Reviewerly/exHarmony huggingface dataset:
Characterization and modeling of solar radiation on the ground, application to the estimate of solar potential available on the coast of Nouakchott

The purpose of this work is to evaluate four empirical models developed in literature such as Ghouard, Perrin Brichambaut, Bird and Hulstrom and Dependent model of the Link trouble factor. The validation of these different theoretical models was carried out using solar global radiation data collected on the Cheikh Zayed Nouakchott power plant. These measurements were carried out at a time step of 5 minutes during the period of one year. (April 1, 2015 to March 31, 2016). The results show that the Ghouard model presents notable performances compared to the other models, with a determination coefficient of 96.2%. This validation also shows that the solar radiation measured and that estimated by the different models have the same pace and have good performance with a coefficient of determination greater than 93%. The available solar potential estimation was carried out using the actual measurements collected on the Cheikh Zayed plant. The results obtained showed that the available solar potential is 5 kWh / m² / d.
Fingerspelling Recognition Using Histogram of Oriented Point Cloud Vectors from Depth Data

The high degree of freedom of hand movements produces a high variability of shapes and hand appearances that still challenges hand gesture recognition algorithms. This paper presents an approach to recognize sign language fingerspelling. Our approach, named histogram of oriented point cloud vectors (HOPC), is based on a new descriptor computed only from depth images. The segmented depth image is mapped into a 3D point cloud and divided into subspaces. In each subspace, 3D point vectors are mapped into their spherical coordinates around its centroid. Next, it is computed their orientations angles $$H_\varphi $$ and $$H_\theta $$ onto two cumulative histograms. Normalized histograms are concatenated to form the image descriptor and used to train a Support Vector Machine classifier (SVM). To assess the feasibility of our approach, we evaluated it on a public data-set of American Sign Language (ASL) composed of more than 60,000 images. Our experiments showed a recognition accuracy average of 99.46%, achieving the state of the art.
Adaptive Solution to Compress Deep Neural Networks for Resource-Constrained Devices

Author(s): Wang, Ruzhuo | Advisor(s): Kim, Hyoseung | Abstract: Prior works applied singular value decomposition and dropout compression methods for fully-connected structure, or pruned the feature map by magnitude of kernel weights for convolutional structure. They only focused on one specific DNN structure. Other than that, prior framework needed numerous time in the compressing process to ensure the least accuracy penalty and they could not take a compression ratio as an input. However, this thesis proposes an idea about combining the different compression methods to generate a unified approach that compresses the whole DNN model. Moreover, we successfully combine singular value decomposition and dropout to be a new method called SVD-based dropout which turns out to be a efficient method for compressing fully-connected structure. Plus, we create a framework called Adaptive-Surgery that can take user input compression ratio and automatically decide the compression parameter $\beta$ for each convolutional layer and fully-connected layer and compresses them based on $\beta$. The compressed models that generated by Adaptive-Surgery can be directly implemented on the Raspberry Pi 3 Model B, which has constrained processing resource. The experiment results show that target two DNN models (Alexnet and Cifar10-quick) can be compressed by Adaptive Surgery to three input compression ratios (43.75\%, 75\% and 93.75\%) with soft accuracy penalty. The experiment results also prove that compared with the models whose initial parameters are randomly chosen, those models that generated by Adaptive-Surgery have less accuracy penalty. More importantly, we can manually set the compression ratio for Adaptive-Surgery, which means in order to implement DNN models on resource constrained devices with specific requirements for real-time correctness, Adaptive-Surgery can compress the DNN models to meet the requirements. For the future work, Adaptive-Surgery could be evaluated on more than two DNN models to prove its performance. Although our SVD-based dropout have a great performance on compressing the fully-connected structure, it can be improved by having a better way to represent the bias matrices in the pruned model rather than ignore the influence of bias matrices. Also, if we can combine two different compression methods to generate a unified compression framework, researchers can try more combinations of different compression methods for compressing the DNN to achieve a unified approach. Last but not the least, in the evaluation of this thesis, we do the experiments to test for three compression ratios. However, since out framework can support arbitrary ratios, it will be interesting to try with more diverse set of compression ratios and analyze their results.
Protection of Information from Leakage by Technical Channels for Sources with Non-Range Distribution of Probability

