OpenRaiser/Intern-Atlas huggingface dataset:
Spatio-temporal graph neural networks for multi-site PV power forecasting
Venue: arXiv cs.LG 2021
Year: 2021

Accurate forecasting of solar power generation with fine temporal and spatial resolution is vital for the operation of the power grid. However, state-of-the-art approaches that combine machine learning with numerical weather predictions (NWP) have coarse resolution. In this paper, we take a graph signal processing perspective and model multi-site photovoltaic (PV) production time series as signals on a graph to capture their spatio-temporal dependencies and achieve higher spatial and temporal resolution forecasts. We present two novel graph neural network models for deterministic multi-site PV forecasting dubbed the graph-convolutional long short term memory (GCLSTM) and the graph-convolutional transformer (GCTrafo) models. These methods rely solely on production data and exploit the intuition that PV systems provide a dense network of virtual weather stations. The proposed methods were evaluated in two data sets for an entire year: 1) production data from 304 real PV systems, and 2) simulated production of 1000 PV systems, both distributed over Switzerland. The proposed models outperform state-of-the-art multi-site forecasting methods for prediction horizons of six hours ahead. Furthermore, the proposed models outperform state-of-the-art single-site methods with NWP as inputs on horizons up to four hours ahead.
Artificial Intelligence in Achieving Sustainable Development Goals
Venue: arXiv cs.CY 2021
Year: 2021

This perspective illustrates some of the AI applications that can accelerate the achievement of SDGs and also highlights some of the considerations that could hinder the efforts towards them. This emphasizes the importance of establishing standard AI guidelines and regulations for the beneficial applications of AI.
Web-Based Learning
Venue: arXiv cs.CY 2021
Year: 2021

Information Technology (IT) has had a number of positive impacts in various fields. In the world of education, IT provides its own style with various modes that may be used. Modern education in the era of globalization and based on Information Technology has transformed towards digital. IT has become the backbone of the modern learning process. The educational process that was previously carried out classically with a direct face-to-face mode has experienced a shift towards distance learning mode. In distance learning mode, students can access lecture material via the internet. Web-based learning is often also called online learning or e-learning because it includes course content and includes all educational interventions that use the internet (or local intranet).
Proof-Theoretic Semantics, a Problem with Negation and Prospects for Modality
Venue: arXiv cs.LO 2021
Year: 2021

This paper discusses proof-theoretic semantics, the project of specifying the meanings of the logical constants in terms of rules of inference governing them. I concentrate on Michael Dummett's and Dag Prawitz' philosophical motivations and give precise characterisations of the crucial notions of harmony and stability, placed in the context of proving normalisation results in systems of natural deduction. I point out a problem for defining the meaning of negation in this framework and prospects for an account of the meanings of modal operators in terms of rules of inference.
Who Owns the Data? A Systematic Review at the Boundary of Information Systems and Marketing
Venue: arXiv cs.DL 2021
Year: 2021

This paper gives a systematic research review at the boundary of the information systems (IS) and marketing disciplines. First, a historical overview of these disciplines is given to put the review into context. This is followed by a bibliographic analysis to select articles at the boundary of IS and marketing. Text analysis is then performed on the selected articles to group them into homogeneous research clusters, which are refined by selecting "distinct" articles that best represent the clusters. The citation asymmetries between IS and marketing are noted and an overall conceptual model is created that describes the "areas of collaboration" between IS and marketing. Forward looking suggestions are made on how academic researchers can better interface with industry and how academic research at the boundary of IS and marketing can be further developed.
Self-Driving Cars and Driver Alertness
Venue: arXiv cs.CY 2021
Year: 2021

Recent years have seen growing interest in the development of self-driving vehicles that promise (or threaten) to replace human drivers with intelligent software. However, current self-driving cars still require human supervision and prompt takeover of control when necessary. Poor alertness while controlling self-driving cars could hinder the drivers' ability to intervene during unpredictable situations, thus increasing the risk of avoidable accidents. In this paper we examine the key factors that contribute to drivers' poor alertness, and the potential solutions that have been proposed to address them. Based on this examination we make some recommendations for various stakeholders, such as researchers, drivers, industry and policy makers.
The ghost of AI governance past, present and future: AI governance in the European Union
Venue: arXiv cs.CY 2021
Year: 2021

The received wisdom is that artificial intelligence (AI) is a competition between the US and China. In this chapter, the author will examine how the European Union (EU) fits into that mix and what it can offer as a third way to govern AI. The chapter presents this by exploring the past, present and future of AI governance in the EU. Section 1 serves to explore and evidence the EUs coherent and comprehensive approach to AI governance. In short, the EU ensures and encourages ethical, trustworthy and reliable technological development. This will cover a range of key documents and policy tools that lead to the most crucial effort of the EU to date: to regulate AI. Section 2 maps the EUs drive towards digital sovereignty through the lens of regulation and infrastructure. This covers topics such as the trustworthiness of AI systems, cloud, compute and foreign direct investment. In Section 3, the chapter concludes by offering several considerations to achieve good AI governance in the EU.
Syllepsis in Homotopy Type Theory
Venue: arXiv cs.LO 2021
Year: 2021

