Reviewerly/exHarmony huggingface dataset:
Drone Networks and Monitoring Systems in Smart Cities

Drones are commonly called unmanned aerial vehicles (UAVs) for they do not have an onboard pilot. They are aircraft that can be controlled autonomously. In fact, UAVs and drones are similar and interchangeable. Earlier, in the ’80s, there was a time when drones were considered aerial weapons in military lingo. Their mode of usage was as an aerial missile deployer. However, drones have seen advancement and rapid evolution, leading to their contributions in many areas. The integration of machine learning and the Internet of Things (IoT) paved the way for drones as an application rather than as deployers. When compared to previous generations, drones are available in different forms and sizes, each modeled and textured for their respective needs. Evolution played a major role in their urbanization. IoT-embedded drones could be useful in fields like traffic management, crowd control, and pollution monitoring; to increase efficiency in agriculture; for safety and surveillance, and disaster management. It has always been a challenge to implement and monitor remote locations. Here, UAVs can help in such difficulties. They can survey and monitor the places difficult for a person to navigate. The output that we expect is the ultimate advantage in the implementation of drones. High-resolution imagery is flexible for acquiring an expected output. The concept of a smart city is the integration of ICT (information and communication technology). Drones can be a major contributor to various stages of development and sustenance of smart cities. They have also been used for devastation purposes. With such scope, it has found its way into virtually every field and smart city. Now there are several ways that a drone can accelerate the creation and maintenance of smart cities. As drones and related technologies become more advanced and sophisticated, new cases continue to emerge. This is obvious, particularly in connected ecosystems. This greatly improves its operation, citizen engagement, and detailed data. This chapter discusses the implementation of drone networks and monitoring systems in smart cities.
Dual-valued Neural Networks

The majority of existing neural networks operate with real-valued representation of data. However, there are multiple tasks in which the input is complex-valued. The complex-valued data is considered to be more informative in terms of larger representational capacity. These reasons motivate researchers to develop neural networks using complex numbers instead of real-valued ones. In this paper, we take a step forward in the generalization of neural networks. We develop the basic building blocks for dual-valued neural networks based on dual numbers. We adjust basic layers such as Linear, Convolution, Average Pooling, ReLU to the dual domain and present an algorithm for Dual Batch Normalization. We construct several dual-valued neural networks for classification tasks basing on classical CV problems and the MusicNet and G2Net datasets. We show that dual-valued models outperform analogous complex-valued neural networks in execution time and have higher or at least the same accuracy.
Generative and reproducible benchmarks or comprehensive evaluation machine learning classifiers

Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial to determine their scope of application. Here, we introduce the Diverse and Generative ML Benchmark (DIGEN), a collection of synthetic datasets for comprehensive, reproducible, and interpretable benchmarking of ML algorithms for classification of binary outcomes. The DIGEN resource consists of 40 mathematical functions that map continuous features to binary targets for creating synthetic datasets. These 40 functions were found using a heuristic algorithm designed to maximize the diversity of performance among multiple popular ML algorithms, thus providing a useful test suite for evaluating and comparing new methods. Access to the generative functions facilitates understanding of why a method performs poorly compared to other algorithms, thus providing ideas for improvement.
Visual Exploration of Financial Data with Incremental Domain Knowledge

Abstract Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina 2.0 , a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation‐wide aggregate data. Our solution is coupled with a pipeline for the generation of firm‐to‐firm financial transaction networks, fusing information about individual firms with sector‐to‐sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight‐based evaluation. The analysis shows how Sabrina 2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.
Application of mixed reality-based surgical navigation system in craniomaxillofacial trauma bone reconstruction.

