Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrafast High-Resolution Solar Cell Cracks Detection Process

This article presents the advancement of an ultrafast high-resolution cracks detection in solar cells manufacturing system. The aim of the developed process is to: first, improve the quality of the calibrated image taken by a low-cost conventional electroluminescence (EL) imaging setup; second, propose a novel methodology to enhance the speed of the detection of the solar cell cracks, and finally develop a proper procedure to decide whether to accept or reject the solar cell due to the existence of the cracks. The proposed detection process has been validated on various cracked/free-crack solar cell samples, evidently it was found that the cracks type, size, and orientation are more visible using the proposes method, while the speed of calibrating the EL images are in the range of 0.1–0.3 s, excluding the EL imaging time.
Moving beyond parallel data for neutral machine translation

The goal of neural machine translation (NMT) is to build an end-to-end system that automatically translates sentences from the source language to the target language. Neural machine translation has become the dominant paradigm in machine translation in recent years, showing strong improvements over prior statistical methods in many scenarios. However, neural machine translation relies heavily on parallel corpora for training; even for two languages with abundant monolingual resources (or with a large number of speakers), such parallel corpora may be scarce. Thus, it is important to develop methods for leveraging additional types of data in NMT training. This thesis explores ways of augmenting the parallel training data of neural machine translation with non-parallel sources of data. We concentrate on two main types of additional data: monolingual corpora and structural annotations. First, we propose a method for adding target-language monolingual data into neural machine translation in which the monolingual data is converted to parallel data through copying. Thus, the NMT system is trained on two tasks: translation from source language to target language, and autoencoding the target language. We show that this model achieves improvements in BLEU score for lowand medium-resource setups. Second, we consider the task of zero-resource NMT, where no source ↔ target parallel training data is available, but parallel data with a pivot language is abundant. We improve these models by adding a monolingual corpus in the pivot language, translating this corpus into both the source and the target language to create a pseudo-parallel source↔ target corpus. In the second half of this thesis, we turn our attention to syntax, introducing methods for adding syntactic annotation of the source language into neural machine translation. In particular, our multi-source model, which leverages an additional encoder to inject syntax into the NMT model, results in strong improvements over non-syntactic NMT for a high-resource translation case, while remaining robust to unparsed inputs. We also introduce a multi-task model that augments the transformer architecture with syntax; this model improves translation across several language pairs. Finally, we consider the case where no syntactic annotations are available (such as when translating from very low-resource languages). We introduce an unsupervised hierarchical encoder that induces a tree structure over the source sentences based solely on the downstream task of translation. Although the resulting hierarchies do not resemble traditional syntax, the model shows large improvements in BLEU for low-resource NMT.
Training Anfis System with Moth-Flame Optimization Algorithm

Adaptive Neuro Fuzzy Inference System (ANFIS) is an adaptive network that can use the computation and learning abilities of artificial neural network together with the inference feature of fuzzy logic. The ANFIS system, which is used in the solution of many problems such as classification and estimation of deep learning applications, meets the needs in many different areas such as modeling, control, and parameter estimation. In recent years, heuristic methods have been used for the training of this network, which requires initial and result parameters by its structure. Moth-Flame Optimization Algorithm (MFO) is one of the current heuristic methods modeled by the influence of the spiral movement of the moths towards the light source. In this study, the MFO algorithm was used for the first time for the optimization of initial and result parameters in the ANFIS system. In the determination of parameters, nonlinear system identification, time series estimation, classification problems were tried to be solved. When the results obtained for the ANFIS trained with the known heuristic methods such as Particle Swarm Optimization(PSO), Genetic Algorithm(GA) and Whale Optimization Algorithm(WOA) and the results of ANFIS trained by the MFO were examined, it was observed that the MFO had lower error values.
Research on Multi-uav Cooperative Tracking Target Based on PSO Predictive Control Algorithm

In the multiple unmanned aerial vehicle (multi-uav) cooperative tracking target control system, the control system is extremely complex due to the nonlinear control model and constraints. In this paper, multi-uav cooperative tracking target motion modeling is adopted, optimization performance indicators is established, and multi-objective rolling optimization is transformed to single-objective optimization. Finally, particle swarm optimization (PSO) prediction control algorithm is adopted for optimization solution, realizing real-time control of multi-uav cooperative tracking target, and its feasibility is verified by simulation.
Assessing the Revised Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Food Packages by Surveying Native American WIC Participants and Administering a Metabolic, Dietary Study of the Revised and Original WIC Food Packages

