Reviewerly/exHarmony huggingface dataset:
Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting

Accurately forecasting power generation in photovoltaic (PV) installations is a challenging task, due to the volatile and highly intermittent nature of solar-based renewable energy sources. In recent years, several PV power generation forecasting models have been proposed in the relevant literature. However, there is no consensus regarding which models perform better in which cases. Moreover, literature lacks of works presenting detailed experimental evaluations of different types of models on the same data and forecasting conditions. This paper attempts to fill in this gap by presenting a comprehensive benchmarking framework for several analytical, data-based and hybrid models for multi-step short-term PV power generation forecasting. All models were evaluated on the same real PV power generation data, gathered from the realisation of a small scale pilot site in Thessaloniki, Greece. The models predicted PV power generation on multiple horizons, namely for 15 min, 30 min, 60 min, 120 min and 180 min ahead of time. Based on the analysis of the experimental results we identify the cases, in which specific models (or types of models) perform better compared to others, and explain the rationale behind those model performances.
Agent-Based Simulation of Blockchains

In this paper, we describe LUNES-Blockchain, an agent-based simulator of blockchains that is able to exploit Parallel and Distributed Simulation (PADS) techniques to offer a high level of scalability. To assess the preliminary implementation of our simulator, we provide a simplified modelling of the Bitcoin protocol and we study the effect of a security attack on the consensus protocol in which a set of malicious nodes implements a filtering denial of service (i.e. Sybil Attack). The results confirm the viability of the agent-based modelling of blockchains implemented by means of PADS.
Gradient Estimation with Stochastic Softmax Tricks

The Gumbel-Max trick is the basis of many relaxed gradient estimators. These estimators are easy to implement and low variance, but the goal of scaling them comprehensively to large combinatorial distributions is still outstanding. Working within the perturbation model framework, we introduce stochastic softmax tricks, which generalize the Gumbel-Softmax trick to combinatorial spaces. Our framework is a unified perspective on existing relaxed estimators for perturbation models, and it contains many novel relaxations. We design structured relaxations for subset selection, spanning trees, arborescences, and others. When compared to less structured baselines, we find that stochastic softmax tricks can be used to train latent variable models that perform better and discover more latent structure.
Modular Structures and Atomic Decomposition in Ontologies

With the growth of ontologies used in diverse application areas, the need for module extraction and modularisation techniques has risen. The notion of the modular structure of an ontology, which comprises a suitable set of base modules together with their logical dependencies, has the potential to help users and developers in comprehending, sharing, and maintaining an ontology. We have developed a new modular structure, called atomic decomposition (AD), which is based on modules that provide strong logical properties, such as locality-based modules. In this article, we present the theoretical foundations of AD, review its logical and computational properties, discuss its suitability as a modular structure, and report on an experimental evaluation of AD. In addition, we discuss the concept of a modular structure in ontology engineering and provide a survey of existing decomposition approaches.
A novel particle-filled carbon-fibre reinforced polymer model composite tailored for the application of digital volume correlation and computed tomography

This paper presents the development of novel Carbon-Fibre Reinforced Polymer (CFRP) laminates, tailored for the application of Digital Volume Correlation (DVC) and Computed Tomography (CT) to experimental mechanics analyses of these materials. Analogous to surface-based Digital Image Correlation (DIC), DVC is a relatively novel volumetric method that utilizes CT data to quantify internal three-dimensional (3D) displacements and implicit strain fields. The highly anisotropic and somewhat regular/self-similar microstructures found in well-aligned unidirectional (UD) materials at high fibre volume fractions are intrinsically challenging for DVC, especially along the fibre direction at microstructural length-scales on the order of a few fibre diameters. To permit the application of DVC to displacement and/or strain measurements parallel to the fibre orientation, the matrix was doped with a sparse population of sub-micrometre particles to act as displacement trackers ( i.e. fiducial markers). Barium titanate particles (400 nm, ∼1.44 vol. %) were found to offer the most favourable compromise between contrast in CT images and the ability to obtain a homogeneous distribution in 3D space with sufficient particle compactness for local DVC analyses. This property combination was selected following an extensive Micro-focus Computed Tomography (µCT)-based qualitative assessment on a wide test matrix, that included 38 materials manufactured with a range of possible particle compositions, mean sizes and concentrations. By comparing the tensile behaviour of the particle-adapted material alongside its particle-free counterpart, we demonstrate through the application of in situ Synchrotron Radiation Computed Tomography (SRCT) that the macro- and micromechanical responses of the newly developed CFRP are consistent with standard production materials indicating its suitability as a model system for mechanistic investigations.
Dynamic obstacle avoidance path planning of UAV Based on improved APF

