Reviewerly/exHarmony huggingface dataset:
Metalearning Approaches for Algorithm Selection I (Exploiting Rankings)

Summary This chapter discusses an approach to the problem of algorithm selection, which exploits the performance metadata of algorithms (workflows) on prior tasks to generate recommendations for a given target dataset. The recommendations are in the form of rankings of candidate algorithms. The methodology involves two phases. In the first one, rankings of algorithms/workflows are elaborated on the basis of historical performance data on different datasets. These are subsequently aggregated into a single ranking (e.g. average ranking). In the second phase, the average ranking is used to schedule tests on the target dataset with the objective of identifying the best performing algorithm. This approach requires that an appropriate evaluation measure, such as accuracy , is set beforehand. In this chapter we also describe a method that builds this ranking based on a combination of accuracy and runtime, yielding good anytime performance. While this approach is rather simple, it can still provide good recommendations to the user. Although the examples in this chapter are from the classification domain, this approach can be applied to other tasks besides algorithm selection, namely hyperparameter optimization (HPO), as well as the combined algorithm selection and hyperparameter optimization (CASH) problem. As this approach works with discrete data, continuous hyperparameters need to be discretized first.
Automating Workflow/Pipeline Design

Summary This chapter discusses the design of workflows (or pipelines ), which represent solutions that involve more than one algorithm. This is motivated by the fact that many tasks require such solutions. This problem is non-trivial, as the number of possible workflows (and their configurations) can be rather large. This chapter discusses various methods that can be used to restrict the design options and thus reduce the size of the configuration space. These include, for instance, ontologies and context-free grammars. Each of these formalisms has its merits and shortcomings. Many platforms have resorted to planning systems that use operators. These can be designed to be in accordance with the given ontologies or grammars. As the search space may be rather large, it is important to leverage prior experience. This topic is addressed in one of the sections, which discusses rankings of plans that have proved to be useful in the past. The workflows/pipelines that have proved successful in the past can be retrieved and used as plans in future tasks. Thus, it is possible to exploit both planning and metalearning.
Denoising of Maritime Unmanned Aerial Vehicles Photography Based on Guidance Correlation Pixel Sampling and Aggregation

In this paper, we proposed a guidance correlation pixel Sampling and aggregation image denoising for maritime Unmanned Aerial Vehicles(UAVs) photography, providing a reliable data base for maritime reconnaissance work. The overall step is mainly composed of two parts, pixel sampling and pixel aggregation. To improve the content correlation of sampled pixels, we propose a guided sampling scheme based on the basic estimated map and extend this algorithm to the restoration of maritime UAVs image. Finally, a UAVs image denoising system is shown. Our experimental results show that the proposed algorithm can effectively remove noise and achieves <inline-formula> <tex-math notation="LaTeX">${32.98}{dB}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${31.83}{dB}$ </tex-math></inline-formula> in Set12 and BSD68 datasets with less and image distortion. In the actual scene, the PSNR of our denoising algorithm has reached <inline-formula> <tex-math notation="LaTeX">${35.33}{dB}$ </tex-math></inline-formula>, meets the basic needs of practical vision and follow-up research.
Improving the efficiency of virtual-reality-based ergonomics assessments with digital human models in multi-agent collaborative virtual environments

Often new digital tools are introduced alongside existing tools and workflows to augment and fill gaps in current processes. Virtual and augmented reality (XR) tools are currently being deployed in this way within design processes, allowing for interactive visualization in virtual environments including the use of DHM tools. Currently, the focus is on how to implement XR as a stand-alone tool for single-user scenarios. However, in collaborative design contexts, screen-based and XR tools can be used together to leverage the benefits of each technology maximizing the potential of multi-user design processes. XR allows for an immersive exploration of designed objects in 3D space, while screen-based tools allow for easier notetaking and integration of additional non-3D software and meeting tools. Ensuring that these technologies are integrated in a mutually beneficial manner requires a framework for determining the best combination of technologies and interfaces for diverse design teams. This paper presents a framework for performing collaborative design reviews in a digital environment that can be accessed using both XR and 2D screen devices simultaneously. It enables asymmetric collaboration to provide each design team member with the technology that best fits their workflow and requirements.
Image processing based package volume measurement system using kinect sensor

