Miracle-dz/UniFAR-FTUs huggingface dataset:
The Example Guru: Suggesting Examples to Novice Programmers in an Artifact-Based Context

OF THE DISSERTATION The Example Guru: Suggesting Examples to Novice Programmers in an Artifact-Based Context by Michelle Ichinco Doctor of Philosophy in Computer Science Washington University in St. Louis, 2018 Professor Caitlin Kelleher, Chair Programmers in artifact-based contexts could likely benefit from skills that they do not realize exist. We define artifact-based contexts as contexts where programmers have a goal project, like an application or game, which they must figure out how to accomplish and can change along the way. Artifact-based contexts do not have quantifiable goal states, like the solution to a puzzle or the resolution of a bug in task-based contexts. Currently, programmers in artifact-based contexts have to seek out information, but may be unaware of useful information or choose not to seek out new skills. This is especially problematic for young novice programmers in blocks programming environments. Blocks programming environments often lack even minimal in-context support, such as auto-complete or in-context documentation. Novices programming independently in these blocks-based programming environments often plateau in the programming skills and API methods they use. This work aims to encourage novices in artifact-based programming contexts to explore new API methods and skills. One way to support novices may be with examples, as examples are effective for learning and highly available. In order to better understand how to use examples for supporting novice programmers, I first ran two studies exploring novices’ use and focus on example code. I used those results to design a system called the Example Guru. The
Implementation of Steganographic Model using Inverted LSB Insertion

The most important thing in this insecure world is the secrecy of everything. In today’s world, any important data costs more than money. Steganography is the technique in which one can hide data as a secrete in selected image. In case of spatial domain, LSB approach is most popular in steganography, where all the LSBs of pixels of image are replaced by the bits of secret data. But the problem is that the secrete can be easily guessed by the hacker and the data is obtained by extracting it from direct LSBs. To make the system more robust and to improve the signal to noise ratio, the conventional LSB insertion method is replaced by inverted LSB technic. The decision to invert or not the LSB depends on combination of the 2nd and 3rd LSB. As not each and every LSB is inverted, it makes the steganalysis very difficult.
The Finite Element Method in Engineering 5th edition

Finite Element Analysis is an analytical engineering tool originated by the Aerospace and nuclear power industries to find usable, approximate solutions to problems with many complex variables. It is an extension of derivative and integral calculus, and uses very large matrix arrays and mesh diagrams to calculate stress points, movement of loads and forces, and other basic physical behaviors. Rao provides a thorough grounding of the mathematical principles for setting up finite element solutions in civil, mechanical, and aerospace engineering applications. The new edition includes examples using modern computer tools such as Matlab, Ansys, Nastran, and Abaqus. Professional engineers will benefit from the introduction to the many useful applications of finite element analysis, and will gain a better understanding of its limitations and special uses. New to this edition: Examples and applications in Matlab, Ansys, and Abaqus Structured problem solving approach in all worked examples New discussions throughout, including the direct method of deriving finite element equations, use of strong and weak form formulations, complete treatment of dynamic analysis, and detailed analysis of heat transfer problems More examples and exercises All figures revised and redrawn for clarity
Design and validation of a measurement scale of intergenerational family solidarity

This paper aims to develop a multidimensional scale to measure intergenerational family solidarity (IFS) to assess its psychometric properties and to examine the invariance of the instrument across gender. In order to do that, we initially developed a scale with six dimensions. Using the theoretical framework of Bengtson and his colleagues (1991), we measured five out of the six conceptual dimensions of the IFS, together with a new one developed by us and called systematic solidarity. Furthermore, items about partner are introduced in the questionnaire. Using a sample of 201 valid questionnaires, different analyses were conducted to validate the new scale. The definitive scale proposed to measure IFS was composed by four dimensions: normative solidarity and three affectual solidarities (related to parents, partner, and children). Finally, we provide evidences of the consistency of this scale across gender differences.
SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map

With the development of indoor localization technology, the location-based services such as product advertising recommendation in the shopping mall attract widespread attention, as precise user location significantly improves the efficiency of advertising push and brings broader profits. However, most of the Wi-Fi-based indoor localization approaches requiring professionals to deploy expensive beacon devices and intensively collect fingerprints in each location grid, which severely limits its extensive promotion. We introduce a zero-cost indoor localization algorithm utilizing crowdsourcing fingerprints to obtain the shop recognition where the user is located. Naturally utilizing the Wi-Fi, GPS, and time-stamp fingerprints collected from the smartphone when user paid as the crowdsourcing fingerprint, we avoid the requirement for indoor map and get rid of both devices cost and manual signal collecting process. Moreover, a shop-level hierarchical indoor localization framework is proposed, and high robustness features based on Wi-Fi sequences variation pattern in the same shop analysis are designed to avoid the received signal strength fluctuations. Besides, we also pay more attention to mine the popularity properties of shops and explore GPS features to improve localization accuracy in the Wi-Fi absence situation effectively. Massive experiments indicate that SP-Loc achieves more than 93% localization accuracy.
Indentation for investigation of strain rate effect on mechanical properties in structural steel weld zone

