Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithm of Video Semantic Classification Based on IAGA and Deep Convolution Neural Network

The rise of convolutional neural network (CNN) has greatly improved the disadvantage of the traditional video classification method. However, in the pre-training process of classification network, often due to over-fitting, gradient disappearance and other factors lead to training data convergence performance is poor, thus would affect the accuracy of the classifier. Aiming at the problem of network optimization, this paper proposes an algorithm to combine improved adaptive genetic algorithm (IAGA) with deep convolution neural network (DCNN) classifier. The weighting of the network is initialized by the IAGA algorithm, and the weight is corrected by combining the gradient descent (GD) algorithm. Finally, the fusion of global feature extracted by the network is input into the extreme learning machine (ELM) for classification. The results of the news video classification show that the algorithm can combine the global search ability of IAGA with the local optimization ability of gradient descent algorithm to improve the accuracy of the training network with less parameters, and the average classification accuracy rate can reach 90.03%. Compared with the three existing algorithms, the algorithm has higher classification accuracy. Compared with the four kinds of network pretraining methods, the algorithm presented in this article is more dominant.
Evaporation Estimation Using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Techniques

Accurate estimation of potential evaporation has been of a great as its importance is obvious in many water resources applications such as management of hydrologic, hydraulic and agricultural systems. Although there are empirical formulas available for Evaporation estimation, but their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. For this purpose, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed to forecast monthly potential evaporation in Pantagar, U.S. Nagar (India) based on four explanatory climatic factors. Observations of relative humidity, solar radiation, temperature, wind speed and evaporation for the past 19 years and 8 months (total 236 months) have been used to train and test the developed models. Results revealed that the models were able to well learn the events they were trained to recognize. Moreover, they were capable of effectively generalizing their training by predicting evaporation for sets of unseen cases. These encouraging results were supported by high values of coefficient of correlation and low mean square errors. It has been found that ANN and ANFIS techniques have good performances (for the test data set, correlation coefficient for ANN is 0.9236 and root mean square error is 0.9863 and for ANFIS correlation coefficient is 0.9562 and root mean square error is 1.2812. Between ANN and ANFIS, ANFIS model is slightly better albeit the difference is small. Although ANN and ANFIS techniques seem to be powerful, their data input selection process was done by trial and error method.
Online Sensor Information and Redundancy Resolution Based Obstacle Avoidance for High DOF Mobile Manipulator Teleoperation

Abstract High degrees of freedom (DOF) mobile manipulators provide more flexibility than conventional manipulators. They also provide manipulation operations with a mobility capacity and have potential in many applications. However, due to high redundancy, planning and control become more complicated and difficult, especially when obstacles occur. Most existing obstacle avoidance methods are based on off-line algorithms and most of them mainly focus on planning a new collision-free path, which is not appropriate for some applications, such as teleoperation and uses many system resources as well. Therefore, this paper presents an online planning and control method for obstacle avoidance in mobile manipulators using online sensor information and redundancy resolution. An obstacle contour reconstruction approach employing a mobile manipulator equipped with an active laser scanner system is also introduced in this paper. This method is implemented using a mobile manipulator with a seven-DOF manipulator and a four-wheel drive mobile base. The experimental results demonstrate the effectiveness of this method.
The Multi-Liaison Algorithm

In this paper we present an approach for extracting multiple connections or links between subject and object from natural language input (English), which can have one or more than one subject, predicate and object. The parse tree visualization and the dependencies generated from the Stanford Parser are used to extract this information from the given sentence. Using the dependencies we have generated an output which displays which subject is related to which object and the connecting predicate. Finding the subjects and objects helps in determining the entities involved and the predicates determine the relationship that exists between the subject and the object. An algorithm has been developed to do so and this algorithm is elucidated in detail step-wise. We have named our algorithm 'The Multi-Liaison Algorithm' since the liaison between the subjects and objects would be displayed. The word 'liaison' has been used since we are displaying the relationship and association between the subjects and predicates. This output would be useful for natural language processing (NLP), information retrieval, information extraction and also text mining applications.
Optimizing monitoring and eradication of invasive species: new frameworks and applications

