Reviewerly/exHarmony huggingface dataset:
Small Aerial Target Detection Algorithm Based on Improved YOLOv5

Small target detection is a difficult problem in the field of target detection. Small targets have many characteristics, such as less visual information, difficult to extract discriminative features, easy background confusion and so on, as a result, its detection accuracy is often only half of the large target. To solve these problems, we study small target detection in UAV aerial scene, and propose an improved YOLOv5-based small target detection algorithm. First, we performed two optimizations on the YOLOv5 prediction scale, reducing the missed detection of small targets and reducing model parameters and computational complexity, while reducing the negative impact of scale mismatches. Then, based on the idea of BiFPN, we introduce the skip connection from backbone network to PAN, and use Concat weighted fusion instead of the weighted fusion in BiFPN to improve the detection precision of the model. Finally, we do some experiments on the data set of Visdrone2019. The experimental results show that the mAP@0.5 and mAP@0.5:0.95 of the improved model are improved by 6.6% and 4.2%, respectively, the parameters of the model are reduced and YOLOv5 is light weight.
A Bilevel Genetic Algorithm for Global Optimization Problems

Genetic algorithm is an important intelligent optimization algorithm that operates on specific population by simulating the natural evolution process and using artificial evolution to continuously optimize the population so as to search for the optimal solution. At present, there are a large number of methods focus on improving genetic algorithms, but the current stage of genetic algorithm tends to have the problems of falling into local optimal premature and slow convergence. In this paper, we try to design a bilevel evolutionary particle swarm optimization algorithm based on the idea of genetic algorithm within the framework but without increasing the complexity, using a data-driven idea, and verify it by the genetic algorithm in the commercial software MATLAB. Numerical experiments show that the data-driven a bilevel genetic algorithm-based algorithm significantly improves the algorithm performance.
An Atomic Retrospective Learning Bare Bone Particle Swarm Optimization

In order to increase the diversity of bare-bone particle swarm optimization (BBPSO) population search range, enhance the ability to jump out of local optimum, we propose an atomic retrospective learning bare-bone particle swarm optimization (ARBBPSO) algorithm based on BBPSO. Different from the renewal strategy of BBPSO, inspired by electron motion around protons in ARBBPSO, we use a strategy of motion around nuclei to increase population diversity. At the same time, the retrospective learning strategy is used to allow the proton particles to have a chance to correct errors during the process of updating, thus allowing the population to have a chance to evade falling into a local optimum. To verify the performance of the proposed algorithm, 29 benchmark functions of CEC2017 are chosen to compare with four well-known BBPSO-based algorithms. The experimental results indicate that ARBBPSO is superior to several other algorithms for improving BBPSO from a comprehensive consideration.
Hybrid Algorithm Based on Phasor Particle Swarm Optimization and Firefly Algorithm

This paper proposes a new hybrid algorithm (HBPPSO) based on the Phasor Particle Swarm Optimization (PPSO) and the Firefly Algorithm (FA) to improve particle velocity and position updates. Initially, a trigonometric updating approach is presented for nonlinearly adjusting particle velocity. The particle position is then controlled using an exponential updating technique. Following that, six groups of experiments are performed to validate the optimization performance of the proposed HFPPSO method. The phasor particle swarm optimization algorithm and particle swarm optimization are chosen as the comparison algorithms. Finally, the experimental findings show that HBPPSO unifies the strengths of managing the learning parameters in both PPSO and FA algorithms while alleviating the weakness of premature convergence, improving searching accuracy and avoiding slipping into local optimum.
Moving Toward the Physical Internet: A Model that Moves Toward Sustainability Against a Necessary Backdrop of Industrial Transformation

A new idea is emerging more and more strongly in the European UnionEuropean Union (EU), of the so-called Physical InternetPhysical internet, a model that aims to handle physical goods in their storage and transport processes in a parallel way to how the Internet handles data, which involves efficiencyEfficiency and sustainabilitySustainability. Some steps and considerations will be required, and in the background, we perceive an appreciable necessary transformation in the volumes and dimensions of the goods to be transported and therefore in the industrial and consumer products.
“What do you read, my lord?”: Purposes and Potential of English Literary Studies

