Predictive Software Engineering: Transform Custom Software Development into Effective Business Solutions

The paper examines the principles of the Predictive Software Engineering (PSE) framework. The authors examine how PSE enables custom software development companies to offer transparent services and products while staying within the intended budget and a guaranteed budget. The paper will cover all 7 principles of PSE: (1) Meaningful Customer Care, (2) Transparent End-to-End Control, (3) Proven Productivity, (4) Efficient Distributed Teams, (5) Disciplined Agile Delivery Process, (6) Measurable Quality Management and Technical Debt Reduction, and (7) Sound Human Development.

Detecting Distributional Shift Responsible for Predictive Model’s Failure*

<div>Any predictive software deployed with this hypothesis that test data distribution will not differ from training data distribution. Real time scenario does not follow this rule, which results inconsistent and non-transferable observation in various cases. This makes the dataset shift, a growing concern. In this paper, we’ve explored the recent concept of Label shift detection and classifier correction with the help of Black Box shift detection(BBSD), Black Box shift estimation(BBSE) and Black Box shift correction(BBSC). Digits dataset from ”sklearn” and ”LogisticRegression” classifier have been used for this investigation. Knock out shift was clearly detected by applying Kolmogorov–Smirnov test for BBSD. Performance of the classifier got improved after applying BBSE and BBSC from 91% to 97% in terms of overall accuracy.</div>

Detecting Distributional Shift Responsible for Predictive Model’s Failure*

<div>Any predictive software deployed with this hypothesis that test data distribution will not differ from training data distribution. Real time scenario does not follow this rule, which results inconsistent and non-transferable observation in various cases. This makes the dataset shift, a growing concern. In this paper, we’ve explored the recent concept of Label shift detection and classifier correction with the help of Black Box shift detection(BBSD), Black Box shift estimation(BBSE) and Black Box shift correction(BBSC). Digits dataset from ”sklearn” and ”LogisticRegression” classifier have been used for this investigation. Knock out shift was clearly detected by applying Kolmogorov–Smirnov test for BBSD. Performance of the classifier got improved after applying BBSE and BBSC from 91% to 97% in terms of overall accuracy.</div>

Detecting Distributional Shift Responsible for Predictive Model’s Failure*

<div>Any predictive software deployed with this hypothesis that test data distribution will not differ from training data distribution. Real time scenario does not follow this rule, which results inconsistent and non-transferable observation in various cases. This makes the dataset shift, a growing concern. In this paper, we’ve explored the recent concept of Label shift detection and classifier correction with the help of Black Box shift detection(BBSD), Black Box shift estimation(BBSE) and Black Box shift correction(BBSC). Digits dataset from ”sklearn” and ”LogisticRegression” classifier have been used for this investigation. Knock out shift was clearly detected by applying Kolmogorov–Smirnov test for BBSD. Performance of the classifier got improved after applying BBSE and BBSC from 91% to 97% in terms of overall accuracy.</div>

Predictive Policing and Crime Control in The United States of America and Europe: Trends in a Decade of Research and the Future of Predictive Policing

There has been a significant focus on predictive policing systems, as law enforcement agents embrace modern technology to forecast criminal activity. Most developed nations have implemented predictive policing, albeit with mixed reactions over its effectiveness. Whilst at its inception, predictive policing involved simple heuristics and algorithms, it has increased in sophistication in the ever-changing technological environment. This paper, which is based on a literature survey, examines predictive policing over the last decade (2010 to 2020). The paper examines how various nations have implemented predictive policing and also documents the impediments to predictive policing. The paper reveals that despite the adoption of predictive software applications such as PredPol, Risk Terrain Modelling, HunchLab, PreMap, PRECOBS, Crime Anticipation System, and Azevea, there are several impediments that have militated against the effectiveness of predictive policing, and these include low predictive accuracy, limited scope of crimes that can be predicted, high cost of predictive policing software, flawed data input, and the biased nature of some predictive software applications. Despite these challenges, the paper reveals that there is consensus by the majority of the researchers on the importance of predictive algorithms on the policing landscape.

Development of CORPS-STIF 1.0 with application to ultra-high performance concrete (UHPC)

This report introduces the first release of CORPS-STIF (Concrete Observations Repository and Predictive Software – Structural and Thermodynamical Integrated Framework). CORPS-STIF is envisioned to be used as a tool to optimize material constituents and geometries of mass concrete placements specifically for ultra-high performance concretes (UHPCs). An observations repository (OR) containing results of 649 mechanical property tests and 10 thermodynamical tests were recorded to be used as inputs for current and future releases. A thermodynamical integrated framework (TIF) was developed where the heat transfer coefficient was a function of temperature and determined at each time step. A structural integrated framework (SIF) modeled strength development in cylinders that underwent isothermal curing. CORPS-STIF represents a step toward understanding and predicting strength gain of UHPC for full-scale structures and specifically in mass concrete.

Fintech Frontiers in Quantum Computing, Fractals, and Blockchain Distributed Ledger: Paradigm Shifts and Open Innovation

Among the hot research topics, Fintech is leading the trend in terms of the newest technology applications. The relatively new emerging paradigms in various sciences, such as geometry (fractals), physics (quantum), and database systems (distributed ledger—blockchain), seem to potentially contribute to a greater shift in the framework of the finance industry, bringing also some concerns (cyber-threats). Consistent and extensive investigation of the reasonable potential impact of these new models (and their underlying technologies) is performed, and then tested through a SWOT analysis, as the main objective of this research. Threats and opportunities are always intrinsically driven by the introduction of technological advancements (revolutions). This research confirms that information availability and the increasing interconnection of crosswise applications of each discovery to the different fields of science is determining the rapid succession of revolutions identified by evident large shifts in economic paradigms. The growing computing capacity and the development of increasingly powerful predictive software are leading to a competitive, extremely dynamic, and challenging system. In this context, as shown by history, there is a high possibility of market concentration in which, however, only a few corporations—digital giants—can afford to develop these technologies, consolidating their dominance.

SAPPHIRE: a neural network based classifier for σ70 promoter prediction in Pseudomonas

Background In silico promoter prediction represents an important challenge in bioinformatics as it provides a first-line approach to identifying regulatory elements to support wet-lab experiments. Historically, available promoter prediction software have focused on sigma factor-associated promoters in the model organism E. coli. As a consequence, traditional promoter predictors yield suboptimal predictions when applied to other prokaryotic genera, such as Pseudomonas, a Gram-negative bacterium of crucial medical and biotechnological importance. Results We developed SAPPHIRE, a promoter predictor for σ70 promoters in Pseudomonas. This promoter prediction relies on an artificial neural network that evaluates sequences on their similarity to the − 35 and − 10 boxes of σ70 promoters found experimentally in P. aeruginosa and P. putida. SAPPHIRE currently outperforms established predictive software when classifying Pseudomonas σ70 promoters and was built to allow further expansion in the future. Conclusions SAPPHIRE is the first predictive tool for bacterial σ70 promoters in Pseudomonas. SAPPHIRE is free, publicly available and can be accessed online at www.biosapphire.com. Alternatively, users can download the tool as a Python 3 script for local application from this site.