Reviewerly/exHarmony huggingface dataset:
Estimation of Solar Power by Combining Ground Measured and Satellite Data Using Artificial Neural Networks

The inherent intermittency of solar energy requires better understanding of its characteristics for the planning and operation of power systems. Solar energy measurements are normally collected at the ground level or using satellite data. Ground measurements are accurate but with limited coverage, in terms of location and covering period. Satellite data is usually available for any location and with decades of records but lacking the accuracy needed. To merge and align these two sources of solar energy measurements is rapidly becoming part of the chores for system planners and operators. To tackle this issue, this paper proposes a series of multi-layered backpropagation neural network (MLBPNN) to estimate historic or future ground measured data from overlapping existing satellite and ground measurements. The study successfully demonstrates that the proposed method can indeed achieve accurate projection. We also demonstrate the sensitivity of the mismatch between satellite data and ground measurements is highly depending on geographical and seasonal factor. Finally, we present a summary of analysing the mismatch comparison amongst different locations in Asia Pacific and discuss various applications using such method in system operation and planning.
Design of an S-ECIES Cryptoprocessor Using Gaussian Normal Bases Over GF(2<sup> <i>m</i> </sup>)

In this article, we present the design of a high-performance simplified elliptic curve integrated encryption scheme (S-ECIES) cryptoprocessor. The cryptoprocessor was designed using a Montgomery ladder scalar multiplier, which was implemented with three finite field multipliers to improve the computational time of the scalar multiplication kP, and using random curves and Gaussian normal bases over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">233</sup> ). Also, considering the National Institute of Standards and Technology (NIST) recommendations, a true random number generator is implemented to generate a secret key k, which is used during the encryption process. The S-ECIES cryptoprocessor was synthesized on field-programmable gate array (FPGA) Stratix IV EP4SGX230KF40C2, simulated in ModelSim, and verified in hardware using the DE4 board and SignalTap tool. According to the synthesis results, the scalar multiplication operation is performed in 5.31 and 8.77 μs for GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">233</sup> ), respectively. Also, the encryption process is performed in 20.70 and 30.90 μs for GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">233</sup> ), respectively, and the decryption process is calculated in 8.10 and 11.9 μs for GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">233</sup> ), respectively. The consumption power for the S-ECIES is 921 and 935 mW for GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">163</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">233</sup> ), respectively.
Korean court cases regarding research and publication ethics from 2009 to 2020

Research and publication misconduct may occur in various forms, including author misrepresentation, plagiarism, and data fabrication. Research and publication ethics are essentially not legal duties, but ethical obligations. In reality, however, legal disputes arise over whether research and publication ethics have been violated. Thus, in many cases, misconduct in research and publication is determined in the courts. This article presents noteworthy legal cases in Korea regarding research and publication ethics to help editors and authors prevent ethical misconduct. Legal cases from 2009 to 2020 were collected from the database of the Supreme Court of Korea in December 2020. These court cases represent three case types: 1) civil cases, such as affirmation of nullity of dismissal and damages; 2) criminal cases, such as fraud, interference with business, and violations of copyright law; and 3) administrative cases related to disciplinary measures against professors affiliated with a university. These cases show that although research and publication ethics are ethical norms that are autonomously established by the relevant academic societies, they become a criterion for case resolution in legal disputes where research and publication misconduct is at issue.
Metric Learning on the Manifold of Oriented Ellipses: Application to Facial Expression Recognition

In this paper we propose a new family of metrics on the manifold of oriented ellipses centered at the origin in Euclidean n-space, the double cover of the manifold of positive semi-definite matrices of rank two, in order to measure similarities between landmark representations. The metrics, whose distance functions are remarkably simple, are parametrized by the choice of a n-by-n positive semi-definite matrix P. This allows us to learn the parameter P from the training data and increase the efficiency of the metric. We evaluate the proposed metric on facial expression recognition from 2D facial landmarks. The conducted experiments demonstrate the effectiveness of the learned metric to classify facial shapes under different expressions.
Designing Synthetic Overhead Imagery to Match a Target Geographic Region: Preliminary Results Training Deep Learning Models