Based on the question of the relationship between “deep reading” and “skim reading”, this article explores the question of which techniques, contents and competences can be promoted and taught in the context of a literary studies programme in English. It outlines a number of techniques, contents and competences that students can or should acquire in the course of their studies in literary studies in English. These include the techniques of deep reading and skim reading as well as self-expression in academic exchange. After a brief presentation of the most important “educational contents” (Dehn et al., Lesesozialisation, Literaturunterricht und Leseförderung in der Schule, in: Bodo Franzmann et al. (Ed.): Handbuch Lesen. Baltmannsweiler: Schneider Verlag Hohengehren 2001, pp. 568–637, 2001), five central competences are outlined on the basis of selected examples of English literature (and above all from Shakespeare’s plays), which can be fostered and developed in the examination of English literature in the context of an English studies programme.
Pull Requests Integration Process Optimization: An Empirical Study

Pull-based Development (PbD) is widely used in collaborative development to integrate changes into a project codebase. In this model, contributions are notified through Pull Request (PR) submissions. Project administrators are responsible for reviewing and integrating PRs. In the integration process, conflicts occur when PRs are concurrently opened on a given target branch and propose different modifications for a same code part. In a previous work, we proposed an approach, called IP Optimizer, to improve the Integration Process Efficiency (IPE) by prioritizing PRs. In this work, we conduct an empirical study on 260 open-source projects hosted by GitHub that use PRs intensively in order to quantify the frequency of conflicts in software projects and analyze how much the integration process can be improved. Our results indicate that regarding the frequency of conflicts in software projects, half of the projects have a moderate and high number of pairwise conflicts and half have a low number of pairwise conflicts or none. Futhermore, on average 18.82% of the time windows have conflicts. On the other hand, regarding how much the integration process can be improved, IP Optimizer improves the IPE in 94.16% of the time windows and the average improvement percentage is 146.15%. In addition, it improves the number of conflict resolutions in 67.16% of the time windows and the average improvement percentage is 134.28%.
Software Project Management Approaches Used in the Context of Agile Development: A Systematic Literature Mapping

Project management is of significant importance to the success of software development, as experts remind us. However, projects have a high degree of uncertainty and are complex in nature. This suggests that specific approaches are needed to manage these threats. Using the right approach can be critical to the success of a project. Knowing the importance of project management and how the approaches can be fundamental to help managers, this work sought to identify in the literature the project management approaches that are used in agile software development. To achieve the objective, a systematic literature mapping study was carried out, which made it possible to identify eight distinct categories of approaches that are used in agile software project management. In addition, it was possible to identify that the approaches are most commonly applied to manage the schedule, quality and communication of projects.
BPMN4FRSS: An BPMN Extension to Support Risk-Based Development of Forensic-Ready Software Systems

The importance of systems secure-by-design is well recognised. However, incidents or disputes requiring thorough investigation might occur even in highly secure systems. Forensic-ready software systems aim to ease the investigations by including requirements for reliable, admissible, and on-point data - potential evidence. Yet, the software engineering techniques for such systems have numerous open challenges. One of them, representation and reasoning, is tackled in this chapter by defining the syntax and semantics of modelling language BPMN for Forensic-Ready Software Systems (BPMN4FRSS). In addition to representing the requirements and specific controls, a semantic mapping to forensic-ready risk management is defined to support risk-oriented design. This approach of designing forensic-ready software systems, supported by BPMN4FRSS models, is then demonstrated.
Opportunities to Improve the Effectiveness of Online Learning Based on the Study of Student Preferences as a “Human Operator”

The article explores the possibilities of improving the effectiveness of online learning based on the analysis of a student as a “human operator” engaged in working with information in the “man-machine” system. The results of the empirical research conducted by the author are presented. With the help of the author's questionnaire, the preferences of students of different levels of education and different areas of training in relation to individual electronic didactic tools, their characteristics and methods of organizing interaction with students in theoretical classes (lectures) used by teachers in the educational process were studied. The preferences common to the whole group of respondents were revealed: trainees prefer a variety of templates and slides used, the use of different information visualization tools (figures, tables), the practical orientation of theoretical classes and their interactive nature. Undergraduate students prefer lectures to a greater extent than undergraduates, which include practical tasks, tests, that is, interactive elements. Master's students perceive lectures – videos and abstract-schematic images of people on slides more positively. For students of technical fields of training, the most important factor was the readability of the slides. Slides with color highlighting of formulas and important elements of the text turned out to be attractive for students of the humanities. Men prefer the Canva program for creating presentations and using a light background of slides, women prefer highlighting important information elements (definitions, formulas) in color and font. Conclusions are drawn that when teaching the same subjects, the style of presentation of information itself should differ if the teacher works with different bachelor's and master's degrees, as well as with different areas of training. Both the format of the slides and the organization of interactions with students is important for getting pleasure in the learning process and, accordingly, for the effectiveness of learning. It is indicated that the search for opportunities to convert presentations and interaction formats within the framework of training sessions from those given to those subjectively convenient for the learner is the way of individualization of digital education. #CSOC1120.
Anthropological dilemmas of information society development modern stage in the context of globalisation challenges