Assessing the Revised Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) Food Packages by Surveying Native American WIC Participants and Administering a Metabolic, Dietary Study of the Revised and Original WIC Food Packages. (December 2011) Rachel Condie, B.S., University of Texas at El Paso Chair of Advisory Committee: Dr. Peter Stephen Murano WIC food packages are undergoing major revisions to accommodate the unique requirements of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) in the twenty-first century. This dissertation is an evaluation of the impact of the revised WIC food packages through national-level surveys obtaining data before and after food package changes. In addition, a community-level metabolic/dietary study of obese women consuming either the original or revised food packages was performed. Since WIC has never revamped its entire program before, this is an opportunity to learn more about the impact of the revisions on WIC participants across the country, including Native Americans from Indian Tribal Organizations (ITOs). The National Food and Nutrition food frequency questionnaire (NATFAN) evaluates food and consumption practices of WIC participants, including Native Americans, for both the original and revised WIC food packages. The baseline surveys (NATFAN ) were administered before the 2009 rollout of the revised WIC food
Towards easy-to-implement misinformation automatic detection for online social media

The introduction of social media technologies has fostered the diffusion of misinformation. The speed and format with which misinformation is created and the pace at which it dif-fuses poses new challenges for practitioners and policy-makers alike. In this paper, we show that a simple network using character-level inputs given to a CNN is well suited to detect misinformation. We believe the effectiveness of such a simple architecture is because the CNN can exploit morphological differences between misinformation and other types of information. To test our intuition, we compare the character CNN inputs to different others like word embeddings. We ﬁnd that the network using character CNN out-performs models that do not take into consideration morphologies and matches the performance of others that consider it to some extent. We argue that the nature of misinformation, the low availability of training data and the multidisciplinary background of individuals implementing misinformation detection algorithms make simple, easy-to-implement networks such as the character CNN good alternatives to other models.
Interactive Visualization of Search Results of Large Document Sets

Anderson, James D. M.S.C.E. , Department of Computer Science and Engineering, Wright State University, 2018. Interactive Visualization of Search Results of Large Document Sets . When presented with many search results, finding information or patterns within the data poses a challenge. This thesis presents the design, implementation and evaluation of a visualization enabling users to browse through voluminous information and comprehend the data. Implemented with the JavaScript library Data Driven Documents (D3), the visualization represents the search as clusters of similar documents grouped into bubbles with the contents depicted as word-clouds. Highly interactive features such as touch gestures and intuitive menu actions allow for expeditious exploration of the search results. Other features include drag-and-drop functionality for articles among bubbles, merging nodes, and refining the search by selecting specific terms or articles to receive more similar results. A user study consisting of a survey questionnaire and user tracking data demonstrated that in comparison to a standard text-browser for viewing search results, the visualization performs commensurate or better on most metrics.
The Tandem Counting Bloom Filter - It Takes Two Counters to Tango

Set representation is a crucial functionality in various areas such as networking and databases. In many applications, memory and time constraints allow only an approximate representation where errors can appear for some queried elements. The Variable-Increment Counting Bloom Filter (VI-CBF) is a popular data structure for the representation of dynamically-changing sets, achieving a good tradeoff between memory efficiency and queries accuracy. For some applications, the required accuracy is higher than that enabled by the VI-CBF. In this paper, we present the Tandem Counting Bloom Filter (T-CBF), a new data structure that relies on the interaction among counters to describe sets with higher accuracy. We analyze its performance and show that by a joint consideration of counters, the T-CBF always performs better than the VI-CBF and it can for some configurations reduce its false positive probability by an order of magnitude. The overhead of such an approach is expressed upon an element insertion or query as read or write operations to a pair of counters rather than a single counter in each hash location. The operations themselves also require considering a larger number of scenarios.
A Parallel Neural Network Approach for Faster Rumor Identification in Online Social Networks

The unprecedented scale of rumor propagation in online social networks urges the necessity of faster rumor identification and control. The identification of rumors in the inception itself is imperative to bring down the harm it could cause to the society at large. But, the available information regarding rumors in inception stages is minimal. To identify rumors with data sparsity, we have proposed a twofold convolutional neural network approach with a new activation function which generalizes faster with higher accuracy. The proposed approach utilizes prominent features such as temporal and content for the classification. This rumor detection method is compared with the state-of-the-art rumor detection approaches and results prove the proposed method identifies rumor earlier than other approaches. Using this approach, the detected rumors with 88% accuracy and 92% precision for experimental datasets is 5% to 35% better than the existing approaches. This automated approach provides better results for larger and scale-free networks.
Buildings-to-Grid Integration with High Wind Power Penetration

