Miracle-dz/UniFAR-FTUs huggingface dataset:
Evowave: A Multiple Domain Metaphor for Software Evolution Visualization

The software evolution produces a lot of data during software development. Software engineers need to interpret these data to extract information that will help them in carrying out their daily activities. The use of Software Evolution Visualization (SEV) has been a promising approach to support this interpretation. This approach makes use of visual attributes that facilitate the interpretation of such data. Still, it is not trivial to visually represent all the data generated during the software development because software have different entities and attributes. Nevertheless, it is still necessary to deal with the temporal dimension of evolution. The SEV are usually built with the goal of helping activities related to a specific domain of software engineering. Many of these visualizations focus only on presenting an overview of the software development, without focusing on the details. However, most software development activities requires both: combine different domains and detailed information. Visual metaphors (i.e., concepts, associations and analogies to specific entities) used in these visualizations are very specific, aiming to assist only a certain domain. The use of multiple visualizations of the software to build the mental model of the system has been touted as an effective approach for the complete understanding of it. In most cases, these visualizations have a sets of visual metaphors. Because of this, the software engineer need to understand and become familiar with the visual metaphors of each of the visualizations while using them. One way to mitigate this problem is to use visualization that have a unique visual metaphor to view various aspects and perspectives of software. This work presents a new visual metaphor, called EVOWAVE, able to be used in multiple domains and to visualize the data in a comprehensive and detailed way. EVOWAVE is inspired by concentric waves as seen from above. This metaphor can represent large amounts of data and concepts cut across domains in the software engineering field. The development of this metaphor went through iterative phases that have refined the concepts associated with it. First we developed a prototype that has validated the ability of metaphor to represent large amounts of data. Then, studies were performed to validate the ability to represent information in different domains. The results indicate that the proposed metaphor can be used effectively in different domains of software engineering to assist in the execution of maintenance and development activities.
The effects on clinical trial activity of direct funding and taxation policy interventions made by government: A systematic review

Context: Governments have attempted to increase clinical trial activity in their jurisdictions using a range of methods including targeted direct funding and industry tax rebates. The effectiveness of the different approaches employed is unclear. Objective: To systematically review the effects of direct government financing interventions and allowing companies to reduce their tax payable on clinical trial activity. Data sources: Pub Med, Scopus, Sage, ProQuest, Google Scholar and Google were searched to the 11 th of April 2022. In addition, the reference lists of all potentially eligible documents were hand searched to identify additional reports. Following feedback from co-authors, information on a small number of additional interventions were specifically sought out and included. Data extraction: Summary information about potentially eligible reports were reviewed independently by two researchers, followed by extraction of data into a structured spreadsheet for eligible studies. The primary outcomes of interest were the number of clinical trials and the expenditure on clinical trials but data about other evaluations were also collected. Results: There were 1694 potentially eligible reports that were reviewed. Full text assessments were done for 304, and 30 reports that provided data on 43 interventions were included - 29 that deployed targeted direct funding and 14 that provided tax rebates or exemptions. There were data describing effects on a primary outcome for 25/41 of the interventions. The most common types of interventions were direct funding to researchers via special granting mechanisms and tax offsets to companies and research organisations. All 25 of the studies for which data were available reported a positive impact on numbers and/or expenditure on clinical trials though the robustness of evaluations was limited for many. Estimates of the magnitude of effects of interventions were reported inconsistently, varied substantially, and could not be synthesised quantitatively, though targeted direct funding interventions appeared to be associated with more immediate impact on clinical trial activity. Conclusion: There is a high likelihood that governments can increase clinical trial activity with either direct or indirect fiscal mechanisms. Direct funding may provide a more immediate and tangible return on investment than tax rebates.
UDCA and INT-777 suppress cardiac fibrosis triggered by IL- 11 through involvement of TGR5

Cardiac fibrosis occurs in a wide range of cardiac diseases and is characterised by the transdifferentiation of cardiac fibroblasts (FB) into myofibroblasts (MFB). Myofibroblasts produce large quantities of extracellular matrix proteins, resulting in myocardial scar. The antifibrotic effect of the bile acid ursodeoxycholic acid (UDCA) is established in cases of liver fibrosis but not the adult myocardium.Our hypothesis is: UDCA is antifibrotic in the adult heart, mediated by the membrane bile acid receptor Takeda G protein-coupled receptor 5 (TGR5). We constructed a predictive network of fibrosis using RNA-seq datasets. We found that UDCA and its semi-synthetic analogue INT-777, both reduced MFB markers in rat and human FBs and living myocardial slices. Utilising a knock-out mouse model, we show that the antifibrotic effect of UDCA is mediated by TGR5. Finally, we performed RNA-seq upon UDCA-treated human FB and integrated with our network of fibrosis, establishing the mechanism of action of TGR5 agonists.
NEAT (NicotinE As Treatment) Trial: Protocol of a randomised controlled trial of vaporised nicotine products compared with nicotine replacement therapy following discharge from residential withdrawal services.

