Miracle-dz/UniFAR-FTUs huggingface dataset:
Eugenic Evolution Strategy Genetic Algorithms for Estimating Parameters of Heavy Oil Thermal Cracking Model

A eugenic evolution strategy was proposed to improve the searching efficiency of the conventional simple genetic algorithm (SGA). The Eugenic evolution genetic algorithm (EGA) collects the population information along the evolution of children generations and constructs a deterministic optimization algorithm, which will be embedded in the evolution process at appropriate stage to speed up the genetic algorithm searching. Besides, the SGA is also modified by proposing an adaptive variation factor to keep the diversity of population, and a novel crossover rule to widen the distribution space of children generation. Within the possible deterministic searching methods, the Powell method is found to be feasible in integrating with the genetic algorithm. Two typical examples indicated the good performance of the proposed method. Finally, we have applied the EGA successfully to the nonlinear parameter estimation of mathematical model for heavy oil thermal cracking.
Clarity of Online Communication

This paper will investigate what determines the clarity of text messages. A number of important factors have been found for writing clear text in general, but none are specific to online communication. An experiment has been performed to determine the influence of spelling in chat messages. Eighteen students, split in groups of two, were asked to describe a number of drawn routes to their partner. This was either done by talking face-to-face or through a chat program. When done by chat, the messages were sometimes cluttered with spelling errors. The results of this experiment show that face-to-face communication is more effective than chatting for giving directions on a map and that spelling errors increase the time it takes for the participants to understand the messages.
Learning and inference in collective knowledge bases

Truly intelligent action requires large quantities of knowledge. Acquiring this knowledge has long been the major bottleneck preventing the rapid spread of AI systems. Hand-building comprehensive knowledge bases is slow and costly. Machine learning can be much faster and cheaper, but is limited in the depth and breadth of knowledge it can acquire. The spread of the Internet has made possible a new solution: building large knowledge bases by mass collaboration, combining information from a multitude of sources. While such collective knowledge bases (CKBs) promise a breakthrough in coverage and cost-effectiveness, they can only succeed if the quality, relevance, and consistency of the knowledge is kept at acceptable levels. This dissertation introduces an architecture for collective knowledge bases that addresses these problems. It operates in two loops of interaction, one with users and one with contributors. Knowledge from contributors is used to answer questions from users, and feedback from users is used to evaluate the knowledge from contributors. By informing contributors what knowledge was used, and what may be lacking, the CKB remains relevant to the needs of its users. Because they are developed collectively, CKBs must deal with knowledge that is inconsistent and noisy. To be of practical use, they must also be able to handle complex domains involving objects, relations, etc. Thus, this dissertation introduces Markov logic networks (MLNs), a knowledge representation that combines probability with the full power of first-order logic. MLNs are robust to inconsistent knowledge because they view logical statements as soft constraints as opposed to hard ones: when a world violates a formula in the KB it is less probable, but not impossible. This dissertation introduces MLNs and develops algorithms for inference and learning in them. The dissertation also provides an approach to combining weaker knowledge from multiple users (specifically, knowledge about which variables in a domain depend on which others), and an approach to determining the quality of contributors when insufficient data from them is available (using a web of trust among them). The utility of CKBs and the associated methods has been demonstrated with experiments in real world domains, including a public bibliography server (www.bibserv.org), a CKB about a university domain, and a CKB for printer troubleshooting.
Directional local ternary patterns for multimedia image indexing and retrieval

In this paper, we proposed a novel multimedia image indexing and retrieval algorithm using Directional Local Ternary Pattern (DLTP) for Content-Based Image Retrieval (CBIR). The standard Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) encode the relationship between reference pixel and its surrounding neighbours by computing grey-level difference. The proposed method extracts the directional edge information based on local ternary value (−1, 0, 1) instead of binary value (0, 1) in 0°, 45°, 90°, and 135° directions of an image. The performance of the proposed method is compared with local binary pattern LBP, Block-based LBP (BLK-LBP), Centre-Symmetric LBP (CS-LBP), Local Edge Patterns for Segmentation (LEPSEG), Local Edge Patterns for Image Retrieval (LEPINV) and other existing transform domain methods based on Corel-5000 (DB1) and Corel-10000 (DB2) databases. The retrieval performance of DLTP shows better improvement in terms of their performance measures as compared with the standard LBP.
Teaching Speaking as a Productive Skill

The term of productive skills according to Harmer (2007) is the language skills where the students produce the language themselves. The language language skills that are categorized into productive skills are speaking and writing. While, reading and listening are as receptive skills.Both components of skills depict how language as means of communication works. These skill components are messages delivered through spoken and printed texts that are produced by language learners. The messages produced by students in spoken form are categorized as speaking; while the messages produced by students through written form are writing. Both skills are prescribed in the following chart.
Translating sound adjectives by collectively teaching abstract representations