This thesis develops new statistical, and decision making tools for the detection and eradication of invasive species. The first section develops models to predict locations of invasive species so monitoring effort can be concentrated there. The first chapter develops an advanced species distribution model that includes dispersal and environmental tolerances and outperforms traditional species distribution models of just environmental tolerances. In addition this model is able to capture estimates of the target species fecundity, dispersal range, environmental tolerances and conspecific larval aggregation. The second chapter compares the performance of this statistical model to a popular form of mechanistic hydrodynamic model that is widely used to model dispersal, but which is rarely validated. By comparing the two approaches I show how advanced species distribution models can be used as alternative models to assess the power of more expensive, computationally demanding mechanistic models and how in some cases the statistical models may perform as well for a fraction the cost.The second section of my thesis examines how to formulate a response to an invasion once it is detected. The first chapter develops the first quantitative framework for eradication. It suggests that eradication can proceed without directly targeting all life stages and the number of treatment passes and their timing can be calculated from basic biological data like maturation rates. The final chapter incorporates guidance from other published eradication frameworks. It modifies and amends them to form a decision tree of necessary experiments to determine eradication feasibility and timing. It adds to the previous chapter explicit consideration of mobility, immigration, and spatio-temporal variation in vulnerability to form a more complete eradication framework. %%%%Cette these developpe de nouveaux outils statistiques qui peuvent etre utilises dans les prises de decision afin de detecter et d'eradiquer les especes invasives. La premiere section de la these developpe des modeles permettant de prevoir la localisation des especes invasives afin d'identifier les lieux ou les efforts de surveillance devenaient etre concentres. Le premier chapitre developpe un modele de distributions d'especes qui incluent les tolerances environnementales et la dispersion et ce faisant performe mieux que les modeles traditionnels qui n'incluent que les tolerances environnementales. De plus, ce modele peut estimer des parametres cles d'especes cibles, comme la fecondite, le potentiel de dispersion, la tolerance environnementale et l'agregation des larves de conspecifics. Le deuxieme chapitre compare la performance du modele statistique du premier chapitre a un modele mecanistique hydrodynamique grandement utilise en modelisation de dispersion, mais, en revanche, qui est rarement valider. En comparant les deux approches, je demontre comment les modeles de distributions d'especes peuvent etre utilises comme modeles alternatifs pour evaluer le pouvoir de modeles…
User-Feature Model for Hybrid Recommender System

Recommender systems provide relevant items to users from a large number of choices. In this work, we are interested in personalized recommender systems where user model is based on an analysis of usage. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Each technique has its drawbacks, so hybrid solutions, combining the two techniques, have emerged to overcome their disadvantages and benefit from their strengths. In this paper, we propose a hybrid solution combining collaborative filtering and content-based filtering. With this aim, we have defined a new user model, called user- feature model, to model user preferences based on items' features and user ratings. The user-feature model is built from the user item model by using a fuzzy clustering algorithm: the Fuzzy C Mean (FCM) algorithm. Then, we used the user-feature model in a user-based collaborative filtering algorithm to calculate the similarity between users. Applying our approach to the MoviesLens dataset, significant improvement can be noticed comparatively to the main CF algorithm, denoted as user-based collaborative filtering.
VisioMap: Lightweight 3-D Scene Reconstruction Toward Natural Indoor Localization

Most existing proposals for indoor localization are “unnatural,” as they rely on sensing abilities not available to human beings. While such a mismatch causes complications in human–computer interactions and thus potentially reduces the usability and friendliness of a localization service, it is partially entailed by the need for low-cost/effort sensing with resource-limited mobile devices. Fortunately, recent developments in smart glasses (e.g., Google Glasses) signal a trend toward realistic visual sensing and hence make the sensing ability of mobile devices more compatible to that of human users. Leveraging such front-end developments, we propose VisioMap as a natural indoor localization system that intentionally mimics the human skills in visual localization. VisioMap uses very sparse photograph samples to reconstruct 3-D indoor scenes; this is facilitated by the facts that photographs are taken at the eye-level with high stability and regularity, and that the reconstruction is lightweight as it exploits geometric features rather than image pixels. Localization is in turn performed by matching the geometric features extracted online to the reconstructed 3-D scene, making VisioMap: 1) natural to users as they can see the matched 3-D scene and 2) dispensed with the need for dense fingerprints/POIs toward accurate localization.
Efficient Cache Organization For Application Specific And General Purpose Processors

