Reviewerly/exHarmony huggingface dataset:
Performance Assessment of Global Horizontal Irradiance Models in All-Sky Conditions

Solar irradiance models contribute to mitigating the lack of measurement data at a ground station. Conventionally, the models relied on physical calculations or empirical correlations. Recently, machine learning as a sophisticated statistical method has gained popularity due to its accuracy and potential. While some studies compared machine learning models with other models, a study has not yet been performed that compares them side-by-side to assess their performance using the same datasets in different locations. Therefore, this study aims to evaluate the accuracy of three representative models for estimating solar irradiance using atmospheric variables measurement and cloud amount derived from satellite images as the input parameters. Based on its applicability and performance, this study selected the fast all-sky radiation model for solar applications (FARMS) derived from the radiative transfer approach, the Hammer model that simplified atmospheric correlation, and the long short-term memory (LSTM) model specialized in sequential datasets. Global horizontal irradiance (GHI) data were modeled for five distinct locations in South Korea and compared with hourly measurement data of two years to yield the error metrics. When identical input parameters were used, LSTM outperformed the FARMS and the Hammer model in terms of relative root mean square difference (rRMSD) and relative mean bias difference (rMBD). Training an LSTM model using the input parameters of FARMS, such as ozone, nitrogen, and precipitable water, yielded more accurate results than using the Hammer model. The result shows unbiased and accurate estimation with an rRMSD and rMBD of 23.72% and 0.14%, respectively. Conversely, the FARMS has a faster processing speed and does not require significant data to make a fair estimation.
Scatterplot Selection Applying a Graph Coloring Algorithm

Scatterplot selection is an effective approach to represent essential portions of multidimensional data in a limited display space. Various metrics for evaluating scatterplots, such as scagnostics, have been devised and applied to scatterplot selection. This paper presents a new scatterplot selection technique that applies multiple metrics. First, the technique calculates the scores of scatterplots with multiple metrics and then constructs a graph by connecting similar scatterplots. Next, it uses a graph coloring algorithm to assign different colors to similar scatterplots. We can extract a set of various scatterplots by selecting them that the specific same color is assigned. This paper introduces a visualization example with a retail dataset containing multidimensional climate and sales values.
Determinant Factors Influencing the Adoption of Blockchain Across SMEs in South Africa

The adoption of blockchain necessitates the attention of a wide range of elements, much beyond the primarily technological focus of most present investigations. The present inquiries do not provide appropriate models as to how SMEs ought to go about blockchain adoption. Thus, this research study sort to investigate the key factors that drive blockchain adoption and propose a research model that could be used to inform blockchain adoption by SMEs in the context of South Africa. Drawing on the innovation diffusion theory and technological, organizational and environmental factors a model was proposed. Quantitative study was carried out; survey data was collected from 167 SMEs. SPSS was used to analyze the collected data and AMOS to fit the model. The results revealed that organizational readiness, trading partner pressure, perceived benefits, complexity, top management support, and compatibility could significantly influence the adoption of blockchain. However, in the South African context, competitive pressure and government regulations &#x0026; support were found to be insignificant.
ArTu: A Tool for Generating Goal Models from User Stories

User stories are widely used to capture the desires of the users in agile development. A set of user stories is easy to read and write but incapable of representing the hierarchical relations and synergies among the user stories. By contrast, goal models are uncommon in industrial projects however they can express the structure and other relations among requirements captured as goals. This paper presents ArTu, a tool for generating goal models from user stories to effortlessly benefit from both. Given a set of user stories, our tool generates goal models with different structures depending on the heuristic selected by the user. Users can import, edit, and export model data in different formats.
Unveiling of How Image Restoration Contributes to Underwater Object Detection

Most underwater image restoration works indicate that restoring images can improve their visual quality and performance of computer vision tasks that use the restored data. This paper aims to investigate how image restoration contributes to underwater object detection. We found that utilizing image restoration through data augmentation rather than using it as a pre-processing step can boost underwater object detection accuracy (up to a 1.9% gain in mAP) on URPC2019 dataset.
A Two-Filter Approach for State Estimation Utilizing Quantized Output Data

Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the system state based on the sequentially available noisy measurements of the system output. In a real-world system, the noisy measurements can suffer a significant loss of information due to (among others): (i) a reduced resolution of cost-effective sensors typically used in practice or (ii) a digitalization process for storing or transmitting the measurements through a communication channel using a minimum amount of resources. Thus, obtaining suitable state estimates in this context is essential. In this paper, Gaussian sum filtering and smoothing algorithms are developed in order to deal with noisy measurements that are also subject to quantization. In this approach, the probability mass function of the quantized output given the state is characterized by an integral equation. This integral was approximated by using a Gauss-Legendre quadrature; hence, a model with a Gaussian mixture structure was obtained. This model was used to develop filtering and smoothing algorithms. The benefits of this proposal, in terms of accuracy of the estimation and computational cost, are illustrated via numerical simulations.
Fine-Grained and Real-Time Gesture Recognition by Using IMU Sensors

Gesture recognition by using Inertial Measurement Unit (IMU) sensors plays an important role in various Internet of Things (IOT) applications, e.g., smart home, intelligent medical system and so on. Traditional technologies usually utilize machine learning algorithms to train different gestures during the offline phase, then recognize the gesture during the online phase. However, such technologies cannot recognize these gestures without prior training. Even for the same gesture, with different gesture amplitude may result in unsuccessful recognition. Also if we change the person to perform the same gesture, the algorithms fails. In order to overcome these drawbacks, we propose an approach, which will be able to track the human body motion in real-time and also recognize complicated gestures. It utilizes the accelerometer information and proposes comprehensive localization algorithms for each deployed sensor attached on the human body. Then, it takes the correlation and limitation among body parts into account to recognize the gesture. Our experiments results show that, the successful recognition rate of our algorithm is 100%. Furthermore, any part of the human body can be well tracked, the tracking accuracy can reach <inline-formula><tex-math notation="LaTeX">$0.06m$</tex-math></inline-formula> .
Simultaneous variable selection, clustering, and smoothing in function‐on‐scalar regression

We address the problem of multicollinearity in a function‐on‐scalar regression model by using a prior that simultaneously selects, clusters, and smooths functional effects. Our methodology groups the effects of highly correlated predictors, performing dimension reduction without dropping relevant predictors from the model. We validate our approach via a simulation study, showing superior performance relative to existing dimension‐reduction approaches described in the function‐on‐scalar literature. We also demonstrate the use of our model on a data set of age‐specific fertility rates from the United Nations Gender Information database.
Open-Pub: A Transparent yet Privacy-Preserving Academic Publication System based on Blockchain

Academic publication of latest research results are crucial to advance the development of all disciplines. However, the current academic publication procedure lacks transparency in the review process, and reviewers do not have strong incentives to provide quality reviews for their peers. This may lead to misconducts in the review process, e.g. an anonymous reviewer may give biased comments to a paper without being noticed since the comments are seldom published for evaluation. In addition, it is ironic that the research community has to pay a lot to access papers reviewed by the community for free, though free sharing of research results is important for research advances.To address the above problems, we propose Open-Pub, a decentralized, transparent yet privacy-preserving academic publication scheme based on the blockchain technology. The blockchain promises transparency in the review process and provides an incentive mechanism based on cryptocurrency, but the privacy requirement in the double-blind review process still needs to be fulfilled. To this end, we first design a threshold identity-based group signature (TIBGS) using verifiable secret sharing to protect identity confidentiality. Then we develop a strong double-blind procedure to protect the identities of authors and reviewers. With this strong double-blind procedure, authors can choose to submit papers anonymously, and validators distribute papers anonymously to reviewers on the blockchain according to their research interests. This process is publicly recorded and traceable on the blockchain so as to realize transparent peer preview. To evaluate its efficiency, we implement Open-Pub based on Ethereum and conduct comprehensive experiments to evaluate its performance, including computation costs and processing delay. The experiment results show that Open-Pub is highly efficient in computation and processing anonymous transactions.
Sensor-Based Human Activity Recognition Using Adaptive Class Hierarchy

In sensor-based human activity recognition, many methods based on convolutional neural networks (CNNs) have been proposed. In the typical CNN-based activity recognition model, each class is treated independently of others. However, actual activity classes often have hierarchical relationships. It is important to consider an activity recognition model that uses the hierarchical relationship among classes to improve recognition performance. In image recognition, branch CNNs (B-CNNs) have been proposed for classification using class hierarchies. B-CNNs can easily perform classification using hand-crafted class hierarchies, but it is difficult to manually design an appropriate class hierarchy when the number of classes is large or there is little prior knowledge. Therefore, in our study, we propose a class hierarchy-adaptive B-CNN, which adds a method to the B-CNN for automatically constructing class hierarchies. Our method constructs the class hierarchy from training data automatically to effectively train the B-CNN without prior knowledge. We evaluated our method on several benchmark datasets for activity recognition. As a result, our method outperformed standard CNN models without considering the hierarchical relationship among classes. In addition, we confirmed that our method has performance comparable to a B-CNN model with a class hierarchy based on human prior knowledge.
HybNet: A Hybrid Deep Learning-Matched Filter Approach for IoT Signal Detection