This study aimed to build a surgical navigation system based on mixed reality (MR) and optical positioning technique and evaluate its clinical applicability in craniomaxillofacial trauma bone reconstruction. Me-thods We first integrated the software and hardware platforms of the MR-based surgical navigation system and explored the system workflow. The systematic error, target registration error, and osteotomy application error of the system were then analyzed via 3D printed skull model experiment. The feasibility of the MR-based surgical navigation system in craniomaxillofacial trauma bone reconstruction was verified via zygomatico-maxillary complex (ZMC) reduction experiment of the skull model and preliminary clinical study.The system error of this MR-based surgical navigation system was 1.23 mm±0.52 mm, the target registration error was 2.83 mm±1.18 mm, and the osteotomy application error was 3.13 mm±1.66 mm. Virtual surgical planning and the reduction of the ZMC model were successfully conducted. In addition, with the guidance of the MR-based navigation system, the frontal bone defect was successfully reconstructed, and the clinical outcome was satisfactory.The MR-based surgical navigation system has its advantages in virtual reality fusion effect and dynamic navigation stability. It provides a new method for doctor-patient communications, education, preoperative planning, and intraoperative navigation in craniomaxillofacial surgery.目的: 搭建基于混合现实（MR）及红外光学定位的手术导航系统，并评估其在颅颌面创伤骨整复中的临床适用性。方法: 集成基于MR技术和光学定位追踪的软硬件系统，并完善系统工作流程；通过3D打印头颅模型实验，分析该系统的系统误差、目标配准误差和截骨应用误差；进一步通过颧上颌骨复合体（ZMC）复位的模型实验和额骨缺损修复的初步临床应用，探讨该系统的适用性。结果: 本MR手术导航系统的系统误差为（1.23±0.52）mm，目标配准误差为（2.83±1.18）mm，截骨应用误差为（3.13±1.66）mm；ZMC模型虚拟手术规划流畅，复位顺利；本系统引导下的额骨缺损修复重建手术过程顺利，重建效果满意。结论: 本研究搭建的MR手术导航系统具有虚实融合效果佳、动态导航稳定性好的优点，其在颅颌面外科的医患沟通、医学教育、术前规划和术中导航等各方面都具有应用潜力。.
A framework for benchmarking clustering algorithms

The evaluation of clustering algorithms can involve running them on a variety of benchmark problems, and comparing their outputs to the reference, ground-truth groupings provided by experts. Unfortunately, many research papers and graduate theses consider only a small number of datasets. Also, the fact that there can be many equally valid ways to cluster a given problem set is rarely taken into account. In order to overcome these limitations, we have developed a framework whose aim is to introduce a consistent methodology for testing clustering algorithms. Furthermore, we have aggregated, polished, and standardised many clustering benchmark dataset collections referred to across the machine learning and data mining literature, and included new datasets of different dimensionalities, sizes, and cluster types. An interactive datasets explorer, the documentation of the Python API, a description of the ways to interact with the framework from other programming languages such as R or MATLAB, and other details are all provided at https://clustering-benchmarks.gagolewski.com.
Active anomaly detection based on deep one-class classification

Active learning has been utilized as an efficient tool in building anomaly detection models by leveraging expert feedback. In an active learning framework, a model queries samples to be labeled by experts and re-trains the model with the labeled data samples. It unburdens in obtaining annotated datasets while improving anomaly detection performance. However, most of the existing studies focus on helping experts identify as many abnormal data samples as possible, which is a sub-optimal approach for one-class classification-based deep anomaly detection. In this paper, we tackle two essential problems of active learning for Deep SVDD: query strategy and semi-supervised learning method. First, rather than solely identifying anomalies, our query strategy selects uncertain samples according to an adaptive boundary. Second, we apply noise contrastive estimation in training a one-class classification model to incorporate both labeled normal and abnormal data effectively. We analyze that the proposed query strategy and semi-supervised loss individually improve an active learning process of anomaly detection and further improve when combined together on seven anomaly detection datasets.
Formation and Development of Comfortable Information Environment of Science at the University: Impact of the Library

The main activities of the Scientific Library of Belarusian National Technical University (ВNTU) aimed at the formation and development of a comfortable information environment for the University’s academic activities are described, including: organizing of redundancyof content and relative limitation of volume in the presented documentary flow, organizing of assistance in creating documents and promoting them to the “intellectual market”. Among the concrete forms of activities there are selection of serial publications for reading and submission of articles; development of the journals published by BNTU; creation and maintenance of open access resources; advisory work on registration in various systems of identification ofauthor profiles; various bibliometric studies. The issue of the coincidence of the implementationof the principles of “open science” with the conditions for the formation of a comfortable information environment of science is touched upon.
Similarity Analysis in Understanding Online News in Response to Public Health Crisis

Background: The “Syphilis No!” campaign the Brazilian Ministry of Health (MoH) launched between November 2018 and March 2019, brought forward the concept "Test, Treat and Cure" to remind the population of the importance of syphilis prevention. In this context, this study aims to analyze the similarity of syphilis online news to comprehend how public health communication interventions influence media coverage of the syphilis issue. Methods: This paper presented a computational approach to assess the effectiveness of communication actions on a public health problem. Data were collected between January 2015 and December 2019 and processed using the Hermes ecosystem, which utilizes text mining and machine learning algorithms to cluster similar content. Results: Hermes identified 1049 google-indexed web pages containing the term ’syphilis’ in Brazil. Of these, 619 were categorized as news stories. In total, 157 were grouped into clusters of at least two similar news items and a single cluster with 462 news classified as “single” for not featuring similar news items. From these, 19 clusters were identified in the pre-campaign period, 23 during the campaign, and 115 in the post-campaign. Conclusions: The findings presented in this study show that the volume of syphilis-related news reports has increased in recent years and gained popularity after the SNP started, having been boosted during the campaign and escalating even after its completion.
Interpretable decision trees through MaxSAT