In this study, an image processing based volume measurement system is presented.The proposed scheme provides high accuracy, low cost and ease of calibration.There a r e n o additional requirements such as alignment of objects during measurement, positioning the objects at a certain position in the measuring field, and these advantages reduce the labor effort in real-time.In the first stage, a problem-specific calibration process was performed and then Hough transform was applied in the image processing phase.Although the proposed method has been developed primarily to measure the volume of box-type objects, it also provides an approximate volume measurement for objects of random geometry.The system tested under various external disturbances has produced satisfactory results.
Determinants of university students' satisfaction with information technology based classroom use by pandemic Covid19

Educational activities have changed due to the pandemic and it was necessary to respond by giving continuity to the formative processes with virtual platforms as it happened at the Universidad Nacional del Centro del Perú - School of Social Work. The objective was to know the level of satisfaction of students in the use of the virtual platform chosen, using the Questionnaire for the evaluation of learning environments in distance education (Sp-DELES) of 42 questions with dimensions: teacher support, student interaction and collaboration, personal relevance, real learning, active learning, autonomy and satisfaction. A total of 213 students were consulted, the instrument was distributed digitally and anonymously. For the descriptive analysis, the results were ranked in three response categories: agree, indifferent and disagree; the Autonomy dimension was valued with 69.01% agreement and 9.39% disagreement. The dimension with the lowest appreciation was Satisfaction with 25.35% agreement and 28.64% disagreement. Structural equation modeling was performed with SmartPLS, obtaining that Faculty support (FS) and Active learning (AL) have a direct relationship with Satisfaction (S) in the use of the virtual platform with beta values (β) of 0.279 and 0.872 respectively. Contrarily, Real learning (RL) and Autonomy (A) have an inverse relationship with Satisfaction (S) with beta values (β) of -0.025 and -0.122 respectively. The study reflects that there is no relationship between Interaction and Communication among Students (ICS), Personal Relevance (PR) and Satisfaction (S) as the p-values were greater than 0.05. The value of the coefficient of determination (R2) was 0.778 being the representative model. The study is non-experimental, descriptive, explicative and cross-sectional.
Correction to: A nomogram model for the risk prediction of type 2 diabetes in healthy eastern China residents: a 14‑year retrospective cohort study from 15,166 participants

[This corrects the article DOI: 10.1007/s13167-022-00295-0.].
Analysis of the giralda tower geotechnical profile and its settlements

The preservation of the cultural heritage is a current and challenging issue for the sustainable development of countries, such is the case in the European Union.Seville is a Spanish city located in the southern Iberian Peninsula, and it is well-known for the importance of its cultural heritage.It is one of the main sources for its economic growth, employment and cultural development.The Giralda tower of the Cathedral of Seville is its most representative building.It has been declared as World Heritage Site by the UNESCO due to its patrimonial value.The Giralda was constructed in 1384 as the Aljama of the major mosque of Seville.However, it has undergone different construction phases over time.One of the most relevant modifications was the addition of the bell tower designed by the architect Hernán Ruíz in the Renaissance.Moreover, the tower has been affected by several historic earthquakes.The goal of this study is to define the geotechnical profile of the soil under the tower and to analyse its settlements.This study will focus on the several modifications that the building has suffered throughout its history.Moreover, it will properly and exhaustively characterise the foundation, which has not been carried out to date.To do so, the geotechnical profile has been defined accurately with the information of the boreholes drilled at its base.Then, finite elements have been used to model the different load phases, which correspond to the different construction phases.Finally, this analysis has shown a great agreement between the settlements of the tower and its real top displacements.
FRAMING THE ATTACKER IN ORGANIZED CYBERCRIME

When large values are at stake, the attacker and the attacker’s motives cannot be easily modeled, since both the organization at stake and the possible attackers are unique and have complex motives. Hence, rather than using stereotypical attacker models, recent work proposes realistic profiling of the opponent by the use of user-centered design principles in form of the persona methodology. Today, cybercrime is often organized, i.e., attacks are planned and executed by an organization that has put together a tailor- made team consisting of the necessary skills for the task. The actual individuals taking part in the attack might not be aware of or interested in the overall organizational motives. Rather, taking motives behind espionage, fraud, etc., into account requires consideration of the attacking organization rather than the individuals. In this paper, based on interviews with IT security experts, we build on the attacker persona methodology and extend it with methodology to also handle organizational motives in order to tackle organized cybercrime. The resulting framework presented in the paper extends the attacker persona methodology by also using narratives in order to assess the own organization’s security. These narratives give rise to intrigue sketches involving any number of attacker personas, which hence, make it possible to take organized cybercrime into account. Keywords: organization, attacker models, espionage, fraud, cybercrime, security experts, framework, persona methodology.
A Question Answering Method of Knowledge Graph Based on BiLSTM-CRF and Seq2Seq

