Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Traffic Prediction Based on Feed-forward Neural Network with PLS Pruning Algorithm

To improve the prediction accuracy and reduce the computational complexity of network traffic prediction based on feed-forward neural network (FNN), the partial least squares (PLS) pruning algorithm was proposed to optimize the network topology structure. The data of network traffic has the characteristics of burst, nonlinear and time variation, which results in the traditional neural network has the disadvantages of slow convergence rate and easy to fall into local minimum for network traffic prediction. The performance of FNN is closely related to the number of nodes in the hidden layer, which affects the computational complexity, convergence rate and convergence accuracy. The proposed method uses PLS pruning algorithm to simply the network topology structure, which can obtain the ideal number of hidden layer nodes of the FNN, and then the prediction accuracy of network traffic is improved. The computer simulation results show that the proposed method has faster convergence rate and higher convergence accuracy compared with traditional FNN.
Hybrid optimization algorithm based on differential evolution and particle swarm optimization

To take advantage of different algorithms and overcome their limitations,a hybrid optimization algorithm(DPA) is proposed,based on the combination of differential evolution(DE) and particle swarm optimization(PSO) . In the first step of DPA,differential mutation and selection operators are employed to produce a new population for effective variation. Next,PSO is carried out for local exploration with high efficiency,followed by crossover and selection operations. Thus,the extent of search region for the population is increased and decreased sequently at each iteration of the DPA progress,and eventually resulted in convergence to an optimal solution. Numerical tests on four benchmark functions are conducted for the algrithom evaluation. The results show the improvement in the convergence speed,searching ability,and computation stability by comparison with the DE and PSO.
An algorithm of multi-target tracking based on improved particle filter

For the multi-target tracking problems with complex background,the data association techniques are discussed.Combing the target detection algorithm with particle filter and using color histograms as observation model,the global nearest neighbor(GNN)performs data association.An improved multi-target tracking algorithm is presented based on particle filter.The proposed algorithm is robust to the problems of the appearance and disappearance of targets,the similar appearance of targets,the cross movement of targets and short-time occlusion.
Decision Tree Induction Approach for Data Classification Using Peano Count Trees

Many organizations have large quantities of data collected in various application areas. Classification of data is a major issue which leads less efficiency and scalability. In this paper, we developed a new method for decision tree for classification of data using a data structure called Peano Count Tree (P-tree) which enhances the efficiency and scalability. We apply Data Smoothing and Attribute Relevance techniques along with a classifier. Experimental results show that the P-tree method is significantly faster than existing classification methods, making it the preferred method for mining on data to be classified.
Evolved Developmental Strategies of Artificial Multicellular Organisms

We present the use of a new computationaly efficient 3D physics model for the simulation of cells in a virtual aquatic world. In this model, cells can freely assemble and disconnect along the simulation without any separation between the development and evaluation stages, as is the case in most evo-devo models which only consider one cell cluster. While allowing for the discovery of interesting behaviors through the addition of new degrees of freedom, this 3D center-based physics engine and its associated virtual world also come with their drawbacks when applied to evolutionnary experiments: larger search space and numerous local optima. In this paper, we have designed an experiment in which cells must learn to survive by keeping their genome alive as long as possible in a demanding world. No morphology or strategy is explicitly enforced; the only objective the cells have to optimize is the survival time of the organism they build. We show that a novelty metric, adapted to our evo-devo matter, dramatically improves the outcome of the evolutionary runs. This paper also details some of the developmental strategies the evolved multicellular organisms have found in order to survive.
Improved Cluster Tracking for Visualization of Large Dynamic Graphs

Analysis and visualization of dynamic graphs is a challenging problem. Clustering can be applied to dynamic graphs in order to generate interactive visualizations with both high stability and good layout quality. However, the existing implementation is naive and unoptimized. Here we present new algorithms to improve both the temporal clustering results and the efficiency of the cluster tracking calculation, and evaluate the results and performance.
Utilization-focused evaluation for program development: The need for teaching experience in the bachelor of arts program in second language studies

