Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Influencing the Faculty Adoption of Web Media Objects: Identification and Recommendations

The current and increasing advances in computer technology has afforded more ubiquitous use of multimedia design for information presentation. This research addresses university faculty's use of web media objects: text, images/graphics, animation, audio and video (Oracle, Inc., 1996) for their course instruction. The framework used to assess the inclusion of web media objects in university course instruction was Rogers' (1995) Diffusion of Innovations model. The innovation was the inclusion of web media objects into university course instruction. The innovation was diffused through a faculty development workshop. It was determined that three factors influenced a faculty member's decision to adopt and implement the innovation: technology, pedagogy, and presentation style. Quantitative, qualitative, and observational data were used to draw conclusions about the influential factors associated with adoption of the innovation. This research resulted in a number of pertinent outcomes. Those outcomes included: information regarding faculty members' perceptions towards the innovation adoption, adoption barriers and general lessons learned, potential design guidelines for advanced multimedia training developers and training support systems, and recommendations for improving the adoption of web media objects for university course instruction. Results revealed that innovation characteristics: compatibility, trialability, relative advantage and observabilty are most important to consider for the adoption of web media objects for university course instruction. Discussion includes recommendations for improving faculty perception regarding these innovation characteristics and other related issues. just months short of the completion of this research. Nevertheless, your presence in my life, your leadership, your dedication and your commitment to our family was absolutely " Stellar. " Never did your life lead you to college, or even high school; however, because of your dedication to your family, your children awarded you an honorary doctorate in " Fatherhood. " Grandfather, you are still the greatest example of perseverance and achievement that I know. Your example has taught me, and is still teaching me, that " good success " in life is not a simple measure of the academic degrees and accolades I can accumulate; but more so, a measure of the character and integrity that I display in everything that I do. Thank you for constructing a solid foundation, upon which I can stand today, and build upon for future generations. For your diligence, work ethic, and love, I honor you with the completion of my research. Grandfather, I present to you an honorary Master's degree in Industrial and Systems Engineering (by the way of …
Gender Classification Using Hybrid of Gabor Filters and Binary Features of an Image

Face is one of the most important biometric of human and contains lots of useful information such as gender, age, race and identity. Gender classification is very easy for human but it considers a challenge for computers. Gender classification through face images has recently been considered so much. Gender recognition can be useful in interaction between human and computer like identifying individual’s identity. It is also applicable in TV networks in order to study the rate of viewers. Various algorithms have been designed for this issue and each of them has unraveled that to some extent. The last obtained rate to identify gender was through article written by Dr. Mozaffari who obtained mean rate of 83% for identification. It is the proposed method of the present study which has brought identification rate to 92.5. in this method we draw out face features based on Gabor filters and local binary patterns. These features are resistant against noise and they select proper features against bottleneck of images. In order to obtain a proper classification, we use self-organized map (SOM) (type of artificial neural network). This neural network finds the proper weights for each gender with very little error. Obtained results are compared with existing datasets and therefore, superiority of the proposed method would be evident. DOI: http://dx.doi. org/10.11591/ijece.v4i4. 5926
Plasma bubble irregularity occurrence and zonal velocities under quiet and disturbed conditions, from polarimeter observations

Amplitude scintillation and Faraday rotation angle fluctuations of satellite VHF beacons registered by electronic polarimeters operated at two stations spaced 110 km apart in the magnetic east-west direction namely, Cachoeira Paulista, 22°41′S, 45°W, dip angle of −26°, and Sao Jose dos Campos, 23°12′S, 45°51′W, dip angle of −26°, in Brazil are analyzed for equinoctial and summer months of 1982–1983 to determine the irregularity patch occurrence characteristics and their zonal velocities under varying levels of magnetic disturbances. The scintillation patch occurrences and total electron content fluctuations representing plasma depletion structures decrease during premidnight hours and are enhanced in the postmidnight period. The occurrences often extend to morning hours, with increasing magnetic activity index. While the night events are associated with eastward irregularity patch motion, the morning events have westward velocities (the reversal) occurring around 0400 LT, reflecting the morning velocity reversal in the F region bulk plasma motion reported earlier from Jicamarca radar measurements. The westward plasma bubble motions reported here represent the first observation of this kind. Another important finding from the present investigation is a clear tendency for the zonal plasma motion, namely, the equatorial meridional electric field, to become enhanced mainly in the premidnight hours immediately following strong magnetic activity.
Differentiating Instruction for Students With Learning Disabilities: Best Teaching Practices for General and Special Educators