Aims: The purpose of the article is to highlight the anthropological dilemmas of contemporary philosophy related to the establishment of the information society. The global, local, and individual dimensions of informatization are projected onto modern human, which leads to his/her new positioning in the scientific and worldview picture of the world. The task of scientific research is to concretise the anthropological dilemmas of our time that are caused by information and technological transformations in society, namely: autonomy-dependence, traditionalism-innovation, and security-risk. Methodology: When the key research question is a dilemma, the most appropriate methodological philosophical approach is the dialectic one. It is based on the search for the truth of knowledge or conditions of development through contradictions and confrontations. At the same time, the entire arsenal of general scientific methods is also mostly oriented towards the dialectical format of research, actualising elements of comparative analysis, comparison, and modelling in the person interpretation in the current information and technological space. Results: The results of the article determine the dominance of pragmatic dimensions of efficiency in almost all anthropological dichotomies proposed in the study. Information and technology form a digital holistic system where the elimination of human-dimensional elements from the existential, epistemological, and axiological segments of the worldview paradigm of our time is initiated. Scientific Novelty: The scientific novelty of the study emphasises that classical anthropological constants such as autonomy, security, style, and traditions acquire contradictory characteristics in the permanent information and digital process of society development. Conclusion: Philosophy, by maintaining a constant methodological balance at the level of: contradiction-unity, makes another (in the cultural and historical dimension) attempt to resolve the problem of human positioning in the context of the dynamic development of a new type (in this case, a global information and technological) society.
An Exploration of Technical Debt over the Lifetime of Open-Source Software

Technical debt represents unwanted issues that result from decisions made to speed up the design or implementation of software at the expense of resolving existing issues. Like financial debt, it consists of the principal and an interest. Debt is usually paid back through code rewrites, refactoring, or the introduction of test code. When unchecked, interest can accumulate over time and lead to development crises where focus and resources must be shifted to resolve existing debt before the development process can be resumed. Existing software tooling allows practitioners to quantify the level of debt and identify its sources, allowing decision makers to measure and control it. We propose a detailed exploration of the characteristics of source code technical debt over the lifetime of several popular open-source applications. We employed a SonarQube instance configured for longitudinal analysis to study all publicly released versions of the target applications, amounting to over 15 years’ worth of releases for each. We found that a small number of issue types were responsible for most of the debt and observed that refactoring reduced debt levels across most application packages. We observed increased variance in technical debt distribution and composition in early application versions, which lessened once applications matured. We addressed concerns regarding the accuracy of SonarQube estimations and illustrated some of its limitations. We aim to continue our research by including additional tools to characterize debt, leverage existing open data sets and extend our exploration to include additional applications and types of software.
A Simple yet Smart Welding Path Generator

In this paper, we propose a novel approach for automated path planning in robotic welding using point cloud data obtained from laser seam tracking. Our approach involves hierarchical segmentation of the point cloud using 3D and 2D RANSAC [12] techniques, combined with Alpha Shapes [10] and appropriate distance measures to deliver different pathfinding results. We conduct experiments using point cloud data from a real welding scenario to evaluate the effectiveness of our proposed approach. The results show that our approach can generate accurate welding paths with minimal manual intervention and in a fraction of the time required for manual path generation. Future work could explore the use of advanced path planning algorithms to generate more complex paths that take into account welding parameters.
Contextual Text Mining on Social Media of Political Leaders Using Machine Learning Algorithms

Now a day’s most of the political leaders use social media to easily communicate with the people, such as sharing their ideas, promoting their policies etc. Contextual text mining is used to acknowledge the opinions of the political leaders as well as attitude on different subjects such as opinions, discussions and microblogs. Natural language processing (NLP) is included for performing the contextual text mining in order to provide the communication between the human and the machine with natural language. A new model has been implemented to compare the dataset of the political leaders which is extracted from facebook and twitter with different machine learning algorithms like Support Vector Machine (SVM), Naive Bayes Classifier (NBC) and Ensemble Learning Methods (ELM) to provide better and accurate results than other machine learning algorithms.
Placement Analysis for Students using Machine Learning

