Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature recognition of prismatic parts for inspection planning

Automated feature recognition has recognized as the front end of fully automated computer aided process planning (CAPP) systems. Automated feature recognition is considered to be the link between computer aided design (CAD) and CAPP systems. Feature recognition converts the geometrical and topological data contained in CAD file into application orientated features for planning purposes. This work introduces an automated rule based feature recognition algorithm to extract prismatic features from a boundary representation (Brep) solid model. The automatic feature recognition algorithm is developed using ACIS geometric kernel and C++ object oriented programming. The recognition system input is a Brep solid model in sat format. The developed algorithm is a part of a complete computer aided inspection planning system.
Correlating features of malicious software to increase insight in attribution

This paper discusses research done on malware attribution. Attribution of malware is complex and with cyber crime becoming more and more popular, law enforcement is facing an uphill battle. Unless the attacker makes a rookie mistake it will be difficult and sometimes impossible to determine who the author of a malware sample is (if there is a single author). This research consists of two parts. The first part experiments with the malware analysis tool Cuckoo Sandbox and different machine learning models to determine possible attribution accuracy. The second part analyzes malware samples to determine the effects of different obfuscation tools on the analysis and the analysis results. Both static and dynamic behavior of the samples are used in the analysis. The results show that even when using only features from static analysis, accuracies of up to 57\% and 72\% can be achieved. Furthermore obfuscation tools can have an impact on both static and dynamic features although the simpler obfuscation tools only influence the static ones. It is argued that with more research this type of analysis will be useful to law enforcement with respect to malware attribution. The usefulness will be limited to providing a list of possible suspects.
State of the Art Lower Limb Robotic Exoskeletons for Elderly Assistance

The number of elderly populations is rapidly increasing. Majority of elderly people face difficulties while walking because the muscular activity or other gait-related parameters start to deteriorate with aging. Therefore, the quality of life among them can be suffered. To make their life more comfortable, service providing robotic solutions in terms of wearable powered exoskeletons should be realized. Assistive powered exoskeletons are capable of providing additional torque to support various activities, such as walking, sit to stand, and stand to sit motions to subjects with mobility impairments. Specifically, the powered exoskeletons try to maintain and keep subjects’ limbs on the specified motion trajectory. The state of the art of currently available lower limb assistive exoskeletons for weak and elderly people is presented in this paper. The technology employed in the assistive devices, such as actuation and power supply types, control strategies, their functional abilities, and the mechanism design, is thoroughly described. The outcome of studied literature reveals that there is still much work to be done in the improvement of assistive exoskeletons in terms of their technological aspects, such as choosing proper and effective control methods, developing user friendly interfaces, and decreasing the costs of device to make it more affordable, meanwhile ensuring safe interaction for the end-users.
COHERENCY IDENTIFICATION FOR POWER SYSTEM DYNAMIC EQUIVALENTS.

Coherency-based approach to the problem of constructing power system dynamic equivalents has been very successful in applications. The key step in this approach is to identify groups of coherent generators. An analytic study of coherency is conducted. An algebraic characterization of coherency is given. An algorithm, based on the algebraic characterization, to identify coherent groups directly from system data is developed. A physical interpretation of the algebraic characterization of coherency is presented, where the condition for coherency is described in terms of generator inertia constants and their equivalent admittances to the bus of the disturbance.
How the Academics Qualification Influence the Students Learning Development

bh-This paper presents an analysis of data from the National Fund of Education Development (FNDE) in relation to the teachers in its database, relating them according to the demographic density of each region, the number of teachers from the Brazilian Capitals who have courses graduate, specialization, master's and doctoral degrees and the percentages of the Development index of basic education of the students (IDEB) of the respective regions. The goal is to analyze if the academic qualification of the teachers impact on the IDEB of the schools students of the regions with more representative indexes. In our verification we used the Apriori association algorithm to identify and analyze the data set to collect the results. The results demonstrate that the maximum academic qualification of male teachers is mostly of specialization, whereas female teachers in most of the regions have a specialization and master's degree course. Furthermore, it was possible to verify that the region with the highest IDEB is the South region with 6.27%, the region with the highest number of teachers with a master degree (4.2%) and a doctor (0.5%). The Central West region has an IDEB of 6.20% and the professors have the majority of the undergraduate course (56.7%) and specialization (31.9%). It is possible to affirm with the results obtained that the regions with the highest IDEB are the regions with the highest level of training by the teachers.
Blind Deconvolution Meets Phase Retrieval in Optical Wireless Communications