About the Author Acknowledgments Introduction 1. Differentiated Instruction for Students With Learning Disabilities 2. Brain-Compatible Instruction and Personal Responsibility for Learning 3. Differentiating the Learning Process: Scaffolded Instruction and Metacognition 4. Strategy Training: The Learning Solution 5. Tutoring in the Inclusive Classroom: Differentiating Instruction and Increasing Instructional Time - Cecil Fore III, William N. Bender 6. Supporting Students Through Performance Monitoring 7. Reading and Literacy Instruction for Students With Learning Disabilities 8. What's Next? References Index
The effect of class distribution on classifier learning: an empirical study

In this article we analyze the effect of class distribution on classifier learning. We begin by describing the different ways in which class distribution affects learning and how it affects the evaluation of learned classifiers. We then present the results of two comprehensive experimental studies. The first study compares the performance of classifiers generated from unbalanced data sets with the performance of classifiers generated from balanced versions of the same data sets. This comparison allows us to isolate and quantify the effect that the training set’s class distribution has on learning and contrast the performance of the classifiers on the minority and majority classes. The second study assesses what distribution is "best" for training, with respect to two performance measures: classification accuracy and the area under the ROC curve (AUC). A tacit assumption behind much research on classifier induction is that the class distribution of the training data should match the “natural” distribution of the data. This study shows that the naturally occurring class distribution often is not best for learning, and often substantially better performance can be obtained by using a different class distribution. Understanding how classifier performance is affected by class distribution can help practitioners to choose training data—in real-world situations the number of training examples often must be limited due to computational costs or the costs associated with procuring and preparing the data.
Grammatical evolution in developing optimal inventory policies for serial and distribution supply chains

Recently, there has been a growing literature on biologically inspired algorithms, particularly genetic algorithms and genetic programming, applied to supply chain modelling and inventory control optimisation. Due to the rigidity of the genetic algorithms approach, it is difficult to change the underlying model logic and add richness to the supply chain. While genetic programming provides a more flexible approach than that provided by genetic algorithms, to date its application has been limited to small supply chain modelling problems in relation to optimal inventory policies. This research applies Grammatical Evolution, a relatively new biologically inspired algorithm, to the field of supply chain optimisation, employing human readable rules called grammars. These grammars provide a single mechanism to describe a variety of complex structures and can incorporate the domain knowledge of the practitioner to bias the algorithm towards regions of the search space containing better solutions. Results are presented showing Grammatical Evolution is at least competitive in cost terms, and superior in flexibility, with these methods applicable to any supply chain of the serial or distribution type. Furthermore, Grammatical Evolution shows an adaptive ability that augurs well for supply chains in dynamic environments, such as disruption.
Building-blocks for event-based vision sensors

A nalyzing and understanding a visual scene and categorizing novel objects is so easy for us that we do not have to spend any effort to do it. Artificial vision systems on the other hand struggle in similar tasks. Despite the recent progress in computer vision and its increasing variety of applications, their functionality remains limited to well controlled environments solving very specific and well-defined tasks. Biological vision systems are orders of magnitude more powerful and powerefficient than computer vision systems. Part of this efficiency comes from more efficient vision sensors. The mammalian retina (the light-sensing tissue at the back of the eye) is a highly developed neural circuit that preprocesses the visual input. The amount of sensory data sent to the brain is reduced by this preprocessing, and important visual features like local contrast, motion direction, etc. are extracted. Computer vision systems rely on concepts and cameras that are optimized for recording picture sequences. These cameras and the entire processing chain of computer vision systems rely on the concept of ‘frames’. A frame is a matrix of values representing the intensities at every spatial location. The camera sends frames at a fixed rate to the processing chain. Frame-based cameras have been developed and optimized for several decades and allow very small pixels and thus high resolution. But using frames has several disadvantages. The fixed frame rate limits the temporal resolution of the system. Each pixel of every frame must be read and processed, even if its value did not change. This repeated processing of redundant data is highly inefficient. The research domain of neuromorphic engineering uses concepts found in biological systems to build devices that are more efficient than conventional devices. During the last twenty years neuromorphic engineers have built vision sensors that differ fundamentally from traditional cameras. However most of these devices have low resolution and suffer from strong fixed-pattern noise and low usability. Hence they are not suited to be employed in real-world applications. At the Institute of Neuroinformatics, Patrick Lichtsteiner and Tobi Delbruck developed the dynamic vision sensor, a neuromorphic vision sensor that proved to be useful in very different applications. In this device, the pixels emit asynchronous events that indicate a relative change of intensity with very low latency. This kind of output completely suppresses temporal redundancy. Thanks to the reduced data rate, less computing power is necessary to process the data; and the low latency
Applying Music Psychology to Music Education: Can Perceptual Theory Inform Undergraduate Harmony?