Every University/College want their students to get placed in a good company with a better package. They create the syllabus so, that students will gain the most knowledge out of the study period. But they are not sure whether the students are getting trained with proper guidance and instruction to be placed. There is a need for metric to find the progress of the student’s placement. So, the student can speed up his/her preparation to meet the demands of the minimum criteria of placement. This metric is called the placement analysis system, it takes attributes like internships completed, papers published, aptitude scores, etc to predict where the individual will get placed i.e., Dream company, Core company, Normal company or not get placed. For this the machine learning algorithms are used to predict the results using three basic algorithms Random Forest, Decision tree and K-means clustering the accuracy of the algorithms are determined to find the optimal algorithm. The past data on the placement results were fed as the training dataset and a part of it is used for testing the accuracy of the model. Then if the accuracy is good, this can be used to predict the possibility of a student getting placed. If the student is unhappy with the result, then the model can be used to find the area where the student needs to improve to get to his desired goal. If properly implemented and the students work consistently, this aids in providing solutions to meet every student's goal.
Adaptive weighting strategy for fault detection and diagnosis of rotating machinery components incorporating multiple operational conditions

Driven by the increasing needs for production safety in mass varieties production process, an adaptive weighting strategy is proposed for fault detection and diagnosis to weaken the influence brought by the change of operational conditions. To this end, considering the correlation between features and operational conditions, representative features extracted from multi-domain are selected using max-relevance and min-redundancy (mRMR) to establish the connection between features and operational condition. On this basis, a health index (HI) is fused for fault detection based on adaptive weight coefficients calculated according to the difference of features. Moreover, the input weight matrix of extreme learning machine (ELM) is redesigned based on the adaptive weight coefficients and the historical information to curb the impact of random factors. The effectiveness of the proposed method is demonstrated by rolling bearing test rig and industrial reciprocating pump. The results show that the rate of detection using the HI designed in this paper can reach 100% and the average diagnosis accuracy can reach [Formula: see text] and [Formula: see text].
Search framework for neutral bits and boomerangs in SHA‐1 collision attacks

Abstract Neutral bits and boomerangs are key techniques for accelerating collision search in SHA‐1 attacks. The current acceleration techniques for SHA‐1 near‐collision attacks are reviewed and a generic search framework for neutral bits and boomerangs is presented. The framework can efficiently construct auxiliary paths for a given differential path and message bit relations and find and store ideal boomerangs. The framework was applied to free‐start attacks for 76‐step SHA‐1, and the complexity was reduced from to . Relaxing the boomerang's restrictions on message words, the authors propose an accelerating technique termed semi‐boomerangs, combined with the search framework of boomerangs, which increases the speed of collision search by 3.48 times in a free‐start attack for 80‐step SHA‐1 and the complexity for 80‐step attack was reduced from to .
Image Captioning Using Deep Transfer Learning

It has been interesting to generate captions from an image due to its real-life significance and applicability in day-to-day applications. Moreover, this phenomena is related to field of computer vision for object identification and natural language processing (NLP) for representation as well. In this research, we proposed a model which employs transfer learning approach to generate a caption from an input image. To train our model, we used Flickr8k dataset for our experiment. This dataset has images of six unique Flickr groups and in total it consists of around 8000 image and each image contains five types of captions. At first, we extracted features from the images using CNN. Subsequently these features are trained through transfer learning. Subsequently a RNN is used as a decoder for generating the language text. The trained model performs better for other datasets also.
Prolegomena to a Political Economy of Knowledge beyond Rentiership

This chapter begins by situating academic rentiership in the deeper issues surrounding ‘cognitive economy’ that lie at the heart of the Western philosophical tradition. The psychological phenomenon of the ‘Gestalt shift’ exemplifies various ways in which the flow of information can be channelled to enable or disable certain possible ways of regarding the world, or ‘modal power’. Against this background, such familiar concepts from academic practice as ‘quality control’ and ‘plagiarism’ acquire a new look. Quality control sustains rentiership, whereas plagiarism undermines it. Aesthetics – especially modernist concept of ‘artworld’ – offers guidance on how to understand this point. The rest of the chapter is largely concerned with the historical swings back and forth between rentiership and anti-rentiership in the history of knowledge production. Rentiership’s decline is associated with corruption of its quality control processes, which reveals academia’s reliance on equally corrupt power elites in society. Moreover, the periodic introduction of new communication media over which academia cannot exert proprietary control – from the printing press to the internet – has historically served to redistribute the sphere of plausible knowledge claims, and modal power more generally. The chapter ends with a consideration of the stakes in the reduction of knowledge to information, an explicit aim shared by Silicon Valley enthusiasts and neoliberals alike.