Reviewerly/exHarmony huggingface dataset:
Optimal Orientation of Solar Panels for Multi-Apartment Buildings

In this paper, we present a mathematical algorithm for the optimal orientation of solar panels for multi-apartment buildings. Currently, photovoltaic power generation has increasingly become an effective method. It has the advantage of not causing environmental pollution; however, it has the disadvantage of relatively low power generation efficiency. To increase the power efficiency of the panel, one can consider a rotation. However, if there is a limitation to the rotation angle of the solar panel, especially in multi-apartment buildings, it is desirable to install the panel at the optimal angle under given constraints. Therefore, we present a simple and practical method to evaluate the optimal installation angle of the panel. Using the proposed method, it is easy to find an optimal installation angle to achieve the best power generation efficiency based on the latitude and azimuth angles. To demonstrate the effectiveness of the proposed algorithm, several numerical simulation results are provided.
Notation as visual representation of sound-based music

This text describes the musical evaluation of a hybrid music notation system that combines traditional notation with symbols and concepts from spectromorphological analysis. During three academic years from 2017 to 2020, three groups of composition students learned to work with sound notation, recreating and interpreting short electroacoustic music sketches based solely on their notation transcriptions – they had not heard the original sketches. The students’ score interpretations bore obvious similarities to the original music sketches and their written reflections showed that there were no major difficulties in understanding the notation although some difficulties existed concerning finding suitable sounds, especially sounds with stable pitch.
The Pragma-Dialectical Approach to the Fallacies Revisited

Abstract This article explains the design and development of the pragma-dialectical approach to fallacies. In this approach fallacies are viewed as violations of the standards for critical discussion that are expressed in a code of conduct for reasonable argumentative discourse. After the problem-solving validity in resolving differences of opinion of the rules of this code has been discussed, their conventional validity for real-life arguers is demonstrated. Starting from the extended version of the theory in which the strategic maneuvering taking place in argumentative discourse is included, the article explains that the violations of the rules that are committed in the fallacies involve derailments of strategic maneuvering. This culminates in a discussion of the exploitation of hidden fallaciousness as an unreasonable way of increasing the effectiveness of argumentative discourse – a vital topic of research in present-day pragma-dialectics.
High Costs and Low Benefits: Analysis and Evaluation of the “I’m Not Stupid” Argument

Abstract This article presents an analysis and evaluation of what I call the “I’m not stupid” argument. This argument has ancient roots, which lie in Aristotle’s famous description of the weak man’s and strong man’s arguments. An “I’m not stupid” argument is typically used in a context of accusation and defense, by a defendant who argues that they did not commit the act of which they have been accused. The analysis of this type of argument takes the shape of an argumentative pattern, which displays a full-fledged representation of its argumentation structure. It is based on a collection of ten contemporary instances of the “I’m not stupid” argument. Although ten instances constitute a small collection, the wide variation in the argumentative elements that they express explicitly or leave implicit made it possible to identify five new key premises in comparison with previous analyses of the weak man’s and strong man’s arguments (Walton, Tindale and Gordon 2014 in Argumentation 28:85–119, 2014; Walton 2019 in Argumentation 33:45–74, 2019). These new premises show that the crucial point of an evaluation of this argument is the arguer's supposedly rational character in making a gain-loss calculation. They also show that we need empirical data to strengthen our analyses of argument schemes and argumentation structures.
Multi-sample dual-decoder graph autoencoder

Self-supervised learning has shown superior performance on graph-related tasks in recent years. The most advanced methods are based on contrast learning, which severely limited by structured data augmentation techniques and complex training methods. Generative self-supervised learning, especially graph autoencoders (GAEs), can prevent the above dependence and has been demonstrated as an effective approach. In addition, most previous works only reconstruct the graph topological structure or node features. Few works consider both and combine them together to obtain their complementary information. To overcome these problems, we propose a generative self-supervised graph representation learning methodology named Multi-View Dual-decoder Graph Autoencoder (MDGA). Specifically, we first design a multi-sample graph learning strategy which benefits the generalization of the dual-decoder graph autoencoder. Moreover, the proposed model reconstructs the graph topological structure with a traditional GAE and extracts node attributes by masked feature reconstruction. Experimental results on five public benchmark datasets demonstrate that MDGA outperforms state-of-the-art methods in both node classification and link prediction tasks.
Harpia: A hybrid system for agricultural UAV missions

