Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Code Readability and Legibility: An Examination of Human-centric Studies

Reading code is an essential activity in software maintenance and evolution. Several studies with human subjects have investigated how different factors, such as the employed programming constructs and naming conventions, can impact code readability, i.e., what makes a program easier or harder to read and apprehend by developers, and code legibility, i.e., what influences the ease of identifying elements of a program. These studies evaluate readability and legibility by means of different comprehension tasks and response variables. In this paper, we examine these tasks and variables in studies that compare programming constructs, coding idioms, naming conventions, and formatting guidelines, e.g., recursive vs. iterative code. To that end, we have conducted a systematic literature review where we found 54 relevant papers. Most of these studies evaluate code readability and legibility by measuring the correctness of the subjects’ results (83.3%) or simply asking their opinions (55.6%). Some studies (16.7%) rely exclusively on the latter variable. There are still few studies that monitor subjects’ physical signs, such as brain activation regions (5%). Moreover, our study shows that some variables are multi-faceted. For instance, correctness can be measured as the ability to predict the output of a program, answer questions about its behavior, or recall parts of it. These results make it clear that different evaluation approaches require different competencies from subjects, e.g., tracing the program vs. summarizing its goal vs. memorizing its text. To assist researchers in the design of new studies and improve our comprehension of existing ones, we model program comprehension as a learning activity by adapting a preexisting learning taxonomy. This adaptation indicates that some competencies, e.g., tracing, are often exercised in these evaluations whereas others, e.g., relating similar code snippets, are rarely targeted.
Towards Data-Flow Parallelization for Adaptive Mesh Refinement Applications

Adaptive Mesh Refinement (AMR) is a prevalent method used by distributed-memory simulation applications to adapt the accuracy of their solutions depending on the turbulent conditions in each of their domain regions. These applications are usually dynamic since their domain areas are refined or coarsened in various refinement stages during their execution. Thus, they periodically redistribute their workloads among processes to avoid load imbalance. Although the defacto standard for scientific computing in distributed environments is MPI, in recent years, pure MPI applications are being ported to hybrid ones, attempting to cope with modern multi-core systems. Recently, the Task-Aware MPI library was proposed to efficiently integrate MPI communications and tasking models, providing also the transparent management of communications issued by tasks. In this paper, we demonstrate the benefits of porting AMR applications to data-flow programming models leveraging that novel hybrid approach. We exploit most of the application parallelism by taskifying all stages, allowing their natural overlap. We employ these techniques on the miniAMR proxy application, which mimics the refinement, load balancing, communication, and computation patterns of general AMR applications. We evaluate how this approach reduces the time in its computation and communication phases while achieving better programmability than other conventional hybrid techniques.
Impact of monensin on methane production and performance of cows fed forage diets

Monensin is an ionophore used to increase the efficiency of feed utilisation when grain-based rations are fed to beef cattle, but it can also lower energy losses to methane. A series of feeding experiments has been undertaken to measure the impact of monensin on methane production by dairy cows fed ryegrass-dominant pastures alone or with white clover, or maize silage. Methane production measured from 15 sets of identical twin cows fed indoors was reduced by 9%, from 16.9 to 15.3 g/kg dry matter (DM; P < 0.01), and remained 10% lower (P < 0.05) than Controls 72 days after the monensin capsule was given. When pasture was substituted with maize silage at 12, 24 and 36% of DM intake, monensin reduced methane (g/kg DM) by 1, 6.5 and 10.5 % respectively (P = 0.05). However, methane emissions were not affected in cows fed pasture with white clover at 15, 30 or 60% of DM intake. Although monensin provides an important opportunity for methane reduction, the data demonstrate a need to understand reasons for variable responses to treatments under differing nutritional regimes. The added benefits of improved feed efficiency and reduced bloat suggest further investigation of monensin for forage fed ruminants is warranted.
Studying Software Developer Expertise and Contributions in Stack Overflow and GitHub

Knowledge and experience are touted as both the necessary and sufficient conditions to make a person an expert. This paper attempts to investigate this issue in the context of software development by studying software developer’s expertise based on their activity and experience on GitHub and Stack Overflow platforms. We study how developers themselves define the notion of an "expert", as well as why or why not developers contribute to online collaborative platforms. We conducted an exploratory survey with 73 software developers and applied a mixed methods approach to analyze the survey results. The results provided deeper insights into how an expert in the field could be defined. Further, the study provides a better understanding of the underlying factors that drive developers to contribute to GitHub and Stack Overflow, and the challenges they face when participating on either platform.The quantitative analysis showed that JavaScript remains a popular language, while knowledge and experience are the key factors driving expertise. On the other hand, qualitative analysis showed that soft skills such as effective and clear communication, analytical thinking are key factors defining an expert. We found that both knowledge and experience are only necessary but not sufficient conditions for a developer to become an expert, and an expert would necessarily have to possess adequate soft skills. Lastly, an expert’s contribution to GitHub seems to be driven by personal factors, while contribution to Stack Overflow is motivated more by professional drivers (i.e., skills and expertise). Moreover, developers seem to prefer contributing to GitHub as they face greater challenges while contributing to Stack Overflow.
Automatic Modeling of Logic Device Performance Based on Machine Learning and Explainable AI