Random access schemes are widely used in IoT wireless access networks. They enable a reduced complexity and overcome power consumption constraints. Nevertheless, random access results in high packet losses which are caused by overlapping transmissions. Signal detection methods for digital modulation techniques are typically based on the well-established <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">matched filter</i> , which is proven as the optimal filter under additive white Gaussian noise for minimizing error probability. However, with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">colored</i> interference arising from the overlapping IoT transmissions, deep learning approaches are being considered a suitable alternative. In this paper, we present a hybrid framework, dubbed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HybNet</i> , that alternates between deep learning and match filter pathways based on the perceived interference level. This helps the detector to work in a broader range of conditions, optimally leveraging both the matched filter and deep learning advantages. We compare the performance of several possible data modalities and detection architectures concerning the interference-to-noise ratio, demonstrating that leveraging domain knowledge by pre-processing the input data in conjunction with the proposed HybNet surpasses the complex conjugate matched filter performance under interference-limited scenarios.
Learned Image Compression With Separate Hyperprior Decoders

Learned image compression techniques have achieved considerable development in recent years. In this paper, we find that the performance bottleneck lies in the use of a single hyperprior decoder, in which case the ternary Gaussian model collapses to a binary one. To solve this, we propose to use three hyperprior decoders to separate the decoding process of the mixed parameters in discrete Gaussian mixture likelihoods, achieving more accurate parameters estimation. Experimental results demonstrate the proposed method optimized by MS-SSIM achieves on average 3.36% BD-rate reduction compared with state-of-the-art approach. The contribution of the proposed method to the coding time and FLOPs is negligible.
Fisher and Skew Information Correlations of Two Coupled Trapped Ions: Intrinsic Decoherence and Lamb-Dicke Nonlinearity

It is well known that many quantum information processing methods in artificial atoms depend largely on their engineering properties and their ability to generate quantum correlations. In this paper, we investigate the non-classical correlation dynamics of two trapped ions by using local quantum Fisher information, local quantum uncertainty, as well as logarithmic negativity. The system engineering is designed such that the two-trapped-ions work as two diploe-coupled qubits in a Lamb-Dicke regime. The center-of-mass vibrational modes are initially in coherent/even coherent states. It is found that the two-trapped-ions correlations can be controlled by the Lamb-Dicke nonlinearity, the nonclassicality effect of the initial center-of-mass vibrational mode, as well as the trapped-ion coupling and the intrinsic decoherence. The sudden changes in the non-classical correlations and their stability are shown against Lamb-Dicke nonlinearity, the nonclassicality, the trapped-ion coupling, and the intrinsic decoherence.
Evaluating the Robustness of Embedding-Based Topic Models to OCR Noise

Unsupervised topic models such as Latent Dirichlet Allocation (LDA) are popular tools to analyse digitised corpora. However, the performance of these tools have been shown to degrade with OCR noise. Topic models that incorporate word embeddings during inference have been proposed to address the limitations of LDA, but these models have not seen much use in historical text analysis. In this paper we explore the impact of OCR noise on two embedding-based models, Gaussian LDA and the Embedded Topic Model (ETM) and compare their performance to LDA. Our results show that these models, especially ETM, are slightly more resilient than LDA in the presence of noise in terms of topic quality and classification accuracy.
Characterising the Gap Between Theory and Practice of Ontology Reuse

Ontology reuse is a complex process that requires the support of methodologies and tools to minimise errors and to keep the ontologies consistent and coherent. Although the vast majority of ontology engineering methodologies include a reuse phase, and reuse has been investigated for different tasks and purposes (e.g.ontology integration), this body of work does not seem to translate into practice, neither in the form of strict criteria for reuse, nor as a set of community proposed guidelines. In this paper, we report the salient results from a study aimed at ontology developers and practitioners, whose objective is to gain an insight into the gap between the theory and the practice of ontology reuse. Thefocus of our study is to gain practitioners' views on i) their preferred reuse approaches; ii) the types of ontologies they tend to reuse (e.g. specific domain ontologies or upper-level ontologies)iii) what reporting information they deem useful when deciding which ontology to reuse; iv) what are the main reasons deterring them from reusing an ontology. Our findings confirm and extend established results from the literature, but in addition, the study provides a fresh view on the practice of reuse with an explicit focus on highly experienced developers and moderately experienced ones. The study corroborates the need for a comprehensive set of recommendations, that are widely accepted by the community, and are possibly implemented in development tools.
Multisensory Virtual Reality for Delivering Training Content to Machinery Operators