This paper presents a framework to handle wind power production in the so-called Building-to-Grid (BtG) framework by using buildings demand flexibility. A stochastic dynamical BtG model is first developed by extending the existing BtG models to integrate wind power and explicitly formulating the interactions between the Transmission System Operator (TSO), Distribution System Operators (DSO), and buildings. Then, a novel unified BtG framework is formulated to handle the wind power by the demand flexibility of individual buildings together with the traditional reserve scheduling, which is in general hard to solve due to the unknown and unbounded distribution of wind power generation. Finally, a tractable robust reformulation together with probabilistic feasibility certificates is provided. Our simulation study shows that the proposed BtG flexibility framework can substitute with the traditional reserve scheduling without losing stability properties of the power grid and violating the buildings thermal comfort of occupants.
Prediction of Wind Power Model Using Hybrid Method Based on WD-SVM Algorithm: Case Study Pandansimo Wind Farm

Wind power prediction with original data that has a nonstationary pattern and randomness becomes a major problem when the data is used for the preparation of the generation. Especially the wind power directly connected to the network. In this research, we proposed wavelet decomposition model and support vector machine (WD-SVM) to predict the power scale of wind power in Pandansimo wind farm. Time series data that is built in interval of 1 hour mean in 24 formed day data. The data is parsed using WD that generates the IMF component. The output of the WD model is reprocessed using the SVM model to do the clustering process which the outputs are the scale of selected wind power strength in 1 month approaching the historical data of measurement. Finally after going through the experiment, obtained the data prediction scale of wind power strength that has a high accuracy near the actual conditions. The WD-SVM hybrid model provides a smaller error than the predicted model of NN and WD-NN.
Oracle assessment, improvement and placement

The oracle problem remains one of the key challenges in software testing, for which little automated support has been developed so far. This thesis analyses the prevalence of failed error propagation in programs with real faults to address the oracle placement problem and introduces an approach for iterative assessment and improvement of the oracles. To analyse failed error propagation in programs with real faults, we have conducted an empirical study, considering Defects4J, a benchmark of Java programs, of which we used all 6 projects available, 384 real bugs and 528 methods fixed to correct such bugs. The results indicate that the prevalence of failed error propagation is negligible. Moreover, the results on real faults differ from the results on mutants, indicating that if failed error propagation is taken into account, mutants are not a good surrogate of real faults. When measuring failed error propagation, for each method we use the strongest possible oracle as postcondition, which checks all externally observable program variables. The low prevalence of failed error propagation is caused by the presence of such a strong oracle, which usually is not available in practice. Therefore, there is a need for a technique to assess and improve existing weaker oracles. We propose a technique for assessing and improving test oracles, which necessarily places the human tester in the loop and is based on reducing the incidence of both false positives and false negatives. A proof showing that this approach results in an increase in the mutual information between the actual and perfect oracles is provided. The application of the approach to five real-world subjects shows that the fault detection rate of the oracles after improvement increases, on average, by 48.6%. The further evaluation with 39 participants assessed the ability of humans to detect false positives and false negatives manually, without any tool support. The correct classification rate achieved by humans in this case is poor (29%) indicating how helpful our automated approach can be for developers. The comparison of humans’ ability to improve oracles with and without the tool in a study with 29 other participants also empirically validates the effectiveness of the approach.
A High Reliability 3D Object Tracking Method for Robot Teaching Application

3D Object tracking is the task of capturing the 3D position and pose of an object from each time-series image frame. As we known, 3D sensing technique can be realized by stereo vision, structured light, and ToF (time-of-flight) camera. All of them can capture the point cloud data for describing the depth information in a workspace. In past research, the reliability in 3D object tracking was a big problem for real industrial application, therefore, we address a different way to enhance the tracking accuracy and stabilize the tracing path for raising the reliability. In order to build a 3D tracking model and the workspace environment, we adopted an RGB-D camera which is the Intel® RealSense™ D400 Series depth modules to collect the cloud point data and RGB values. The built 3D tracking model should contains the information which includes points, normal and texture for producing many 2D object images with different perspectives. Then the produced images were fed to a SSD (single-shot detector) neural network to learn the object’s features for 2D tracking. In dynamic tracking process, the image frames were through the sematic image segmentation by DeepLabV3+ for only extracting the object information without hands and background. Thus, the reserved data only included object’s cloud point data and texture information in workspace. Then we use the iterative closest point (ICP) algorithm and the RGB intensity correlation method to confirm the object’s position and posture in workspace. The result shows that our method has a better performance than SSD method for tracking a self-predefined object.
Wireless Energy Harvesting-Based Relaying: A Finite-SNR Diversity-Multiplexing Tradeoff Perspective