In this paper, we propose a machine learning framework for predicting performances of semiconductor devices that can automatically reflect modifications in process conditions. While standard TCAD simulators require intensive modeling and calibration works to capture new process conditions, our proposed framework can learn these conditions from data efficiently and directly. Furthermore, by applying recently attention-getting explainable AI techniques, important factors that affecting device performances can be discovered automatically from the proposed model. Specifically, our model quantifies significance of each process step as the game-theoretic Shapley value, that cannot be achieved by TCAD simulators.
Optimal Switching Frequency Variation Range Control for Critical Conduction Mode Boost Power Factor Correction Converter

Critical conduction mode boost power factor (PF) correction converter is widely used in low-to-medium power applications for its advantages of high PF, zero-current turn-on of the switch, and no reverse recovery in diode. Nevertheless, the switching frequency varies continually with the input voltage phase angle with traditional constant on-time control. This makes the electromagnetic interference (EMI) spectra display great differences and complicates the design of EMI filter. An optimal switching frequency variation range control strategy is proposed in this article. The optimal harmonics amounts for the lowest switching frequency variation range without and with a limited PF are both figured out. In addition, a lower output voltage ripple is obtained. A prototype has been built and tested in the lab to demonstrate the validity of the theoretical analysis.
Extending the learning shout-ahead architecture with user-defined exception rules – a case study for traffic light controls

We present an extension of the shout-ahead agent architecture that allows for adding human user-defined exception rules to the rules created by the hybrid learning approach for this architecture. The user-defined rules can be added after learning as reaction to weaknesses of the learned rules or learning can be performed with the user-defined rules already in place. We applied the extended shout-ahead architecture and the associated learning to a new application area, cooperating controllers for the traffic lights of intersections. In our experimental evaluations, adding user-defined exception rules to the learned rules for several traffic flow instances increased the efficiency of the resulting controllers substantially compared to just using the learned rules. Performing learning with user-defined exception rules already in place decreased the learning time substantially for all flows, but had mixed results with respect to efficiency. But both variants of adding user-defined exception rules create controllers that are much more flexible than what using the original shout-ahead architecture with its learning is able to create as experiments with variations of flows show.
Automatic Voltage Regulator with Particle Swarm Optimized Model Predictive Control Strategy

Automatic Voltage Regulator (AVR) plays an important role in keeping voltage stability in the power system network. Since the performance of AVR gets affected due to the presence of load inertia and field circuit dynamics, it is important to take it into account while designing a control strategy. In this paper, efforts have been made to design a Model Predictive Controller based robust control strategy for the AVR system which gives good transient response characteristics as well as robustness against the external disturbance and parameter variation. The Model Predictive Controller weights of the system have been fine-tuned with the help of Particle Swarm Optimization (PSO) algorithm for further improvement in vital performance indices.
Texture Analysis of Knee Osteoarthritis Using Contrast Limited Adaptive Histogram Equalization Based Gray Level Co-occurrent Matrix

Osteoarthritis (OA) is a joint disease associated with degenerative damage to joint cartilage. WHO reports that 40% of the world’s population will suffer from OA, in which 80% of them have limited motion. due to the number of osteoarthritis sufferers is increasing and in the detection of osteoarthritis orthopedic doctors use manual x-ray images, detection of osteoarthritis using x-ray image input is necessary. This study uses x-ray image processing using the CLAHE method which is used for image enhancement to analyze bone texture using GLCM. The results showed that the accuracy rate of KL-0 was 20%, KL-15%, KL-220%, KL-355%, and KL-4 5%. Based on these results, adding methods will give better results
Model of Probe Transition Including Probe Mispositioning

As the frequency of mm-wave and sub-mm-wave applications frequency is rising, it is important to find easier methods for characterizing uncertainty in on-wafer measurements. In this study, we are exploring the possibility of extracting the probe pads propagation characteristics from several probe touchdowns. By forcing the probe mispositioning at one port, it is possible to capture the changes of measured S-parameters of any two-port device with relatively high transmission, e.g., Thru, Line, etc. After developing lumped element models of the transition, it may be possible to extract its values thanks to a least square optimization process.
Automatic Identification of Rollback Edit with Reasons in Stack Overflow Q&A Site