Correctly translating adjectives that describe sound (e.g. “heavy”, “tinny”, “dry”, “soothing”) can be a difficult task [Zannoni 1997]. Resources such as the Oxford English Dictionary (OED) [Oxford University Press 1992], typically list the “audio sense” for only a small subset of the words commonly used to describe sound. For example, “warm” is a very commonly used sound adjective and the OED does not mention the audio sense. Directly translating the predominant (i.e. first) sense of a sound adjective into another language often results in an incorrect translation. For example, when “a warm sound” is typed into Google Translate [Google Inc. 2014], it responds with “un sonido cálido. ” While the wordfor-word translation is correct, the appropriate translation to correctly express the meaning is “un sonido profundo. ” The word-for-word translation to English of “un sonido profundo” is “a deep sound,” not “a warm sound. ” As a result, people relying on current translation technology may fail to communicate while believing they have. This, for example, would make it difficult for an English-speaking audiologist to correctly diagnose hearing problems for people whose primary language is not English. Here, we describe a system that builds a translation map between sound adjectives of two languages: English and Spanish. This map is built from the collective intelligence of hundreds of participants who teach the system sound adjectives by indicating how well example sounds embody the adjectives. When two words are both strongly embodied by the same sound examples, they are considered synonyms. When the two words come from different languages, we consider one a translation of the other. The more frequently a pairing between two words occurs, the more certain the translation.
New Hybrid Algorithm Based on Multiswarm Particle Swarm Optimization and Shuffled Frog Leaping Algorithm

We propose a new hybrid algorithm,countering the shortcoming of particle Swarm optimization and shuffled frog leaping algorithm being easy to fall into local optimum in high-dimensional complex function optimization.The algorithm uses multisw arm particle sw arm optimization,and after each evolution,groups the best particles in the sub-sw arms into a frog group and uses shuffled frog leaping algorithm to evolve it.Inthe evolutionary model of each sub-sw arm,in addition to considering the best particle of the subsw arm,the best particle of the w hole sw arm is also considered.Compared w ith some improved particle sw arm optimization or shuffled frog leaping algorithm,the hybrid algorithm is simple in concept,easy to implement,has a good global search capability and faster convergence speed.The MPSO-SFLA has comprehensively been evaluated on 8 unimodal and multimodal benchmark functions.Results show that MPSO-SFLA is better then some of the common improved particle sw arm optimization.
Architecture for Real-Time, Low-SWaP Embedded Vision Using FPGAs

ions and hardware construction interfaces. Table recreated from [49]. Registers Logic Memory (Kbits) Fmax (MHz) Throughput Implementation FF % incr. LUT % incr. ALM* MB/s % LoC bitonic analytical 10 240 0.00% 2 560 0.00% bitonic Verilog 10 250 0.10% 2 640 3.13% 7 210.8 0.00 311.43 38 016.36 100.00% 134 bitonic BSV 10 250 0.10% 2 640 3.13% 6 997.5 0.00 313.97 38 326.42 100.82% 57 bitonic Chisel 10 272 0.31% 2 649 3.48% 5 571.0 0.00 314.96 38 447.27 101.13% 114 bitonic LegUp 4 210 5 180 3 973.4 0.00 211.86 1 034.47 2.72% 101 bitonic OpenCL 38 455 5 221 15 842.6 361.38 307.12 1 317.21 3.46% 140 STL sort C++ (host CPU) 2 300.00 570.42 1.5% 3 spatial analytical 2 048 0.00% 640 0.00% spatial Verilog 2 081 1.61% 641 0.16% 1 359.5 0.00 341.30 1 301.96 100.00% 98 spatial BSV 2 112 3.13% 1 701 165.75% 1 081.0 0.00 343.52 1 310.42 100.65% 181 spatial Chisel 2 112 3.13% 720 12.50% 1 053.0 0.00 345.30 1 317.21 101.17% 87 spatial LegUp 1 115 823 612.5 0.00 309.12 3.13 0.24% 28 spatial OpenCL 26 059 14 667 15 072.3 877.84 236.85 660.53 50.73% 66 median analytical 4 544 0.00% 2 240 0.00% median Verilog 4 555 0.24% 2 352 5.00% 4 009.5 0.00 302.76 1 154.94 100.00% 159 median BSV 4 554 0.22% 6 168 175.36% 3 359.5 0.00 334.67 1 276.66 110.54% 70 median Chisel 4 577 0.73% 2 351 4.96% 3 321.5 0.00 338.98 1 293.11 111.96% 132 median LegUp 10 449 5 262 3 781.4 0.00 174.98 34.25 2.97% 97 median OpenCL 19 366 7 590 9 309.5 190.06 312.10 920.60 79.71% 84 median C++ (host CPU) 2 300.00 836.10 72.39% 6 hash probe Verilog 995 174 327.5 3 136.00 174.06 995.98 100.00% 66 hash probe BSV 1 150 166 365.5 3 136.00 181.46 1 038.32 104.25% 124 hash probe Chisel 1 020 179 333.5 4 096.00 171.59 981.85 98.58% 83 hash probe LegUp 345 397 262.5 4 096.00 302.85 61.90 6.22% 50 hash probe OpenCL 35 536 21 854 19 175.6 3 876.08 270.19 2.14 0.21% 59 hash probe C++ (host CPU) 2 300.00 433.32 43.51% 18 *ALM (Adaptive Logic Module): Altera’s basic cell blocks, with an 8-input fracturable LUT and four 1-bit registers. Figure 4.1: Chisel outputs C++ for use in simulations and Verilog for implementation in either FPGAs or ASICs.
The Nessus Software System for Probabilistic Structural Analysis