The present paper introduces Harpia, a hybrid artificial intelligent planning system for UAVs. Harpia aims to execute tasks for agricultural applications with minimum human intervention. The mission's execution is on-board, running under the Robotic Operating System and executing re-planning for tasks and path planning with obstacle avoidance. The re-planning can happen after mission changes in real-time or unpredictable UAV behavior. It combines Planning Domain Definition Language for task planning, Bayesian Network to evaluate mission execution, and K-Nearest Neighbors algorithm to select a path planner. Thus, Harpia's novelty focuses on robustness for autonomously planning and re-planning the sequence of tasks and trajectories to regions of interest. The main contributions include an autonomous system architecture for planning missions with minimum human intervention, no boundary by specific tasks, and computationally simple for operating within non-convex scenarios. The computational tests report results for 21 simulated scenarios, where Harpia handled all situations properly, e.g., making decisions about task re-planning with 97.57% accuracy based on battery health and choosing the better path-planning for each case with at least 95% of accuracy.
SynBERT: Chinese Synonym Discovery on Privacy-Constrain Medical Terms with Pre-trained BERT

Discovering medical synonym sets (i.e.,set of terms referring to a similar medical concept) is an important task in real-world, which can benefit many downstream applications such as medical information retrieval system and clinical decision support system. Recent synonym discovery methods take words as the input unit and leverage raw text as contextual information. However, they are ill-suited in Chinese participle as taking word as the input unit leads to serious Out-of-Vocabulary (OOV) problems. Additionally, it is hard to get large-scaled raw clinical texts in medical domain because of the privacy and security. Therefore, we define a new task discovering Chinese synonym from Privacy-Constrain terms (i.e., only terms without raw corpus) and propose a framework SynBERT to solve it. SynBERT consists of a binary classifier, inferring whether two term sets can form a synonym set, and two-phase clustering algorithm, applying classifier to cluster given terms into different synonym sets. In particular, SynBERT composes term’s embedding with character’s embedding to address the OOV problems. SynBERT introduces a BERT model pre-trained on public large-scaled corpus before to eliminate the need of raw context information. Ȧccording to our experiment, SynBERT outperforms better than baseline methods such as Kmeans, L2C, SynSetMine, etc.
Relating Kleene Algebras with Pseudo Uninorms

This paper explores a strict relation between two core notions of the semantics of programs and of fuzzy logics: Kleene Algebras and (pseudo) uninorms. It shows that every Kleene algebra induces a pseudo uninorm, and that some pseudo uninorms induce Kleene algebras. This connection establishes a new perspective on the theory of Kleene algebras and provides a way to build (new) Kleene algebras. The latter aspect is potentially useful as a source of formalism to capture and model programs acting with fuzzy behaviours and domains.
Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling

There are two de facto standard architectures in recent computer vision: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Strong inductive biases of convolutions help the model learn sample effectively, but such strong biases also limit the upper bound of CNNs when sufficient data are available. On the contrary, ViT is inferior to CNNs for small data but superior for sufficient data. Recent approaches attempt to combine the strengths of these two architectures. However, we show these approaches overlook that the optimal inductive bias also changes according to the target data scale changes by comparing various models' accuracy on subsets of sampled ImageNet at different ratios. In addition, through Fourier analysis of feature maps, the model's response patterns according to signal frequency changes, we observe which inductive bias is advantageous for each data scale. The more convolution-like inductive bias is included in the model, the smaller the data scale is required where the ViT-like model outperforms the ResNet performance. To obtain a model with flexible inductive bias on the data scale, we show reparameterization can interpolate inductive bias between convolution and self-attention. By adjusting the number of epochs the model stays in the convolution, we show that reparameterization from convolution to self-attention interpolates the Fourier analysis pattern between CNNs and ViTs. Adapting these findings, we propose Progressive Reparameterization Scheduling (PRS), in which reparameterization adjusts the required amount of convolution-like or self-attention-like inductive bias per layer. For small-scale datasets, our PRS performs reparameterization from convolution to self-attention linearly faster at the late stage layer. PRS outperformed previous studies on the small-scale dataset, e.g., CIFAR-100.
Breast cancer dataset with biomarker Biglycan

This dataset is composed of photomicrographs of the immunohistochemical expression of Biglycan (BGN) in breast tissue, with and without cancer, using only the staining of 3-3' diaminobenzidine (DAB), after processing images with color deconvolution plugin, from Image J. The immunohistochemical DAB expression of BGN was obtained using the monoclonal antibody (M01) (clone 4E1-1G7 - Abnova Corporation, mouse anti-human). Photomicrographs were obtained, under standard conditions, using an optical microscope, with UPlanFI 100x objective (resolution: 2.75 mm), yielding an image size of 4800 × 3600 pixels. After color deconvolution, the dataset with 336 images was divided into 2 two categories: (I) with cancer and (II) without cancer. This dataset allows the training and validation of machine learning models to diagnose, recognize and classify the presence of breast cancer, using the intensity of the colors of the BGN.
Coherent dimension reduction with integrated photonic circuits exploiting tailored disorder