Made reasoning of the information protection from leakage through technical channels for uneven distribution of source probabilities and set the negative sequences in case of its non-consideration.Shown relations of probability information security risk conditions with power at the input of intercept receiver and given requires analytical values, which allow to assess the risk relative to the current signal/noise ratio and for the given probability of the safety risk of the required values of the maximum allowable indicators, namely the throughput of the technical channel of leakage, the probability of correct reception with possible interception and the signal/noise ratio.These ratios differ from previously known by those that take into account the uneven distribution of the signs probability on output of the source of leakage.Ratios allow automated analysis of information security risk in real time with the use of modern information systems and technologies.
From Blurry Space to a Sharper Sky: Keeping Twenty-Three Years of Astronomical Data Alive

Author(s): Boscoe, Bernadette Marie | Advisor(s): Borgman, Christine L. | Abstract: In compute-heavy and data-driven scientific fields, digital data play a central role in the creation of knowledge. For science fields that rely on data that can only be observed once, the preservation of these data is crucial to analytic processes. Time-domain astronomy is one such field, involving celestial observations such as exploding supernovae and passing comets. This dissertation examines the data and code practices of a university-based astronomy research group who have managed to keep their data “alive” for twenty-three years with limited resources. Keeping data alive means both understanding the knowledge contained within and having the associated technologies operable. The human aspects necessary to keep data alive are equally as important as the technological elements. This three-year ethnographic research project examines the factors involved in analyzing, preserving, and curating astronomy data as these data wend their way through socio-technical, physical, and digital infrastructures that shape and are shaped by the knowledge contained within the data. Dissertation research took place at six field sites: the case study’s university, the observatory of the case study, the archive of the observatory, and three other astronomy archives. The findings of this study show two main ways that data are kept alive in astronomy: 1) Publicly: Data are curated and preserved with the intention to be made available via web interfaces, engendering relationships among stakeholders, and 2) Privately: Data are preserved by astronomy research groups at universities that continually reuse them and their associated code, in a collaborative way. Findings suggest how a better understanding of the relationships between public and private astronomy data can inform scientific data preservation and curation practices.
A Tunable Loss Function for Classification

Recently, a parametrized class of loss functions called $\alpha$-loss,$\alpha \in [1,\infty]$, has been introduced for classification. This family,which includes the log-loss and the 0-1 loss as special cases, comes withcompelling properties including an equivalent margin-based form which isclassification-calibrated for all $\alpha$. We introduce a generalization ofthis family to the entire range of $\alpha \in (0,\infty]$ and establish howthe parameter $\alpha$ enables the practitioner to choose among a host ofoperating conditions that are important in modern machine learning tasks. Weprove that smaller $\alpha$ values are more conducive to faster optimization;in fact, $\alpha$-loss is convex for $\alpha \le 1$ and quasi-convex for$\alpha >1$. Moreover, we establish bounds to quantify the degradation of thelocal-quasi-convexity of the optimization landscape as $\alpha$ increases; weshow that this directly translates to a computational slow down. On the otherhand, our theoretical results also suggest that larger $\alpha$ values lead tobetter generalization performance. This is a consequence of the ability of the$\alpha$-loss to limit the effect of less likely data as $\alpha$ increasesfrom 1, thereby facilitating robustness to outliers and noise in the trainingdata. We provide strong evidence supporting this assertion with severalexperiments on benchmark datasets that establish the efficacy of $\alpha$-lossfor $\alpha > 1$ in robustness to errors in the training data. Of equalinterest is the fact that, for $\alpha < 1$, our experiments show that thedecreased robustness seems to counteract class imbalances in training data.
VHDL Implementation of 4096-bit RNS Montgomery Modular Exponentiation for RSA Encryption

Modular exponentiation is the basis needed to perform RSA encryption and decryption. Execution of 4096-bit modular exponentiation using an embedded system requires many arithmetic operations. This work aims to improve the performance of modular exponentiation for an existing FPGA platform containing a soft core RISC-V processor. The solution is to introduce a peripheral that performs Montgomery multiplication with 4096-bit operands. These operands are represented using the Residue Number System (RNS) and each residue is assigned to a RNS processor core. In total, the system consists of 121 RNS cores and each core is responsible for a 34-bit residue. RNS Montgomery multiplication requires a base extension algorithm, which will be represented by the Bajard and the Shenoy base extensions. Two designs are proposed: one using a tree-based reduction circuit and one with an iterative reduction circuit. The former focuses on performance overall, while the latter is more efficient in terms of performance per area. Synthesis for a XCKU035 FPGA gives an area usage of 83484 LUTs for the tree-based design and 41857 LUTs for the iterative design. Both designs also require 132.5 BRAMs and 485 DSP blocks and are running at a clock frequency of 400 MHz. The tree-based design performs Montgomery multiplication using 4096-bit operands in 434 cycles (1.09 µs), while the iterative design does that in 577 cycles (1.44 µs). For performing modular exponentiation, the sliding window method is used with an optimal window size of seven. A modular exponentiation with 4096-bit operands takes on average 5.09 ms for the tree-based design, while the iterative version needs 6.78 ms. Few other implementations were found performing Montgomery multiplication using 4096-bit operands. However, compared to those the proposed design provides better performance.
CHALLENGES ON INTRODUCING INFORMATION SECURITY STANDARDS: A CASE STUDY