Convolutional Neural Networks (CNNs) have dominated performance on benchmark problems for object recognition in remote sensing imagery. However, recent work has shown that they may perform poorly when tested on imagery collected over a geographic location that was not present in its training imagery. In this work we explore the potential of designing synthetic overhead imagery to match a target geographic region (e.g., city or county), after which the synthetic imagery could be used to train deep learning models to recognize the unique visual features of objects/background in the target geographic location. We term this approach geographic domain matching. Towards this goal, we utilize a publicly-available dataset of synthetic overhead imagery, Synthinel-1. We systematically alter individual visual features of Synthinel-1 in an effort to match one real-world testing city: Vienna, Austria. We then evaluate whether these individual alterations improve the performance benefits of Synthinel-1 on Vienna and other cities. The results suggest that our proposed methodologies for altering the synthetic imagery to match Vienna were effective, thereby taking first step towards developing methods for designing synthetic overhead imagery for domain matching.
Multi-Class Object Detection Using 2D Poses

Object detection (OD) methods are finding application in various fields. The OD problem can be divided into two sub-problems, namely object classification and localization. While the former aims to answer the question what class a given object belongs to, the latter focuses on locating an object within a given image. For localization, both implicit representations, which border the object and its features (e.g. bounding boxes, polygons and masks), and explicit representations, which describe the object's pose in an image (e.g. 6D pose, keypoints), are used. The 2D pose is a simple, yet effective representation that has so far been overlooked. In this paper, we therefore motivate and formulate the use of 2D poses for object localization. Furthermore, we present RetinaNet-2DP, an anchor-based convolutional neural network (CNN) that is capable of detecting objects using 2D poses. To do so, we propose the idea of Anchor Poses and the Gaussian Kernel Distance as a similarity metric between poses. Experiments on the DOTA dataset and two robotics use cases from industry emphasize the performance of the network architecture and more generally demonstrate the potential of the proposed localization representation. Finally, we critically assess our findings and present an outlook of future work.
Propositional Constraint Graphs: An Intuitive, Domain-General Tool for Diagramming Knowledge, Assumptions, and Uncertainties

A diagramming method called Propositional Constraint (PC) graphing was developed as an aid for tasks involving argumentation, planning, and design. Motivated by several AI models of defeasible (or non- monotonic) reasoning, PC graphs were designed to represent knowledge according to an analogical framework in which constraints (e.g., evidence, goals, system constraints) may elicit or deny possibilities (e.g., explanations, decisions, behaviors). In cases of underspecification, an absence of constraints yields uncertainty and competition among plausible outcomes. In cases of overspecification, no plausible outcome is yielded until one of the constraints is amended or forfeited. This framework shares features with theoretical models of reasoning and argumentation, but despite its intuitiveness and applicability, we know of no modeling language or graphical aid that explicitly depicts this defeasible constraint structure. We describe the syntax and semantics for PC graphing and then illustrate potential uses for it.
A Semiparametric Approach for Structural Equation Modeling with Ordinal Data

There is currently a lack of methods for non-linear structural equation modeling (NSEM) for non-parametric relationships between latent variables when data are ordinal. To this end, a semiparametric approach for flexible NSEMs without parametric forms is developed for ordinal data. An indirect application of a finite mixture of structural equation models (SEMM) is employed for modeling the conditional expected mean of endogenous latent variables. In this context, the latent classes are not to be interpreted as groups of observations belonging to those classes, rather they serve as means to model flexible non-linear functions as locally linear functions which together approximate a globally non-linear function. The proposed method is based on a hybrid of direct maximization and expectation-maximization algorithms. Two simulation studies are performed which show that parameter estimates are associated with low bias and a non-linear functional form is satisfactorily estimated using the proposed approach.
Thyroid Nodules Recognition in Ultrasound Images Based on ImageNet Top-Performing Deep Convolutional Neural Networks

Deep learning algorithms, especially convolutional neural networks, have quickly become a methodology of choice for medical image analysis. In this article, we are interested in thyroid cancer, a disease that is triggered off by the malignant transformation of some of the cells that make up the thyroid gland. For this purpose, our study focuses on the use of deep learning on ultrasound images for the recognition of malignant tissue. The proposed approach is based on ImageNet top-performing deep convolutional neural networks such as VGG16, Inception and Inception-Resnet. These networks were evaluated on the public database, Digital Database Thyroid Image (DDTI). However, the latter contains only 451 thyroid images which is little to work with as sophisticated models trained on small datasets tend to overfit quickly. Thus, we carried out several data augmentation strategies on the training images prior to classification in order to enlarge the variety of training data. The obtained results showed a test accuracy of 93%, 97% and 99% for VGG16, Inception-Resnet and Inception, respectively.
Learning Secured Modulation With Deep Adversarial Neural Networks