In natural language processing, intelligent question answering based on knowledge graph has received great attention. In the previous knowledge base question answering, the traditional word vector is difficult to express the text semantic information, and the cyclic neural network is easy to cause gradient disappearance and gradient explosion. At the same time, it is lack of comprehensive consideration of text context information. This paper proposes an intelligent Q &#x0026; A method based on knowledge graph, which uses BiLSTM-CRF model to realize entity recognition. The intelligent Q &#x0026; A model is constructed based on Seq2Seq, and the above methods are verified by taking the intelligent Q &#x0026; A as an example, which effectively improves the accuracy of intelligent Q &#x0026; A.
Security Issues in Cloud Computing

Cloud computing has aided the evolution of IT by improving the ability and flexibility of data storage, as well as providing scalable computation and processing capabilities that match the dynamic data sets. Many corporate application programs have been transferred to public and hybrid clouds due to the multiple benefits of cloud computing. On the other hand, many businesses identify privacy and data security as major concerns that prevent them from adopting cloud computing. The only way to achieve successful cloud implementation is to improve and manage data security and privacy in the cloud. This research paper looks at data privacy and security in cloud computing at all stages of the data lifecycle, providing a general overview of the technology while highlighting important security difficulties and concerns that must be addressed. It also examines a number of existing solutions and gives recommendations for new ones that can improve data privacy and security in the cloud. Finally, future research on data privacy and security in cloud systems is discussed in the study report.
OCR of Kannada Characters Using Deep Learning

Kannada, A dravidian language of south India that consists of kannada numerals from 0 to 9 and 49 letters that are further classified into swara, vyanjana and yogavahagalu. The task Optical Character Recognition(OCR) is to transform printed or handwritten text into digital form. This technique can be explored to extract kannada numerals and letters from images of handwritten documents, processed using image processing techniques such as segmentation, skewing and slanting using OpenCV. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Convolutional neural network(CNN) is a deep learning technique that can be used to train the model and classify kannada characters using Tensorflow and Keras. Our study has showed that our model has outperformed present methods to classify Kannada numerals and characters with 100% accuracy.
ArXiv in the Open Access Era: its usage and impact on physics researchers

ArXiv is an Open Access (OA) e-prints repository of physics, mathematics, computer science, and related subjects.This study aims to determine the use and impact of arXiv on physics researchers of selected research institutes in Bengaluru, India.The study was conducted using the survey method, and a structured questionnaire was distributed to physics researchers working in five research institutions in Bengaluru.The first part of the questionnaire consists of demographic information second part consists of the usage of arXiv, and the last section includes the impact of usage and satisfaction level of users.The response was 73%, and analysis revealed that 94% use arXiv.The study showed that more than 50% contribute to arXiv and that they are highly satisfied with using this resource.Further, the study has also shown that, by contributing to arXiv, the researchers benefited from significant impacts, such as getting good feedback, collaborations, and increased citations.We believe that this study will encourage researchers to use and contribute to OA resources and also motivate libraries to promote OA repositories.
Web Based UCloud Application Using CodeIgniter Framework

Internet-based data storage services or called the cloud are becoming a global technology trend, the cloud that offers unlimited scalability is becoming very reliable. Educational institutions such as Qamarul Huda Badaruddin University are always constrained by poor data management because there is no centralized data storage area that can be accessed together. With the presence of UCloud, data management problems can be resolved. Designed using the web-based CodeIgniter framework, Ucloud has been successfully implemented and optimally utilized by the academic community, so that data can be managed properly.
GeoSPARQL-Jena: Implementation and Benchmarking of a GeoSPARQL Graphstore

This work presents an RDF graphstore implementation for all six modules of the GeoSPARQL standard using the Apache Jena Semantic Web library. Previous implementations have provided only partial coverage of the GeoSPARQL standard. There is discussion of the design and development of on-demand indexes to improve query performance without incurring lengthy data preparation delays. A supporting benchmarking framework is also discussed for the evaluation of any SPARQL compliant queries with interfaces provided for integrating additional test systems. This benchmarking framework is utilised to examine the performance of the implementation against two existing GeoSPARQL systems using the Geographica benchmark. It is found that the implementation achieves comparable or faster query responses than the alternative systems while also providing much faster dataset loading and initialisation durations.
Intelligent Tutoring of Learners In E-learning Systems and Massive Open Online Courses (Mooc)