An undergraduate harmony course is presented that is grounded in recent research on the perception of harmony and tonality, and makes relevant aspects of that research accessible to music students. Perceptual theory can shed light on such general basic issues as intonation and the role of acoustics in harmonic theory, principles of chord construction, voicing and voice-leading in diatonic and chromatic progressions, stability and tension of harmonies in tonal contexts, modulation and tonicization, and the degree of finality of cadential progressions in different voicings. The proposed course is intended to complement and enrich more familiar historical and notational approaches. At various stages of the course, all possible chords, functions, or harmonic sequences conforming to clearly defined perceptual constraints are systematically enumerated using pitch-class set theory. Relevant perceptual properties (consonance, interrelationships) of the elements, calculated according to available models, are then compared with both their musical function in conventional harmonic theory and with their frequency of occurrence in relevant musical literature. At every stage of the course, musical and perceptual theories are integrated with ear training, analysis, keyboard skills, and composition.
Information Theory And Sensory Perception

In this chapter, we explore Shannon’s information theory and what it may tell us about the biological processes of sensory perception. We begin by discussing some historical theories of sensory perception, including concepts of objects and invariances as well as principles from Gestalt psychology. We consider the ecological principle that perception should be useful for an organism, and introduce information theory as a possible way to measure this usefulness. After introducing some of the key concepts from information theory, such as entropy, information, redundancy and factorial coding, we draw parallels between communication in engineering systems and sensory perception. We discuss Attneave’s early proposal that perception should create an economical description of sensory inputs, as measured by information theory, and Barlow’s suggestion of lateral inhibition as a possible mechanism to achieve this. We discuss the important role played by noise in an information processing system, and its importance when determining how to optimise the communication of information. This leads to the concept of information-optimal filters which change their characteristics at different signal-to-noise levels, a feature exhibited by fly and human visual systems. For information processed across topographic maps, optimal information processing leads to a principle of uniform information density, whereby a larger area in the cortex allows higher information density, and hence higher accuracy or sensitivity, at the corresponding sensors. We also discuss some recent investigations of information theory applied to spiking neural systems, and the concept of economy of impulses. Finally, we discuss some related concepts such as structure in a perceptual stimulus and implications for Gibson’s ideas about perceptual systems, and we speculate about the possible importance of attention and active perception for efficient information processing in a sensory system.
Scalable (re)design frameworks for optimal, distributed control in power networks

In this thesis, we develop scalable frameworks to (re)design a class of large-scale network systems with built-in control mechanisms, including electric power systems and the Internet, in order to improve their economic efficiency and performance while guaranteeing their stability and robustness. After a detailed introduction relating to power system control and optimization, as well as network congestion control, we turn our attention to merging primary and secondary frequency control for the power grid. We present modifications in the conventional generation control using a consensus design approach while considering the participation of controllable loads. The optimality, stability and delay robustness of the redesigned system are studied. Moreover, we extend the proposed control scheme to (i) networks with more complexity and (ii) the case where controllable loads are involved in the optimization. As a result, our controllers can balance power flow and drive the system to an economically optimal operating point in the steady state. We then study a real-time control framework that merges primary, secondary and tertiary frequency control in power systems. In particular, we consider a transmission level network with tree topology. A distributed dynamic feedback controller is designed via a primal-dual decomposition approach and the stability of the overall system is studied. In addition, we introduce extra dynamics to improve system performance and emphasize the trade-off when choosing the gains of the extra dynamics. As a result, the proposed controller can balance supply and demand in the presence of disturbances, and achieve optimal power flow in the steady state. Furthermore, after introducing the extra dynamics, the transient performance of the system significantly improves. A redesign framework for network congestion control is developed next. Motivated by the augmented Lagrangian method, we introduce extra terms to the Lagrangian, which is used to redesign the primal-dual, primal and dual algorithms. We investigate how the gains resulting from the extra dynamics influence the stability
PREMO: an emerging standard for multimedia presentation

ISO/IEC JTC1/SC24 are developing a standard for the presentation of multimedia objects, called PREMO (Presentation Environments for Multimedia Objects). PREMO is aimed at application developers who want to include multimedia effects into the applications, but do not want to restrict themselves to model of multimedia documents, which is prevalent in multimedia applications today. This report gives an overview of the current status of PREMO.
An Efficient Heuristic for the Minimum Connected Dominating Set Problem on Ad Hoc Wireless Networks

