Reviewerly/exHarmony huggingface dataset:
Interactive maps, productivity and user experience: A user study in the e‐mobility domain

Abstract E‐mobility has been gaining importance in recent years in Germany, with the National Platform for E‐mobility infrastructure in Germany is expected to change fast, therefore many charging stations will appear. These need to be managed, and the aim of this work is to explore how web applications could best support productivity and user experience in this context. We compared a text‐form‐based approach to a map‐based approach for the addition and update of locations and chargepoints. We found that the map‐based approach yields better learning effects and offers a better compromise between pragmatic and hedonic properties. The results are useful for a better understanding of the impact of interactive maps on work and user experience in the e‐mobility domain and beyond.
Path Planning Algorithms Benchmarking for Grapevines Pruning and Monitoring

Labour shortage is a reality in agriculture. Farmers are asking for solutions to automate agronomic tasks, such as monitoring, pruning, spraying, and harvesting. The automation of these tasks requires, most of the time, the use of robotic arms to mimic human arms capabilities. The current robotic arm based solutions available, both in the market and in the scientific sphere, have several limitations, such as, low-speed manipulation, the path planning algorithms are not aware of the requirements of the agricultural tasks (robotic motion and manipulation synchronisation), and require active perception tuning to the end-target point. This work benchmarks algorithms from open manipulation planning library (OMPL) considering a cost-effective six-degree freedom manipulator in a simulated vineyard. The OMPL planners shown a very low performance under demanding pruning tasks. The best and most promising results are performed and obtained by BiTRRT. However, further work is needed to increase its performance and reduce planning time. This benchmark work helps the reader to understand the limitations of each algorithm and when to use them.
Energy-Efficient task scheduling in cloud data center- A temperature aware approach

Most of the previous studies based on task scheduling deals with either optimizing computing energy or performance upgradation only. However with advancement of cloud data center the users requirements are being diversified that is not achieved through traditional scheduling algorithm. One of the demand is to minimize cost related to cooling the data center. From the literature survey we found that the cooling energy cost half millions dollar to computing energy cost. In order to provide cost-effective executions in cloud environment, appropriate task scheduling strategy is necessary. Temperature affects not only the reliability but also the performance, power and cost of the embedded system. This paper proposes a thermalaware task allocation and scheduling algorithm for cloud data center. The task scheduling is designed in such a way that not only minimizes computation cost, it also reduces cooling. The rigorous simulation has been performed and compared with state of the art algorithms. The experimental results show that the thermalaware task scheduling algorithm outperforms compared to other strategies.
Ubiquitous music: Perspectives and challenges

This special volume features a selection of proposals that prompted positive and extensive discussions within the context of the Ubiquitous Music Workshop held at the Federal University of São João del Rei, MG, Brazil. In its eighth edition and featuring contributions from a host of international participants, this event provided a demonstration of the various approaches that characterise the research and practice of the ubimus field. In addition to the papers selected for this issue and the oral presentations, the workshop also included round-table discussions and artistic realisations, which are an important part of the work of many ubimus practitioners. These musical and artistic results are also addressed in the articles included in this special issue.
A hybrid regularized extreme learning machine for automated detection of pathological brain

This paper presents an automated method for detection of pathological brain using magnetic resonance (MR) images. The proposed method suggests to derive features using fast discrete curvelet transform. A combined feature reduction algorithm principal component analysis + linear discriminant analysis (PCA + LDA) is then applied to generate a low-dimensional and discriminant feature vector. Finally, the classification is carried out using a hybrid regularized extreme learning machine (RELM). The proposed hybrid classifier combines RELM and sine cosine algorithm to not only overcome the drawbacks of conventional learning algorithms but also provide good generalization performance with a compact and well-conditioned network. Besides root mean square error, the norm of the output weights and the condition value of the hidden layer output matrix have been separately taken into consideration to optimize the parameters of RELM. Extensive simulations on three benchmark datasets demonstrate that the proposed scheme obtains promising results as compared to state-of-the-art approaches. The effectiveness of the proposed hybrid classifier is compared with its counterparts. Moreover, the impact of noise in the training data is analyzed using the suggested scheme. The proposed approach can aid the radiologists for screening pathological brain at a larger scale.
Application Selection Lending Houses Subsidized by the Method of AHP and SAW