This paper analyzes the inapplicability of the traditional APF in the dynamic environment and the existence of local minimum. In this paper, a new potential field function is proposed. Combined with the fuzzy algorithm, the ratio of the absolute value of the difference between the gravitational force and the repulsive force of the UAV to the resultant force and their angle difference are taken as two fuzzy inputs. With the help of the fuzzy rules, the fuzzy output force is calculated to break the local minimum value.in the dynamic environment, the relative velocity repulsion potential field between UAV and dynamic obstacles is introduced to solve the problem of dynamic obstacle avoidance. The experimental simulation results show that the obstacle avoidance track drawn by the improved APF regulation in this paper is smoother and the obstacle avoidance track correlation data is better.
IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition

Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.
Constant Velocity Constraints for Self-Supervised Monocular Depth Estimation

We present a new method for self-supervised monocular depth estimation. Contemporary monocular depth estimation methods use a triplet of consecutive video frames to estimate the central depth image. We make the assumption that the ego-centric view progresses linearly in the scene, based on the kinematic and physical properties of the camera. During the training phase, we can exploit this assumption to create a depth estimation for each image in the triplet. We then apply a new geometry constraint that supports novel synthetic views, thus providing a strong supervisory signal. Our contribution is simple to implement, requires no additional trainable parameter, and produces competitive results when compared with other state-of-the-art methods on the popular KITTI corpus.
Decoy-state phase-matching quantum key distribution with source errors

The phase-matching quantum key distribution (PM-QKD), one of the variants of Twin-Field (TF) QKD protocol, was recently proposed to overcome the rate-distance limits of point to point protocol without quantum repeaters. In this paper, we propose a more practical PM-QKD protocol version with four-intensity decoy states and source errors, since neither the infinite decoy states nor the precise control of the light source is available in practice. We present the formulation of the secure key rate of the proposed protocol and analyze the performances of the protocol with and without source errors by numerical simulations.
Trust in “Trust-free” Digital Networks: How Inter-firm Algorithmic Relationships Embed the Cardinal Principles of Value Co-creation

In this exploratory research, I develop new knowledge on trust in inter-firm cooperation that leverages recent technologies such as blockchain and the Internet of things in a digital platform ecosystem.In a digital network, advanced algorithms govern and shape inter-firm business processes.While such algorithms introduce efficiency in inter-firm business processes, their limitations, especially their apparent lack of transparency, may affect the key trust dimensions (i.e., reliability, fairness, and goodwill) in the relationships among the participating firms.I introduce algorithmic relationship, a label that embeds the concepts of smart contracts in inter-firm cooperation.Algorithmic relationships involve autonomous and semi-autonomous implementations of smart contracts in all lifecycle stages of inter-firm cooperation.By analyzing extant literature on trust, inter-firm cooperation, business model innovation, and digital platforms, I demonstrate how various factors influence whether firms adopt smart contracts: perceptions about other participants' trustworthiness, participants' own propensity to trust, participants' shared goals and resource embeddedness in the network, perceived risks in inter-firm interactions, and complexity and time criticality of inter-firm interactions.Taking a temporal perspective, I also recognize the present lacunae with smart contracts from various perspectives (algorithm development, algorithm implementation, algorithm governance, and the availability of appropriate legal resources in the event that disputes occur) and demonstrate how these drawbacks impede shared value creation.
Three dimensional mineral prospectivity modeling by evidential belief functions, a case study from Kahang porphyry Cu deposit