Connected dominating set (CDS) problem in unit disk graph has signi£cant impact on an ef£cient design of routing protocols in wireless sensor networks, where the searching space for a route is reduced to nodes in the set. A set is dominating if all the nodes in the system are either in the set or neighbors of nodes in the set. In this paper, a simple and ef£cient heuristic method is proposed for £nding a minimum connected dominating set (MCDS) in ad hoc wireless networks based on the new parameter support of vertices. With this parameter the proposed heuristic approach effectively £nds the MCDS of a graph. Extensive computational experiments show that the proposed approach outperforms the recently proposed heuristics found in the literature for the MCDS. Keywords—ad hoc wireless networks, dominating sets, unit disk graphs, heuristic.
Optimizing Smith-Waterman for the Cell Broadband Engine

Motivation: As new processors become available, the SingleInstruction Multiple-Data Smith-Waterman implementations need to be adapted to the processors instruction set to get maximum performance. One recent processor, the Cell Broadband Engine has eight independent vector processors. To take advantage of the Cell’s vector engines, the implementation needs to take into account the limited resources of the vector engine and the limits of the instruction set. Results: The adapted Smith-Waterman implementation running on a single 3.2 GHz Cell Broadband Engine achieved speeds of >16 billion cell update per second with the ability to handle sequences of 32K residues. Availability: http://farrar.michael. googlepages.com/striped.tgz
Effect of Memory Load on Visual Search

Visual search functions require visual attention for the detection of particular stimuli from large amounts of information. In a state of storing a target stimulus, visual search is performed to determine whether the stimulus in front of the eye is a target stimulus. Therefore, the number of stimuli to remember is believed to affect performance of the task. This study examined the effects of memory load on visual search using the cancellation task. Participants were 28 healthy adults. Participants performed six type cancellation tasks consisting of the number of target types: 1-6. Results showed that canceling efficiency became lower as the error increased and that the time to completion is prolonged by an increase in the number of target types. The effects of increasing the memory load on the visual search showed strong correlation in the logarithmic function, which suggests that when the target types are few, the effect of the number of target types on the task performance is strong, but for numerous target types, the effect of the number of target types on the task performance is weak.
Feature Selection using Random Forest Method for Sentiment Analysis

Background/Objectives: Online review has become important decision support system for the customers to decide on the subscription or purchse. This paper is aiming to suggest a method that improves the accuracy of the classifier. Methods/Statistical analysis: Feature selection for sentiment analysis using decision forest method and Principal Component Analysis (PCA) is used for the feature reduction. The proposed method is evaluated using twitter data set. Findings: It is proved, that the proposed decision forest based feature extraction improves the precision of the classifiers in the range of 12.49% to 62.5% when compared to PCA and by 49.5% to 62.5% when compared to decision tree based feature selection. Application/Improvements: This method is applicable to product reviews, emotion detection, Knowledge transformation, and predictive analytics.
Proceedings of the EACL 2003 Workshop on Evaluation Initiatives in Natural Language Processing: are evaluation methods, metrics and resources reusable?

Systems that accomplish different Natural Language Processing (NLP) tasks have different characteristics and therefore, it would seem, different requirements for evaluation. However, are there common features in evaluation methods used in various language technologies? Could the evaluation methods established for one type of systems be ported/adapted to another NLP research area? Could automatic evaluation metrics be ported? For instance, could Papineni's MT evaluation metric be used for the evaluation of generated summaries? Could the extrinsic evaluation method used within SUMMAC be applied to the evaluation of Natural Language Generation systems? What are the reusability obstacles encountered and how could they be overcome? What are the evaluation needs of system types such as dialogue systems, which have been less strenuously evaluated till now, and how could they benefit from current practices in evaluating Language Engineering technologies? What are the evaluation challenges that emerge from systems that integrate a number of different language processing functions (e.g. multimodal dialogue systems such as Smartkom)? Could resources (e.g. corpora) used for a specific NLP task, be reused for the evaluation of an NLP system and if so, what adaptations would this require?
Advances in Automatic Text Summarization

From the Publisher: Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user or task. "Until now there has been no state-of-the-art collection of the most important writings in automatic text summarization. This book presents the key developments in the field in an integrated framework and suggests future research areas. The book is organized into six sections. Classical Approaches, Corpus-Based Approaches, Exploiting Discourse Structure, Knowledge-Rich Approaches, Evaluation Methods, and New Summarization Problem Areas.