Optical wireless communication becomes a key enabling technology for achieving ultra-high data rate requirements in beyond 5G systems. In this paper, to reduce both channel signaling overhead and hardware cost in optical wireless communications, we present a blind deconvolutional phase retrieval approach to recover the source signals from phaseless measurements without a priori channel information. To deal with the coupled challenges of phaseless measurements and bilinear signaling model, we recast the signal recovery problem into a rank-one matrices recovery problem via matrix lifting, followed by relaxing each phaseless matrix measurement into its convex hull. We further propose a difference-of-convex-functions (DC) programming algorithm to solve the low-rank matrix optimization problem. This is achieved by proposing the DC representation for the rank function based on the convex Ky Fan k-norm, thereby exactly detecting the fixed-rank constraints. The numerical results demonstrate that the proposed DC approach outperforms the state-of-the-art methods in terms of signal recovery performance and the robustness to the noise.
Nonlinear Model Predictive Control for a Managed Pressure Drilling with High-Fidelity Drilling Simulators

Nonlinear Model Predictive Control for a Managed Pressure Drilling with High-Fidelity Drilling Simulators Junho Park Department of Chemical Engineering, BYU Master of Science The world’s energy demand has been rapidly increasing and is projected to continue growing for at least the next two decades. With increasing global energy demand and competition from renewable energy, the oil and gas industry is striving for more efficient petroleum production. Many technical breakthroughs have enabled the drilling industry to expand the exploration to more difficult drilling such as deepwater drilling and multilateral directional drilling. For example, managed pressure drilling (MPD) offers ceaseless operation with multiple manipulated variables (MV) and wired drill pipe (WDP) provides two-way, high-speed measurements from bottom hole and along-string sensors. These technologies have maximum benefit when applied in an automation system or as a real-time advisory tool. The objective of this study is to investigate the benefit of nonlinear model-based control and estimation algorithms with various types of models. This work presents a new simplified flow model (SFM) for bottomhole pressure (BHP) regulation in MPD operations. The SFM is embedded into model-based control and estimation algorithms that use model predictive control (MPC) and moving horizon estimation (MHE), respectively. This work also presents a new Hammerstein-Wiener nonlinear model predictive controller for BHP regulation. Hammerstein-Wiener models employ input and output static nonlinear blocks before and after linear dynamics blocks to simplify the controller design. The control performance of the new Hammerstein-Wiener nonlinear controller is superior to conventional PID controllers in a variety of drilling scenarios. Conventional controllers show severe limitations in MPD because of the interconnected multivariable and nonlinear nature of drilling operations. BHP control performance is evaluated in scenarios such as drilling, pipe connection, kick attenuation, and mud density displacement and the efficacy of the SFM and Hammerstein-Wiener models is tested in various control schemes applicable to both WDP and mud pulse systems. Trusted high-fidelity drilling simulators are used to simulate well conditions and are used to evaluate the performance of the controllers using the SFM and Hammerstein-Wiener models. The comparison between nonWDP (semi-closed loop) and WDP (full-closed loop) applications validates the accuracy of the SFM under the set of conditions tested and confirms comparability with model-based control and estimation algorithms. The SFM MPC maintains the BHP within ± 1 bar of the setpoint for each investigated scenario, including for pipe connection and mud density displacement procedures that experience a wider operation range than normal drilling.
Assessing Preparedness of Non-Hospital Health Centers to Provide Primary Emergency Care; A Systematic Review.

Objectives To identify prevalent domains related to the concept of assessing preparedness of non-hospital centers to provide primary emergency care in order to develop a comprehensive framework. Methods Five databases including PubMed, Scopus, Web of science, Barakat Knowledge Network Systems (BKNS) and Scientific Information Database (SID) were searched in English and/or Persian languages with no time limit until March, 2018. Manual search and grey literature were also done. According to the eligibility criteria, all the studies were independently tracked by two researchers. Studies were appraised using the Mixed Methods Appraisal Tool (MMAT). The findings were synthesized through directed content analysis method. Results Out of 3014 studies, 15 studies were included for data synthesis. The synthesis of literature resulted in the emergence of 13 domains and 25 sub-domains. Then, they were categorized based on Donabedian's triple model and a conceptual framework was developed. In this framework, 6 domains were put in input, 6 in processes, and 1 domain in outcome. Of the 15 included studies, 1 study considered 10 domains and 14 other studies considered 4 to 8 domains out of 13 synthesized domains. The most prevalent synthesized domains were "medical supplies and equipment" and "human resources", which were considered in 15 studies. Conclusion In this study, a conceptual framework was constructed that identifies elements that significantly affect the preparedness of these centers. This framework may assist managers to take a comprehensive approach to assess these centers.
Deep recurrent neural networks with word embeddings for Urdu named entity recognition

