Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Mathematical Framework for the Analysis of Neural Networks

Over the past decade, Deep Neural Networks (DNNs) have become very popular models for processing large amounts of data because of their successful application in a wide variety of fields. These models are layered, often containing parametrized linear and non-linear transformations at each layer in the network. At this point, however, we do not rigorously understand why DNNs are so effective. In this thesis, we explore one way to approach this problem: we develop a generic mathematical framework for representing neural networks, and demonstrate how this framework can be used to represent specific neural network architectures. In chapter 1, we start by exploring mathematical contributions to neural networks. We can rigorously explain some properties of DNNs, but these results fail to fully describe the mechanics of a generic neural network. We also note that most approaches to describing neural networks rely upon breaking down the parameters and inputs into scalars, as opposed to referencing their underlying vector spaces, which adds some awkwardness into their analysis. Our framework strictly operates over these spaces, affording a more natural description of DNNs once the mathematical objects that we use are well-defined and understood. We then develop the generic framework in chapter 3. We are able to describe an algorithm for calculating one step of gradient descent directly over the inner product space in which the parameters are defined. Also, we can represent the error backpropagation step in a concise and compact form. Besides a standard squared loss or cross-entropy loss, we also demonstrate that our framework, including gradient calculation, extends to a more complex loss function involving the first derivative of the network. After developing the generic framework, we apply it to three specific network examples in chapter 4. We start with the Multilayer Perceptron (MLP), the simplest type of DNN, and show how to generate a gradient descent step for it. We then represent the Convolutional Neural Network (CNN), which contains more complicated input spaces, parameter spaces, and transformations at each layer. The CNN, however, still fits into the generic framework. The last structure that we consider is the Deep Auto-Encoder (DAE), which has parameters that are not completely independent at each layer. We are able to extend the generic framework to handle this case as well. In chapter 5, we use some of the results from the previous chapters to develop a framework for Recurrent Neural Networks (RNNs), the sequence-parsing DNN architecture. The parameters are shared across all layers of the network, and thus we require some additional machinery to describe RNNs. We describe a generic RNN first, and then the specific case of the vanilla RNN. We again compute gradients directly over inner product spaces.
The Influence of Positive Affect on Students Motivation in both Sport and Academic Achievement

Objective: positive emotion causes to pleasure and increases the potential enjoyable activities because it is believed that positive emotion can influence the job motivation, as more important duties have more motivation. This research studies the effectiveness of positive affect induction on the students' motivation in sport and the role of increasing this motivation on Tehran high school students' academic achievements. Method: In this study the population included all Tehran high schools. Harter's classroom affect and motivational scale and the Sport Motivation Scale (SMS) were used as the instruments. Generally, 4 groups of students have been chosen. In 3 groups of positive affect induction, the first group watched a comic movie, the second group received a gift and the third group listened to a joke, while the control group didn't receive any change. Again the students' academic achievement motivation and their motivation in sport were tested. Results: the results showed that positive affect induction had increased the boys' academic achievement motivation significantly but had no effect on the girls'. Conclusions: This study showed that when the motivation in sport increases the academic achievement motivation increases as well.
Do Demographic Variables Affect Organizational Role Stress

The study explores the impact of demographic variables on the organizational role stress on the bank employees. The questionnaire employed in this study is organizational role stress scale developed by Prof. Udai Pareek (1983) where gender, age, education number of family members and income are independent variables and organizational role stress is dependent variable. The sample for the study consists of 325 bank employees from public and private sector banks. Simple random sampling is used as a sampling technique. Data was analyzed using multiple regressions. The results revealed that age, education and income significantly impact the organizational role stress on the bank employees. Keywords: Demographic variables, organizational role stress, bank employees
How can materialism affect the intention to create brand-related user-generated content about style on social media sites?

