Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Using Weakly Supervised and Semi-Supervised for Cross-Domain Sentiment Classification

Supervised machine learning techniques can analyze sentiment very effectively. However, in many languages, there are few appropriate data for training sentiment classifiers. Thus, they need a large corpus of training data. In this paper, weakly-supervised techniques using a large collection of unlabeled text to determine sentiment is presented. The performance of this method maybe less depends on the domain, topic and time period represented by the testing data. In addition, semi-supervised classification using a sentiment-sensitive thesaurus is mentioned. It can be applicable when it does not have any labeled data for a target domain but have some labeled data for other multiple domains designated as the source domains. This method can learn efficiently from multiple source domains. The results show that the weakly-supervised techniques are suitable for applications requiring sentiment classification across some domains and semi-supervised techniques can learn efficiently from multiple source domains.
Improve Abstract Data with Feature Selection for Classification Techniques

A universal problem with text classification has a problem due to the high dimensionality of feature space, e.g. word frequency vectors. To overcome this problem, this paper proposed a feature selection which focuses on statistical pattern based on SVM Attribute. Experiments have shown that the determination of word importance may increase the speed of the classification algorithm and save their resource used significantly. The proposed method was studied by comparing classification performance among Decision Tree, Naïve Bayes, and Support Vector Machine. The results showed that Support Vector Machine was found to be the best algorithm with F-measure 93.6%. It is found that the feature selection can reduce dimensionality of data significantly.
Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems

Modeling and implementing dynamical systems is a central problem in artificial intelligence, robotics, software agents, simulation, decision and control theory, and many other disciplines. In recent years, a new approach to representing such systems, grounded in mathematical logic, has been developed within the AI knowledge-representation community. This book presents a comprehensive treatment of these ideas, basing its theoretical and implementation foundations on the situation calculus, a dialect of first-order logic. Within this framework, it develops many features of dynamical systems modeling, including time, processes, concurrency, exogenous events, reactivity, sensing and knowledge, probabilistic uncertainty, and decision theory. It also describes and implements a new family of high-level programming languages suitable for writing control programs for dynamical systems. Finally, it includes situation calculus specifications for a wide range of examples drawn from cognitive robotics, planning, simulation, databases, and decision theory, together with all the implementation code for these examples. This code is available on the book's Web site.
Sharpening the focus: methodological issues in analysing on-line conferences

Abstract As on-line discussion becomes an increasingly important and significant aspect of teachers' professional education, most particularly as part of distance learning environments, there is a need to understand and develop effective analytic techniques that provide insights into the processes at work within these systems. The computer conferencing environment at the centre of this study is a complex, unstructured discussion forum, of a type increasingly found as a component of continuing professional development courses. In these situations the evidence of knowledge building processes is a key concern. This study concentrates on a close examination of some existing analytic tools and considers their contribution to identifying the processes at work within these on-line environments, as teachers discuss aspects of education.
Marketing Library Resources and Services to Distance Faculty

Abstract As academic libraries and librarians experience an increasingly complex higher education environment, it is essential to market library resources and services effectively. One component of a library's marketing plan for distance learners can be an assessment of the needs of distance faculty. Teaching faculty are influential in affecting student perceptions and their use of the library. This paper reports on an assessment of the needs of faculty who teach classes in distance graduate degree programs. Results from the survey provide strategic direction for delivering and marketing services to distance learners and distance faculty.
SAR image despeckling using over-complete dictionary

A new methodology for speckle suppression of SAR images using an over-complete dictionary is presented. The approach taken is based on sparse and redundant representations by employing a combined dictionary consisting of wavelets, shearlets and learned dictionaries. Wavelets provide sparse expansions for point-like structures, shearlets provide sparse expansions for curve-like structures, and learning dictionaries are taken as obtaining the best performance on a training set. The experimental results demonstrate that the proposed algorithm provides more effective speckle reduction as well as detail preservation.
High-speed 3D imaging by DMD technology