The Probabilistic Structural Analysis Methods (PSAM) project developed at SwRI integrates state-of-the-art structural analysis techniques with probability theory for the design and analysis of complex large-scale engineering structures. An advanced efficient software system (NESSUS) capable of performing complex probabilistic analysis has been developed. A number of software components are contained in the NESSUS system and include: an expert system, a probabilistic finite element code, a probabilistic boundary element code and a fast probability integrator. This paper discusses the NESSUS software system and its ability to carry out the goals of the PSAM project.
A novel computational system for identification of biological processes from multi-dimensional high-throughput genomic data

A Novel Computational System for Identification of Biological Processes from Multi-dimensional High-throughput Genomic Data Julian Marshall Dymacek Identifying potential toxicity signaling pathways could guide future animal studies and support human risk assessment and intervention efforts. This thesis describes a novel computational approach for identifying biological processes and pathways that are significantly associated with a disease pathology from time series, dose response, gene expression data. Our system employs a novel constrained non-negative matrix factorization algorithm and Monte Carlo Markov chain simulation to identify underlying patterns in mRNA gene expression data. Quantitative pathology can be used as a pattern constraint. The found patterns can be thought of as functions that influence a gene’s expression. Using a database of curated gene sets, we can identify biological processes that are significantly related to a pathology. We also developed a computational model for integrating miRNA with mRNA time series microarray data along with disease pathology. The dynamic temporal regulatory effects of miRNA are not well known and a single miRNA may regulate many mRNA. The integrated analysis includes identifying both mRNA and miRNA that are significantly similar to the quantitative pathology. Potential regulatory miRNA/mRNA target pairs are then identified through databases of both predicted and validated pairs. Finally, potential target pairs are filtered, keeping only pairs that demonstrate regulatory effects in the expression data. Multi-walled carbon nanotubes (MWCNT) are known for their transient inflammatory and progressive fibrotic pulmonary effects; however, the mechanisms underlying these pathologies are unknown. In this thesis, we used time series microarray data of global lung mRNA and miRNA expression isolated from 160 C57BL/6J mice exposed by pharyngeal aspiration to vehicle or 10, 20, 40, or 80 μg MWCNT at 1, 7, 28, or 56 days post-exposure. Quantitative pathology patterns of MWCNT-induced inflammation (bronchoalveolar lavage score) and fibrosis (Sirius Red staining, quantitative morphometric analysis) were obtained from separate studies. Understanding the regulatory networks between mRNA and miRNA in different stages would be beneficial for understanding the complex path of disease development. These identified genes and pathways may be useful for determining biomarkers of MWCNT-induced lung inflammation and fibrosis for early detection of disease. Our computational approach detects biologically relevant processes with and without pathology information. The identified significant processes and genes are supported by evidence in the literature and with biological validation.
Design and Realization of the Software for Computer Room Management Based on VB and Batch Processing

On the basis of analyzing respective characteristics of VB and batch processing,the idea of combining VB and batch processing for software development was proposed,then a design thought and implementation method of the software for computer room management based on VB and batch processing were proposed aiming at the miscellaneous computer room management.The man-machine interface and function modules of the software were designed based respectively on VB and batch processing.The realization code of the software was given in part.The software integrated functional advantages of VB and batch processing,solved the question of data transfer between the two kinds of program.It is simple in using,occupies less disk space,and is free from installing.The software can reduce the labor intensity of manager,and can improve the management efficiency.
Bright Visible Photoluminescence from Nanotube Titania Grown by Soft Chemical Process