The present study has focused on the performance of the evidential belief functions (EBFs) model to predict the three dimensional (3D) mineralization of copper (Cu) at Kahang porphyry Cu deposit (KPCD) in the northeast of Isfahan, Iran. The 3D deposit modeling and reserve estimation are of the most important steps in mineral exploration program. Determining the boundaries of the deposit and estimating the quantity and quality of reserves are among the most essential issues in mining designing. Normally, a 3D model of the sought deposit is prepared based on the mineral grade parameter without considering other parameters and deposit information. In this regard, 3D integration models at the deposit scale facilitate the consideration of other parameters and available underground information. The results of these 3D integration models are mostly used to introduce mineral prospectivity areas. However, in this study, the integrated model was used as an auxiliary parameter in estimation for the geostatistical co-kriging method. Of note is that the accuracy of the 3D model of the deposit improved whereas the estimation error demonstrated a reduction. . The information of 22 boreholes drilled by the National Iranian Copper Industry Company was used in this research. To implement resource modeling, the number of blocks in the eastern, northern and depth directions were 30 × 28 × 145, respectively and the size of each block was designed equal to15 × 15 × 5 m. Auxiliary geospatial models were divided into three main categories of geological, geochemical, and geophysical data sets, and all 3D evidential models were obtained quantitatively. Subsequently, the Dempster-Shafer evidence theory was applied to construct a 3D prospectivity modeling of Kahang deposit. Using this knowledge-driven theory, four 3D models of belief, uncertainty, plausibility, and disbelief were produced, which have substantial impact on more accurate Cu resource modeling. After validation of the prospectivity models with the drilling results, the desired 3D auxiliary model had a correlation of 0.92 with the Cu grades. Thus, the Cu grade was estimated by a co-kriging method using a 3D EBFs model as an auxiliary variable, and output was compared with the one from an ordinary kriging (OK) method. The significance of this study lies in improvement of the performance of the Cu grade modeling through an auxiliary variable, where the root-mean-square error (RMSE) of the estimates reduces from 0.437 to 0.288 when implementing a co-kriging method rather than the ordinary kriging.
Learning User Representations with Hypercuboids for Recommender Systems

Modeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifically, the key novelty behind our model is that it explicitly models user interests as a hypercuboid instead of a point in the space. In our approach, the recommendation score is learned by calculating a compositional distance between the user hypercuboid and the item. This helps to alleviate the potential geometric inflexibility of existing collaborative filtering approaches, enabling a greater extent of modeling capability. Furthermore, we present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests. A neural architecture is also proposed to facilitate user hypercuboid learning by capturing the activity sequences (e.g., buy and rate) of users. We demonstrate the effectiveness of our proposed model via extensive experiments on both public and commercial datasets. Empirical results show that our approach achieves very promising results, outperforming existing state-of-the-art.
SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization

Nowadays, as data becomes increasingly complex and distributed, data analyses often involve several related datasets that are stored on different servers and probably owned by different stakeholders. While there is an emerging need to provide these stakeholders with a full picture of their data under a global context, conventional visual analytical methods, such as dimensionality reduction, could expose data privacy when multi-party datasets are fused into a single site to build point-level relationships. In this paper, we reformulate the conventional t-SNE method from the single-site mode into a secure distributed infrastructure. We present a secure multi-party scheme for joint t-SNE computation, which can minimize the risk of data leakage. Aggregated visualization can be optionally employed to hide disclosure of point-level relationships. We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding. We demonstrate the effectiveness of our approach with three case studies, one of which is based on the deployment of our system in real-world applications.
Optimum Codesign for Image Denoising Between Type-2 Fuzzy Identifier and Matrix Completion Denoiser

With the wide deployment of digital image capturing equipment, the need of denoising to produce a crystal clear image from noisy capture environment has become indispensable. In this article, a novel type-2 fuzzy-based filter is proposed for denoising images corrupted by impulse noise, especially for the high density of salt-and-pepper noise. It operates two stages, namely, type-2 fuzzy identifier and matrix completion denoiser. In the proposed method, the type-2 fuzzy identifier is first employed to identify and trim the entries contaminated by impulse noise in the data matrix from fuzzy system. Then, the trimmed data matrix is utilized to retrieve the noiseless data matrix with the matrix completion technology. Herein, a novel matrix completion technique is developed without <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$priori$</tex-math></inline-formula> rank information compared to its counterparts. Simulation results are presented, which vividly show the denoised images obtained by the proposed method can achieve crystal clear image with strong structural integrity, and are showing good performance in terms of peak signal-to-noise ratio.
A Classifier to Detect Informational vs. Non-Informational Heart Attack Tweets