Decision Support System (DSS) is a system that can help somebody in making an accurate decision and precisely. many problems can be solved by using DSS, one of them is the determination of giving a subsidized home loans. Giving loans has a higher risk and can affect the pace of company's development if there's a bad credit. A company needs a DSS to help the credit analysis' team in making decision by the recomendation if the consumer deserves the loan or not. the calculation method that's used in this research is to compare two methods of Analytical Hierarchy Process (AHP) and the Simple Additive Weighting (SAW). . The result shows by using SAW method for consumers V4 scored 0,25; while using AHP to consumers A004 scored 0,31.
Automatic Drone Navigation in Realistic 3D Landscapes using Deep Reinforcement Learning

We present a study where a drone navigates through diverse 3D obstacles by finding a 3D path and reaches the goal using deep reinforcement learning (RL) in a 3D realistic landscape. The drone has two inputs: first RGB provides a first person view of the landscape and secondly depth map gives it 3D information of the environment. For training the drone for automatic navigation, deep reinforcement learning is extensively used. For the same task, human pilot navigates through the obstacles with a radio controller (RC) using a hardware-in-the-loop setup. Racing performance between human and several deep RL algorithms such as Deep Q-Network (DQN), Double DQN, Dueling DQN and Double Dueling DQN (DD-DQN) are evaluated. Results suggest that DD-DQN outperforms other algorithms and, for the racing between humans and algorithms, DD-DQN performs better than a novice and yet an expert or intermediate-level pilot outperforms any other algorithms. The present study demonstrates that time and resource for training a drone can be saved using a realistic and yet controllable platform.
The Privacy Barrier for Blockchain in Logistics: First Lessons from the Port of Hamburg

Blockchain technology is associated with greatly beneficial applications for supply chain and logistics (SC&L), two of which are to trace goods across many actors, and to decentralize asset transfers without needing an intermediary. As a first use-case, actors from the Port of Hamburg are planning to implement blockchain to improve the sea freight container release by providing a common data platform for sea freight carriers, terminals, truck companies, and freight forwarders. Currently, releasing containers from the port's terminals to trucks requires proof of ownership for the recipient to take custody. In practice, this proof passes through many hands causing duplication of information flow and ownership evidence. We conducted workshops and short interviews with experts providing first-hand insight into the use-case. Using blockchain in the process provides improvements such as traceable proof of ownership. The technology also faces barriers, with privacy concerns as one of the most prominent obstacles. A decentralized system could lead to business networks and company information being disclosed through data triangulation. We argue that privacy is a vital design consideration that affects the use of blockchain in SC&L generally.
Comparison of HD and UHD video quality with and without the influence of the labeling effect

The past decades have shown a steady rise in the pixel resolution of digital visualization technologies. New TVs and computer displays available on the consumer market are commonly UHD/4K, and 8K recently started appearing in the commercial world. The added value of such higher resolution is not only indicated by the purported level of visual performance, but also by the labels that emphasize the devices' properties, especially the resolution. However, the genuinely perceived difference between two resolutions, such as HD and UHD, may not have the same effect on user experience as the labels attached to the devices or sequences themselves. In this paper we present four subjective studies in which we investigated the influence of the labeling effect in the context of HD and UHD video. Subjects were shown pairs of either HD or UHD video sequences and had to determine whether there was a difference in quality between the sequences. Two of the subjective tests involved labels in these paired comparisons, indicating to the subject which sequence was which, while the other two tests excluded these labels. For both test sets with and without labels, one test used a 3-point comparison scale for the subjective assessment of visual quality, while the other used a more fine-grained 7-point scale. Our findings show that the sole inclusion of quality labels can strongly impact subjective rating behavior and the overall opinion on UHD quality; also, visual differences between HD and UHD video were rarely noticeable by the subjects.
Semantic-Enhanced Recommendation of Video Lectures

Learning support systems explore several audio-visual resources to consider individual needs and learning styles aiming to stimulate learning experiences. However, the large amount of educational content in different formats and the possibility of making them available in a fragmented way turns difficult the tasks of accessing these resources and understanding the concepts under study. Although literature has proposed approaches to explore explicit semantic representation through artifacts such as ontologies in learning support systems, this research line still requires further investigation efforts. In this paper, we propose a method for recommending educational content by exploring the use of semantic annotations over textual transcriptions from video lessons. Our investigation addresses the difficulties in extracting entities from natural language texts as subtitles of videos. We report on major challenges to achieve the representation of video transcriptions as semantic annotations for automatic recommendation of educational content.
Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction

Deep reinforcement learning has proven to be a great success in allowing agents to learn complex tasks. However, its application to actual robots can be prohibitively expensive. Furthermore, the unpredictability of human behavior in human-robot interaction tasks can hinder convergence to a good policy. In this paper, we present an architecture that allows agents to learn models of stochastic environments and use them to accelerate learning. We descirbe how an environment model can be learned online and used to generate synthetic transitions, as well as how an agent can leverage these synthetic data to accelerate learning. We validate our approach using an experiment in which a robotic arm has to complete a task composed of a series of actions based on human gestures. Results show that our approach leads to significantly faster learning, requiring much less interaction with the environment. Furthermore, we demonstrate how learned models can be used by a robot to produce optimal plans in real world applications.
Fuzzy Logic Control of Goal-Seeking 2-Wheel Differential Mobile Robot Using Unicycle Approach

In this work, fuzzy logic-based navigation control for a 2-wheel mobile robot is proposed. An autonomous mobile robot must be able to move safely in an environment, to reach its target (or goal). The work began with the mathematical model of the robot that involved the kinematic model. After which the Simulink model was developed and then fuzzy logic controller was designed for goal seeking. The problem with the differential drive model is that separate fuzzy rules need to be developed for both left and right wheels of the robot and the steering angle cannot be controlled directly from the fuzzy rules. However, these limitations have been taken care of in this work. The linear velocity and steering angle of the mobile robot were directly controlled from a single set of fuzzy rules and this has helped to reduce the time of navigation of the robot. The performance was satisfactory and the results of the simulation showed that the robot could reach its goal during navigation in shorter time than if differential drive model alone was used.
Frequent itemset-based feature selection and Rider Moth Search Algorithm for document clustering

Document clustering has recently been paid great attention in retrieval, navigation, and summarization of huge volumes of documents. With a better document clustering approach, computers can organize a document corpus automatically to a meaningful cluster for enabling efficient navigation, and browsing of the corpus. Document navigation and browsing is a valuable complement to the deficiencies of information retrieval technologies. This paper introduces Modsup-based frequent itemset and Rider Optimization-based Moth Search Algorithm (Rn-MSA) for clustering the documents. At first, the input documents are given to the pre-processing step, and then, the extraction is carried out based on TF-IDF and Wordnet features. Once the extraction is done, the feature selection is carried out based on frequent itemset for the establishment of feature knowledge. At last, the document clustering is done using the proposed Rn-MSA, which is designed by combining Rider Optimization Algorithm (ROA), and the Moth Search Algorithm (MSA). The performance of the document clustering based on proposed Modsup + Rn-MSA is evaluated in terms of precision, recall, F-Measure, and accuracy. The developed document clustering method achieves the maximal precision of 95.90%, maximal recall of 96.41%, maximal F-Measure of 96.41%, and the maximal accuracy of 95.12% that indicates its superiority.
Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category

Abstract Classification is a method of data analysis that is used to create models that describe data classes that are considered important. For the classification of the classification process, the data used is THPS Visitor data which consists of 4 classes including Education, Gender, Age and Visit. The classification used as a comparison of results is the Naive Bayes Classifier. By classifying the number of visitors who are most dominant visiting by age category consisting of adults, adolescents and children. This study aims to classify the highest number of values for visitors by age category. This study was reviewed using the Naive Bayes algorithm. The results of this study indicate that the visitor data that is the most dominant visiting by age category is children who have 77% accuracy is the age of children. This accuracy value is the age that most often visits THPS.
Two Subpopulations Cuckoo Search Algorithm Based on Mean Evaluation Method for Function Optimization Problems

In order to better apply the cuckoo search (CS) algorithm in solving the problem of function extremum optimization, and further improve the phenomenon of low precision and slow convergence in the optimization process of algorithm, the two subpopulations CS algorithm based on mean value evaluation is proposed. On the one hand, the algorithm introduces dynamic inertia weight to adjust the lévy flight mechanism, thus dynamically constraining the moving step-size of each generation of population, so that the algorithm has certain self-adaptability. On the other hand, the algorithm changes the way of mutation in the preference random walk. First, the average fitness evaluation mechanism is used to divide the current population into two subpopulations: good and bad. Then, it adopts a directional mutation strategy for the better population, so that the individual can search purposefully. The worse population uses differential mutation mechanism of the disturbance items with the [Formula: see text]-distribution characteristics, and makes the individual to search in the best orientation of current, so as to enhance the local search performance and accelerate the convergence rate of the algorithm. Theoretical analysis proves the convergence and time complexity of the algorithm in this paper. The simulation results show that the improved algorithm has good applicability in solving the function optimization problem, and the optimization results and convergence speed have been significantly improved in the algorithm.
Type Checking Program Code Using SHACL