The paper presents an advanced solution for capturing the height of an object in addition to the 2D image as it is frequently desired in machine vision applications. Based upon the active fringe projection methodology, the system takes advantage of a series of patterns projected onto the object surface and observed by a camera to provide reliable, accurate and highly resolved 3D data from any scattering object surface. The paper shows how the recording of a projected image series can be significantly accelerated and improved in quality to overcome current limitations. The key is ALP - a metrology dedicated hardware design using the Discovery 1100 platform for the DMD micromirror device of Texas Instruments Inc. The paper describes how this DMD technology has been combined with latest LED illumination, high-performance optics, and recent digital camera solutions. The ALP based DMD projection can be exactly synchronized with one or multiple cameras so that gray value intensities generated by pulse-width modulation (PWM) are recorded with high linearity. Based upon these components, a novel 3D measuring system with outstanding properties is described. The “z-Snapper” represents a new class of 3D imaging devices, it is fast enough for time demanding in-line testing, and it can be built completely mobile: laptop based, hand-held, and battery powered. The turnkey system provides a “3D image” as simple as an usual b/w picture is grabbed. It can be instantly implemented into future machine vision applications that will benefit from the step into the third dimension.
DETECTING HUMANS IN VIDEO SEQUENCES USING STATISTICAL COLOR AND SHAPE MODELS

2001 ii ACKNOWLEDGMENTS I would like to thank my parents for their endless support, guidance, and love. Their unbounded faith makes nothing seem unconquerable. I would also like to thank Dr. Michael Nechyba for sharing his extensive knowledge, as well as for his patience and time. I would like to thank Dr. A. Antonio Arroyo for giving me the opportunity to find and explore new fields. Special thanks go to Dr. Keith Doty who gave me the tools to explore these fields, and allowed me the freedom to do so on my own terms. Thanks go to Scott Nichols for providing an avenue to express and develop new ideas, and delivering undissembled feedback at all times. Additional thanks go to
Feature-based statistical analysis of structural MR data for automatic detection of focal cortical dysplastic (FCD) lesions

We present a framework for automatic detection of focal cortical dysplastic (FCD) lesions from MR images of human brain. Our method extends, and improves the lesion detection specificity of a previously published voxel-based technique using cortical thickness and signal gradient as discriminating features of FCD lesions. In absence of any prior anatomical hypothesis regarding the spatial location of the lesion, the method examines each intracerebral voxel individually and simultaneously, and constructs a statistical parametric map indicating evidence against a null hypothesis of no effect in the patient versus a normal control group. Upon interrogation of the statistical map with an optimally selected threshold, the voxels demonstrating the improbability of the null hypothesis are reported as lesions. The method correctly detects 5 out of the 10 cases with a very high significance. The cases we did not detect were in deep gray matter regions, where the variance in the feature maps was high, decreasing the significance of the effect.
Data Protection Comes of Age: The Data Protection Clauses in the European Constitutional Treaty

This article traces the evolution of the right to data protection in European law, with a particular focus on the inclusion of the right in the European Union Charter of Fundamental Rights and its proposed legal status under the new Constitutional Treaty. In particular, the discussion considers the legal impact of this ‘constitutionalisation’ of this area of legal protection. It is argued that this should be viewed as more than just a formal development in that entrenchment of the right may strengthen ground-level interpretation and implementation of data protection principles.
A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models

Abstract This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, better data splitting, in terms of data representation and enhanced performance of developed ANN models, can be achieved. Compared with standard KS algorithm and another improved KS algorithm (data division based on joint x - y distances (SPXY) method), the proposed method has also shown a better performance. Therefore, the proposed technique can be used as an advantageous alternative to other existing methods of data splitting for developing ANN models. Care should be taken when dealing with large amount of dataset since they may increase the computational load for MDKS due to its variance-covariance matrix calculations.
Image analysis for adaptive noise reduction in super high-definition image coding

The encoding of Super High Definition Images presents new problems with regard to the effect of noise on the quality of images and on coding performance. Although the information content of images decreases with increasing resolution, the noise introduced in the image acquisition or scanning process, remains at a high level, independently of resolution. Although this noise may not be perceptible in the original image, it will effect the quality of the encoded image, if the encoding process introduces correlation and structure in the coded noise. Further, the coder performance will be affected by the noise, even if the noise is not perceived. Therefore, there is a need to reduce the noise by pre-processing the SHD image, so as to maintain image quality and improve the encoding process. The reduction of noise cannot be performed by low pass filtering operations that will degrade image quality. We are applying to this problem image analysis for adaptive noise removal. We discuss first the information theoretic issues on the effect of noise on coders. We then consider adaptive noise removal techniques to the perceptually transparent and very high quality coding of still SHD images.
Fracture and Wellbore Spacing Optimization in Multistage Fractured Horizontal Wellbores: Learnings from Our Experience on Canadian Unconventional Resources

