Reviewerly/exHarmony huggingface dataset:
Exploring solar and wind energy resources in North Korea with COMS MI geostationary satellite data coupled with numerical weather prediction reanalysis variables

Despite their potential as a naturally-available clean energy option, the renewable energy resources of the Democratic People's Republic of Korea (i.e., North Korea) have rarely been evaluated. Therefore, to estimate the availability of land surface solar irradiance necessary for solar applications and to model available energy potential, physically-based models drawing on Communication, Ocean and Meteorological Satellite (COMS) data and associated statistics for key atmospheric constituents, were employed. To assess wind energy resources, model output statistics (MOS) were integrated from post-processed Local Data Assimilation and Prediction System (LDAPS) variables, thereby removing any systematic bias arising from long-term regression methods. The root mean square error (RMSE) and mean bias error (MBE) served to compare pyranometer- and satellite-sourced solar radiation, under instantaneous (87.90 W m−2 and 16.84 W m−2, respectively) and daily ‘all sky conditions’ (624.98 Wh m−2 d−1 and 13.89 Wh m−2 d−1, respectively). These low values indicate that satellite-based solar irradiance is sufficiently accurate to be used to model future land surface solar energy in North Korea. In the evaluation of wind energy resources, daily wind speeds obtained from Numerical Weather Prediction (NWP) reanalysis fields showed good accuracy compared to a meteorological tower measurement (RMSE = 0.37 m s−1 and MBE = 0.24 m s−1). In the study region, mean wind energy potential (from 2013–2015) was 3.44 kWh m−2 d−1, whereas solar energy potential was slightly lower at 3.36 kWh m−2 d−1; this can be attributed to the nation's mountainous terrain and high latitude. Although the region's mountainous terrain may be an obstacle for future development of renewable energy infrastructure, these initial annual mean solar and wind power density results illustrate the significant renewable energy potential of North Korea. This situates the country in a position to promote the United Nations Sustainable Development Goal (SDG #7) of integrating cleaner and more sustainable energy resources through solar and wind power.
Estimation of Behind-the-Meter Solar Generation by Integrating Physical with Statistical Models

Accurate estimation of solar photovoltaic (PV) generation is crucial for distribution grid control and optimization. Unfortunately, most of the residential solar PV installations are behind-the-meter. Thus, utilities only have access to the net load readings. This paper presents an unsupervised framework for estimating solar PV generation by disaggregating the net load readings. The proposed framework synergistically combines a physical PV system performance model with a statistical model for load estimation. Specifically, our algorithm iteratively estimates solar PV generation with a physical model and electric load with the Hidden Markov model regression. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is validated against net load and solar PV generation data gathered from residential customers located in Austin, Texas. The validation results show that our method reduces mean squared error by 44% compared to the state-of-the-art disaggregation algorithm.
Mental Models of Mere Mortals with Explanations of Reinforcement Learning

How should reinforcement learning (RL) agents explain themselves to humans not trained in AI? To gain insights into this question, we conducted a 124-participant, four-treatment experiment to compare participants’ mental models of an RL agent in the context of a simple Real-Time Strategy (RTS) game. The four treatments isolated two types of explanations vs. neither vs. both together. The two types of explanations were as follows: (1) saliency maps (an “Input Intelligibility Type” that explains the AI’s focus of attention) and (2) reward-decomposition bars (an “Output Intelligibility Type” that explains the AI’s predictions of future types of rewards). Our results show that a combined explanation that included saliency and reward bars was needed to achieve a statistically significant difference in participants’ mental model scores over the no-explanation treatment. However, this combined explanation was far from a panacea: It exacted disproportionately high cognitive loads from the participants who received the combined explanation. Further, in some situations, participants who saw both explanations predicted the agent’s next action worse than all other treatments’ participants.
Digital policy sociology: software and science in data-intensive precision education

Software code, algorithms, data analytics and infrastructures have become inseparable from policy processes and modes of governance. This article introduces 'digital policy sociology' as a way of studying the role and influence of digital technologies in education policy. Building on existing 'policy sociology' approaches combined with emerging insights rom 'digital sociology', digital policy sociology extends the analytical gaze to new technical actors – nonhuman software and hardware, as well as human experts, technology companies, and promotional organizations. As a case study exemplar, the analysis focuses on an emerging domain of data-intensive science and technology with significant implications for education policy in the future. 'Precision education' is an emerging combination of psychological, neuroscientific and genetic expertise, with a particular emphasis on using advanced computational technologies to produce 'intimate data' about students' bodies and biological associations with learning. These intimate data have potential to become new sources of biological policy knowledge, raising significant methodological and analytical challenges for policy sociology.
Asset tokenization in plain English