Abstract The issue of training operators in the use of machinery is topical in the industrial field and in many other contexts, such as university laboratories. Training is about learning how to use machinery properly and safely. Beyond the possibility of studying manuals to learn how to use a machine, operators typically learn through on-the-job training. Indeed, learning by doing is in general more effective, tasks done practically are remembered more easily, and the training is more motivating and less tiresome. On the other hand, this training method has several negative factors. In particular, safety may be a major issue in some training situations. An approach that may contribute overcoming negative factors is using Virtual Reality and digital simulation techniques for operators training. The research work presented in this paper concerns the development of a multisensory virtual reality application for training operators to properly use machinery and personal protective equipment (PPE). The context selected for the study is a university laboratory hosting manufacturing machinery. The application allows user to navigate the laboratory, to approach a machine and learn about how to operate it, and also to use proper PPE while operating a machine. Specifically, the paper describes the design and implementation of the application and presents the results of preliminary testing sessions.
Notes on Iterated Rationality Models of Scalar Implicatures

Abstract In the linguistics literature, the derivation of scalar implicatures has often been handled in a relatively modular way, using computations that are sensitive to logical relations among alternatives such as entailment but are blind to other notions such as the probabilities that participants in a conversation might associate with these alternatives (or with related propositions). In recent years, a family of models that we refer to as iterated rationality models (IRMs) have offered an interestingly different perspective on such alternative-sensitive processes in terms of multiple iterations of (typically probabilistic) reasoning. Our paper investigates what at first sight seems like a very interesting argument for the adequacy of IRMs as models of scalar implicatures, coming from the conjunctive interpretation of disjunctive sentences. We then outline challenges for the argument based on a theoretical comparison with the grammatical theory of scalar implicatures. The comparison focuses on the full distribution of conjunctive interpretation, on the question of sensitivity to priors, and on other results achieved within the grammatical theory that the IRM literature has yet to engage with.
DAR-Net: Dense Attentional Residual Network for Vehicle Detection in Aerial Images

With the rapid development of deep learning and the wide usage of Unmanned Aerial Vehicles (UAVs), CNN-based algorithms of vehicle detection in aerial images have been widely studied in the past several years. As a downstream task of the general object detection, there are some differences between the vehicle detection in aerial images and the general object detection in ground view images, e.g., larger image areas, smaller target sizes, and more complex background. In this paper, to improve the performance of this task, a Dense Attentional Residual Network (DAR-Net) is proposed. The proposed network employs a novel dense waterfall residual block (DW res-block) to effectively preserve the spatial information and extract high-level semantic information at the same time. A multiscale receptive field attention (MRFA) module is also designed to select the informative feature from the feature maps and enhance the ability of multiscale perception. Based on the DW res-block and MRFA module, to protect the spatial information, the proposed framework adopts a new backbone that only downsamples the feature map 3 times; i.e., the total downsampling ratio of the proposed backbone is 8. These designs could alleviate the degradation problem, improve the information flow, and strengthen the feature reuse. In addition, deep-projection units are used to reduce the impact of information loss caused by downsampling operations, and the identity mapping is applied to each stage of the proposed backbone to further improve the information flow. The proposed DAR-Net is evaluated on VEDAI, UCAS-AOD, and DOTA datasets. The experimental results demonstrate that the proposed framework outperforms other state-of-the-art algorithms.
Real Time Detection of Social Bots on Twitter Using Machine Learning and Apache Kafka

Social media networks, like Facebook and Twitter, are increasingly becoming important part of most people's lives. Twitter provides a useful platform for sharing contents, ideas, opinions, and promoting products and election campaigns. Due to the increased popularity, it became vulnerable to malicious attacks caused by social bots. Social bots are automated accounts created for different purposes. They are involved in spreading rumors and false information, cyberbullying, spamming, and manipulating the ecosystem of social network. Most of the social bots detection methods rely on the utilization of offline data for both training and testing. In this paper, we use Apache Kafka, a big data analytics tool to stream data from Twitter API in real time. We use profile information (metadata) as features. A machine learning technique is applied to predict the type of the incoming data (human or bot). In addition, the paper presents technical details of how to configure these different tools.