Horizontal wellbore drilling and completion technology with multistage fracturing has revolutionized the exploitation of unconventional resources in North America in recent years. Many unconventional oil and gas reservoirs with ultra-low permeability have become economical as a result. Yet, the development and completion costs of these resources can be further improved by optimizing the number of fracture stages placed on each wellbore and the number of wellbores drilled per section of land. This study highlights our operational and analytical experience on an integrated workflow for optimization of fracture and wellbore spacing to develop the unconventional resource in Western Canadian Sedimentary Basin (WCSB). The study is based on fracturing design and optimization, microseismic fracture mapping, reservoir modelling and production analysis for numerous case studies on various formations in Canada including Beaverhill Lake, Cadomin, Cardium, Doig, Dunvegan, Duvernay, Montney, Viking, Slave Point and Sprit River formations. The typical workflow for fracture and well spacing optimization studies includes multiple and iterative steps: minifrac tests, fracture modelling and calibration, fracture job execution, microseismic monitoring, reservoir simulation and production data analysis. In this integrated process, hydraulic fracture models were built based on fracture job data, rock mechanics and log data, and then calibrated with minifrac data and microseismic fracture mapping results. Three-dimensional reservoir simulation models were constructed using laboratory core data, petrophysical and geological data, and reservoir fluid PVT properties. The calibrated fracture models were integrated into reservoir simulation models. The reservoir models were fine-tuned by history matching the production data. The fine-tuned models were then used to run multiple scenarios by varying fracture size, the number of fracturing stages per wellbore and wellbores per section. Fracturing treatments with different pump rate, proppant size, pumping schedule and proppant tonnage were further investigated to optimize fracture geometry and conductivity for production enhancement. Optimal fracture and wellbore spacing scenarios were recommended for subsequent drilling and completion planning in the field. Such optimization studies have helped to minimize operation cost and improve the economics of resource development. Our workflow and experience in the WCSB can be a useful guideline to improve economic success of unconventional resources in other basins around the world. To order the full paper, visit https://doi.org/10.2523/IPTC-17853-MS
Real time gait event detection during FES paraplegic walking

Functional electrical stimulation (FES) can used for standing and stepping in complete spinal cord injury (SCI) patients. A proposed method to enhance the performance of FES gait is through the use of feedback control to modify the muscle stimulation on a cycle-to-cycle or next-step basis. An essential component of such a controller is the knowledge of the timing of the gait cycle and gait events. The innovative feature in this work is the use of a multi-level gait event detector. The gait events are detected from insole force measurements in real time. The lower level of the detector, which is based upon previous classification work, estimates the phase of gait at each time sample. The upper level of the detector uses recently developed supervisory rules to detect the transitions between the phases, i.e. the gait events. The gait events of paraplegic patients walking with FES are detected in real time. The gait events of most interest, heel strike and toe off are detected within /spl plusmn/9% of the gait cycle duration.
Experimental identification and evolutionary analysis of homotypic transmembrane helix-helix interfaces

The homotypic interaction of transmembrane helices is a key type of protein-protein interaction in the membrane. It supports the dimerisation and oligomerisation of many bitopic membrane protein, and it is therefore vital for many cellular processes. However, the properties of the interface residues are poorly understood. This study quantify the importance of factors such as residue conservation, residue polarity, and the GxxxG motif. Experiments were performed using the ToxR assay, to identification of homotypic TM interfaces in a natural membrane environment.
A Methodological Framework for Researching the Usability of the Space-Time Cube

Abstract In recent years, an increased interest in the use of the Space-Time Cube (STC) to visualize movement data can be witnessed. However, little is known about whether the cube is truly efficient and effective to satisfactorily display complex movement datasets. The limited usability research that has been done did not lead to details on how the STC content should be designed and whether the cartographic design influences the exploration process. The conceptual framework that is proposed in this paper offers an approach to the evaluation of the STC content with special attention for design aspects and the environment in which the STC has to function. Systematic usability studies have to identify the strengths and weaknesses of the STC under different conditions. Therefore, the framework contains phased studies in which real-world data of four use cases of different complexity are used.
Synthesis techniques for discrete time neural network models

The authors establish a qualitative theory for synchronous, discrete-time, Hopfield-type neural networks. Their objectives are accomplished in two phases. They analyze the class of neural networks considered and use the results to develop a synthesis procedure for them. The analysis utilizes techniques from the theory of large-scale interconnected dynamical systems to derive tests for the asymptotic stability of an equilibrium of the neural network. Estimates are obtained for the rate at which the trajectories of the network will converge from an initial condition to a final state. The authors utilize the stability tests as constraints to develop a design algorithm for content-addressable memories. The algorithm guarantees that each desired memory will be stored as an equilibrium and will be asymptotically stable. The applicability of the results is demonstrated for a 13-neuron and for an 81-neuron network. <<ETX>>
Determine Execution Time and Selection Probabilities in Process Mining via Petri Nets