Abstract Asset tokenization is an expansion of blockchain technology that allows digital assets to be bought, sold, and traded on blockchains. Digitizing real assets will help create universally accessible, fast, liquid, and transparent investment and financial systems. However, blockchain and related asset tokenization are still in their infancy, and there are obstacles that need to be overcome before widespread adoption occurs. This article describes what it means to tokenize an asset on a blockchain, how tokenization is accomplished, and why it has the potential to disrupt a variety of industries, particularly the financial and real estate industries. Challenges that are associated with implementing asset tokenization are also explored.
Design Thinking: Challenges for Software Requirements Elicitation

Agile methods fit well for software development teams in the requirements elicitation activities. It has brought challenges to organizations in adopting the existing traditional methods, as well as new ones. Design Thinking has been used as a requirements elicitation technique and immersion in the process areas, which brings the client closer to the software project team and enables the creation of better projects. With the use of data triangulation, this paper brings a literature review that collected the challenges in software requirements elicitation in agile methodologies and the use of Design Thinking. The result gave way to a case study in a Brazilian public organization project, via user workshop questionnaire with 20 items, applied during the study, in order to identify the practice of Design Thinking in this context. We propose here an overview of 13 studied challenges, from which eight presented strong evidence of contribution (stakeholders involvement, requirements definition and validation, schedule, planning, requirement details and prioritization, and interdependence), three presented partial evidence of contribution and two were not eligible for conclusions (non-functional requirements, use of artifacts, and change of requirements). The main output of this work is to present an analysis of the use of Design Thinking to see if it fits properly to be used as a means of solving the challenges of elicitation of software requirements when using agile methods.
Unsupervised Procedure Learning via Joint Dynamic Summarization

We address the problem of unsupervised procedure learning from unconstrained instructional videos. Our goal is to produce a summary of the procedure key-steps and their ordering needed to perform a given task, as well as localization of the key-steps in videos. We develop a collaborative sequential subset selection framework, where we build a dynamic model on videos by learning states and transitions between them, where states correspond to different subactivities, including background and procedure steps. To extract procedure key-steps, we develop an optimization framework that finds a sequence of a small number of states that well represents all videos and is compatible with the state transition model. Given that our proposed optimization is non-convex and NP-hard, we develop a fast greedy algorithm whose complexity is linear in the length of the videos and the number of states of the dynamic model, hence, scales to large datasets. Under appropriate conditions on the transition model, our proposed formulation is approximately submodular, hence, comes with performance guarantees. We also present ProceL, a new multimodal dataset of 47.3 hours of videos and their transcripts from diverse tasks, for procedure learning evaluation. By extensive experiments, we show that our framework significantly improves the state of the art performance.
Landmark-based approaches for goal recognition as planning

Recognizing goals and plans from complete or partial observations can be efficiently achieved through automated planning techniques. In many applications, it is important to recognize goals and plans not only accurately, but also quickly. To address this challenge, we develop novel goal recognition approaches based on planning techniques that rely on planning landmarks. In automated planning, landmarks are properties (or actions) that cannot be avoided to achieve a goal. We show the applicability of a number of planning techniques with an emphasis on landmarks for goal recognition tasks in two settings: (1) we use the concept of landmarks to develop goal recognition heuristics; and (2) we develop a landmark-based filtering method to refine existing planning-based goal and plan recognition approaches. These recognition approaches are empirically evaluated in experiments over several classical planning domains. We show that our goal recognition approaches yield not only accuracy comparable to (and often higher than) other state-of-the-art techniques, but also result in substantially faster recognition time over existing techniques.
Matching RGB and Infrared Remote Sensing Images with Densely-Connected Convolutional Neural Networks

We develop a deep learning-based matching method between an RGB (red, green and blue) and an infrared image that were captured from satellite sensors. The method includes a convolutional neural network (CNN) that compares the RGB and infrared image pair and a template searching strategy that searches the correspondent point within a search window in the target image to a given point in the reference image. A densely-connected CNN is developed to extract common features from different spectral bands. The network consists of a series of densely-connected convolutions to make full use of low-level features and an augmented cross entropy loss to avoid model overfitting. The network takes band-wise concatenated RGB and infrared images as the input and outputs a similarity score of the RGB and infrared image pair. For a given reference point, the similarity scores within the search window are calculated pixel-by-pixel, and the pixel with the highest score becomes the matching candidate. Experiments on a satellite RGB and infrared image dataset demonstrated that our method obtained more than 75% improvement on matching rate (the ratio of the successfully matched points to all the reference points) over conventional methods such as SURF, RIFT, and PSO-SIFT, and more than 10% improvement compared to other most recent CNN-based structures. Our experiments also demonstrated high performance and generalization ability of our method applying to multitemporal remote sensing images and close-range images.
Efficient Information-Theoretic Secure Multiparty Computation over $$\mathbb {Z}/p^k\mathbb {Z}$$ via Galois Rings