In this study, instrumented indentation testing was conducted at room temperature for the investigation of the effect of strain rate on the hardness and yield strength in the weld zone of a commonly used structural steel, SM520. A number of indentation tests were undertaken at a number of strain rate values from 0.02 s-1 to 0.2 s-1 in the weld metal (WM), heat-affected zone (HAZ), and base metal (BM) regions of the weld zone. The mechanical properties including yield strength (σy) and hardness (H) in WM, HAZ, and BM were then computed from the applied load-penetration depth curves using a proposed method. As the result, the effects of strain rate indentation on yield strength and hardness in all regions of the weld zone were evaluated. The results displayed that hardness and yield strength in the weld zone’s components are influenced on the strain rate, where both hardness and yield strength decrease with the decreasing strain rate. Keywords: indentation; mechanical properties; strain rate effect; structural steel; weld zone.
High brightness ultrafast transmission electron microscope based on a laser-driven cold-field emission source: principle and applications

ABSTRACT We report on the development of an ultrafast Transmission Electron Microscope based on a laser-driven cold-field emission source. We first describe the instrument before reporting on numerical simulations of the laser-driven electron emission. These simulations predict the temporal and spectral properties of the femtosecond electron pulses generated in our ultrafast electron source. We then discuss the effects that contribute to the spatial, temporal and spectral broadening of these electron pulses during their propagation from the electron source to the sample and finally to the detectors of the electron microscope. The spectro-temporal properties are then characterized in an electron/photon cross-correlation experiment based on the detection of electron energy gains. We finally illustrate the potential of this instrument for ultrafast electron holography and ultrafast electron diffraction. Graphical Abstract
Optical Simulation of Planar CH3NH3PbI3 Perovskite Solar Cells

In this work, optical simulation results of planar CH3NH3PbI3 solar cells using a MATLAB script developed by McGehee’s group (Stanford University) will be presented. The device structure is FTO/HEL/AL/ETL/LiF/Al, where HEL is a hole-extraction layer, AL is an active layer (CH3NH3PbI3), and EEL is an electron-extraction layer. This MATLAB script uses the transfer matrix method, where transmission and reflection are calculated for each interface in the stack as well as attenuation in each layer. The wavelength-dependent optical constants (n and k) of each layer were measured by spectroscopic ellipsometry (SE). The exciton generation rates within the active layer were calculated and with an assumption that the Internal Quantum Efficiency (IQE) equals 100% at all wavelengths, the predicted short-circuit currents (JSC) were also estimated. These results show a good agreement with the experimental values of JSC measured on real devices.
Nanoquasicrystalline Al-Fe-Cr-alloy Performed by Quasihydrostatic Compression: Structural Characterization and Mechanical Properies

The present research is aimed at the nanoquasicrystalline Al-Fe-Cr alloy consolidated under conditions of quasihydrostatic compression. Water atomized powder of nominal composition $\mathrm{A}1_{94}\text{Fe}_{3}\text{Cr}_{3}$ and fraction volume of nano-sized quasicrystalline particles of i-phase about 30% has been used in experiments. Prior quasihydrostatic compression the powder was sieved to the size ranged from 40 to 60 μm. Structural stability of consolidated Al-Fe-Cr alloy in response to heat treatment has been examined by precise method of differential scanning calorimetry and X-ray analysis. The results obtained for consolidated Al-Fe-Cr alloy have been specified and compared with those of the feedstock powder. Mechanical parameters of as-receive and heat treated consolidated Al-Fe-Cr alloy have been determined by using indentation technique and discussed by considering specific features of structure. Nanoquasicrystalline Al-Fe-Cr alloy subjected to quasihydrostatic compression showed high strength and sufficient ductility, maintaining them at the unchanged level up to 400 °C.
Cranberry Beans Derived Carbon Dots as a Potential Fluorescence Sensor for Selective Detection of Fe3+ Ions in Aqueous Solution