Abstract We present an approach to improve the accuracy-interpretability trade-off of Machine Learning (ML) Decision Trees (DTs). In particular, we apply Maximum Satisfiability technology to compute Minimum Pure DTs (MPDTs). We improve the runtime of previous approaches and, show that these MPDTs can outperform the accuracy of DTs generated with the ML framework sklearn .
A novel practical algorithm for strong and weak synonyms extraction with simple equality operation of web operational machine translation systems results

In this paper, we address synonyms extraction through a simple algorithm based on equality operation of translated segments generated by four free of charge web machine translation systems namely: Google Translate, Promt, Babylon and Bing. The synonyms are classified into two kinds: strong synonyms with different vocabularies, and weak synonyms with the same vocabularies and different part of speech nature. The obtained results, using a dataset of ten theses abstracts, published into the web, seem to be very promising. Moreover, according to this study, it is advised that weak synonyms should receive more attention especially into synonyms substitution for machine translation evaluation.
METAVERSE VE NFT DÜNYASINA TASARIM AÇISINDAN BİR BAKIŞ

Metaverse is an indicator of interactions in the virtual world with the support of the internet, software, and hardware to create an immersive user experience. In simple terms, the metaverse is the virtual universe that brings the virtual and physical worlds together in a three-dimensional space and allows users to discover a different internet experience. With the innovation it brought to the internet world, it is often referred to as web 3.0. NFT stands for immutable token. It is usually created using the same type of program as cryptocurrencies, such as Bitcoin or Ethereum. The term metaverse is now used everywhere and it takes an important place in our lives by developing in different areas. Visual design plays an important role in many areas such as the creation of NFT artwork, games, film, and television. At the same time, it creates a three-dimensional world where avatars representing real people live and digital versions can interact in a digital environment. This article researched problems between what is the metaverse and NFT? What are the main features of Metaverse? What does crypto art involve? seeks answers to questions such as Its general purpose is to make a descriptive literature content analysis of the concepts of the metaverse and NFT in the internet database in a general framework, and finally, to examine how crypto art plays a role in today's world together with technology with examples.
Learning Disentanglement with Decoupled Labels for Vision-Language Navigation

Vision-and-Language Navigation (VLN) requires an agent to follow complex natural language instructions and perceive the visual environment for real-world navigation. Intuitively, we find that instruction disentanglement for each viewpoint along the agent’s path is critical for accurate navigation. However, most methods only utilize the whole complex instruction or inaccurate sub-instructions due to the lack of accurate disentanglement as an intermediate supervision stage. To address this problem, we propose a new Disentanglement framework with Decoupled Labels (DDL) for VLN. Firstly, we manually extend the benchmark dataset Room-to-Room with landmark- and action-aware labels in order to provide fine-grained information for each viewpoint. Furthermore, to enhance the generalization ability, we propose a Decoupled Label Speaker module to generate pseudo-labels for augmented data and reinforcement training. To fully use the proposed fine-grained labels, we design a Disentangled Decoding Module to guide discriminative feature extraction and help alignment of multi-modalities. To reveal the generality of our proposed method, we apply it on a LSTM-based model and two recent Transformer-based models. Extensive experiments on two VLN benchmarks (i.e., R2R and R4R) demonstrate the effectiveness of our approach, achieving better performance than previous state-of-the-art methods.
Initial clustering center optimization and feature auto-weighting for k-Means clustering algorithm

We focus on two main issues. First, the effectiveness of clustering is strongly related to selecting the initial clustering center. Traditional algorithms and their tendency to select multiple initial clustering centers in the same cluster, we use the maximum distance principle, which ensures the initial clustering centers attribute to different categories to avoid this problem. Second, the k-means algorithm cannot assign greater weights to essential features in high dimensions because it treats all features equitably in the clustering process. We acquire a proposed algorithm that is more efficient and accurate than the traditional k-means by improving the algorithm with the multidimensional feature weights technique to give more weight to the more essential features. Experimentally, our enhancements have significantly improved efficiency by 33% and accuracy by 36%.
SMART: Semantic-Aware Masked Attention Relational Transformer for Multi-label Image Recognition