The article discusses the current state and prospects for the further development (modernization) in the area of information security (IS) in Kazakhstan.Special attention is paid to the challenges that may arise when taking cyber security measures in relation to special requirements of standards to security and an independent IS audit at essential objects of the information and communication infrastructure (EOICI).The purpose of the study is to analyze the challenges on introducing modern standards of IS in the context of forming the national cyber security system in the Republic of Kazakhstan.The study has determined that the current challenges on introducing modern IS standards to maintain a high level of cyber security are related to the underdevelopment of the regulatory framework for the list of EOICI, the creation of an IS audit system and an information and analytical system to form national IS indicators.Recommendations have been given, and areas for the further study have been identified.
A New Feature Extraction Algorithm to Extract Differentiate Information and Improve KNN-based Model Accuracy on Aquaculture Dataset

In the world of aquaculture, understanding the condition of a pond is very important for a farmer in deciding which action should they take to prevent any bad condition occurred. Condition of a pond can be justified by measuring plenty of water parameters which can be divided into 3 categories that are physical, chemical and biological. The physical parameter is any physical quantity that can be measured in the pond. The chemical parameter is any kind of chemical substances that are dissolved in water. The biological parameter is any organic matter that lives in water. However, all of these parameters are not so distinguishable in representing the condition of a pond. Therefore, the farmer experience difficulties in justifying the condition and taking proper action to their pond. Even with the help of the K-Nearest Neighbors (KNN) algorithm combined with grid search optimization to model the data, the result is still not satisfying where the model only achieve accuracy of 0.701 in leave one out validation. To overcome this problem, a kind of feature extraction algorithm is needed to extract more information and make the data become more differentiate in representing the condition of the pond. With the help of our proposed feature extraction algorithm, optimized KNN can model the data easier and achieve higher accuracy. From the experiment results, the proposed feature extraction algorithm gives an impressive performance where it increases the accuracy to 0.741. A comparison with other feature extraction algorithms such as Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), and Singular Value Decomposition (SVD) is also conducted to validate how good the proposed feature extraction algorithm is. As a result, the proposed algorithm is surpassing the other algorithms which only achieve the accuracy of 0.707, 0.718, and 0.718, respectively.
A comparison between Neural networks, Lasso regularized Logistic regression, and Gradient boosted trees in modeling binary sales

The primary purpose of this thesis is to predict whether or not a customer will make a purchase from a specific item category. The historical data is provided by the Nordic online-based IT-retailer ...
A Generalization of the Goresky–Klapper Conjecture, Part II

Suppose that f(x)=Axk mod p is a permutation of the least residues mod p. With the exception of the maps f(x) = Ax and Ax(p+1)/2 mod p we show that for fixed n≥2 the image of each residue class mod n contains elements from every residue class mod n, once p is sufficiently large. If f(x) = Ax mod p, then for each p and n there will be exactly (1+o(1))6π2n2 readily describable values of A for which the image of some residue class mod n misses at least one residue class mod n, even when p is large relative to n. A similar situation holds for f(x)=Ax(p+1)/2 mod p.
A Trie Based Model for SMS Text Normalization

Language takes varied forms as used on different platforms. The instinctive nature of humans to use shorter message length, facilitating faster typing while maintaining semantic clarity, shapes the structure of a non-standard form of written text known as the texting language. The present work focuses on developing a Trie-based technique to model words in texting language for normalizing SMS text to Standard English text. The model is conceived and developed through systematic analysis of training data on user behaviour of texting language. Trie as a data structure is not only compact, but also is easy to manipulate for performing operations related to text normalization. Although Trie is a well-known data structure, its application to model SMS text has so far been unexplored. The results obtained using this model, despite the scheme being computationally cheap, are comparable with existing HMM based schemes that are available in the literature.
A Deep Learning Approach to 3D Segmentation of Brain Vasculature

The segmentation of blood-vessels is an important preprocessing step for the quantitative analysis of brain vasculature. We approach the segmentation task for two-photon brain angiograms using a fully convolutional 3D deep neural network.
Touch Sensing on the Forearm Using the Electrical Impedance Method