It is a strength of graph-based data formats, like RDF, that they are very flexible with representing data. To avoid run-time errors, program code that processes highly-flexible data representations exhibits the difficulty that it must always include the most general case, in which attributes might be set-valued or possibly not available. The Shapes Constraint Language (SHACL) has been devised to enforce constraints on otherwise random data structures. We present our approach, Type checking using SHACL (TyCuS), for type checking code that queries RDF data graphs validated by a SHACL shape graph. To this end, we derive SHACL shapes from queries and integrate data shapes and query shapes as types into a $$\lambda $$ -calculus. We provide the formal underpinnings and a proof of type safety for TyCuS. A programmer can use our method in order to process RDF data with simplified, type checked code that will not encounter run-time errors (with usual exceptions as type checking cannot prevent accessing empty lists).
Analysis of the ELECTRE Method on the Selection of Student Creativity Program Proposals

Abstract Creativity is a potential that is owned by every human being and not received from outside the individual. Human creativity, born together with the birth of the human being. Student Creativity Program is one form of effort carried out by students and lecturers in improving the quality of students’ education in universities so that they can apply, develop, and disseminate knowledge, technology and culture and enrich national culture. This study aims to analyze the selection of proposals that are eligible to be submitted to the Directorate General of Learning and Student Affairs, Ministry of Research Technology, and Higher Education through the url https://simbelmawa.risteldikti.go.id using the ELECTRE (ELimination and Choice Expressing Reality) method by using 8 assessment criteria namely Title (C1), Abstract (C2), Introduction (C3), Objective (C4), Method (C5), Results and Discussion (C6), Conclusions (C7), Bibliography (C8). And the results of the study obtained 10 proposals worth submitting from the 15 proposals selected. The results of this study can answer the formulation of the problem and become a recommendation in the selection of the Student Creativity Program which aims to improve the quality of students and improve the value of accreditation for related institutions, especially in the STIKOM Tunas Bangsa environment.
Pseudonymization for research data collection: is the juice worth the squeeze?

The collection of data and biospecimens which characterize patients and probands in-depth is a core element of modern biomedical research. Relevant data must be considered highly sensitive and it needs to be protected from unauthorized use and re-identification. In this context, laws, regulations, guidelines and best-practices often recommend or mandate pseudonymization, which means that directly identifying data of subjects (e.g. names and addresses) is stored separately from data which is primarily needed for scientific analyses.When (authorized) re-identification of subjects is not an exceptional but a common procedure, e.g. due to longitudinal data collection, implementing pseudonymization can significantly increase the complexity of software solutions. For example, data stored in distributed databases, need to be dynamically combined with each other, which requires additional interfaces for communicating between the various subsystems. This increased complexity may lead to new attack vectors for intruders. Obviously, this is in contrast to the objective of improving data protection. What is lacking is a standardized process of evaluating and reporting risks, threats and countermeasures, which can be used to test whether integrating pseudonymization methods into data collection systems actually improves upon the degree of protection provided by system designs that simply follow common IT security best practices and implement fine-grained role-based access control models. To demonstrate that the methods used to describe systems employing pseudonymized data management are currently heterogeneous and ad-hoc, we examined the extent to which twelve recent studies address each of the six basic security properties defined by the International Organization for Standardization (ISO) standard 27,000. We show inconsistencies across the studies, with most of them failing to mention one or more security properties.We discuss the degree of privacy protection provided by implementing pseudonymization into research data collection processes. We conclude that (1) more research is needed on the interplay of pseudonymity, information security and data protection, (2) problem-specific guidelines for evaluating and reporting risks, threats and countermeasures should be developed and that (3) future work on pseudonymized research data collection should include the results of such structured and integrated analyses.
Shape description and recognition by implicit Chebyshev moments

Chebyshev Moments(CMs) have been applied to representation and recognition of 2D object shapes in image processing and computer vision. However they still suffer from poor representation power and difficulty in computing invariants for shapes. In this work, we present Implicit Chebyshev Moments (ICMs) to overcome these issues. Firstly, we use Euclid distance transformation to generate a series of level sets based on a given shape. Secondly, we fit an implicit Chebyshev polynomial to the data set consisting of the obtained level sets together with all the boundary points on the original shape and call the obtained coefficients of the fitted implicit Chebyshev polynomial ICMs. Finally, we propose a new approach to derive geometric invariants based on ICMs. In addition, we also develop an algorithm for the determination of a suitable degree for implicit Chebyshev polynomials before representing a given shape. Experimental results show the ICMs are more efficient for representing complex shapes than CMs.