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state‐of‐the‐art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short‐term memory and back propagation through time approaches. The proposed models consider both language‐dependent features, such as part‐of‐speech tags, and language‐independent features, such as the “context windows” of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f‐measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.
3D web visualization of continuous integration big data

Continuous Integration (CI) is a practice that is used to automate the software build and its test for every code integration to a shared repository.CI runs thousands of test scripts every day in a software organization. Every test produces data which can be test results logs such as errors, warnings, performance measurements and build metrics. This data volume tends to grow at unprecedented rates for the builds that are produced in the Continuous Integration (CI) system. The amount of the integrated test results data in CI grows over time. Visualizing and manipulating the real time and dynamic data is a challenge for the organizations. The 2D visualization of big data has been actively in use in software industry. Though the 2D visualization has numerous advantages, this study is focused on the 3D representation of CI big data visualization and its advantage over 2D visualization. Interactivity with the data and system, and accessibility of the data anytime, anywhere are two important requirements for the system to be usable. Thus, the study focused in creating a 3D user interface to visualize CI system data in 3D web environment. The three-dimensional user interface has been studied by many researchers who have successfully identified various advantages of 3D visualization along with various interaction techniques. Researchers have also described how the system is useful in real world 3D applications. But the usability of 3D user interface in visualizations in not yet reached to a desirable level especially in software industry due its complex data. The purpose of this thesis is to explore the use of 3D data visualization that could help the CI system users of a beneficiary organization in interpreting and exploring CI system data. The study focuses on designing and creating a 3D user interface for providing a more effective and usable system for CI data exploration. Design science research framework is chosen as a suitable research method to conduct the study. This study identifies the advantages of applying 3D visualization to a software system data and then proceeds to explore how 3D visualization could help users in exploring the software data through visualization and its features. The results of the study reveal that the 3D visualization help the beneficiary organization to view and compare multiple datasets in a single screen space, and to see the holistic view of large datasets, as well as focused details of multiple datasets of various categories in a single screen space. Also, it can be said from the results that the 3D visualization help the beneficiary organization CI team to better represent big data in 3D than in 2D.
Free roaming: A system for ubiquitous computing

Based on the Umbilical Cord system, this project enriches the Ubiquitous Computing system with a Free Roaming system. This Free Roaming consists of a distributed authentication system, a data caching system and a communication system between them. It allows user to roam within this system and access his data everywhere. Together with the Umbilical Cord system, a Ubiquitous Computing system is functionally completed as a prototype, and is ready to be deployed into the Internet.
Implementing Relational-Algebraic Operators for Improving Cognitive Abilities in Networks of Neural Cliques

Associative memories are devices capable of retrieving previously stored messages from parts of their content. They are used in a variety of applications including CPU caches, routers, intrusion detection systems, etc. They are also considered a good model for human memory, motivating the use of neural-based techniques. When it comes to cognition, it is important to provide such devices with the ability to perform complex requests, such as union, intersection, difference, projection and selection. In this paper, we extend a recently introduced associative memory model to perform relational algebra operations. We introduce new algorithms and discuss their performance which provides an insight on how the brain performs some high-level information processing tasks.
Data-Adaptive Robust Transmission Network Planning Incorporating Post-Contingency Demand Response

The significant development and increasing deployment of renewable generation in the modern power system introduces the challenge for dealing with uncertainty. In this paper, data-adaptive robust optimization is applied to the transmission network planning. By taking historical data correlation into account, the proposed model can achieve a lower expansion investment without sacrificing the robustness. Demand response is embedded in this model to relieve the overflow incurred by renewable generation fluctuation and $N-1$ contingency. The model is decomposed into a master problem and several slave problems by column and constraint generation algorithm and then solved iteratively. The numerical simulation tested on Garver 6-bus system and the IEEE 118-bus system demonstrates the effect of demand response in reducing or postponing network construction. The proposed data-adaptive robust optimization is proved to be cost-effective and computationally efficient.
Multilevel Solvers for Stochastic Fluid Flows

