Miracle-dz/UniFAR-FTUs huggingface dataset:
Man in the Mirror: A Mythology-Driven Exploration of Multiple User-Interpretations in a Multimedia Space

Artists, designers and writers have long employed ambiguity as a tool in compelling their audience to deduce a personal meaning to their work. As computing becomes less of a strictly workspace, task-oriented phenomenon and more of a ubiquitous, life-space one, it is increasingly important to consider the intelligence of the user in the design of everyday computer-based things. Support of multiple user interpretation through ambiguity is an element whose appropriate inclusion in system design can compel the user to deduce a personal interpretation of the system’s meaning and utility. The work in this paper explores the process by which users may come to deduce a meaning to an ambiguous work, both as individuals and collaboratively. Incorporating elements of ambiguity, we created SenSpace, an immersive physical environment that embeds the Greek myth of Narcissus within itself. The subsequent user study provided insight on the process by which naive visitors may come to deduce their meanings of a work, both individually and collaboratively. Our results showed that there exists a tradeoff between a user’s level of interaction and depth of the interpretation of the multimedia environment. We also show how ambiguity can be used as a design method, by incorporating observed user expectations into the system. This paper uses experimental evidence to advocate the design of systems that support not only the system goal the designer has in mind, but also the multiple perspectives and meanings that the user often brings to the system.
Learning embeddings: efficient algorithms and applications

Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and one scenario in efficiently estimating an empirical expectation. We present novel algorithmic solutions and demonstrate their applications on a wide range of data-sets. The first scenario deals with learning from small data with large number of classes. This setting is common in computer vision problems such as person re-identification and face verification. To address this problem we present a new algorithm called Weighted Approximate Rank Component Analysis (WARCA), which is scalable, robust, non-linear and is independent of the number of classes. We empirically demonstrate the performance of our algorithm on 9 standard person re-identification data-sets where we obtain state of the art performance in terms of accuracy as well as computational speed. The second scenario we consider is learning embeddings from sequences. When it comes to learning from sequences, recurrent neural networks have proved to be an effective algorithm. However there are many problems with existing recurrent neural networks which makes them data hungry (high sample complexity) and difficult to train. We present a new recurrent neural network called Kronecker Recurrent Units (KRU), which addresses the issues of existing recurrent neural networks through Kronecker matrices. We show its performance on 7 applications, ranging from problems in computer vision, language modeling, music modeling and speech recognition. Most of the machine learning algorithms are formulated as minimizing an empirical expectation over a finite collection of samples. In this thesis we also investigate the problem of efficiently estimating a weighted average over large data-sets. We present a new data-structure called Importance Sampling Tree (IST), which permits fast estimation of weighted average without looking at all the samples. We show successfully the evaluation of our data-structure in the training of neural networks in order to efficiently find informative samples.
MODAL ANALYSIS AND TESTING OF MODIFIED RE-ENTRANT HEXAGONAL HONEYCOMB CORES

In the present work, the vibrational behaviors of cellular auxetic honeycomb cores were investigated. Classical hexagonal re-entrant honeycomb structure and two newly developed designs namely, splined and stiffened re-entrant honeycomb structures were used in this investigation. The newly developed structures were found to have in-plane negative Poisson’s ratios with in-plane stiffness higher than that of classical re-entrant hexagonal structure. Finite elements models were developed for the three structures to obtain the mechanical properties and the natural frequencies. The finite elements results showed that the honeycombs exhibit different modes of vibrations namely, bending, torsional and axial modes. It was found that the natural frequencies in bending decreases with increase of the rib length. This behavior was directly linked to the increase of the in-lane relative density and stiffness of the honeycomb. Experiments on the honeycomb cores of the three structures were carried out to obtain the cores natural frequencies and their respective mode shapes. The finite element results were verified with the experimental results for various geometrical parameters and good agreement between the results was obtained.
Fast spectral multiplication for real-time rendering