Recently, synthesis, characterization, and application of carbon dots have received much attention. Natural products are the effectual carbon precursors to synthesize carbon dots with fascinating chemical and physical properties. In this study, the fluorescent sensor of carbon dots derived from cranberry beans without any functionalization and modification was developed. The carbon dots were prepared with a cheap, facile, and green carbon precursor through a hydrothermal treatment method. The synthetic process was toxic chemical-free, convenient, and environmentally friendly. To find the optimized synthetic conditions, the temperature, heating time duration, and carbon precursor weight were evaluated. The prepared carbon dots were characterized by UV light, transmission electron microscopy, Raman, Fourier transform infrared, UV–vis, and fluorescence spectroscopy. The resulting carbon dots exhibit stable fluorescence with a quantum yield of approximately 10.85%. The carbon dots emitted the broad fluorescence emission range between 410 and 540 nm by changing the excitation wavelength and were used for the detection of Fe3+ ions at the excitation of 380 nm. It is found that Fe3+ ions induced the fluorescence intensity quenching of the carbon dots stronger than other heavy metals and the Fe3+ ion detection can be achieved within 3 min. Spectroscopic data showed that the obtained carbon dots can detect Fe3+ ions within the wide concentration range of 30–600 μM with 9.55 μM detection limit.
Direct laser texturing using two-beam interference patterning on biodegradable magnesium alloy

Structured surfaces provide different functions exploitable in different areas, ranging from aerospace to to biomedicine. In particular, in biomedical applications feature dimension control in micrometric and nanometric range is a crucial point. In this context, direct laser interference patterning (DLIP) with the use of high power pulsed lasers is a promising method, which can be used to overcome dimensional limitations. Pattern period is directly related to the laser wavelength and angle between the interference beams, ensuring higher resolution and dimensional flexibility. As a matter of fact, it is possible to move from micrometric feature size and period to nanometric scale in a single step process. In this work, an industrial grade ns-pulsed fiber laser with green wavelength was used to realize two-beam interference patterning on a biodegradable Mg alloy. The low melting point of Mg alloy combined with ns pulses generates further difficulties in terms of the machining quality. Laser process parameters were studied in order to define a correlation between fluence values and feature quality.Structured surfaces provide different functions exploitable in different areas, ranging from aerospace to to biomedicine. In particular, in biomedical applications feature dimension control in micrometric and nanometric range is a crucial point. In this context, direct laser interference patterning (DLIP) with the use of high power pulsed lasers is a promising method, which can be used to overcome dimensional limitations. Pattern period is directly related to the laser wavelength and angle between the interference beams, ensuring higher resolution and dimensional flexibility. As a matter of fact, it is possible to move from micrometric feature size and period to nanometric scale in a single step process. In this work, an industrial grade ns-pulsed fiber laser with green wavelength was used to realize two-beam interference patterning on a biodegradable Mg alloy. The low melting point of Mg alloy combined with ns pulses generates further difficulties in terms of the machining quality. Laser process paramete.
Every word is valuable: Studied influence of negative words that spread during election period in social media

Studying the influence of negative words that spread during election period is an important work in social media. Most of current methods rely on sentiment analysis of tweets to determine the users' preference. However, sentiment analysis can only makes use of emotional words (ie, adverbs and adjectives), which only take 30 percent of the context in the Internet. According to our empirical analysis based on real datasets, the bias on word selection largely reduced the accuracy of the context in the Internet. In order to address this critical problem, we propose a new method that makes use of nouns with emotional context to determine the election preference of each user. By collecting the frequencies of words in context, we weigh the impact of each supportive/objective noun to strengthen the determination of users' preference. Final results will further be integrated to examine the effectiveness and efficiency of our proposed method. To indicate this idea, we collect and adopt real datasets (UK Prime Minister 2017 and US President Campaign 2016) in the experiments. All the experiment results suggested that our integrated method largely outperformed previous prediction methods. In particular, the prediction results were quite similar to the final results of the UK and US election. Meanwhile, for UK election, we found that the daily approval rate is closely related to the event happened everyday.
Towards a Smart Tourism Destination Development Model: Promoting Environmental, Economic, Socio-cultural and Political Values

ABSTRACT The rapid increase in urban population poses significant challenges to the administration of cities worldwide. Thus, a growing number of initiatives are being implemented to alleviate such strain by attempting to transform urban centers into smart cities. Alongside the concept of smart city, a specific variation of this concept is emerging in the Tourism sector, namely smart tourism destinations (STD). Despite the growing number of STD initiatives in different countries, the extant literature remains silent on this growing phenomenon. As such, the objective of this paper is to propose a model—The Smart Tourism Destination Development Model—aimed at indicating a strategic path that a tourism destination should follow to become a smart one. As such, this study discusses the STD concept, and argues that besides enhancing the destination competitiveness, an STD project should also be grounded on a stainable paradigm in order to create public value for the hosting community.
Multiliteracies: Literacy Learning and the Design of Social Futures

