence.
Compressed caching and modern virtual memory simulation

CPU speeds have increased so quickly that few program suffer from a lack of CPU capacity. However, many program in all domains of computing are hindered by the slowness of memory at some level of the virtual memory hierarchy. One of the greatest pitfalls in system performance is that of virtual memory paging. Improving the performance of this part of the virtual memory hierarchy is essential. However, the understanding of program reference behavior and its relation to virtual memory design and performance has not been significantly advanced in the past thirty years. Recent ideas that could improve virtual memories have been evaluated through direct implementation as part of an operating system kernel. Unfortunately, this approach does not allow for controlled, reproducible experiments that help a researcher to understand how program use their memory. Trace-driven simulation allows for these kinds of controlled, scientific experiments. However, the difficulties in gathering, storing, and processing large reference traces have kept researchers from adopting this approach. This dissertation begins by outlining an approach to reducing reference traces for use in simulations of virtual memory. We have used reference traces to explore compressed caching—the insertion of a new, compressed level of RAM into the memory hierarchy. We will present the trace-driven investigations into the compression of in-memory data. We will also present a new, principled approach to making page replacement and compressed caching agilely adapt to a program's reference behavior, made possible by controlled simulations.
Development of ECoG based multi-channel recording BMI system using UWB wireless communications

To realize a low-invasive and high accuracy BMI (Brain-machine interface) system, we have already developed a fully-implantable wireless BMI system which consists of ECoG neural electrode arrays, neural recording LSIs, a Wi-Fi based wireless data transmitter and a wireless power receiver with a rechargeable battery. For accurate estimation of movement intentions, it is important for a BMI system to have a large number of recording channels. In this paper, we report a new multi-channel BMI system which is able to record up to 4096ch ECoG data by multiple connections of 64-ch LSIs and time division multiplexing of recorded data. This system has an ultra-wide-band (UWB) wireless unit for transmitting the recorded neural signals to outside the body. By preliminary experiments with a human body equivalent liquid phantom, we confirmed 4096-ch UWB wireless data transmission at 128Mbps mode below 25mm distance.
Joint channel estimation and symbol detection for space-time block code

The simplified joint channel estimation and symbol detection based on the EM (expectation-maximization) algorithm for space-time block code (STBC) are proposed. By assuming channel to be invariant within only one STBC word and utilizing the orthogonal structure of STBC, the computational complexity and cost of this algorithm are both very low, so it is very suitable to implementation in real systems.
Adaptive filtering based DS/SS code acquisition in frequency selective and flat Rayleigh fading channels

The mean acquisition time performance for a code acquisition scheme utilizing an FIR adaptive filter is investigated. Both frequency selective and frequency nonselective Rayleigh fading channels are considered. It is shown that fading causes a performance degradation of less than 3 dB for both the coherent and noncoherent cases when the carrier phase is varying slowly, and less than 5 dB for the noncoherent case with fast phase variations. This degradation is significantly smaller than that suffered by other systems based on using matched filters (about 10-15 dB degradation).
Speech based emotion classification

In this paper, a speech based emotion classification method is presented. Six basic human emotions including anger, dislike, fear, happiness, sadness and surprise are investigated. The recognizer presented in this paper is based on the discrete hidden Markov model and a novel feature vector based on mel frequency short time speech power coefficients is proposed. A universal codebook is constructed based on emotions under observation for each experiment. The databases consist of 90 emotional utterances each from two speakers. Several experiments including ungrouped emotion classification and grouped emotion classification are conducted. For the ungrouped emotion classification, an average accuracy of 72.22% and 60% are obtained respectively for utterances of the two speakers. For grouped emotion classification, higher accuracy of 94.44% and 70% are achieved.
STRIPPLOT: Stata module for strip plots (one-way dot plots)

stripplot plots data as a series of marks against a single magnitude axis, horizontal or vertical. It is an Stata 8 alternative to gr7, oneway or dotplot with more flexibility in several respects. Its predecessor, onewplot, remains available to users of Stata 7 or Stata 6 as a separate module.
A Tale of Two Web Spaces: Comparing Sites Using Web Impact Factors.

Ingwersen has suggested the web impact factor (WIF) as a means for comparing relative attractiveness or influence of web spaces. The WIF of a web space is the number of pages linking to a web space, divided by the number of pages in the web space. The numbers of links and pages can be determined using web search engines such as AltaVista, and this paper examines methodological issues associated with this determination. Three distinct WIFs can be calculated: the external WIF, reflecting the number of pages linking from outside the web space being measured; the self-link WIF, reflecting links made from inside the web space; and an overall WIF, combining external and self-links. External WIFs are probably the most significant. WIFs can be calculated for top level domains such as countries; however these appear to be unreliable. This paper compares WIFs for two different types of web spaces: Australasian universities and Australasian electronic journals. For large organisations such as universities or research institutions, WIFs seem to be a useful measure of the overall influence of the web space. However for smaller spaces such as electronic journals the WIF is less reliable as a measure.