Background: Tobacco smoking rates in alcohol and other drug (AOD) treatment settings is much higher than in the general Australian population. As a result, people seeking treatment for AOD use experience a greater tobacco-related burden of illness. Attempts to reduce smoking rates in AOD treatment consumers have failed to identify smoking cessation strategies with long term effectiveness. The primary aim of this study is to examine the effectiveness of nicotine vaporised products (NVPs) or nicotine replacement therapy (NRT)on self-reported 6 months continuous abstinence at the 9-month follow-up (6 months following end-of 12 weeks of nicotine treatment) for people leaving smoke-free residential withdrawal treatment. Both groups will also receive Quitline telephone counselling. Secondary outcomes and process measures will also be collected. Methods: A two-arm, single-blinded, parallel-group randomised trial with a 6-month post-intervention follow-up (9 months following baseline) will be conducted. The setting is five residential and inpatient government-funded AOD withdrawal units across three cities in three states of Australia (New South Wales, Queensland, Victoria). Participants will be service users aged 18 years or over who smoked at least 10 cigarettes per day, interested in quitting in the next 30 days and have capacity to give informed consent. Research assistants will recruit participants during intake, who then complete a baseline survey, will be randomised to a condition, and receive their first Quitline call during AOD treatment. At discharge, all participants receive a discharge pack containing either NVPs or NRT, depending on condition allocation. Discussion: This is the first study we know of that will be testing intervening with a tobacco smoking cessation approach during the transition phase from AOD treatment to community. From a public health perspective, this approach has the potential to have tremendous reach into a priority population for smoking cessation. Trial registration: Australian and New Zealand Clinical Trials Registry (ACTRN12619001787178)
The impact of trade and investment agreements on the implementation non-communicable disease policies, 2014-2019: protocol for a statistical study

Introduction Regulating tobacco, alcohol, and unhealthy foods and drinks is a cornerstone of global efforts to combat the Non-Communicable Disease (NCD) pandemic, but implementation of these policies remains slow. It has been suggested that producers of these unhealthy commodities use rules in Trade and Investment Agreements (TIAs) to delay and undermine NCD policy implementation. Yet, there is no systematic empirical evidence linking TIA participation to reduced implementation. Here we present a study protocol for a statistical analysis of the relationship between TIA participation and the implementation of regulations on tobacco, alcohol, and unhealthy food and drink in 154 countries, 2014-2019. Methods and analysis We aim to examine whether participation in TIAs with the EU and US is associated with implementation of regulations targeting tobacco, alcohol, and unhealthy food and drink. We focus on TIAs with these countries as their TIAs create multiple opportunities to contest health regulations, and a majority of the major unhealthy commodity producers are registered in these jurisdictions. Partial and full implementation is captured in a recently published dataset which systematically coded implementation of 11 NCD policies in 2014, 2016 and 2019. We will combine these outcome data with TIA membership and covariate data from multiple sources. We will calculate descriptive statistics and use both regression adjustment and matching to conduct covariate-adjusted, quasi-experimental comparisons of implementation levels and progress according to whether or not countries have a TIA with the EU or US. Further analyses and robustness checks will examine additional TIA participation arrangements and test the sensitivity of our results to our model specifications. Ethics and dissemination Ethics approval will not be required as the study uses anonymised and pre-aggregated data. Findings will be disseminated to policymakers via personal contacts and press releases in parallel with scientific papers and conference presentations.
Maintenance Record Analysis and Failure Data Regression

Abstract This paper presents how maintenance record analysis and failure data regression was done as part of a Reliability, Availability and Maintainability (RAM) study on an acetic acid plant in Europe. Specifically, it describes how to build a failure data bank (reliability database) from which the site can benefit to make the right decisions on maintenance strategies. By choosing the most appropriate maintenance strategy, it can increase the process availability and reduce cost and risks caused by unexpected shutdowns. This unique database will reflect the reality of the individual items and system failures in the process of the plant.
Robust Statistical Methods for Electricity Load Forecasting