In computer graphics, the complex phenomenon of color appearance, involving the interaction of light, matter and the human visual system, is modeled by the multiplication of RGB triplets assigned to lights and materials. This efficient heuristic produces plausible images because the triplets assigned to materials usually function as color specifications. To predict color, spectral rendering is required, but the O(n) cost of computing reflections with n-dimensional point-sampled spectra is prohibitive for real-time rendering. Typical spectra are well approximated by m-dimensional linear models, where m n, but computing reflections with this representation requires O(m) matrixvector multiplication. A method by Drew and Finlayson [JOSA A 20, 7 (2003), 1181-1193], reduces this cost to O(m) by “sharpening” an n ×m orthonormal basis with a linear transformation, so that the new basis vectors are approximately disjoint. If successful, this transformation allows approximated reflections to be computed as the products of coefficients of lights and materials. Finding the m×m change of basis iv matrix requires solving m eigenvector problems, each needing a choice of wavelengths in which to sharpen the corresponding basis vector. These choices, however, are themselves an optimization problem left unaddressed by the method’s authors. Instead, we pose a single problem, expressing the total approximation error incurred across all wavelengths as the sum of d · m squares for some number d, where, depending on the inherent dimensionality of the rendered reflectance spectra, m ≤ d n, a number that is independent of the number of approximated reflections. This problem may be solved in real time, or nearly, using standard nonlinear optimization algorithms. Results using a variety of reflectance spectra and three standard illuminants yield errors at or close to the best lower bound attained by projection onto the leading m characteristic vectors of the approximated reflections. Measured as CIEDE2000 color differences, a heuristic proxy for image difference, these errors can be made small enough to be likely imperceptible using values of 4 ≤ m ≤ 9. An examination of this problem reveals a hierarchy of simpler, more quickly solved subproblems whose solutions yield, in the typical case, increasingly inaccurate approximations. Analysis of this hierarchy explains why, in general, the lowest approximation error is not attained by simple spectral sharpening, the smallest of these subproblems, unless the spectral power distributions of all light sources in a scene are sufficiently close to constant functions. Using the methods described in this dissertation, spectra can be rendered in real time as the products of m-dimensional vectors of sharp basis coefficients at a cost that is, in a typical application, a negligible fraction above the cost of RGB rendering.
Reliability Study in Despiking of ADV Data to Open Channel Flow

The major problem in collection of data from ADV is generation of spikes due to misidentification of the frequency of Doppler signals, this occurs when the phase difference between outgoing and incoming pulse lies outside the range of -180° to 180°. This shift is caused when the velocity of flow exceeds the pre-set velocity range or when there is contamination from previous pulses reflected from the surface of complex geometrics. For despiking study, different methods are used such as phase space thresholding (PST) method, RC filter (RCF) method, Tukey 53H method (T53H), acceleration thresholding (AT) method, wavelet thresholding (WT) method. Phase space thresholding method uses the concept of three dimensional Poincare maps in which the variables and their derivatives are plotted against each other to determine the spikes. RC filter is a low pass filter used to detect the spikes. Tukey 53H method uses the principle that a median is robust estimator of the mean to generate a smooth time sequence that can be subtracted from original signal. The acceleration threshold method is detection and replacement with two phases one for negative acceleration and other for positive acceleration. WT Method is similar to the previous method except that all the calculations are done in wavelet phase. A review paper is made on the reliability of the most popular methods that are WT, PST and AT method and finally conclusion is prepared showing the most reliable method for ADV despiking
Off-line mask-to-mask registration characterization as enabler for computational overlay

After the introduction of multi-patterning techniques like multiple Litho-Etch (LEn ) steps and/or Spacer Assisted Double/Quadruple Patterning (SADP/SAQP), the amount of masks required to produce a semiconductor device has increased significantly. The main reason was that a functional layer could no longer be exposed in one single litho step due to the elevated pitch requirements. Consequently, the required pattern had to be split-up and divided over multiple masks. One can imagine that this has put a huge constraint on the mask-to-mask on-product overlay requirements and control. It was already shown before that for the LE2 use-case the mask-to-mask contribution is the second largest contributor (after the scanner) to the overall on-product overlay. In order to keep the on-product overlay within specification over time, the number of on-wafer overlay metrology steps inside the fab increased even more. Since more masks are used per layer, multiple combinations are now possible to measure and control both the intra-layer as well as the inter-layer overlay. As a consequence, the increasing number of metrology steps has resulted in a negative impact on the overall wafer/lot cycle time in the fab. It would be beneficial to fully characterize the mask-to-mask overlay off-line and apply computational overlay techniques to compute the on-wafer overlay. This enables smart metrology sampling to address and reduce the overall wafer/lot cycle time inside the fab. In this work, we performed a correlation study between off-line mask-to-mask registration metrology and on-wafer measurements. The off-line overlay measurements were performed on a PROVE® tool while the exposures and scanner readouts were executed on an ASML TWINSCAN™. Two ASML qualification (BaseLiner) masks were used for this purpose. Extensive off-line registration measurements were performed on both reticles including the reticle alignment marks as well as the image field metrology features (gratings). We show an excellent correlation between the measurements on the PROVE® tool and the on-wafer results reaching R2 < 0.96 with an accuracy of 0.58-nm. The accuracy is determined by the reticle alignment accuracy on the scanner and the quality of the masks. We have identified the underlying contributors to the error budget to enable a further improvement of the correlation between the mask-tomask and the on-wafer overlay. Since the results of this first investigation were so promising, the effect of a pellicle mounted on one of the masks was studied as well. The off-line mask-to-mask registration metrology was repeated and the resulting computational overlay has been compared with the on-wafer results.
The antecedents of consumers’ attitude and its consequences on online purchase intention