Crowd-sourced developer forums, such as Stack Overflow (SO), rely on edits from users to improve the quality of the shared knowledge. Unfortunately, suggested edits in SO are frequently rejected by rollbacks due to undesired edits or violation of editing guidelines. Such rollbacks could frustrate and demotivate users to provide future suggestions. We thus need to warn a user of a potential rollback so that he can improve the suggested edit and thus increase its likelihood of acceptance. This study proposes to help users with an automated machine learning classification model that can warn them of potential rollbacks to their suggested edits. We present the conceptual design of EditEx, an online tool that can guide SO users during their editing by highlighting the potential causes of rollback. We offer details of an empirical study to assess the accuracy of the classifiers and a user study to evaluate the effectiveness of EditEx.
EWIDL: Single-Source Web API Documentation Management System

This paper is an overview of the EWIDL documentation management system for Web APIs. We designed EWIDL based on our experience with the development of Tizen Web API. Over the eight years of our work, two requirements have proven to be the most important for successful documentation management.Firstly, the documentation should be machine-readable to support automatic validation tools development. EWIDL achieves this by combining Doxygen documentation comments’ format with Web IDL language.Secondly, the system should support the maintenance of multiple variants of documentation. Tizen Web API behavior varies between different target devices, depending on the software version and hardware profile (mobile, wearable, and TV). Managing documentation for all configurations simultaneously is a challenging task with ever-increasing complexity as new versions are released. We chose a single-sourcing approach by including conditional configuration preprocessor syntax in EWIDL documents.
Constraints on the global carbon budget from variations in total column carbon dioxide

Diagnosing the patterns and trends in the flux of carbon dioxide, $\mathrm{CO_2}$, between the land or ocean and the atmosphere is necessary to predict the response of the carbon cycle to climate change. Atmospheric observations of the vertically averaged mixing ratio of $\mathrm{CO_2}$, $\langle\mathrm{CO_2}\rangle$, provide a new tool that complements existing observations of boundary layer $\mathrm{CO_2}$ in constraining surface fluxes of $\mathrm{CO_2}$. This dissertation explores how variations in $\langle\mathrm{CO_2}\rangle$ arise and how these variations can be used to estimate surface fluxes.
Integration of SCADA Services and Power-Hardware-in-the-Loop Technique in Cross-Infrastructure Holistic Tests of Cyber-Physical Energy Systems

Cyber-physical energy system (CPES)—complex juxtaposition of multiple energy domains and communication and automation technologies—requires a sophisticated testing framework to achieve holistic assessment and validation, especially at large scale. In this article, the real supervision, control, and data acquisition (SCADA) system of CPES is integrated with the advanced techniques—real-time simulation (RTS) and power-hardware-in-the-loop (PHIL), in a cross-infrastructure manner to create a realistic validation environment for CPES. On the one hand, the method can be applied to extend the capacity of the infrastructures as well as creating a common resource and expertise pool. On the other hand, this approach combines the realistic data and advanced SCADA services with the flexibility of the RTS platform, which provides the possibility to emulate extreme and faulty scenarios with virtual equipment and topology. The proposed approach is demonstrated via a case study of analyzing the impact of communication on advanced voltage and frequency restoration in an isolated microgrid. The case study is implemented on two remote platforms PREDIS-PRISMES (70 km apart). The validation framework comprises the RTS and PHIL platform coupled with the OPC UA SCADA system in PRISMES, the control algorithm and the communication network simulator located in PREDIS platform.
pdfPapers: shell-script utilities for frequency-based multi-word phrase extraction from PDF documents

Biomedical research is intensive in processing information in the previously published papers. This motivated a lot of efforts to provide tools for text mining and information extraction from PDF documents over the past decade. The *nix (Unix/Linux) operating systems offer many tools for working with text files, however, very few such tools are available for processing the contents of PDF files. This paper reports our effort to develop shell script utilities for *nix systems with the core functionality focused on viewing and searching multiple PDF documents combining logical and regular expressions, and enabling more reliable text extraction from PDF documents with subsequent manipulation of the resulting blocks of text. Furthermore, a procedure for extracting the most frequently occurring multi-word phrases was devised and then demonstrated on several scientific papers in life sciences. Our experiments revealed that the procedure is surprisingly robust to deficiencies in text extraction and the actual scoring function used to rank the phrases in terms of their importance or relevance. The keyword relevance is strongly context dependent, the word stemming did not provide any recognizable advantage, and the stop-words should only be removed from the beginning and the end of phrases. In addition, the developed utilities were used to convert the list of acronyms and the index from a PDF e-book into a large list of biochemical terms which can be exploited in other text mining tasks. All shell scripts and data files are available in a public repository named \pp\ on the Github. The key lesson learned in this work is that semi-automated methods combining the power of algorithms with the capabilities of research experience are the most promising for improving the research efficiency.
Modeling and Active Shielding of Magnetic Field with Circular Space-Time Characteristic and with different Shielding Coils spatial positions