Uncertainty is ubiquitous in many areas of science and engineering. It may result from the inadequacy of mathematical models to represent the reality or from unknown physical parameters that are required as inputs for these models. Uncertainty may also arise due to the inherent randomness of the system being analyzed. For many problems of practical interest, uncertainty quantification (UQ) can involve computations that are intractable even for the modern supercomputers, if conventional mathematical techniques are utilized. The reason is typically a product of complexity factors associated with many samples needed to compute the statistics, and for each sample, complexity associated with the spatio-temporal scales characteristics to the system. The main objective of this research is to obtain multilevel solvers for stochastic fluid flow problems with high-dimensional uncertainties. In our approach, the complexity arising due to sampling is overcome by the multilevel Monte Carlo (MLMC) method and complexity due to spatio-temporal scales is eliminated via the multigrid solver. Historically, Monte Carlo (MC) type methods have been proven to be the methods of choice for problems with a large uncertainty dimension as they do not suffer from the curse of dimensionality. A well-known computational bottleneck associated with the plain MC method is the slow convergence of the sampling error. For problems involving a wide range of space and time scales, ensuring a low mean square error will require a large number of MC samples on a very fine computational mesh making the estimator very expensive. Inspired by the multigrid ideas, the MLMC method generalizes the standard MC to multiple grids, exhibiting an exceptional improvement. The efficiency of the MLMC method comes from solving the problem of interest on a coarse grid and subsequently adding corrections based on fewer mesh resolutions. On the coarsest grid, a large number of samples can be computed inexpensively. The corrections computed on fewer grids, have smaller variances and can be estimated accurately using only fewer samples. The estimates at different levels are then combined using a telescopic sum.
GPUCheck: Detecting CUDA Thread Divergence with Static Analysis

Graphics Processing Units (GPUs) have been widely used to accelerate the performance of programs. However, such performance gains can be signiﬁcantly degraded by irregular data accesses and by control-ﬂow divergence. Both of these performance issues arise only in the presence of thread-divergent expressions— expressions that evaluate to diﬀerent values for diﬀerent threads. The eﬀect on performance depends on the number of threads that are left idle by control divergence, or on the number of distinct memory accesses that are required to satisfy all the memory references from a single warp of threads. Even experienced programmers may fail to identify control divergence or non-coalesceable memory accesses. This paper introduces GPUCheck: a static analysis tool that detects branch divergence and non-coalesceable memory accesses in GPU programs. GPUCheck relies on a static dataﬂow analysis to ﬁnd thread-dependent expressions and on a novel symbolic analysis to determine when such expressions could lead to performance issues. GPUCheck supports programmers by informing them, at compile time without executing any GPU code, of potential performance problems.
Ranking manufacturing processes from the quality management perspective in the automotive industry

The aim of this study is to propose a fuzzy decision‐making model to rank manufacturing processes from the quality management perspective in the automotive industry. This paper proposes a model for improving quality management through the assessment and ranking of manufacturing subprocesses with respect to key performance indicators (KPIs). The developed model, supported with the fuzzy extended ELECTRE III, allows for the determination of subprocesses' rank. An illustrative example indicates that the proposed model could be very useful in everyday business operations as total quality management asset. The model can handle all uncertain and vague input data by applying the theory of fuzzy sets. The research also suggests different managerial implications because it provides an adequate tool for overall quality improvement. The number of treated KPIs is relatively high, so ELECTRE III method gives an advantage over other multicriteria analysis methods because it embraces less subjective thinking and demands slightly less experts' knowledge during the process of decision making and assessment.
ClustMe: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns

We propose ClustMe, a new visual quality measure to rank monochrome scatterplots based on cluster patterns. ClustMe is based on data collected from a human‐subjects study, in which 34 participants judged synthetically generated cluster patterns in 1000 scatterplots. We generated these patterns by carefully varying the free parameters of a simple Gaussian Mixture Model with two components, and asked the participants to count the number of clusters they could see (1 or more than 1). Based on the results, we form ClustMe by selecting the model that best predicts these human judgments among 7 different state‐of‐the‐art merging techniques (Demp). To quantitatively evaluate ClustMe, we conducted a second study, in which 31 human subjects ranked 435 pairs of scatterplots of real and synthetic data in terms of cluster patterns complexity. We use this data to compare ClustMe's performance to 4 other state‐of‐the‐art clustering measures, including the well‐known Clumpiness scagnostics. We found that of all measures, ClustMe is in strongest agreement with the human rankings.