This paper presents an analytical framework to derive the closed-form expressions for diversity-multiplexing tradeoff (DMT) for wireless energy harvesting (WEH) based amplify-and-forward (AF) and decode-and-forward (DF) protocols in finite signal-to-noise (SNR) regime. The results of this investigation suggest that both AF and DF offer similar performance except for few nuances. At low multiplexing gains, DF offers marginally better performance in the low SNR regime, whereas AF performs better in the high SNR scenarios. However, in the higher multiplexing gain regime, though subtle, DF uniformly dominates AF across all SNRs. Furthermore, an analytical study is presented to evaluate the effect of the fraction of time devoted to WEH (time-sharing parameter, <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>) on the finite SNR DMT (f-DMT). In addition, the impact of relay position on the outage performance is also presented. A distinguishing feature of the proposed work is the characterization of WEH-based f-DMT which reveals the complete interplay between the operating SNR and the time-sharing parameter (<inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>), which is of fundamental importance to system designers. Finally, Monte-Carlo simulations are provided to confirm the veracity of analytical solutions.
A Hybrid Meta-Heuristic Solution to Operation Optimization of Hot Oil Pipeline

In the process of pipeline transportation, the temperature and pressure of waxy and high viscosity crude oil are decreasing. In order to complete the pipeline transportation task, stations are usually set at intervals to overcome the pressure loss caused by friction and impact in the flow and the temperature loss caused by the radial temperature difference. The main equipment of the station is pumps and heaters. The operating fee of pumps and heaters is the core costs of hot oil pipelines, so it is necessary to reasonably optimize the pump and heater operating schemes. Because the hydraulic and thermodynamic of the hot oil pipeline are coupled with each other, and there are a lot of nonlinear constraints and integer variables, this optimization problem is a kind of mixed integer nonlinear programming, which is difficult to solve by using the traditional optimization method. In this paper, a hybrid meta-heuristic algorithm based on particle swarm and pattern search is proposed, which can deal with the problem effectively. The operational cost of the optimized scheme is much lower than that of the actual operating scheme. So this method can provide guidance for the cost reduction and efficiency improvement of the hot oil pipeline.
Analysis and synthesis of musical harmonies based on formal grammars

Music is one of the most fundamental and universal forms of expression of humanity, being part of the human society since its origins. As any artistic manifestation, it is a cultural product and its evolution and use in all kind of scenarios show us how important it is for the humanity. The relation between music and mathematics has been a close one, investigated since the Pythagoreans, and one of the main products of this relation is tonal music, considered one of the greatest contributions of western civilization. So not only is music one of the forms of expression that most directly speak to the emotions, it is also the one whose foundations are more firmly mathematical, making music an ideal testing ground for analyzing the formalization of emotionally charged forms of expression, and to explore the limits of such formalization. Music is not subject to simple deterministic rules, but there is an obvious structure underneath it. Investigating and formalizing such structure is important to better understand music and even to develop new ways of composition using computers as a synergistic partner or as an independent composer. Thus, in this work, we consider the usefulness and the limits of applicability of formal grammars. The use of grammars can give important insights into the harmonic structure of a piece of music, and a lot of researchers have developed grammars and applied them to different styles of music. In musical theory there are harmonic concepts that are not well-defined in a mathematical sense. In this work we considered several of these concepts and see how, and whether, grammars can be modified to accommodate them. However, the ambiguous nature of music limits the possibility of formal analysis of harmonic sequences: using the grammars we could not precisely define (and, consequently, recognize) some musical concepts, such as modulation (the change of key within a musical piece). Trying to formalize modulation leads to unavoidable ambiguities in the grammar. Consequently we tried using other methods to formalize these concepts, and we successfully could determine modulations using a numerical costbased method. Then we used grammars to analyze separately the non-modulating segments in which our algorithm has divided the music. The combination of different methods and technologies developed in this work appears to be a very bright perspective for analyzing musical pieces.