The purpose of this evaluation was to investigate (a) if there was a need for students in a new Bachelor of Arts (BA) program in Second Language Studies (SLS) to gain teaching experience and, if so, (b) how best to meet the need. The project followed a utilization-focused evaluation (UFE) design with the end goal of evaluation use for program development. Evaluation questions were addressed in two phases; the first aimed to determine if students needed to gain teaching experience as a component of the BA degree in SLS, while the second aimed to determine the degree and type of support needed for students to gain teaching experience should it be a need. Data was collected from SLS BA students (n= 35) and faculty (n = 13) in an iterative process using the following instruments: anonymous web-based surveys, focus groups, group interview, and a Delphi technique. The data were analyzed using descriptive statistics and thematic coding. Findings indicated that the gaining of teaching experience was a perceived need for most students in the BA program, though not a universal need. Also, findings suggested that the SLS Department should provide support for students to gain teaching or related professional experience, and ideally this support would be a professional practicum or internship course open to both teaching and non-teaching students. An additional and unexpected finding revealed that stakeholders held different conceptions of students’ professional identity within the degree, perhaps related to changes occurring in the Second Language Teacher Education (SLTE) field. Consequently, the present study explored the use of UFE as a means of program understanding and self-exploration at a time of transition within
An investigation of the use of thinking reader, a universally designed computer-supported reading environment, approach to word recognition and word knowledge with deaf middle-school students: a case study

This case study observed how Deaf middle school students attained word recognition and word knowledge using a researchware, Thinking Reader , developed by the Center for Applied Special Technology. Thinking Reader, a digital book with reading comprehension support embedded, facilitated word comprehension using multimedia, including videos in American Sign Language that corresponded directly with the reading material. The study was implemented with Deaf middle school, or grade 6, 7, 8, students whose average reading ability was at the first and second grade level. The goal of the study was to observe the process and to gain insight of the decisions and interactions students made to enhance their vocabulary with Thinking Reader, a computer-mediated environment. The findings of this study suggested that the students benefited from the multimedia features of Thinking Reader. The multimedia features supported students' word recognition, word knowledge and encouraged independence. It was found that the students used the features of Thinking Reader in different ways, reflecting their individual preferences. In addition, the use of technology to present text bilingually, in both American Sign Language and English, had an impact on students' motivation. The students reported that the decoding view was most successful in motivating them to recognize words.
Exploring Factors That Influence Technology-Based Distractions in Bring Your Own Device Classrooms

Previous research on distractions and the use of mobile devices (personal digital assistants, tablet personal computers, or laptops) have been conducted almost exclusively in higher education. The purpose of the current study was to examine the frequency and influence of distracting behaviors in Bring Your Own Device secondary school classrooms. Quantitative and qualitative data were collected from 181 secondary school students (55 female and 126 male) enrolled in three schools across Canada. Almost 80% of the students reported being on task regularly when using mobile devices in class. However, students also engaged in at least one of five distracting activities occasionally or regularly with their mobile devices including emailing (64%), surfing the web (65%), using social media (52%), instant messaging (32%), and playing games (30%). Female students engaged with social media significantly more than male students, whereas male students played games significantly more than female students. Students were rarely distracted by peer use of mobile technology devices. Students were more distracted by their own use of mobile devices when engaged in independent or group work, and less distracted with lectures and student presentations. Students claimed that teacher and school restrictions were probably the most effective method to limit distracting behavior while learning.
Research on multi-scale non-uniform progressive mesh compression coding

This paper proposes a multi-scale non-uniform progressive mesh simplification algorithm based on edge-collapse mesh simplification algorithm.This way not only simplifies the algorithm faster,but also overcomes the deficiencies of the traditional algorithm whose grids,in the simplified model,are evenly distributed and can not highlight the key features of model.In this paper,folding,corner and embossing in the model represent the key features.The area of the triangle meshes which locate in these regions is relatively small with large quantities,while in the flat regions of the model which carry less information of the model's feature,the area of meshes is large and the number of them is small.After simplify the algorithm with a greater extent,the model still maintains the geometrical and visual features of original grids.Because of the multi-scale progressive encoding through streaming transmission technology,it can generate the model whose file is efficient and convenient to transmit progressively on the network.
A Novel ScanNet Method for Real-Time Position Monitoring System in Radiotherapy

Radiation therapy is a major modality of cancer treatment. To achieve good local control, high radiation dose must be precisely focused to the target region. A geometric missing during radiotherapy could result in tumor regrowth or severe damage of the neighboring normal tissues. To prevent geometric missing, a real-time monitoring the patient movement becomes very important. Using image or infrared technology to detect the displacement has been proposed. However, Promoting the current detection efficiency is too poor to fulfill clinical requirement due to the low resolution of 512 512× pixels and slow complex calculation. To overcome these problems, this study proposed ScanNet algorithm to monitor the patient breathing, cough, or other movement. ScanNet algorithm could detect 1 mm shift under the resolution of 1920 × 1080 pixels in realtime and to meet the quality requirement of modern radiotherapy for monitoring displacement.
Synchronization, coherence, and consistency for high performance shared memory multiprocessing