Social media sites are considered one of the most important sources of data in many fields, such as health, education, and politics. While surveys provide explicit answers to specific questions, posts in social media have the same answers implicitly occurring in the text. This research aims to develop a method for extracting implicit answers from large tweet collections, and to demonstrate this method for an important concern: the problem of heart attacks. The approach is to collect tweets containing “heart attack” and then select from those the ones with useful information. Informational tweets are those which express real heart attack issues, e.g., “Yesterday morning, my grandfather had a heart attack while he was walking around the garden.” On the other hand, there are non-informational tweets such as “Dropped my iPhone for the first time and almost had a heart attack.” The starting point was to manually classify around 7000 tweets as either informational (11%) or non-informational (89%), thus yielding a labeled dataset to use in devising a machine learning classifier that can be applied to our large collection of over 20 million tweets. Tweets were cleaned and converted to a vector representation, suitable to be fed into different machine-learning algorithms: Deep neural networks, support vector machine (SVM), J48 decision tree and naïve Bayes. Our experimentation aimed to find the best algorithm to use to build a high-quality classifier. This involved splitting the labeled dataset, with 2/3 used to train the classifier and 1/3 used for evaluation besides cross-validation methods. The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). In addition, it obtained the highest F1-scores with (73.6%) and (97.4%) for informational and non-informational classes, respectively.
Experimental Realization of a Deterministic Quantum Router with Superconducting Quantum Circuits

A quantum router is an elementary building block for quantum-information processing. It directs the incoming quantum signal to different routes with phase coherence. To realize scalable quantum routing, a single particle needs to deterministically and coherently route the paths of a flying qubit, which requires nonlinear interaction at the single quantum level and complicated path-data qubit control so far beyond the experimental reach. Here we demonstrate a deterministic quantum router working at microwave frequencies based on superconducting circuits, which can naturally integrate with a leading platform for quantum computing. We encode the quantum signal into a weak coherent pulse and use an entanglement witness to rigorously verify the generated entanglement between the control qubit and the path of the data qubit, thus proving quantum coherence of the routing operation. Our experiment lays the foundation for useful applications of quantum routers in quantum networks and quantum random-access memories.
ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines

Dynamic Software Product Lines (DSPLs) are a well-accepted approach to self-adaptation at runtime. In the context of DSPLs, there are plenty of reactive approaches that apply countermeasures as soon as a context change happens. In this paper we propose a proactive approach, ProDSPL, that exploits an automatically learnt model of the system, anticipates future variations of the system and generates the best DSPL configuration that can lessen the negative impact of future events on the quality requirements of the system. Predicting the future fosters adaptations that are good for a longer time and therefore reduces the number of reconfigurations required, making the system more stable. ProDSPL formulates the problem of the generation of dynamic reconfigurations as a proactive controller over a prediction horizon, which includes a mapping of the valid configurations of the DSPL into linear constraints. Our approach is evaluated and compared with a reactive approach, DAGAME, also based on a DSPL, which uses a genetic algorithm to generate quasi-optimal feature model configurations at runtime. ProDSPL has been evaluated using a strategy mobile game and a set of randomly generated feature models. The evaluation shows that ProDSPL gives good results with regard to the quality of the configurations generated when it tries anticipate future events. Moreover, in doing so, ProDSPL enforces the system to make as few reconfigurations as possible.
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors

In this work, we present an uncertainty-based method for sensor fusion with camera and radar data. The outputs of two neural networks, one processing camera and the other one radar data, are combined in an uncertainty aware manner. To this end, we gather the outputs and corresponding meta information for both networks. For each predicted object, the gathered information is post-processed by a gradient boosting method to produce a joint prediction of both networks. In our experiments we combine the YOLOv3 object detection network with a customized $1D$ radar segmentation network and evaluate our method on the nuScenes dataset. In particular we focus on night scenes, where the capability of object detection networks based on camera data is potentially handicapped. Our experiments show, that this approach of uncertainty aware fusion, which is also of very modular nature, significantly gains performance compared to single sensor baselines and is in range of specifically tailored deep learning based fusion approaches.