The goal of this paper is to present a robust statistical methodology to model and forecast electricity load demand. The paper is focused solely on hourly short-term forecasts (from one- to seven-days-ahead), but the developments can be directly extended to data observed at higher frequencies (half-hourly, for example). Longer forecasting horizons can be also considered. Following many successful applications in the literature, a different model is constructed for each hour of the day. Each model is based on a decomposition of each of the 24 daily series in three components. The first component is related to possible long-run trends, seasonality, and special days effect. The second one measures the impact of weather variables and the third component describes the short-run dynamics and the dependence among adjacent hours. The volatility of the series is also carefully considered, such that robust confidence intervals are computed. All the modeling cycle is based on rigorous statistical arguments and both linear and nonlinear models (neural networks) are considered. Our proposal is put into proof with the famous dataset from Puget Sound Power and Light Company, USA.
Study and comparison on several different electronic transformer calibration methods

According to the different output signals from electronic transformer, this paper classifies electronic transformers into analog output and digital output. And this paper also has made the classification about calibration methods applied to these two kinds of electronic transformers in theory and according to their implementing modes. Through the research, analysis and comparison on the various calibration methods’ structure, theoretical basis, the operation ability and application forecast, this paper hereby points out their respective advantages and disadvantages. At the last part of the paper, the applicable calibration methods applied to analog output and digital output electronic transformers are provided. In addition, this paper brings forward the point that using the standard electronic transformer as standard’s electronic transformer is the final development objective of the electronic transformer calibration method.
Development of an electromyogram-based controller for functional electrical stimulation-assisted walking after partial paralysis.

66 Introduction.66 Methods . 68 Subjects.68 Data Acquisition and Processing.70 Muscle Selection . 72 Classifier Development and Offline Testing.73 Classifier Testing During FES-assisted Ambulation.75 Results.76 Classifier Performance . 76 Repeatability of the Classifier Performance.77 Discussion.77 Conclusion . 79 References.80 Figures . 84 Surface EMG-triggered FES-assisted gait parameters during over-ground walking in the laboratory . 91 Abstract.9
Typical characteristics of cloud manufacturing and several key issues of cloud service composition

In order to realize sharing and collaboration of manufacturing resources and manufacturing ability based on knowledge and to realize added value of manufacturing resource,through analyzing typical characteristics of Cloud Manufacturing(CMfg),relationships among resources,manufacturing cloud service and manufacturing cloud were discussed.Function structure of CMfg service management prototype system was designed.Requirements of Cloud Service Composition(CSC)in whole-lifecycle implementation of CMfg were studied.Several key issues for CSC,including modeling,description and consistency check,correlation relationship,composition flexibility,composition network as well as its dynamic characteristics,modeling evaluation,and optimal-selection were studied in particular.It provided theoretical foundation for realizing effective and intelligent CMfg service management.
Joint Distribution of Ages of Information in Networks

We study a general setting of status updating systems in which a set of source nodes provide status updates about some physical process(es) to a set of monitors. The freshness of information available at each monitor is quantified in terms of the Age of Information (AoI), and the vector of AoI processes at the monitors (or equivalently the age vector) models the continuous state of the system. While the marginal distributional properties of each AoI process have been studied for a variety of settings using the stochastic hybrid system (SHS) approach, we lack a counterpart of this approach to systematically study their joint distributional properties. Developing such a framework is the main contribution of this paper. In particular, we model the discrete state of the system as a finite-state continuous-time Markov chain, and describe the coupled evolution of the continuous and discrete states of the system by a piecewise linear SHS with linear reset maps. Using the notion of tensors, we first derive first-order linear differential equations for the temporal evolution of both the joint moments and the joint moment generating function (MGF) for an arbitrary set of age processes. We then characterize the conditions under which the derived differential equations are asymptotically stable. The generality of our framework is demonstrated by recovering several existing results as special cases. Finally, we apply our framework to derive closed-form expressions of the stationary joint MGF in a multi-source updating system under non-preemptive and source-agnostic/source-aware preemptive in service queueing disciplines.
An Approach for Automatic Construction of an Algorithmic Knowledge Graph from Textual Resources

There is enormous growth in various fields of research. This development is accompanied by new problems. To solve these problems efficiently and in an optimized manner, algorithms are created and described by researchers in the scientific literature. Scientific algorithms are vital for understanding and reusing existing work in numerous domains. However, algorithms are generally challenging to find. Also, the comparison among similar algorithms is difficult because of the disconnected documentation. Information about algorithms is mostly present in websites, code comments, and so on. There is an absence of structured metadata to portray algorithms. As a result, sometimes redundant or similar algorithms are published, and the researchers build them from scratch instead of reusing or expanding upon the already existing algorithm. In this paper, we introduce an approach for automatically developing a knowledge graph (KG) for algorithmic problems from unstructured data. Because it captures information more clearly and extensively, an algorithm KG will give additional context and explainability to the algorithm metadata.
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection

Model attributions are important in deep neural networks as they aid practitioners in understanding the models, but recent studies reveal that attributions can be easily perturbed by adding imperceptible noise to the input. The non-differentiable Kendall's rank correlation is a key performance index for attribution protection. In this paper, we first show that the expected Kendall's rank correlation is positively correlated to cosine similarity and then indicate that the direction of attribution is the key to attribution robustness. Based on these findings, we explore the vector space of attribution to explain the shortcomings of attribution defense methods using $\ell_p$ norm and propose integrated gradient regularizer (IGR), which maximizes the cosine similarity between natural and perturbed attributions. Our analysis further exposes that IGR encourages neurons with the same activation states for natural samples and the corresponding perturbed samples, which is shown to induce robustness to gradient-based attribution methods. Our experiments on different models and datasets confirm our analysis on attribution protection and demonstrate a decent improvement in adversarial robustness.
Efficient Learning of Interpretable Classification Rules

Machine learning has become omnipresent with applications in various safety-critical domains such as medical, law, and transportation. In these domains, high-stake decisions provided by machine learning necessitate researchers to design interpretable models, where the prediction is understandable to a human. In interpretable machine learning, rule-based classifiers are particularly effective in representing the decision boundary through a set of rules comprising input features. Examples of such classifiers include decision trees, decision lists, and decision sets. The interpretability of rule-based classifiers is in general related to the size of the rules, where smaller rules are considered more interpretable. To learn such a classifier, the brute-force direct approach is to consider an optimization problem that tries to learn the smallest classification rule that has close to maximum accuracy. This optimization problem is computationally intractable due to its combinatorial nature and thus, the problem is not scalable in large datasets. To this end, in this paper we study the triangular relationship among the accuracy, interpretability, and scalability of learning rule-based classifiers. The contribution of this paper is an interpretable learning framework IMLI, that is based on maximum satisfiability (MaxSAT) for synthesizing classification rules expressible in proposition logic. IMLI considers a joint objective function to optimize the accuracy and the interpretability of classification rules and learns an optimal rule by solving an appropriately designed MaxSAT query. Despite the progress of MaxSAT solving in the last decade, the straightforward MaxSAT-based solution cannot scale to practical classification datasets containing thousands to millions of samples. Therefore, we incorporate an efficient incremental learning technique inside the MaxSAT formulation by integrating mini-batch learning and iterative rule-learning. The resulting framework learns a classifier by iteratively covering the training data, wherein in each iteration, it solves a sequence of smaller MaxSAT queries corresponding to each mini-batch. In our experiments, IMLI achieves the best balance among prediction accuracy, interpretability, and scalability. For instance, IMLI attains a competitive prediction accuracy and interpretability w.r. t. existing interpretable classifiers and demonstrates impressive scalability on large datasets where both interpretable and non-interpretable classifiers fail. As an application, we deploy IMLI in learning popular interpretable classifiers such as decision lists and decision sets. The source code is available at https://github.com/meelgroup/mlic.
Reductive MDPs: A Perspective Beyond Temporal Horizons

Solving general Markov decision processes (MDPs) is a computationally hard problem. Solving ﬁnite-horizon MDPs, on the other hand, is highly tractable with well known polynomial-time algorithms. What drives this extreme disparity, and do problems exist that lie between these di-ametrically opposed complexities? In this paper we identify and analyse a sub-class of stochastic shortest path problems (SSPs) for general state-action spaces whose dynamics satisfy a particular drift condition. This construction generalises the traditional, temporal notion of a horizon via decreasing reachability: a property called reductivity. It is shown that optimal policies can be recov-ered in polynomial-time for reductive SSPs—via an extension of backwards induction—with an efﬁcient analogue in reductive MDPs. The practical considerations of the proposed approach are discussed, and numerical veriﬁcation provided on a canonical optimal liquidation problem.
An Experimental Comparison of Floating Base Estimators for Humanoid Robots with Flat Feet

Extended Kalman filtering is a common approach to achieve floating base estimation of a humanoid robot. These filters rely on measurements from an Inertial Measurement Unit (IMU) and relative forward kinematics for estimating the base position-and-orientation and its linear velocity along with the augmented states of feet position-and-orientation, thus giving them their name, flat-foot filters. However, the availability of only partial measurements often poses the question of consistency in the filter design. In this paper, we perform an experimental comparison of state-of-the-art flat-foot filters based on the representation choice of state, observation, matrix Lie group error and system dynamics evaluated for filter consistency and trajectory errors. The comparison is performed over simulated and real-world experiments conducted on the iCub humanoid platform.