It is well-known that in homotopy type theory (HoTT), one can prove the Eckmann-Hilton theorem: given two 2-loops p, q : 1 = 1 on the reflexivity path at an arbitrary point a : A, we have pq = qp. If we go one dimension higher, i.e., if p and q are 3-loops, we show that a property classically known as syllepsis also holds in HoTT: namely, the Eckmann-Hilton proof for q and p is the inverse of the Eckmann-Hilton proof for p and q.
Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data
Venue: arXiv cs.CY 2021
Year: 2021

Increasingly available high-frequency location datasets derived from smartphones provide unprecedented insight into trajectories of human mobility. These datasets can play a significant and growing role in informing preparedness and response to natural disasters. However, limited tools exist to enable rapid analytics using mobility data, and tend not to be tailored specifically for disaster risk management. We present an open-source, Python-based toolkit designed to conduct replicable and scalable post-disaster analytics using GPS location data. Privacy, system capabilities, and potential expansions of \textit{Mobilkit} are discussed.
Developing Open Source Educational Resources for Machine Learning and Data Science
Venue: arXiv cs.CY 2021
Year: 2021

Education should not be a privilege but a common good. It should be openly accessible to everyone, with as few barriers as possible; even more so for key technologies such as Machine Learning (ML) and Data Science (DS). Open Educational Resources (OER) are a crucial factor for greater educational equity. In this paper, we describe the specific requirements for OER in ML and DS and argue that it is especially important for these fields to make source files publicly available, leading to Open Source Educational Resources (OSER). We present our view on the collaborative development of OSER, the challenges this poses, and first steps towards their solutions. We outline how OSER can be used for blended learning scenarios and share our experiences in university education. Finally, we discuss additional challenges such as credit assignment or granting certificates.
Towards developing a realistic robotics simulation environment of an indoor vegetable greenhouse
Venue: arXiv cs.RO 2021
Year: 2021

This article presents a method for developing a realistic robotics simulation environment for application in vegetable greenhouses. The method pipeline starts with the construction of a 3D cloud images of the greenhouse rows. This data is then used to develop a robotics simulation environment using the CoppeliaSim simulation software. The method has been tested using images from a commercial greenhouse.
Modelling and Reasoning Techniques for Context Aware Computing in Intelligent Transportation System
Venue: arXiv cs.AI 2021
Year: 2021

The emergence of Internet of Things technology and recent advancement in sensor networks enabled transportation systems to a new dimension called Intelligent Transportation System. Due to increased usage of vehicles and communication among entities in road traffic scenarios, the amount of raw data generation in Intelligent Transportation System is huge. This raw data are to be processed to infer contextual information and provide new services related to different modes of road transport such as traffic signal management, accident prediction, object detection etc. To understand the importance of context, this article aims to study context awareness in the Intelligent Transportation System. We present a review on prominent applications developed in the literature concerning context awareness in the intelligent transportation system. The objective of this research paper is to highlight context and its features in ITS and to address the applicability of modelling techniques and reasoning approaches in Intelligent Transportation System. Also to shed light on impact of Internet of Things and machine learning in Intelligent Transportation System development.
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
Venue: arXiv cs.LG 2021
Year: 2021

Electricity load forecasting is crucial for the power systems' planning and maintenance. However, its un-stationary and non-linear characteristics impose significant difficulties in anticipating future demand. This paper proposes a novel ensemble deep Random Vector Functional Link (edRVFL) network for electricity load forecasting. The weights of hidden layers are randomly initialized and kept fixed during the training process. The hidden layers are stacked to enforce deep representation learning. Then, the model generates the forecasts by ensembling the outputs of each layer. Moreover, we also propose to augment the random enhancement features by empirical wavelet transformation (EWT). The raw load data is decomposed by EWT in a walk-forward fashion, not introducing future data leakage problems in the decomposition process. Finally, all the sub-series generated by the EWT, including raw data, are fed into the edRVFL for forecasting purposes. The proposed model is evaluated on twenty publicly available time series from the Australian Energy Market Operator of the year 2020. The simulation results demonstrate the proposed model's superior performance over eleven forecasting methods in three error metrics and statistical tests on electricity load forecasting tasks.
Creating Powerful and Interpretable Models with Regression Networks
Venue: arXiv cs.LG 2021
Year: 2021