The performance gap between processor and memory continues to remain a major performance bottleneck in both application specific and general purpose processors. This thesis strives to ease the above bottleneck by exploiting the characteristics of the application domain to improve the cache organization for two distinct processor architectures: (1) application specific processors for packet forwarding, (2) general purpose processors. Packet forwarding algorithms make use of a trie data structure to determine the forwarding route. We observe that the locality characteristics of the nodes at various levels of such a trie are different. Nodes that are closer to the root node, especially those that are immediate children of the root node (level-one nodes), exhibit higher temporal locality than nodes lower down the trie. Based on this observation we propose a novel Heterogeneously Segmented Cache Architecture (HSCA) that uses separate caches for level-one and lower-level nodes, each with carefully chosen sizes. We also propose a new replacement policy to enhance the performance of HSCA. Performance evaluation indicates that HSCA results in up to 32% reduction in average memory access time over a unified cache that shares the same cache space among all levels of the trie. HSCA also outperforms a previously proposed results cache. The use of a large root branching factor in a forwarding trie forcefully introduces a large number of nodes at level-one. Among these, only nodes that cover prefixes from the routing table are useful while the rest, are superfluous. We find that as many as 75% of the level-one nodes are superfluous. This leads to a skewed distribution of useful nodes among the cache sets of the level-one nodes cache. We propose a novel two-level mapping framework that achieves a better nodes to cache set mapping and
Rock and soil cutting slopes stability condition identification based on soft computing algorithms

This study aims to develop a tool able to help decision makers to find the best strategy for slopes management tasks. It is known that one of the main challenges nowadays for every developed or countries undergoing development is to keep operational under all conditions their transportation infrastructures. However, considering the network extension and increased budget constraints such challenge is even more difficult to accomplish. In the framework of transportations networks, particularly for railway, slopes are perhaps the element for which their failure can have a strongest impact at several levels. Therefore, it is important to develop tools able to help minimizing this situation. Aiming to achieve this goal, we take advantage of the high flexible learning capabilities of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have been used in the past to model complex nonlinear mappings. Both data mining algorithms were applied in the development of a classification tool able to identify the stability condition of a rock and soil cutting slopes, keeping in mind the use of information usually collected during routine inspections activities (visual information) to feed them. For that, two different strategies were followed: nominal classification and regression. Moreover, to overcome the problem of imbalanced data, three training sampling approaches were explored: no resampling, SMOTE (Synthetic Minority Over-sampling Technique) and Oversampling. The achieved results are presented and discussed, comparing the performance of both algorithms (ANN and SVM) according to each modelling strategy as well as the effect of the sampling approaches. Also, a comparison between both types of slopes is presented and discussed. An input-sensitivity analysis was applied allowing to measure the relative influence of each model attribute.
Spatially Referring Natural Language Propositions: Information Fusion and Estimation Theory

This work concerns an automatic information fusion scheme for state estimation where the inputs (or measurements) that are used to reduce the uncertainty in the state of a subject are in the form of natural language propositions. In particular, we consider spatially referring expressions concerning the spatial location (or state value) of certain subjects of interest with respect to known anchors in a given state space. The probabilistic framework of random-set-based estimation is used as the underlying mathematical formalism for this work. Each statement is used to generate a generalized likelihood function over the state space. A recursive Bayesian filter is outlined that takes, as input, a sequence of generalized likelihood functions generated by multiple statements. The idea is then to recursively build a map, e.g. a posterior density map, over the state space that can be used to infer the subject state.
A Framework for Off-The-Shelf Software Component Development and Maintenance

In recent years, component-based software engineering (CBSE) has become a promising engineering discipline for software development. However, research in the CBSE field has mainly concentrated on in-house component development and utilization of components that have been constructed internally or acquired from component markets. Not enough attention has been paid to commercial software component development, although disciplined processes have been seen as a focal point in the development of high-quality reusable software Although Off-The-Shelf (OTS) software component development can be considered as development for reuse, which is a broadly studied research topic, development for external markets makes it different from traditional reuse process approaches. OTS software components are developed in an environment in which the developer has no control over the market. This publication presents a framework for OTS software component development and maintenance processes based on IEEE Std 1517 Standard for Reuse Processes and ISO/IEC 12207: 1995 Standard for Software Life Cycle Processes, and introduces general guidelines for OTS component user documentation. OTS software component development follows the incremental and iterative life cycle, as it facilitates recognizing and managing changing requirements and mitigating risks at an early stage. The process framework incorporates aspects of software development for external markets, as well as characteristics deriving from the nature of a component being a unit of composition, such as adhering to component models. The process framework has some limitations: process activities and tasks are presented at a high abstraction level and they have not been validated in practice. Thus, the processes are likely to require revising and further refining once put into use.
Performance of switched beam systems in battlefield TDMA networks