In process mining research, process discovery techniques can produce or rebuild models with the information from logs. There are already algorithms supporting control-flow perspective mining which focus on the order of events and provide understanding workflow paths. But few of them take time perspective and path selection probabilities into consideration, which are important in performance evaluating, delay prediction, decision making, as well as process redesigning and optimizing. This paper provides a novel algorithm which determines the information of time perspective and selection probabilities from a log and integrates them with the control-flow perspective. By applying this algorithm, a stochastic Petri net is provided which is useful in performance analyzing and process optimizing.
Teaching with Blogs: A Case Study of Technologically Mediated Literacy

The mismatch between the print-based literacy focus of most classrooms and the lived electronic lives of teenagers prompted our case study of educational blogging. We conducted classroom observations in an eighth- and a ninth-grade English class over a two-month period and conducted thematic analyses of blogs created by the teacher and 28 eighth-grade students. We observed that the students were engaged in their class and eager to read and write online and to collaborate with their peers. They demonstrated hybrid literacies that met academic, social, and personal goals. We found that digital literacy as taught in this class comprised three aspects. First was proficiency in decoding and comprehending the symbols of print text (reading), as well as encoding (writing) these symbols and integrating them digitally with other expressive modes, such as images. Second was technological proficiency in the use of digital media, or digital fluency. Third was dynamic engagement in the interactive construction of mean.
A unified method for comparison of algorithms of saliency extraction

Extracting salient regions of a still image, which are pertinent areas likely to attract subjects' fixations, can be useful to adapt compression loss according to human attention. In the literature, various algorithms have been proposed for saliency extraction, ranging from region-of-interest (ROI) or point-of-interest (POI) algorithms to saliency models, which also extract ROIs. Implementing such an algorithm within image sensors implies to evaluate its complexity and performance of fixation prediction. However, there have been no pertinent criteria to compare these algorithms in predicting human fixations due to the different nature between ROIs and POIs. In this paper, we propose a novel criterion which is able to compare the prediction performance of ROI and POI algorithms. Aiming at the electronic implementation of such an algorithm, the proposed criterion is based on blocks, which is consistent with processing within image sensors. It also takes into account salient surface, an important factor in electronic implementation, to reflect more accurately the prediction performance of algorithms. The criterion is then used for comparison in a benchmark of several saliency models and ROI/POI algorithms. The results show that a saliency model, which has higher computational complexity, gives better performance than other ROI/POI algorithms.
The SOCs Modular Method for Kinematic Analysis of Complicated Parallel Manipulators

This paper presents a simple and systematic modular approach for kinematic analysis of complicated Parallel Kinematic Manipulators (in short, PKMs) which coupled degrees are more than 2. (1) Single open chains (in short, SOCs) may be regarded as the basic modules of a PKM. Any PKM can always be decomposed automatically into a set of ordered SOCs, and these SOCs can also be used to recognize the basic kinematic chains contained in it. (2) The kinematic analysis algorithms and the compatibility conditions of the SOC modules are offered. (3) Directly applying the above SOC kinematic modules, the kinematic equations of a PKM can be automatically established. (4) In order to solve kinematic equations of complicated parallel manipulators which coupled degrees are more than 2, a new searching algorithm which requires no initial guess has been presented. The procedural approach is demonstrated in parallel manipulators.
A novel frame-level constant-distortion bit allocation for smooth H.264/AVC video quality

It is known that quality fluctuation has a major negative effect on visual perception. In previous work, we introduced a constant-distortion bit allocation method [1] for H.263+ encoder. However, the method in [1] can not be adapted to the newest H.264/AVC encoder directly as the well-known chicken-egg dilemma resulted from the rate-distortion optimization (RDO) decision process. To solve this problem, we propose a new two stage constant-distortion bit allocation (CDBA) algorithm with enhanced rate control for H.264/AVC encoder. In stage-1, the algorithm performs RD optimization process with a constant quantization QP. Based on prediction residual signals from stage-1 and target distortion for smooth video quality purpose, the frame-level bit target is allocated by using a close-form approximations of ratedistortion relationship similar to [1], and a fast stage-2 encoding process is performed with enhanced basic unit rate control. Experimental results show that, compared with original rate control algorithm provided by H.264/AVC reference software JM12.1, the proposed constant-distortion frame-level bit allocation scheme reduces quality fluctuation and delivers much smoother PSNR on all testing sequences.