The circular space-time characteristic of magnetic field generated by overhead power lines with triangular conductor’s arrangement and with different spatial positions of two shielding coils has been simulated. System of active shielding of magnetic field with two degree of freedom robust control and with different spatial positions of two shielding coils are synthesized. The multi-criteria game decision based synthesis. The Maxwell equations quasi-stationary approximation solution based calculated payoff vector. The stochastic particles multiswarm optimization algorithms based game decision. It is show that synthesized system of active shielding reduced initial magnetic flux density to the sanitary standards level in shielding space located near the overhead power lines.
Accurate Charging Demand Estimation of Electric Vehicle via Multi-Data Sources

With the penetration of electric vehicles (EVs) increased, charging demand estimation is more urgent and important. It provides constructive ideas and guidance for building an efficient charging pile network. Unfortunately, most of the existing charging demand estimation methods are based on simulation experiments. They lack real data and ignore the complexity of charging behavior. Therefore, the description of the charging demand is not accurate enough. In this paper, we first analyze the proportion of quick/slow public charging piles and their usage situation, compare the distribution of charging specific gravity of different vehicle types. Then we propose a novel algorithm to estimate charging demands. Through the analysis and observations of the data of the public charging facilities and EVs in Kunming, we obtain the spatial and temporal distribution of charging demands of EVs. In the spatial part, electric orders of the pile, parking locations and charging locations of EVs are analyzed with the Gaussian mixture model(GMM). A similarity-based algorithm is provided to fuse the three models. In the temporal part, charging times per hours of EVs are analyzed with the Autoregressive Integrated Moving Average model(ARIMA). Evaluation results demonstrate that our approach outperforms alternative solutions by reducing the spatial error by up to 66.70% and temporal error by up to 63.41%, respectively.
Accurate Estimation of Tip Force on Tendon-driven Catheters using Inverse Cosserat Rod Model*

Tip force estimation on tendon-driven catheters is of crucial clinical importance for catheter-based surgeries such as radio-frequency ablation (RFA). In this study, a force estimation framework based on Bezier spline shape approximation and inverse Cosserat rod model was proposed and validated. Initially, the necessary condition for a Bezier spline to approximate a constant bending radius catheter was derived. Afterward, the kinematics and balance equations of the catheter were derived based on the proposed shape approximation technique and solved to find the tip force. The proposed method was experimentally tested for accuracy and computation time. The results showed that the estimated forces were in fair agreement with reference with a mean-absolute error of $0.024\pm 0.020\mathrm{N}$ and a computation time of $0.007\pm 0.005\mathrm{s}$ per frame. The exhibited performance was comparable to other studies and was in compliance with the requirements of interventional surgery applications.
Control of synaptogenesis and dendritic arborization by the γ-Protocadherin family of adhesion molecules

During development, the mammalian nervous system wires into a precise network of unrivaled complexity. The formation of this network is regulated by an assortment of molecular cues, both secreted molecules and cell-surface proteins. The γ-Protocadherins (γ-Pcdhs) are particularly good candidates for involvement in these processes. This family of adhesion molecules consists of 22 members, each with diverse extracellular adhesive domains and shared cytoplasmic domains. Thus, cellular interactions with varied adhesive partners can trigger common cytoplasmic responses. Here we investigated the functions of the γPcdhs in two processes involved in neural network formation: dendrite arborization and synaptogenesis. We first asked how γ-Pcdhs regulate synaptogenesis in the spinal cord. We found that the γ-Pcdhs are differentially expressed by astrocytes as well as neurons. In astrocytes, the proteins localize to perisynaptic processes where they can mediate contacts between neurons and astrocytes. In an in vitro coculture system in which either only astrocytes or only neurons were null for the γPcdhs, we found that astrocytic γ-Pcdh is required for an early stage of synaptogenesis in a contact-dependent manner, while neuronal γ-Pcdh is sufficient for later stages. Conversely, if neurons lacked the adhesion molecules, very few synaptic contacts formed at all. By deleting the γ-Pcdhs from astrocytes in vivo, we demonstrated that these contacts are required for the normal progression of synaptogenesis. We also investigated the function of the γ-Pcdhs in the cerebral cortex. We found that cortical-restricted loss of the adhesion molecules resulted in a severe reduction in thickness of layer 1. By crossing the mutant mice to a line in which scattered layer 5 neurons express YFP, we saw that this thinning resulted from a reduced complexity in the apical tufts of dendrites from layer 5 neurons. Sholl