Bright visible photoluminescence was observed from nanotube titania (or dehydrated nanotube titanic acid) prepared by a soft chemical process. Its photoluminescence comes from the Ti−OH complex within the tubular structure. With the improvement of the crystalline structure and the desorption of physically adsorbed water on the inner and outer surfaces of the nanotube titania by annealing below 662 K, the photoluminescence intensity is enhanced. However, the intensity decreases dramatically if the tubular structure is destroyed at higher annealing temperature, and simultaneously Ti−OH bonds within the nanotube titania are broken. Therefore, the one-dimensional tubular structure is a very important factor for nanotube titania to obtain strong photoluminescence.
Flexible birth-death MCMC sampler for changepoint models

This thesis describes a flexible architecture for a Markov chain Monte Carlo sampler which allows Bayesian posterior inference for a variety of changepoint models. The structure of this class of models consists of two stochastic processes. The first process is either observed directly or indirectly through, possibly noisy, observations. The second process is not observed and governs the parameters of the observed process. The main assumption for our class of models is that the hidden process is piecewise constant, i.e. it jumps between discrete states. As the observed process, we discuss mainly the Ornstein-Uhlenbeck and Poisson process. The hidden process can have a fixed number of states, or an unknown number of states. The latter model is based on a hidden Chinese restaurant process and allows Bayesian inference over the number of states of the hidden parameters. The sampler applies a MetropolisHastings random walk on the hidden jump process through proposed birth-death moves. Different kinds of proposal moves on the path of the hidden process are presented. The structure of the sampler makes it very flexible and easy to modify to other combinations of observed and hidden processes compared to other inference methods which are tailor-made for a specific model. Applied to gene expression data the sampler allows Bayesian posterior inference on a more complex model than in previous work. We compute the Bayes factor which indicates that our model, which allows the strength of the system noise to switch, is better in explaining the data. The sampler is used on gene expression data from yeast cells and the results are compared to a variational approximation. The posterior is more confident about the times of transcriptional activity than the approximation suggests. The results from the Chinese restaurant process sampler on the same yeast dataset support the initial assumption about the number of transcription factors involved in the control of the examined genes. When the sampler is used on financial data, changepoints are revealed which can be connected to historic events. This is shown both for the Ornstein-Uhlenbeck model as well as a Cox-Ingersoll-Ross model used in a different thesis. Modifying the sampler to work on Markov modulated Poisson processes allows for very fast posterior inference and this is shown when the time to get an uncorrelated sample is compared to an exact Gibbs sampler for the model. A model combining an observed Poisson process with the Chinese restaurant process is then utilized to find hidden states in the rate of neuronal spike trains and linked to the stimulus. The model’s advantages in finding and estimating bursting of neurons is discussed and compared to a model which assumes a continuous Poisson rate.
Detecting Biomedical Relations using Distant Supervision

This work concerns the detection of relationships between key information in biomedical publications, such as treatments for diseases or side-effects of drugs. Given a sentence containing some medical concepts the goal is to determine their relationship to each other. Supervised machine learning methods are a very popular way to address this problem and often provide reliable results. Those methods require manually labelled examples to extract characteristics of particular relationships in order to detect similar information in unlabelled data. However, manually labelled data is not always available and its generation is time consuming and expensive. The main objective of this thesis is the exploration of distant supervision, a method which generates those labelled examples automatically using prior knowledge to detect relationships between key facts. First, relation extraction using a limited amount of training data is explored to detect adverse-drug effects in natural language. Then, work focuses on automatically labelling data using a large biomedical knowledge base, the Unified Medical Language System (UMLS). The effectiveness of a popular evaluation method that does not require manually labelled data is examined in more detail. The main goal is the investigation of whether UMLS is suitable to be used to label data automatically so as to detect similar information in natural language. Finally, a method to reduce falsely labelled instances in the automatically generated data is presented and found to improve the detection of relationships.
Choosing the Method of Data Collection

This chapter guides researchers in the complex decision which mode to choose for a particular survey. When designing a survey, one has to balance survey errors (coverage, sampling, nonresponse and measurement error) and survey costs. In this chapter four main data collection methods are reviewed: face-to-face interviews, telephone interviews, mail surveys and Internet surveys. The guidelines given are based on theoretical and practical considerations and take into account the large body of empirical research on mode comparison. The chapter starts with a treatise on why one should expect mode difference, based on social psychology and communication theories, and availability of communication channels, media related factors, and interviewer effects are discussed. In the next section an overview is given on the influence of data collection mode and measurement and the consequence of a specific mode for question format and complexity of the questionnaire are discussed. This is followed by a detailed overview of empirical evidence on the influence of data collection mode on measurement error. Next mode choice and its consequences for noncoverage and nonresponse are discussed. The chapter ends with a comparison of data collection modes on logistic points, such as, timeliness, personnel requirements, and costs, and presents the reader with an extensive summary Table of advantages and disadvantages of the four main data collection methods (mail, internet, face-to-face, and telephone