Multiliteracies considers the future of literacy teaching in the context of the rapidly changing English language. Questions are raised about what constitutes appropriate literacy teaching in today's world: a world that is both a global village yet one which local diversity is increasingly important. This is a coherent and accessible overview of the work of the New London Group, with well-known international contributors bringing together their varying national experiences and differences of theoretical and political emphasis. The essays deal with issues such as: * the fundamental premises of literacy pedagogy * the effects of technological change * multilingualism and cultual diversity * social futures and their implications on language teaching. The book concludes with case studies of attempts to put the theories into practice and thereby provides a basis for dialogue with fellow educators around the world.
Development of Novel AlSi10Mg Based Nanocomposites: Microstructure, Thermal and Mechanical Properties

Al matrix nanocomposites are interestingly employed in the automotive, military, aerospace and electronics packaging industries. In this study, Graphene Nanoplatelets (GNPs) reinforced AlSi10Mg nanocomposites were produced via powder metallurgy. The effect of GNPs content on density, microstructure and mechanical characteristics of the AlSi10Mg/GNPs nanocomposites was investigated systematically. To this aim, AlSi10Mg/GNPs nanocomposites reinforced with 0.5, 1.0 and 2.0 wt. % of GNPs were produced by wet mixing method following by hot compaction at 600 °C. To evaluate the effect of GNPs on mechanical properties of the as-fabricated nanocomposite, Vickers hardness and tensile properties of composites analyzed at room temperature. According to the results, it was found that the fabrication of AlSi10Mg/GNPs nanocomposites is faced with several challenges such as agglomeration and non-uniform dispersion of GNPs that should be addressed to achieve the desirable thermal and mechanical properties. For instance, surprisingly, it is revealed that the mechanical and thermal properties of nanocomposites were deteriorated in the presence of a high quantity of GNPs (>1.0 wt.%), which can be attributed to the GNPs agglomeration and accordingly introduction of internal porosity in the nanocomposite. The relatively low fraction of GNPs can uniformly be dispersed in the matrix and improve the performance of the nanocomposite.
Application of graphene in metal corrosion protection

Single-layer defect-free graphene has excellent shielding performance and can prevent corrosion factors such as oxygen and water molecules from reaching the surface of the metal matrix. Based on the great application potential of graphene in the field of metal corrosion protection, this paper systematically summarizes the graphene film protective coating, graphene/organic protective coating and graphene-conductive polymer/organic protective coating. The existing problems of graphene film and the uniform dispersion of graphene in organic coating are analyzed and introduced in detail, and the future development of graphene-based anti-corrosion coating is prospected.
Structure and Properties of Chained Carbon: Recent Ab Initio Studies

Carbon chains or carbyne-like structures represent the next generation of 1D materials whose properties can be tuned by the chain length, doping, and the type of termination. Currently inaccessible technology of the macroscopic carbyne synthesis and characterization makes theoretical work especially valuable. The state of the art methods being applied in the field are density functional theory and molecular dynamics. This paper provides a review of the current state of research on modeling linear carbon structures and related materials. We show that even though the “static” properties of carbon chains (mechanical strength, thermal conduction, band gaps, and phonon spectra) are extensively described, there are only a few simulations of the synthesis processes that constitute the next challenge in 1D research.
Genetic driven multi-relevance vector regression forecasting of hourly wind speed in smart power systems.

Implementation of smart power systems implicitly demands the extensive use of learning. Learning methods may be used for forecasting of several critical parameter values of the power system infrastructure that will lead to efficient management of energy distribution and utilization. This paper places itself in the area of integrating renewable sources in the power grid. In particular the objective is to develop an intelligent data driven method for forecasting the average hourly wind speed. Wind speed is crucial in predicting the power output coming from wind generators. The proposed methodology, implements a two-stage learning method: in the first stage, it employs three relevance vector regressors (RVRs that learn from observations and make predictions, while in the second stage a genetic algorithm is employed to learn and assimilate the discrepancies of predictions with the most recent observations. The proposed method is applied to a set of real world wind speed hourly data, while benchmarked against learning Gaussian processes. Results exhibit the superiority of the multi-kernel method in forecasting wind speed.