This study aims to explain the effects of the quantity of posts, views, and reviews (QPVR) of UGC videos shared on YouTube towards its credibility as a source and the usefulness of the information it shares, as well as how it influences consumers’ attitude towards UGC that ultimately leads to their online purchase intention. The population of this research is every female consumer residing in Denpasar that has seen cosmetics video reviews on YouTube. A sample of 100 respondents was collected through online questionnaires. The data obtained were tested using PLS (Partial Least Square) analysis. The results showed that information usefulness of a UGC video had a positive and significant effect on consumers’ attitude towards UGC, thus resulting in their online purchase intention. Furthermore, it showed that QPVR had a positive and significant effect on both credibility and information usefulness of the cosmetics product content generated by users on YouTube. However, UGC’s credibility does not prove to have a significant effect on consumer’s attitude towards UGC due to lack of trust and dependability.
Towards Fully Data-Driven Tracking of Pedestrians and Vehicles in Videos

Accurately localizing objects in images and videos is a cornerstone of Computer Vision, with a vast number of practical applications. Common approaches in videos typically localize objects in frames and link them across frames. Many works base this linking on hand-designed features, heuristics and hard coded constraints that are suboptimal. In contrast, we learn deep matching models and motion patterns, moving towards a completely data-driven tracking framework. This dissertation has three contributions in the tracking pipeline. First, we aim to speed up object detection, the first step in the tracking pipeline. We introduce a novel algorithm for generic object detection based on Prim’s algorithm. Our algorithm produces a small set of object proposals that are passed down the object detection pipeline. It yields high detection rates and windowproposals that tightly fit objects, without the need class-specific information. Secondly, we study how to track objects from detections for the application of monitoring traffic in surveillance cameras. We observe that motion can be a helpful cue for tracking in these scenes with structured motion patterns. We show that we can use a small set of scene-specific trajectory annotations and unsupervised event recognition to learn how vehicles move and in which regions of the image relying on appearance is not sufficient. We further show that we can leverage such single-target tracking methods to link detections in a tracking-by-detection paradigm. The third and final contribution addresses the more general scenario of tracking pedestrians in street-level moving cameras. In line with our data-driven tracking goal, we argue that more training data is necessary for such scenarios and propose a new way to efficiently annotate trajectories in videos based on weak path annotations. We use our framework to annotate a large-scale dataset and show that a quantity-over-quality annotation strategy is ideal to learn rich detection-matching deep models. We hope that our work spurs research towards a fully data-driven tracking pipeline.
Influence of Electrode Distance on Electrical Energy Production of Microbial Fuel Cell using Tapioca Wastewater

Microbial fuel cell is one alternative technology that can be used to simultaneously solve problems related with wastewater production and energy demand. This study investigates the influence of electrode distance on power density in microbial fuel cell using tapioca wastewater. Graphite sheet without metal catalyst was used for both electrodes, separated by Nafion membranes. Four variations of electrode distance were used. MFC with highest electrode distance give the highest equilibrium OCV (676 mV), while the MFC with shortest electrode distance give the highest power density (7.74 mW/m2). EIS measurement suggested that the charge transfer resistance is dominant in all MFC configuration. Wastewater COD removal were in the range of 35-46 %, which were in accordance with the power density for all MFC.
Effect of long-term sponsorship of the Škoda Auto brand and international sport events on congruence of brand personality

The research examines the influence of long-term sponsorship on congruence (perceived consensus) of personal characteristics between Škoda Auto and sponsored international sport events such as the Ice Hockey World Championship and Tour de France. To identify personalities of these three subjects’ brands we used a standardised personality scale created by Geuens, Weijters and De Wulf in 2009, which was developed to measure brands of various product categories. To measure personality congruence we used the absolute differentiation method (Gwinner & Eaton, 1999). Data were collected via online questionnaires in January and February 2015. The research file formed a quote sample of 250 Prague respondents. Personal characteristics were evaluated via a seven-point Likert’s scale expressing a degree of agreement or disagreement. The results show that the Škoda Auto brand is mostly characterized by the Responsibility dimension, whereas the brands of international sport events such as the Ice Hockey World Championship and Tour de France are mostly characterized by the Activity dimension. Respondents marked Aggresivity and Emotionality as undescriptive dimensions of Škoda Auto, and Simplicity and Emotionality as undescriptive dimensions of international sport events.
Physics Teacher Efforts of Islamic High School in Yogyakarta to Minimize Students' Anxiety When Facing the Assessment of Physics Learning Outcomes