Although improved device technology has increased the performance of computer systems , fundamental hardware limitations and the need to build faster systems using existing technology have led many computer system designers to consider parallel designs with multiple computing elements. Unfortunately, the design of eecient and scalable multiprocessors has proven to be an elusive goal. This dissertation describes a hierarchical bus-based multiprocessor architecture, an adaptive cache coherence protocol, and eecient and simple synchronization support that together meet this challenge. We have also developed an execution-driven tool for the simulation of shared-memory multiprocessors, which we use to evaluate the proposed architectural enhancements. Our simulator ooers substantial advantages in terms of reduced time and space overheads when compared to instruction-driven or trace-driven simulation techniques, without signiicant loss of accuracy. The simulator generates correctly interleaved parallel traces at run time, allowing the accurate simulation of a variety of architectural alternatives for a number of programs. Our results provide a quantitative analysis of the viability of large-scale bus-based memory hierarchies. We evaluate the eeect on performance of several architectural enhancements, and discuss the tradeoos between reducing contention and increasing latency as the number of levels in the memory hierarchy are increased. Toward this end, we have developed iii a cache coherence protocol for a hierarchical bus-based architecture that minimizes total communication overhead by utilizing all available (bus-provided) information. Based on our evaluation, we propose an integrated set of architectural design decisions. These include synchronization using a conditional test&set operation that eliminates excess bus traac and contention, conditional access scheduling, where bus traac is reduced by keeping track of pending bus accesses for every cache line, adap-tive caching, where each cache line is assigned a coherence protocol based upon the expected or observed access behavior for that line, and the use of relaxed memory consistency models, where writes are aggressively buuered. We also present a new classiication of memory consistency models that, in addition to unifying all existing models into a common framework, provides insight into the implications of these models with respect to access ordering. Acknowledgments I would like to express my sincere thanks to the members of my committee, Dr. during the course of this research have been invaluable. I am grateful for their patient reading and incisive comments on the writing of this thesis, without which I would still be writing. My oocemates Bill Foundoulis and Rajat Mukherjee, have made these years as …
Markov logic for machine reading

A long-standing goal of AI and natural language processing (NLP) is to harness human knowledge by automatically understanding text. Known as machine reading, it has become increasingly urgent with the rise of billions of web documents. However, progress in machine reading has been difficult, due to the combination of several key challenges: the complexity and uncertainty in representing and reasoning with knowledge, and the prohibitive cost in providing direct supervision (e.g., designing the meaning representation and labeling examples) for training a machine reading system. In this dissertation, I propose a unifying approach for machine reading based on Markov logic. Markov logic defines a probabilistic model by weighted first-order logical formulas. It provides an ideal language for representing and reasoning with complex, probabilistic knowledge, and opens up new avenues for leveraging indirect supervision via joint inference, where the labels of some objects can be used to predict the labels of others. I will demonstrate the promise of this approach by presenting a series of works that applied Markov logic to increasingly challenging problems in machine reading. First, I will describe a joint approach for citation information extraction that combines information among different citations and processing stages. Using Markov logic as a representation language and the generic learning and inference algorithms available for it, our solution largely reduced to writing appropriate logical formulas and was able to achieve state-of-the-art accuracy with substantially less engineering effort compared to previous approaches. Next, I will describe an unsupervised coreference resolution system that builds on Markov logic to incorporate prior knowledge and conduct large-scale joint inference. This helps compensate for the lack of labeled examples, and our unsupervised system often ties or even outperforms previous state-of-the-art supervised systems. Finally, I will describe the USP system, the first unsupervised approach for jointly inducing a meaning representation and extracting detailed meanings from text. To resolve linguistic variations for the same meaning, USP recursively clusters expressions that are composed with or by similar expressions. USP can also induce ontological relations by creating abstractions to assimilate commonalities among non-synonymous meaning clusters. This results in a state-of-the-art end-to-end machine reading system that can read text, extract knowledge and answer questions, all without any labeled examples. Markov logic provides an extremely compact representation of the USP model, and enables future work to "close the loop" by incorporating the extracted knowledge into the model to aid further extraction.
Reconocimiento biométrico de iris basado en características SIFT