We explore the performance of switched beam systems (SBS) under conditions of angular, delay, and Doppler spread. We offer a derivation of the probability of selecting a given beam for processing when IS-136 TDMA color codes are used for beam validation. This result is then used to derive the average output SIR for the SBS using the given validation process. We simulate the performance of SBS using maximum RSSI validation and compare it to that of the standard sector antenna system for various angle spreads. We also compare the performance of the color code validation scheme to the maximum RSSI scheme.
When pigs fly: a study of computer generated paper folding

When Pigs Fly: A Study of Computer Generated Paper Folding. (December 2008) Elizabeth Jeanette Nitsch, B.E. D. , Texas A&M University Chair of Advisory Committee: Prof. Carol LaFayette The purpose of this thesis is to develop a system for folding an origami model using computer generated, or virtual, paper. This research is detailed according to both the creative and technical aspects of that process, with particular attention given to formulating a solution for animating the paper in a way that is physically realistic. The project is executed in Autodesk Maya, a 3D computer graphics program, and rendered with mental ray, a production quality rendering software. The final results are illustrated via excerpts from When Pigs Fly. , an original 3D short which uses the developed methodology to give life to an origami-based narrative. The techniques employed in this thesis can provide a valuable framework for other artists embarking on similar productions and supply a foundation for more advanced problems related to folding and computer graphics.
Multi-operator Temporal Decision Trees

Rising interest in mining and analyzing time series data in many domains motivates designing machine learning (ML) algorithms that are capable of tackling such complex data. Except of the need in modification, improvement, and creation of novel ML algorithms that initially works with static data, criteria of its interpretability, accuracy and computational efficiency have to be fulfilled. For a domain expert, it becomes crucial to extract knowledge from data and appealing when a yielded model is transparent and interpretable. So that, no preliminary knowledge of ML is required to read and understand results. Indeed, an emphasized by many recent works, it is more and more needed for domain experts to get a transparent and interpretable model from the learning tool, thus allowing them to use it, even if they have few knowledge about ML's theories. Decision Tree is an algorithm that focuses on providing interpretable and quite accurate classification model.More precisely, in this research we address the problem of interpretable time series classification by Decision Tree (DT) method. Firstly, we present Temporal Decision Tree, which is the modification of classical DT algorithm. The gist of this change is the definition of a node's split. Secondly, we propose an extension, called Multi-operator Temporal Decision Tree (MTDT), of the modified algorithm for temporal data that is able to capture different geometrical classes structures. The resulting algorithm improves model readability while preserving the classification accuracy.Furthermore, we explore two complementary issues: computational efficiency of extended algorithm and its classification accuracy. We suggest that decreasing of the former is reachable using a Local Search approach to built nodes. And preserving of the latter can be handled by discovering and weighting discriminative time stamps of time series.
Exploring incentive mechanisms for mobile crowdsourcing: sense of safety in a Thai city

ABSTRACT The rapid adoption of mobile devices enables capture and transmission of a variety of sensor and user-contributed data, creating a new data collection paradigm and a wide range of services, often termed as mobile crowdsourcing. However, we are still investigating factors contributing towards the effectiveness of such systems. One key factor to succeed in mobile crowdsourcing applications is the incentive mechanism, which motivates people to contribute to a crowdsourcing effort. In this research, we conducted field experiments that compared the effectiveness of non-monetary and monetary incentive mechanisms, using both quantitative and qualitative methods. The focus was on an exploration of how these mechanisms motivate users’ performance in a mobile crowdsourcing environment. In the experiment, we developed a smart zoning application that allows users to share areas of the cities they perceived as safe or unsafe. The results from this study contribute to research in mobile crowdsourcing for urban understanding. Taken together, these results suggest that payment-related incentives can not only engage people’s interest in project participation but also help improve their work performance in terms of productivity and quality. Notably, a fixed-price financial reward mechanism is best suited for short period data collection and achieving data quality. Also, referral incentive mechanisms, if properly designed, have the potential to extend user coverage, both spatially and temporally. These results can be helpful with regard to the formulation of guidelines on how to create and organize effective payment-based incentives for crowd involvement in cities.
Silicon micromachining and microfabrication techniques for integrated sensor and actuator systems

Solid-state microsensors and microactuators have recently become attractive for a number of instrumentation and control systems. This paper reviews some of the major and widely used silicon micromachining and microfabrication technologies, including bulk micromachining, surface micromachining, and electroplating techniques along with their applications. <<ETX>>