This study aims to determine the anxiety experienced by Islamic high school students in Yogyakarta and find out the efforts of Islamic high school physics teachers in Yogyakarta in minimizing the anxiety that occurs in their students when facing assessment of physics learning outcomes. This study uses qualitative research methods and descriptive approaches. The subjects of this study were Islamic high school physics teachers and Islamic Senior High School students of class X natural science until the XII natural science class in Yogyakarta. The subject of this study was determined using purposive sampling method. The results of this study indicate that the anxiety experienced by Islamic high school students in Yogyakarta when facing the assessment of physics learning outcomes in the form of a lack of mastery of the concept of learning material. Therefore, the efforts made by Islamic high school physics teachers in Yogyakarta to minimize the anxiety experienced by their students when facing assessment of physics learning outcomes is by conveying the concept of physics material that is clear and easy to understand, provides regular practice questions, provides motivation, and applying learning using existing technology. Therefore, it is expected that teachers can minimize students 'anxiety when facing exams so that students' talents in the academic field are good.
Automated recognition of thinking orders in secondary school student writings

ABSTRACT Despite the rapid development in the area of learning analytics (LA), there is comparatively little focused towards the secondary level of education. This ongoing work presents the latest developed function of Wikiglass, an LA tool designed for automatically recognising, aggregating, and visualising levels of thinking orders in student collaborative writing. Three levels of thinking orders were defined based on frameworks in writing assessment and an adapted Bloom’s taxonomy of cognitive domains. Text categorisation models were constructed and evaluated using machine learning and natural language processing techniques. The best performing model was then integrated into Wikiglass, with visualisations in different modes and scopes (i.e., class, group, individual). Currently being used in classrooms, Wikiglass is expected to assist teachers in identifying at-risk individuals and groups, refining assessment rubrics, and selecting example sentences as teaching materials, as well as to facilitate students in self-monitoring and reflection.
Group based fault-tolerant physical intrusion detection system using fuzzy based distributed RSSI processing

We propose a group based real-time fault-tolerant physical intrusion detection system in an indoor scenario using Received Signal Strength Indicator (RSSI), to enhance security in wireless sensor networks considering its importance. Since there are a lot of techniques available to solve this problem in an outdoor scenario, we focus our research for the indoor environment. We provide a unique and novel approach, by applying a set of Fuzzy Logic (FL) rules on our distributed protocol before merging the beliefs of the fuzzy membership classes using Transferable Belief Model (TBM). Even though other techniques that have been designed earlier provide a solution to this problem, almost all of the techniques depend on incorporating additional sensor hardware. In some cases, sensor technology is even combined with other technologies such as cameras, motion sensors, video camera, etc. This makes the solutions complex, expensive, and difficult to deploy. However, there are published works that address this problem by measuring the drop in the RSSI. At the same time, many of the published works show that RSSI is an unreliable and unstable metric. Hence, we carry out an exhaustive experimentation to identify the behavior of RSSI both indoors and outdoors. The unstable characteristic of RSSI is clearly evident from these results. But, we embrace the unreliability of RSSI by using an additional metric, Link Quality Indicator (LQI) as a filter to localize the node in a network. Our approach helps in obtaining a tighter bound on the number of possible distances that any given two i nodes are away from or to one another. Again, through experimental results, we observe a drastic reduction in the number of possible distances and show how RSSI and LQI can be used in combination for node localization. While, this reduced the number of possible distances, there were still numerous distances. Therefore, we propose a distributed protocol which employed Fuzzy Logic (FL) and Transferable Belief Model (TBM). FL aided in translating the distances in linguistic terms and in calculating the beliefs of each of the membership classes. On the other hand, TBM enabled us to merge these beliefs to arrive at an improved decision. This combination helped us to handle uncertainty with ease. Finally, we carry out a similar approach to the physical intrusion detection problem. So, we model a real-time fault-tolerant intrusion detection system. Our system solution relies only on RSSI and in a distributed manner. We do not depend on any special hardware. In fact, we try to remove the requirement of all but bare minimum hardware. The combination of FL and TBM provided immense advantages. Thus, we propose a readily applicable solution for physical intrusion detection in wireless sensor networks.