This M.Sc. Thesis presents the study, implementation and evaluation of an iris-based automatic recognition system based of SIFT features. We also carry out an study of vulnerabilities of the developed SIFT matcher to direct attacks using fake iris images. We use for our experiments the BioSec Baseline database and a fake iris database developed from the first one, both freely available upon request. The study of vulnerabilities done in the framework of this Project has also included the acquisition of the mentioned fake iris database. We first start by describing the existing methods for iris recognition. After an introduction to biometrics and a review of the state of the art of iris-based systems, an implementation of the selected algorithm is performed, including the proposal of some improvements for the system. In the experimental section, we first evaluate separately the performance of the different stages of the implemented system in order to optimize them. Studies have mainly focused on the two principal steps, which are feature extraction and score matching. The obtained results have been compared with a freely available traditional iris recognition system, which is used as baseline system. The main novelty of the proposed system with respect to traditional approaches is that it does not need to transform the ring-shaped region of the iris to polar coordinates. This avoids the need of a very accurate segmentation, which can be difficult with non-ideal images. Once the system has been optimized, its robustness to direct attacks has been tested through a developed fake iris database. The results are once more compared to those obtained with the baseline iris recognition system. Lastly, our conclusions and some future directions to improve the implemented system are presented.
Against the “system” module

Abstract Modularity is a fundamental doctrine in the cognitive sciences. It holds a preeminent position in cognitive psychology and generative linguistics, as well as a long history in neurophysiology, with roots going all the way back to the early nineteenth century. But a mature field of neuroscience is a comparatively recent phenomenon and has challenged orthodox conceptions of the modular mind. One way of accommodating modularity within the new framework suggested by these developments is to go for increasingly soft versions of modularity. One such version, which I call the “system” view, is so soft that it promises to meet practically any challenge neuroscience can throw at it. In this paper, I reconsider afresh what we ought to regard as the sine qua non of modularity and offer a few arguments against the view that an insipid “system” module could be the legitimate successor of the traditional notion.
A new method on converting polygonal meshes to volumetric datasets

The voxel-based models are used extensively in many fields, such as medical image reconstruction, volume rendering and fluid dynamics, etc. Especially in robotics, voxel-based model is a powerful tool to execute collision detection. Voxelization refers to a process to convert data from one source type into a three dimensional volume dataset. In this paper, an algorithm is proposed to voxelize the polygonal meshes into contour voxel representation based on the octree structure. It is proved that our algorithm is efficient and effective to voxelize 3D polygon mesh models into voxel representation, which makes it possible to be employed in robotics and current CAD systems for true solid modeling with particular interior attributes, such as colors, density and materials, etc.
Temporal patterns in Pacific white-sided dolphin communication at Barkley Canyon, with implications for multiple populations

Acoustic data collection is a cost-effective approach to evaluating activity patterns of otherwise challenging-to-study offshore cetaceans. However, manual analysis of acoustic data is time-consuming and impractical for large data sets. This study evaluates diel and seasonal patterns in Pacific white-sided dolphin communication through automated analysis of 20 months of continuous acoustic data collected from the Barkley Canyon node of Ocean Networks Canada’s NEPTUNE observatory, offshore of Vancouver Island, British Columbia, Canada. In this study, cetacean signals are manually annotated in a sub-set of the data, and 94 time and frequency features of these and other sounds are extracted and used to train random forest classifiers targeting Pacific white-sided dolphin pulsed calls. The performance of binary and multiclass models with various forest sizes, minimum leaf sizes, and confidence thresholds for Pacific white-sided dolphin classification are compared through nested 10-fold cross-validation to select the best model. Vocalizations are classified with the resultant classifier, manually verified, and examined for seasonal and diel patterns. Pacific white-sided dolphins are shown to be vocally active during dawn and day in spring and summer and at dusk and night year-round with reduced overall activity in fall and winter, suggesting both consistent nocturnal and migratory diurnal populations. Acoustic data collection is a cost-effective approach to evaluating activity patterns of otherwise challenging-to-study offshore cetaceans. However, manual analysis of acoustic data is time-consuming and impractical for large data sets. This study evaluates diel and seasonal patterns in Pacific white-sided dolphin communication through automated analysis of 20 months of continuous acoustic data collected from the Barkley Canyon node of Ocean Networks Canada’s NEPTUNE observatory, offshore of Vancouver Island, British Columbia, Canada. In this study, cetacean signals are manually annotated in a sub-set of the data, and 94 time and frequency features of these and other sounds are extracted and used to train random forest classifiers targeting Pacific white-sided dolphin pulsed calls. The performance of binary and multiclass models with various forest sizes, minimum leaf sizes, and confidence thresholds for Pacific white-sided dolphin classification are compared through nested 10-fold cross-validation to sel.