Growing interest in utilizing the wireless spectrum by heterogeneous devices compels us to rethink the physical layer security to protect the transmitted waveform from an eavesdropper. We propose an end-to-end symmetric key neural encryption and decryption algorithm with a modulation technique, which remains undeciphered by an eavesdropper, equipped with the same neural network and trained on the same dataset as the intended users. We solve encryption and modulation as a joint problem for which we map the bits to complex analog signals, without adhering to any particular encryption algorithm or modulation technique. We train to cooperatively learn encryption and decryption algorithms between our trusted pair of neural networks, while eavesdropper’s model is trained adversarially on the same data to minimize the error. We introduce a discrete activation layer with a defined gradient to combat noise in a lossy channel. Our results show that a trusted pair of users can exchange data bits in both clean and noisy channels, where a trained adversary cannot decipher the data.
Visual Word Embedding for Text Classification

The question we answer with this paper is: ‘can we convert a text document into an image to take advantage of image neural models to classify text documents?’ To answer this question we present a novel text classification method that converts a document into an encoded image, using word embedding. The proposed approach computes the Word2Vec word embedding of a text document, quantizes the embedding, and arranges it into a 2D visual representation, as an RGB image. Finally, visual embedding is categorized with state-of-the-art image classification models. We achieved competitive performance on well-known benchmark text classification datasets. In addition, we evaluated our proposed approach in a multimodal setting that allows text and image information in the same feature space.
DDF Library: Enabling functional programming in a task-based model

In recent years, the areas of High-Performance Computing (HPC) and massive data processing (also know as Big Data) have been in a convergence course, since they tend to be deployed on similar hardware. HPC systems have historically performed well in regular, matrix-based computations; on the other hand, Big Data problems have often excelled in fine-grained, data parallel workloads. While HPC programming is mostly task-based, like COMPSs, popular Big Data environments, like Spark, adopt the functional programming paradigm. A careful analysis shows that there are pros and cons to both approaches, and integrating them may yield interesting results. With that reasoning in mind, we have developed DDF, an API and library for COMPSs that allows developers to use Big Data techniques while using that HPC environment. DDF has a functional-based interface, similar to many Data Science tools, that allows us to use dynamic evaluation to adapt the task execution in run time. It brings some of the qualities of Big Data programming, making it easier for application domain experts to write Data Analysis jobs. In this article we discuss the API and evaluate the impact of the techniques used in its implementation that allow a more efficient COMPSs execution. In addition, we present a performance comparison with Spark in several application patterns. The results show that each technique significantly impacts the performance, allowing COMPSs to outperform Spark in many use cases.
RevBloc: A Blockchain-Based Secure Customer Review System

Customer reviews enable customers to share their experiences with others, which allow potential customers to know more about products and consume products with confidence. However, online product sellers and service providers could manipulate customer reviews, such as adding fake positive reviews and removing negative customer reviews, to support their business. Manipulated reviews could result in distorting the original content of customer reviews and misleading customers. State-of-the-art solutions lack a customer review system that is secure, efficient, and usable. In this paper, we propose RevBloc to provide a customer review system with a high level of security, efficiency, and usability. RevBloc is based on blockchain technology that enables customer reviews to be preserved in a distributed ledger, thus a single or subset of malicious parties cannot manipulate the reviews. To show the feasibility of our approach, we implement a proof-of-concept prototype of RevBloc and report its performance.
Quantum communication scheme with freely selectable users

Quantum communication plays an important role in quantum information science due to its unconditional security. In practical implementations, the users of each communication vary with the transmitted information, and hence not all users are required to participate in each communication round. Therefore, improving the flexibility and efficiency of the actual communication process is highly demanded. Here, we propose a theoretical quantum communication scheme that realizes secret key distribution for both the two-party quantum key distribution (QKD) and multi-party quantum secret sharing (QSS) modes. The sender, Alice, can freely select one or more users to share keys among all users, and nonactive users will not participate in the process of secret key sharing. Numerical simulations show the superiority of the proposed scheme in transmission distance and secure key rate. Consequently, the proposed scheme is valuable for secure quantum communication network scenarios.
Design of an Automatic Reader for the Visually Impaired Using Raspberry Pi