As objects usually co-exist in an image, learning the label co-occurrence is a compelling approach to improving the performance of multi-label image recognition. However, the dependencies among the non-exist categories in an image cannot be effectively evaluated. These redundant label dependencies may bring noise and further decrease the performance of classification. Therefore, we proposed SMART, a Semantic-aware Masked Attention Relational Transformer for Multi-label Image Recognition for multi-label image recognition tasks. In addition to leveraging Transformer to model the inter-class dependencies, the proposed masked attention filters out the redundant dependencies among the non-exist categories. SMART is able to explicitly and accurately capture the label dependencies without extra word embeddings. Moreover, our method achieves new state-of-the-art results on two benchmarks for multi-label image recognition: MS-COCO 2014 and NUS-WIDE. In addition, extensive ablation studies and empirical analysis are provided to demonstrate the effectiveness of the essential components of our method under different factors.
A CTR Prediction Model With Double Matrix-Level Cross-Features

CTR prediction is an important task in recommender systems, which is used to estimate the likelihood of a user clicking on an advertisement.In the past, the CTR prediction model based on the deep neural network mainly obtains the implicit feature combination of the model at the bit-wise level, and the interpretability and generalization of the model are poor. At the same time, the prediction accuracy of the model is poor. For the above problems, We propose a click-through rate prediction model (DTM) with double matrix-level cross-features. The model integrates various components such as multi-head self-attention, residual network and interaction network into an end-to-end model, and automatically obtains explicit feature combinations at the vector-wise level and bit-wise level, which not only has better interpretability, generalization and memory, and reduce the inherent flaws and engineering complexity of multi-modules. The experimental results show that on the datasets Criteo and Avazu, compared with other state-of-the-art CTR prediction models, the AUC values of the DTM model are increased by 4% and 3% on average, and the loss values are decreased by 3.5% and 2.8% on average, respectively.
Ontological Architecture for Knowledge Graphs in Manufacturing and Simulation

Semantic technologies and knowledge graphs are becoming increasingly important in academia and industry. Nevertheless, they remain a rarity in the manufacturing industry, and the advantages they offer are only partially exploited. The same is true for semantic technologies for simulations within production systems. Simulation serves as an essential tool for virtual manufacturing, but the required models are still created by experts in an often elaborate and mostly manual way. In this paper, we derive the requirements for semantic models to capture production simulation knowledge and present an ontological architecture for a knowledge graph for manufacturing and simulation that meets these requirements. The different ontologies of the architecture are based on established production and simulation standards, namely ISA-95 and VDI guidelines. By implementing the ISA-95 standard in an ontology, heterogeneous data from the production system can be integrated. Furthermore, the connection of ISA-95 models to simulation-specific concepts allows the representation of simulation models within the knowledge graph. The implementation of the knowledge graph is shown within a prototypical simulation framework.
A Deep Learning-Based Pressure Matching Approach To Soundfield Synthesis

In this paper we propose a technique for soundfield synthesis based on the combination of the Pressure Matching (PM) approach and of deep learning-based methods. The pressure matching approach retrieves the driving signals for soundfield reproduction by minimizing the reproduction error at discrete control points through least squares. In this paper we follow a similar approach, but we perform the minimization by applying a Convolutional Neural Network (CNN). Through simulations, we compare the performance of the original pressure matching approach with the proposed technique and demonstrate how the latter is able to overcome spatial aliasing issues.
Pre-Trained Word Embeddings for Sarcasm Detection in Indonesian Tweets: A Comparative Study

In affective computing, sarcasm detection is vital because sarcasm can affect the polarity of sentiment analysis. Sarcasm is one of the most challenging problems researchers face when conducting sentiment analysis. Sarcasm is difficult to identify in text because the meaning of the words expressed by a person are the opposite to what the person really means. Deep learning is currently widely used for sarcasm detection. For generating vector representations of words, three distinct pre-trained word embedding models were employed in this study, namely GloVe, fastText, and BERT. Furthermore, three distinct deep learning architectures were utilized, namely Bidirectional Long Sort-Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Convolutional Neural Network (CNN), for sentence-level sarcasm detection in tweets written in Indonesian. The dataset was collected through data crawling using Twitter API. The data was then further preprocessed and features were extracted. The experimental results indicate that the combination of fastText embeddings and BiGRU as the classifier produced the best performance, with an accuracy of 93.85%.