At CRYPTO 2018, Cramer et al. introduced a secret-sharing based protocol called SPD$$\mathbb {Z}_{2^k}$$ that allows for secure multiparty computation (MPC) in the dishonest majority setting over the ring of integers modulo $$2^k$$, thus solving a long-standing open question in MPC about secure computation over rings in this setting. In this paper we study this problem in the information-theoretic scenario. More specifically, we ask the following question: Can we obtain information-theoretic MPC protocols that work over rings with comparable efficiency to corresponding protocols over fields? We answer this question in the affirmative by presenting an efficient protocol for robust Secure Multiparty Computation over $$\mathbb {Z}/p^{k}\mathbb {Z}$$ (for any prime p and positive integer k) that is perfectly secure against active adversaries corrupting a fraction of at most 1/3 players, and a robust protocol that is statistically secure against an active adversary corrupting a fraction of at most 1/2 players.
Research on Text Classification Based on Convolutional Neural Network

Text classification is one of the research hotspots in the field of Natural Language Processing (NLP). In this paper, TextCNN model based on Convolutional Neural Network (CNN) is used for classification; the classified corpus is selected from the text extracted from the electronic instruction manual. During the experiment, the text was preprocessed at first, then the processed text was converted intoword vector formatto generate data sets, which were finally input into TextCNN for training. In order to verify the influence of TF-IDF weighted word vectors on training results, data sets made by weighted and unweighted word vectors were used in the comparison experiment to conduct classification model training and to calculate the final accuracy of the model, it is concluded that the classification accuracy can be improved by using weighted word vectors.
Pedestrian Graph: Pedestrian Crossing Prediction Based on 2D Pose Estimation and Graph Convolutional Networks

Pedestrian crossing prediction is a particularly important task for intelligent transportation systems, accurate prediction can guarantee the safety of pedestrians and driving comfort of vehicles. This paper predicts intentions of pedestrians crossing on urban roads based on 2D human pose estimation and Graph Convolutional Network (GCN), achieving the new state-of-the-art in the Joint Attention in Autonomous Driving (JAAD) data set. The major contribution of this work is the development of the 2D pedestrian graph structure and pedestrian graph network to predict whether a pedestrian is going to cross the street. The proposed method obtained an accuracy of 91.94 % in pedestrian crossing prediction.
Recovery of Incomplete IoT Sensed Data using High-Performance Extended-Input Neural-Like Structure

The task of the recovery of incomplete IoT sensed data is considered. A high-performance extended-input neural network, which is constructed using a non-iterative neural-like structure and a Wiener polynomial for its solution, has been applied. Modelling of its work on the data collected by the IoT device was conducted. The research of hyperparameters selection of the proposed technique for missed data recovery was carried out. The number of neurons in the ANN’s hidden layer and the polynomial degree for the effective method operation were selected by experimental way. It is established the significant reduction of the training time as well as the accuracy increase by reducing the ANN’s hidden neurons number. A comparison of the proposed technique effectiveness with the state-of-the-art has been carried out. The highest accuracy of its work is established in comparison with existing methods. The training time of the proposed technique was determined and compared with existing methods.
Sentiment Analysis for Social Media

Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, such as deep learning techniques, have solved some of the traditional challenges in the area caused by the scarcity of lexical resources. In this Special Issue, different approaches that advance this discipline are presented. The contributed articles belong to two broad groups: technological contributions and applications.
The GeoCore ontology: A core ontology for general use in Geology