The number of systems that are investigated for computation in the physical domain has increased substantially in the recent past. Optical and photonic systems have drawn high interest due to their potential for carrying out energy-efficient linear operations and perceived advantages in latency and general computation speed. One of the main challenges remains to scale up integrated photonic designs to integration densities required for meaningful computation, in particular for matrix-vector multiplications. To address upscaling for photonic computing, here we propose an on-chip scheme for dimension reduction of the input data using random scattering. Exploiting tailored disorder allows us to reduce the incoming dimensionality by more than an order of magnitude, which a shallow subsequent network can use to perform image recognition tasks with high accuracy.
Design Principles for Content Creation in Location-Based Games

Location-based games have been around since 2000 across various fields, including education, health, and entertainment. The main challenge facing such games is content generation. In contrast to normal games, content in location-based games is inherently dependent on location. The biggest challenge is the availability of the content globally. Other challenges include player engagement, enjoyable interactions with the real-world environment, safety, and customizability based on player performance and preference. While crowdsourcing has often been adopted as a tool for content creation, this approach requires quality control. Designing high-quality content requires detailed guidelines. In this paper, we introduce design principles for the creation of high-quality content that can survive for long periods of time. These principles are derived from ten years of experience running our in-house orienteering treasure-hunt game called O-Mopsi , which represents a case study in this paper. O-Mopsi allows players to visit pre-defined locations. The design principles are expected to be generalizable to other location-based games as well as to the creation of sightseeing tours more generally.
Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network

Cloud computing plays a vital role in healthcare as it can store a large amount of data known as big data. In the current emerging era of computing technology, big data analysis and prediction is a challenging task in the healthcare industry. Healthcare data are very crucial for the patient as well as for the respective healthcare services provider. Several healthcare industries adopted cloud computing for data storage and analysis. Incredible progress has been achieved in making combined health records available to data scientists and clinicians for healthcare research. However, big data in health cloud informatics demand more robust and scalable solutions to accurately analyze it. The increasing number of patients is putting high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. Predicting mortality among patients with a variety of symptoms and complications is difficult, resulting inaccurate and slow prediction of the disease. This article presents a deep learning based model for the prediction of patient mortality using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Different parameters are used to analyze the proposed model, i.e., accuracy, F1 score, recall, precision, and execution time. The results obtained are compared with state-of-the-art models to test and validate the proposed model. Moreover, this research suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate.
Perception of the Use of Virtual Reality Didactic Tools among Faculty in Mexico

This paper develops descriptive quantitative research of the assessments of virtual reality (VR) technology, used as a didactic tool, by a sample of 712 university professors in Mexico. For this purpose, a validated Likert-type questionnaire was used as an instrument, the responses to which were statistically analyzed. The results obtained show that professors in Mexico report low levels of digital skills, but high valuations of VR. These ratings depend strongly on the professors’ area of knowledge. In this sense, the biggest gap is between Engineering professors, who value VR better, and Humanities professors, who value it worse. There are also gender gaps and gaps due to the digital generation of the participants in the assessments made, whose behavior is also different according to the area of knowledge. As a result, some recommendations are provided to try to reduce the gaps found.
Augmented reality as a participation tool for youth in urban planning processes: Case study in Oslo, Norway

Augmented reality (AR) allows objects to be digitally simulated in the real world through smartphones, tablets, and headsets. While there are interesting AR technology case studies in participatory urban planning, this type of research has yet to be conducted within a real-life municipal planning scenario. Following the UN Habitat recommendation that further studies in AR as a participatory tool seek to integrate planning with real citizens, we studied the use of AR for the Oslo Trees plan in Norway. The case study consists of field work with AR between 2020 and 2021 over five weeks, with five different groups of youth participants from eight different districts of Oslo, who were tasked with planning a portion of Oslo’s 100,000 new trees. We document how these youths used AR in films, images, drawings, interviews, screen recordings, and recorded presentations. We find that AR is a highly intuitive tool for these youth user groups in design and planning and how the AR schemes impacted the final design of the plan. The use of AR aided users’ ability to generate their own planning proposals on site at scale; nearly all participants increased their understanding of participation, urban planning, architecture, and design in the workshops. In addition, the youths experienced an increased sense of confidence in displaying their design intentions and appreciated being given control of the planning process. However, we also found that location tracking and positioning in AR is imprecise and often “buggy” in the current state of the technology, causing irritation among users. Furthermore, despite the high degree of control afforded to users through AR, experts were still needed to verify which tree proposals were viable, offering important insights into how AR could be designed in the future. We conclude with a discussion on opportunities and barriers for the implementation of AR in participatory urban planning, pointing to the need for a more coordinated and holistic approach to both AR technology development and planning policy if the technology is to be developed further for participatory urban planning.
Embedded Federated Learning for VANET Environments