Reading is the most challenging problem for visually impaired people. It makes life more troublesome for them. Though there are braille technologies for blind persons to read any text, they are time taking approach. Some other newly arrived technology is also there for reading purpose. Here we proposed a prototype which focuses on the development of an automatic image-to-speech device for the visually impaired persons which will help with some daily tasks. This prototype is built using Raspberry Pi 4 Model B. The main purpose to develop this automatic reader is to allow a blind user to “read” text (a paper or any book). Optical Character Recognition (OCR), Text to Speech Synthesis (TTS) and some image processing algorithms are integrated into Raspberry Pi 4 (credit card sized mini computer). Tesseract OCR and eSpeak text to speech synthesizer are used here. This model enables the user to take a picture using a Raspberry Pi camera, hear the text (which exists in the paper or the book) by using any speaker and the whole process is controlled by using push buttons. Proposed reader can read the texts which are written in English, Bengali and Hindi languages.
An OCR Pipeline and Semantic Text Analysis for Comics

Optical character recognition has remained a challenge for comics, given the high variability of placement of text on the page, the wide variety of frequently handwritten fonts, and the limited availability and small size of datasets. This paper reports on currently on-going work on an OCR pipeline that includes text spotting with the help of a U-Net based fully convolutional neural network and OCR training with the open-source software Calamari, which was performed on the “Graphic Narrative Corpus” of book-length graphic novels written in English. Based on the results of the OCR training, we then present an analysis of the textual properties of 129 graphic novels correlated with page length, historical development, and genre affiliation.
Aerospace engineering experience and on-board software projects of satellite navigation systems

Abstract The main stages of creating global space navigation systems of the first generation are discussed in the article. The authors give a description of the scope and content of work, stages and timing of this work, and also give the main characteristics of the satellites and on-board software testing processes of these space navigation systems. The list and sequence of technological stages of the on-board software testing process are defined in the description of the methodology for guaranteeing the quality of development and maintenance of the on-board software of communication and navigation satellites.
Investigating the information value of different sources of evidence of developers’ expertise for bug assignment in open‐source projects

Bug assignment (BA), the process of ranking developers according to their potential ability to fix a given bug, is an important software-engineering task. BA usually requires the development of an expertise profile for each developer, and formulation of a similarity metric to estimate the relevance of developers to the bug. This needs us to answer the following question: ‘what is the information value of various contributions of developers in BA research?’ We address this question by making the following contributions. (i) We enhance the expertise metric of our prior work, vocabulary and time-based BA, to consider information regarding various sources of expertise with different importance. We show that this can improve the effectiveness of bug-assignment process. (ii) Using this ‘Multisource’ expertise metric, we investigate the information value of different pieces of information in open-source repositories for BA. We show that in addition to bug-fixing contributions, other technical and even social contributions of developers within the version-control system are useful information for BA. (iii) We provide a curated, up-to-date data set including technical information of 13 popular open-source projects in Github. To the best of our knowledge, this is the most comprehensive data set, currently available for bug-assignment research.
Improved Identification of Imbalanced Multiple Annotation Intent Labels with a Hybrid BLSTM and CNN Model and Hybrid Loss Function

Payment or fund transfer transactions can be annotated by users when they are made through a mobile banking app, for example, SCB Easy app—a mobile banking app by Siam Commercial Bank—allows users to annotate transactions with 40 character texts. The AI\(^2\) framework was used to identify user intentions with the transactions, so that the bank can offer the right product to the right customer at the right time. The framework employed Long Short-Term Memory (LSTM). Commonly, one annotated sample can be interpreted as representing multiple intents, thus we had a multiple label classification problem. However, the original model did not consider the class imbalance, that caused the model to bias toward the majority class. We introduced a new hybrid Bidirectional LSTM and Convolutional Neural Network model in conjunction with a new hybrid loss function to tackle the imbalance. Our model with hybrid loss function performed better than the AI\(^2\) framework with a 4.5% improvement in \(F_1\)-score. Moreover, our hybrid loss function enabled the model to classify minority classes better, when the imbalance ratio became higher, compared with a conventional cross-entropy loss function. In other words, our hybrid loss function made the model to be more efficient in real-world multiple label imbalance problem.