This study examines how materialism can affect the intention to create brand-related user-generated content about style on Facebook and blogs. It contributes to the understanding of how personality traits explain the behaviour of brand-related user-generated content creation on social media sites by providing results of the mechanism and condition that can activate highly materialistic consumers to create brand-related user-generated content about style. This research used content analysis and online experiments.
MyoSpat: A hand-gesture controlled system for sound and light projections manipulation

We present MyoSpat, an interactive system that enables performers to control sound and light projections through hand-gestures. MyoSpat is designed and developed using the Myo armband as an input device and Pure Data (Pd) as an audio-visual engine. The system is built upon human- computer interaction (HCI) principles; specifically, tan- gible computing and embodied, sonic and music interac- tion design (MiXD). This paper covers a description of the system and its audio-visual feedback design. Finally, we evaluate the system and its potential use in exploring em- bodied, sonic and music interaction principles in different multimedia contexts.
Detection of Brain Tumour by Image Fusion using SVM Classifier

Tumor is defined as the abnormal increases of the tissues. Brain Tumor is an abnormal mass of tissue in which cells get increase in size and multiply uncontrollably apparently unchecked by the mechanisms that control normal cells. The proposed system is going to detect this brain tumor of a particular person. That is done by fusion of output of segmented image and input image. Image fusion is a process of combining complementary information from two images of the same patient into an image. The resultant image consists of more informative the usual images alone. The images are pre-processed for feature extraction and data analysis of image is done based on Histogram feature for localizing the tumor. The morphological operations like dilation and erosion are applied on the image for image segmentation using SVM classifier. After this step the output image obtained after segmentation is fused with the input image for knowing the exact position of the tumor in the brain. This technique is used for detection of Brain Tumor. Keywords: convolution filter, svm classifier segmentation, image selection.
Challenges and Future Vision to Improve Nursing Practice- A Systematic Review

Background: Quality nursing care improves health outcomes, but quality nursing care cannot be provided without good vision. The aim: for this review was to identifying the future nursing vision along with challenges and how to overcome them Methodology: It was a literature review in which meta-analysis were utilized. A total of 181 articles related to the review title were accessed. After secondary reviewing, only thirty-three studies were included in the systematic review and metaanalysis during the period from November, 2018 up to February, 2019. The databases that used were PubMed, Google Scholar, SAGE and Academic Search Complete Magazines and journals (EBSCO). Results: The most cited future nursing vision was to improve health globally by raising the profile and status of nurses worldwide. For achieving this vision, there were some major challenges should be overcome. These challenges and obstacles had been categorized into six major groups which were financial, educational and training, clinical and working environment, technological, social and environmental determinants and multi-sectoral approach.
Did You Feel That? Developing Novel Multimodal Alarms for High Consequence Clinical Environments

Additionally, loudness of the alarms can cause ”alarm fatigue”, which is a phenomenon of diminished response due to desensitization of the practitioners [3]. This problem is exacerbated by the fact that free-field audible alarms are not informative, nonlocalizable, and presented to everyone in the room. When the fatigue is severe, it can increase clinician error, potentially leading to adverse patient outcomes. To cope with these problems, we are interested in the possibility of leveraging multimodality to improve the information transfer capacity of alarms. Through this approach to alarm management, we anticipate a reduction in overall sound exposure level in the clinical environment and decreased distractions, as well as a possibility of implementing personalized alarms. An extensive volume of previous literature has described the effects of multisensory integration, in particular involving the auditory and visual modalities. These include evidence for both complementary and inhibitory effects of the combination [4]. However, visual attention is directional, and in the case of a clinical environment, one cannot assume that a visual signal would be attended to by the health-care provider. In contrast, haptic feedback can be provided anywhere, any time, irrespective of current activity, and offers the additional benefit that it can be delivered selectively to the clinician(s) for whom it is relevant. To investigate the possibility of leveraging the audio-haptic modality in this manner, we conducted an experiment to determine the degree to which haptic stimuli can complement audible alarms. To quantify the accuracy to such cues, we compared unisensory auditory and multisensory auditory-haptic stimuli. The results of our experiments did not demonstrate a facilitative effect as expected. However, analyzing them raised important questions regarding fatigue and habituation to vibrotactile stimuli, potential interference of sensory streams, potential benefits of speech over non-speech auditory stimuli, and if supra-threshold stimuli can still be weakly effective as a unisensory stream to contribute to multisensory gain.
Age differences in attitudes about older adults with dementia