In the scope of smart cities, the sensors scattered throughout the city generate information that supplies intelligence mechanisms to learn the city’s mobility patterns. These patterns are used in machine learning (ML) applications, such as traffic estimation, that allow for improvement in the quality of experience in the city. Owing to the Internet-of-Things (IoT) evolution, the city’s monitoring points are always growing, and the transmission of the mass of data generated from edge devices to the cloud, required by centralized ML solutions, brings great challenges in terms of communication, thus negatively impacting the response time and, consequently, compromising the reaction in improving the flow of vehicles. In addition, when moving between the edge and the cloud, data are exposed, compromising privacy. Federated learning (FL) has emerged as an option for these challenges: (1) It has lower latency and communication overhead when performing most of the processing on the edge devices; (2) it improves privacy, as data do not travel over the network; and (3) it facilitates the handling of heterogeneous data sources and expands scalability. To assess how FL can effectively contribute to smart city scenarios, we present an FL framework, for which we built a testbed that integrated the components of the city infrastructure, where edge devices such as NVIDIA Jetson were connected to a cloud server. We deployed our lightweight container-based FL framework in this testbed, and we evaluated the performance of devices, the effectiveness of ML and aggregation algorithms, the impact on the communication between the edge and the server, and the consumption of resources. To carry out the evaluation, we opted for a scenario in which we estimated vehicle mobility inside and outside the city, using real data collected by the Aveiro Tech City Living Lab communication and sensing infrastructure in the city of Aveiro, Portugal.
IDEINFO: An Improved Vector-Weighted Optimization Algorithm

This study proposes an improved vector-weighted averaging algorithm (IDEINFO) for the optimization of different problems. The original vector-weighted optimization algorithm (INFO) uses weighted averaging for entity structures and uses three core procedures to update the positions of the vectors. First, the update rule phase is based on the law of averaging and convergence acceleration to generate new vectors. Second, the vector combination phase combines the obtained vectors with the update rules to achieve a promising solution. Third, the local search phase helps the algorithm eliminate low-precision solutions and improve exploitability and convergence. However, this approach pseudo-randomly initializes candidate solutions, and therefore risks falling into local optima. We, therefore, optimize the initial distribution uniformity of potential solutions by using a two-stage backward learning strategy to initialize the candidate solutions, and a difference evolution strategy to perturb these vectors in the combination stage to produce improved candidate solutions. In the search phase, the search range of the algorithm is expanded according to the probability values combined with the t-distribution strategy, to improve the global search results. The IDEINFO algorithm is, therefore, a promising tool for optimal design based on the considerable efficiency of the algorithm in the case of optimization constraints.
Combining SABSA and Vis4Sec to the Process Framework IdMSecMan to Continuously Improve Identity Management Security in Heterogeneous ICT Infrastructures

Identity management ensures that users have appropriate access to resources, such as ICT services and data. Thereby, identity management does not only identify, authenticate, and authorize individuals, but also the hardware devices and software applications which the users need for access. In consequence, identity management is an important element of information security management (ISM) and data governance. As ICT infrastructures are constantly changing, and new threats emerge, identity management has to be continuously improved, just like any other business process. In order to align the identity management process with business requirements, and provide a systematic approach supported by reporting and supporting visualizations, we apply Sherwood Applied Business Security Architecture (SABSA) and Visualization for Security (Vis4Sec) together in our approach, IdMSecMan (identity management security management). We first introduce IdMSecMan, before applying it to the central technical process activities of identification, authentication, and authorization. Our approach is underlined by a case study. Thereby, we, for example, see that enabling multi-factor authentication in organizations impacts other areas that may be overlooked without a structured approach. With IdMSecMan, we provide a process framework to align all decisions and to constantly improve identity management within organizations and inter-organizational collaborations.