We present a novel on-skin touch sensing approach based on the electrical impedance method (EIM). Our approach enables the user to detect touch across the surface of the forearm by wearing two bands, one each on the lower and upper forearm. EIM uses a conductive substance to identify the area being touched. We focused on the electrical conductivity of the skin and applied EIM to the forearm. The two bands have electrodes on the inside. Signals are applied to these electrodes, and the resulting voltage on the surface of the skin is then measured. The advantages of our approach are that it works over a large area of the forearm, and can recognize both hand gestures and touch.
Dynamic Word Embedding for News Analysis

Author(s): Zhang, Haoxiang | Advisor(s): Chang, Kai-Wei | Abstract: Dynamic word embeddings refer to dividing data into time slices and obtaining the word vector representations of each time slice. By applying dynamic word embeddings, we can analyze how word trends evolve over time. Recently, a number of efforts have been applying dynamic word embeddings to understand how the meanings of words change over years or decades. However, little or no efforts have focused on the monthly changes, which are important for news analysis, such as how a word trend changes monthly. This thesis introduces how to apply dynamic word embeddings for news analysis and presents meaningful word trends evaluations. The application can be extended for news analysis of any time intervals, given enough data within each time slice.
Making Sensors Tangible with Long-exposure Photography

We provide a simple way to make sensors (including wearable sensors) tangible. The result is a photographic depiction of sensing that is a form of photographic art, as well as scientific photography, allowing humans to easily see and understand sensors. Since photography is a preferable form of evidence, making sensors tangible may also have forensic value, e.g. allowing a judge or jury to be presented with photographic evidence of the efficacy or defects in sensors. We use robotics, digital video feedback, wireless communications, and long-exposure photography to see and understand smart city sensors and wearable camera systems in a new light.
Review of the sky temperature and solar decomposition and their impact on thermal modeling

Performing accurate hourly building energy modeling requires the presence of reliable boundary conditions. The required data for energy simulation model entries are exterior air temperature, exterior air relative humidity, solar radiation, sky temperature, wind velocity, and cloud cover. Unfortunately, most available measured solar energy data is in the form of global horizontal radiation. Moreover, measured night sky temperature is normally not available. Proper energy modeling of a full building requires having accurate solar radiation intensity on angled building envelope surfaces as well as precise sky temperature data available. In this study, among several available models, three hourly horizontal global solar radiation decomposition models, four hourly diffuse radiation models on an inclined surface, and five sky temperature estimation models are studied for the Vancouver, Canada, climate. For the purpose of solar radiation validation, 2013 1-year measured total solar radiation on a southeast-oriented wall located at the British Columbia Institute of Technology Burnaby Campus is compared with the results from selected solar models. For both solar radiation and sky temperature models, the impact of using different models on transient heat transfer results of lightweight and mass-type walls (two walls) are reviewed. Results reveal the high impact of both solar and sky temperature models on hourly heat transfer simulation results.
The Relationship among the Academic Achievement Pressure, the Academic Stress and the School Adjustment of Middle School Students: The Moderating Effects of School Sports Club Participation

The purpose of this study was to test the moderating effect of school sports club participation in relation among academic achievement pressure, academic stress, and school adjustment. The participants were 257 middle school students. All the students administered four instruments, academic achievement pressure scale, academic stress scale, school sports club participation scale, school adjustment scale. The results of this study were as follows: First, there are significient relationship among academic achievement pressure, academic stress, school sports club participation and school adjustment. Second, it was proved that school sports club participation exerted as moderating variable between academic achievement pressure and school adjustment. Third, it was proved that school sports club participation exerted as moderating variable between academic stress and school adjustment. The implications of this study were investigated variables regarding school adjustment to help middle school students. Based on the finding results, how to increase school sports club participation and school adjustment and reduce academic achievement pressure and academic stress were suggested.
Learning Time Constant of Continuous-Time Neurons with Gradient Descent

In this paper, we propose a learning method to update the time constant in each continuous-time neuron with gradient descent to generate desired output patterns. Selecting appropriate time constant for each neuron in a continuous-time recurrent neural network is difficult. Hence, the development of adaptive method of the time constant is desired. However, direct update of time constants with gradient descent is significantly unstable. Therefore, to avoid the instability, we propose a learning method applying gradient descent to the logarithm of the time constant. We carried out an oscillator reproducing task in which a learning network is trained to generate the same oscillatory outputs from the teacher network. The training result shows that our proposed method can successfully update the time constants and suggests that leaning of time constants expands the freedom in learning and improve the learning performance.