Abstract Dementia, a term that describes a variety of brain conditions marked by gradual, persistent and progressive cognitive decline, affects a significant proportion of older adults. Older adults with dementia are sometimes perceived less favourably than those without dementia. Furthermore, compared to persons without dementia, those with dementia are often perceived by others as having reduced personhood. This study was aimed at investigating whether differences in attitudes towards dementia and personhood perceptions vary as a function of age group, care-giver status, attitudes towards ageing, dementia knowledge, gender and education. In total 196 younger, middle-aged and older adults were recruited. Findings revealed that being a care-giver as well as having less ageist attitudes were predictive of being more comfortable around persons with dementia, having more knowledge about dementia and ascribing greater personhood to people with dementia. Those with more dementia knowledge (prior to the study) were less comfortable around people with dementia. Finally, when controlling this prior dementia knowledge, older adults were more comfortable around people with dementia compared to younger and middle-aged adults. Gender and education were not associated with any of the variables under study. Findings contribute to a better understanding of the role of age- and care-giver-related factors in the determination of attitudes towards dementia.
Fast Trees for Options with Discrete Dividends

The binomial model is a workhorse for valuing options with early exercise possibilities, including options with American or Bermudan exercise and barrier options. The procedure is straightforward when the underlying asset does not pay dividends, and it can be readily adapted to a payout at a fixed proportional rate. But discrete dividends cause trouble because they make the tree “splinter” rather than recombining at each time step. If the tree recombines, an up move followed by a down move produces the same asset price as a down move followed by an up move. But if the intermediate date corresponds to an ex-dividend date and the same fixed amount is subtracted from the up and down nodes, the tree no longer recombines at the subsequent step. The number of nodes in a non-recombining tree increases at a geometric rate, quickly leading to unreasonably long execution times. A number of alternative procedures have been developed to deal with this problem. In this article, Areal and Rodrigues adopt a new approach, accepting the splintering of the binomial lattice but then applying several techniques to accelerate the calculations. They show that their procedures are faster and more accurate than the current methods that force the tree to recombine despite discrete dividend payouts.
Cutting Tetrahedra Affordances and Limitations of Using Virtual Reality Visualization for Tetrahedral Cutting

Finite element method researchers aim to create algorithms for optimally cutting complex 3D shapes into several tetra-hedra for computational eﬃciency in simulations [5]. It is diﬃcult to create a mental representation of the 3D shapes as they increase in complexity. Our hypothesis is that a virtual reality (VR) visualization could help with creating this mental 3D representation. In this thesis, a desktop tool using a 2D monitor and a virtual reality tool were compared in a controlled within-subject user study. The ﬁndings show that VR gave a better understanding of the 3D objects. Participants reported VR to be more intuitive and enjoyable than the 2D monitor. However, it did not improve the time it took to complete the study tasks and, although it made the users perceive that they were more accurate, the observational data suggests that more accurate cuts were made using the desktop tool.
A Two-Stage Pilot-Assisted CFO Estimation Scheme for OFDM Signals

In this paper, a pilot-assisted two-stage carrier frequency offset (CFO) estimation algorithm is proposed for OFDM signals. The proposed scheme performs a coarse search over a window of possible carrier offsets to obtain an initial estimate of the CFO. Then, a fine search is carried over a much smaller search window to obtain better accuracy of the carrier frequency offset. The pilot signal used in this algorithm results in a slowly varying correlation function with peaks at the correct offset. This is exploited by the proposed algorithm to reduce the search window for the second stage. Simulation results presented in this paper show that a significant improvement in the mean-square error performance is achieved by using this two-stage approach without increasing the complexity of implementation compared to single-stage conventional algorithms. It is also demonstrated that the proposed scheme outperforms the conventional cyclic-prefix based CFO estimation algorithm under both flat and frequency-selective Rayleigh fading channel conditions. Keywords-Carrier Frequency Offset Estimation, OFDM Synchronization; Multi-stage Search.
OBTAINING HIGH ACCURACY VIBRATION MEASUREMENTS WITH LOW-COST SENSORS USING BAYESIAN VIRTUAL SENSING