Domain ontologies assume the role of representing, in a formal way, a consensual knowledge of a community over a domain. This task is especially difficult in a wide domain like Geology, which is composed of diversified science resting on a large variety of conceptual models that were developed over time. The meaning of the concepts used by the various professionals often depends on the particular vision that they have of a domain according to their background and working habits. Ontology development in Geology thus necessitates a drastic elucidation of the concepts and vocabulary used by geologists. This article intends to contribute to solving these difficulties by proposing a core ontology named GeoCore Ontology resting on the BFO top ontology, specially designed for describing scientific fields. GeoCore Ontology contains well-founded definitions of a limited set of general concepts within the Geology field that are currently considered by all geologists whatever their skill. It allows modelers to separately consider a geological object, the substance that constitutes it, the boundaries that limit it and the internal arrangement of the matter inside it. The core ontology also allows the description of the existentially dependent qualities attached to a geological object and the geological process that generated it in a particular geological age. This small set of formally defined and described concepts combined with concepts from BFO provides a backbone for deriving by subsumption more specialized geological concepts and also constitutes a baseline for integrating different existent domain ontologies within the Geology domain. The GeoCore ontology and the methodology that we used for building it, provide solutions for unveiling major misunderstanding regarding the concepts that are commonly used for formulating geological interpretations. This will facilitate the communication of this information to external Geology users and its integration in domain applications.
Non-Additive Cost Functions for Color Image Steganography Based on Inter-Channel Correlations and Differences

Despite the strong presence of color images for communication, scholars have mainly devoted their attention to research on steganography for grayscale images. In contrast to grayscale images, color images have three interrelated color channels, and the relationships among the three channels have a strong impact on the steganography security. In this paper, we present a steganographic scheme for spatial color images by exploiting the correlations and differences between the color channels. We find that the G channel has a stronger correlation with R and B than the one between R and B, and thus, synchronizing the modification directions of the R and B channels with those from the G channel will have better resistance to detection. In addition, the payload capacity and the distribution of complex regions between channels are different. Based on these findings, we design a new strategy for defining non-additive costs for color image steganography, called G-channel-related Inter-channel Non-Additive (GINA) strategy. The GINA strategy can make the modification directions of the R and B channels consistent with those of the G channel and can adaptively distribute the embedding capacity between the three channels. Specifically, this strategy will not violate the Complexity Prior rule. The experimental results show that the proposed GINA strategy can significantly improve the performance in terms of resisting color image steganalysis compared with previous methods.
LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking

LiDAR (Light Detection And Ranging) is an important and widely adopted sensor for autonomous vehicles, particularly for those vehicles operating at higher levels (L4-L5) of autonomy. Recent work has demonstrated the promise of deep-learning approaches for LiDAR-based detection. However, deep-learning algorithms are extremely data hungry, requiring large amounts of labeled point-cloud data for training and evaluation. Annotating LiDAR point cloud data is challenging due to the following issues: 1) A LiDAR point cloud is usually sparse and has low resolution, making it difficult for human annotators to recognize objects. 2) Compared to annotation on 2D images, the operation of drawing 3D bounding boxes or even point-wise labels on LiDAR point clouds is more complex and time-consuming. 3) LiDAR data are usually collected in sequences, so consecutive frames are highly correlated, leading to repeated annotations. To tackle these challenges, we propose LATTE, an open-sourced annotation tool for LiDAR point clouds. LATTE features the following innovations: 1) Sensor fusion: We utilize image-based detection algorithms to automatically pre-label a calibrated image, and transfer the labels to the point cloud. 2) One-click annotation: Instead of drawing 3D bounding boxes or point-wise labels, we simplify the annotation to just one click on the target object, and automatically generate the bounding box for the target. 3) Tracking: we integrate tracking into sequence annotation such that we can transfer labels from one frame to subsequent ones and therefore significantly reduce repeated labeling. Experiments show the proposed features accelerate the annotation speed by 6.2x and significantly improve label quality with 23.6% and 2.2% higher instance-level precision and recall, and 2.0% higher bounding box IoU. LATTE is open-sourced at https://github.com/bernwang/LATTE.
Improved text sentiment classification method based on BiGRU-Attention

Abstract Aiming at the problem that the traditional text sentiment classification method is not sufficient for text context information learning and key feature extraction ability, this paper proposes a BiGRU-Attention based text sentiment classification method to classify Chinese texts. The method used a Bidirectional Gated Recurrent Unit (BiGRU) instead of the Bidirectional Long Short-Term Memory network (BiLSTM) to build a hidden layer, and introduces an attention model to input the result of each time point in the hidden layer to the fully connected layer yields a probability vector. Then use this probability vector to weight each hidden layer result and add it to get the result vector. The experimental shows that the model this paper proposed has better accuracy and effectiveness in text classification.
Jumping with variably scaled discontinuous kernels (VSDKs)

Abstract In this paper we address the problem of approximating functions with discontinuities via kernel-based methods. The main result is the construction of discontinuous kernel-based basis functions. The linear spaces spanned by these discontinuous kernels lead to a very flexible tool which sensibly or completely reduces the well-known Gibbs phenomenon in reconstructing functions with jumps. For the new basis we provide error bounds and numerical results that support our claims. The method is also effectively tested for approximating satellite images.