In the last few years, many terms related to learning environments have emerged.Each one of these terms is distinguished by a set of criteria such as the target audience, the duration of learning, the type and nature of the educational content, the manner of dissemination of knowledge, etc.Among problems encountered in these environments, the lack of support for learners seems to be a serious problem that requires special attention.Tutoring can be seen as a good solution for this problem; it consists of giving assistance to learners who are in need for help.Regardless the nature of this assistance (pedagogical, social, etc.), it can be delivered in many forms: advice, guidance or even recommendation.If tutoring has been applied for a long time in traditional e-learning environments, its application in new systems such as Massive Open Online Courses (MOOC) is still under study.In fact, a considerable number of studies driven on MOOCs had reported the problem of learners' dropout.Several reasons can be listed as causes of such a problem.Among these reasons, we can find learners' isolation as well as learners' loss of motivation.This same problem has been reported by researchers working in the field of Computer-based Environments for Human Learning.In this article, we propose a new vision on how to apply an intelligent tutoring process in human learning systems in general and in MOOCs in particular.This new vision is based on the behaviors and skills of learners.This activity can take many forms and can be carried out by different types of actors (teachers, learners, etc.).
Hierarchical Optimal Path Planning (HOPP) for Robotic Apple Harvesting

Apples are among the most consumed fruits in the United States.Currently, almost all apples destined for the fresh market are picked by the human hand.Due to the shortage of seasonal manual labor and rising costs in manual apple picking, robotic apple harvesting has been explored for years.However, a challenge in developing and deploying an apple harvesting robotic system is how to deal with the unstructured apple tree environment.This work aims to demonstrate that structured 3D model representation of apple trees can significantly enable and facilitate robotic apple harvesting, particularly for optimal 3D path planning.Accordingly, a hierarchical optimal path planning (HOPP) algorithm is designed to significantly reduce or minimize the time cost during robotic apple harvesting in a 3D environment.The core idea of this HOPP algorithm is applying distance-constrained k-means clustering to group apples into 3D harvesting zones first, after which an optimal 3D robotic harvesting path is derived via the Traveling Salesman Problem (TSP) formulation and solution.Within each 3D apple harvesting zone, a second-stage optimal path planning is conducted by the TSP method on individual apples.Experiments showed that the proposed HOPP algorithm is promising.
Extended Target Tracking with Constrained PMHT

This paper aims at adressing specific issues of extended target tracking. Firstly, we propose a method to accurately model the origin of the measurements on the surface of the target. This is achieved by removing the usual hypothesis of the independence of the association of the measurements to possible measurement sources to allow us to assume that a certain number of measurements shall be originating from some specific sources. Secondly, a Gaussian distribution is a poor representation for the length of a target. We developed a method discretizing the length to estimate its distribution without the Gaussian assumption but avoiding the computational burden of a multi-hypothesis tracking for each target. The implementation effectiveness is shown on simulated as well as real data from RADAR.
Particle Flow Gaussian Particle Filter

State estimation in non-linear models is performed by tracking the posterior distribution recursively. A plethora of algorithms have been proposed for this task. Among them, the Gaussian particle filter uses a weighted set of particles to construct a Gaussian approximation to the posterior. In this paper, we propose to use invertible particle flow methods, derived under the Gaussian boundary conditions for a flow equation, to generate a proposal distribution close to the posterior. The resultant particle flow Gaussian particle filter (PFGPF) algorithm retains the asymptotic properties of Gaussian particle filters, with the potential for improved state estimation performance in high-dimensional spaces. We compare the performance of PFGPF with the particle flow filters and particle flow particle filters in two challenging numerical simulation examples.
Development of Learning Media Flipbook Digital Comic Based on Local Wisdom to Increase Learning Interest

This study aims to determine: (1) the feasibility of digital comic flipbook learning media and (2) increase students' interest in learning using digital flipbook comics based on local wisdom. This research uses RnD (Research and Development) research with ADDIE model consisting of: analysis, design, development, implementation, and evaluation. The subjects of this study were fourth grade students of SDN 01 Tirtomoyo Malang with a total of 29 students. Data collection techniques used are interviews, observations, questionnaires, and documentation. The results showed that, (1) digital flipbook comic media based on local wisdom proved to have a feasibility level based on a validation questionnaire, response questionnaire, and readability questionnaire, (2) the percentage increase in student interest in learning in small groups was 44.5%, and the results the percentage increase in student interest in learning is 44.46%. Based on the results of this study, it can be concluded that the learning media of digital flipbook comics based on local wisdom can increase the learning interest of fourth grade elementary school students.