Vibration measurements are utilized in many applications, e.g. structural health monitoring, vibration control, fatigue assessment, system identification, and model updating. Requirements for data accuracy are often high, which can be obtained by using high-quality sensors. However, wireless sensor networks often consist of a large number of low-cost sensor nodes, the accuracy of which may not fulfill the strict requirements of the applications. The possibility to replace high-quality vibration sensors with a larger number of low-cost sensors is studied. In order to achieve the required accuracy with low-cost sensors, empirical virtual sensing is introduced, which uses hardware redundancy for estimation. In empirical virtual sensing, the signal of a single sensor can be estimated from the data acquired by the whole sensor network. A Bayesian virtual sensor is derived, resulting in a posterior mean that is more accurate than the actual measurement provided the sensor noise is known. Also optimal sensor placement is studied to minimize the number of sensors. Numerical simulations are performed for a structure subject to unknown random excitation. Noisy response is measured and the accuracy of virtual sensors is evaluated. Given the original reference sensor network with a small number of high-quality sensors, it is possible to determine the number of low-cost sensors needed to achieve the same accuracy. From this result, the cost-effectiveness can be assessed. 16 Available online at www.eccomasproceedia. org Eccomas Proceedia UNCECOMP (2017) 16-31 ©2017 The Authors. Published by Eccomas Proceedia. Peer-review under responsibility of the organizing committee of UNCECOMP 2017. doi: 10.7712/120217.5348. 16778
Journal of University Teaching & Learning Practice Journal of University Teaching & Learning Practice

Abstract Likert scales traditionally used in student evaluations of teaching (SET) suffer from several shortcomings, including psychometric deficiencies or ambiguity problems in the interpretation of the results. Assessment instruments with Behavioral Anchored Rating Scales (BARS) offer an alternative to Likert-type questionnaires. This paper describes the construction of an appraisal tool with BARS generated with the participation of 974 students and 15 teachers. The resulting instrument eliminates ambiguity in the interpretation of results and gives objectivity to the evaluation due to the use of unequivocal behavioral examples in the final scale. However, BARS methodology presents the problem of losing behavioral information during scale construction. The BARS methodology presented by the authors introduces an additional step to the traditional procedure, which significantly reduces the loss of information during the scale construction. The authors conclude that the qualitative approach of the proposed instrument facilitates the application of the formative function of the evaluation.
Internal sensation of pleasure can be explained as a specific conformation of semblance: Inference from electrophysiological findings

Semblance hypothesis was able to find a solution for the generation of first- person internal sensation of memory along with provisions for behavioral motor actions. The derived inter-postsynaptic functional LINK (IPL) mechanism was able to explain a large number of findings from different levels of the system ranging from perception to sleep. It was possible to explain long-term potentiation (LTP) as the effect of experimental scaling-up of the changes occurring during natural learning. By keeping the latter relationship as a baseline, it was possible to explain long-term depression (LTD) observed in the nucleus accumbens (NAc), a scaled-up change of a mechanism responsible for inducing internal sensation of pleasure. This mechanism provides inter-connectable explanations for the attenuation of postsynaptic potentials, reduced ring of medium spiny neurons and the finding that LTD induced by stimulation of one pathway to NAc occludes the LTD induction by another pathway.
A Determination of an Optimal Clustering Method Based on Data Characteristics

Clustering is a method that collects data objects into groups based on their similary. Performance of the state-of-the-art clustering methods is different according to the data characteristics. There have been numerous studies that performed experiments to compare the accuracy of the state-of-the-art clustering methods by applying various kinds of datasets. A common problem of these studies is that they only consider clustering algorithms that yield the most accurate results for a particular dataset. They do not consider what factors affect the execution time of each clustering method and how they are affected. Nevertheless, execution time is an important factor in clustering performance if there is no significant difference in accuracy. In order to solve the problems of the existing research, through a series of experiments using various types of datasets, we compare the accuracy of four representative clustering methods. In addition, we perform practical clustering performance comparisons by deriving time complexity and identifying factors that influences to its performance.
Assessment of Childhood Social Phobia: Construct, Convergent, and Discriminative Validity of the Social Phobia and Anxiety Inventory for Children (SPAI —C)

The Social Phobia and Anxiety Inventory for Children (SPAI—C) has shown initial promise to assess childhood social fears. In this study, convergent validity was determined by comparing scores on the SPAI—C to daily diary ratings of social distress. Social phobic children scored significantly higher on the SPAI—C, reported more distressing events, and rated these events as more distressful when compared with normal peers. A positive relationship was found between SPAI—C scores and daily diary ratings. Discriminative validity was determined by comparing SPAI—C scores of social phobic children with normal controls and children with externalizing disorders. The SPAI—C successfully differentiated children with social phobia from those with externalizing disorders or no disorder. Finally, internal consistency and factor analytic outcome data are presented. Results are discussed in terms of the SPAI—C's usefulness as a clinical and research tool.