As the discipline has evolved, research in machine learning has been focused more and more on creating more powerful neural networks, without regard for the interpretability of these networks. Such "black-box models" yield state-of-the-art results, but we cannot understand why they make a particular decision or prediction. Sometimes this is acceptable, but often it is not. We propose a novel architecture, Regression Networks, which combines the power of neural networks with the understandability of regression analysis. While some methods for combining these exist in the literature, our architecture generalizes these approaches by taking interactions into account, offering the power of a dense neural network without forsaking interpretability. We demonstrate that the models exceed the state-of-the-art performance of interpretable models on several benchmark datasets, matching the power of a dense neural network. Finally, we discuss how these techniques can be generalized to other neural architectures, such as convolutional and recurrent neural networks.
Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation
Venue: arXiv cs.CV 2021
Year: 2021

There has been a recent surge of research interest in attacking the problem of social relation inference based on images. Existing works classify social relations mainly by creating complicated graphs of human interactions, or learning the foreground and/or background information of persons and objects, but ignore holistic scene context. The holistic scene refers to the functionality of a place in images, such as dinning room, playground and office. In this paper, by mimicking human understanding on images, we propose an approach of \textbf{PR}actical \textbf{I}nference in \textbf{S}ocial r\textbf{E}lation (PRISE), which concisely learns interactive features of persons and discriminative features of holistic scenes. Technically, we develop a simple and fast relational graph convolutional network to capture interactive features of all persons in one image. To learn the holistic scene feature, we elaborately design a contrastive learning task based on image scene classification. To further boost the performance in social relation inference, we collect and distribute a new large-scale dataset, which consists of about 240 thousand unlabeled images. The extensive experimental results show that our novel learning framework significantly beats the state-of-the-art methods, e.g., PRISE achieves 6.8$\%$ improvement for domain classification in PIPA dataset.
Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges
Venue: arXiv cs.SD 2021
Year: 2021

We report on cross-running the recent COVID-19 Identification ResNet (CIdeR) on the two Interspeech 2021 COVID-19 diagnosis from cough and speech audio challenges: ComParE and DiCOVA. CIdeR is an end-to-end deep learning neural network originally designed to classify whether an individual is COVID-positive or COVID-negative based on coughing and breathing audio recordings from a published crowdsourced dataset. In the current study, we demonstrate the potential of CIdeR at binary COVID-19 diagnosis from both the COVID-19 Cough and Speech Sub-Challenges of INTERSPEECH 2021, ComParE and DiCOVA. CIdeR achieves significant improvements over several baselines.
Neural Network Based Model Predictive Control for an Autonomous Vehicle
Venue: arXiv cs.RO 2021
Year: 2021

We study learning based controllers as a replacement for model predictive controllers (MPC) for the control of autonomous vehicles. We concentrate for the experiments on the simple yet representative bicycle model. We compare training by supervised learning and by reinforcement learning. We also discuss the neural net architectures so as to obtain small nets with the best performances. This work aims at producing controllers that can both be embedded on real-time platforms and amenable to verification by formal methods techniques.
Video Based Fall Detection Using Human Poses
Venue: arXiv cs.CV 2021
Year: 2021

Video based fall detection accuracy has been largely improved due to the recent progress on deep convolutional neural networks. However, there still exists some challenges, such as lighting variation, complex background, which degrade the accuracy and generalization ability of these approaches. Meanwhile, large computation cost limits the application of existing fall detection approaches. To alleviate these problems, a video based fall detection approach using human poses is proposed in this paper. First, a lightweight pose estimator extracts 2D poses from video sequences and then 2D poses are lifted to 3D poses. Second, we introduce a robust fall detection network to recognize fall events using estimated 3D poses, which increases respective filed and maintains low computation cost by dilated convolutions. The experimental results show that the proposed fall detection approach achieves a high accuracy of 99.83% on large benchmark action recognition dataset NTU RGB+D and real-time performance of 18 FPS on a non-GPU platform and 63 FPS on a GPU platform.
The bitwise operations in relation to the concept of set
Venue: arXiv cs.PL 2021
Year: 2021

We contemplate this article to help the teachers of programming in his aspiration for giving some appropriate and interesting examples. The work will be especially useful for students-future programmers, and for their lecturers. Some of the strong sides of these programming languages C/C++ and Java are the possibilities of low-level programming. Some of the means for this possibility are the introduced standard bitwise operations, with the help of which, it is possible to directly operate with every bit of an arbitrary variable situated in the computers memory. In the current study, we are going to describe some methodical aspects for work with the bitwise operations and we will discuss the benefit of using bitwise operations in programming. The article shows some advantages of using bitwise operations, realizing various operations with sets.
Marine Locomotion: A Tethered UAV$-$Buoy System with Surge Velocity Control
Venue: arXiv cs.RO 2021
Year: 2021

Unmanned aerial vehicles (UAVs) are reaching offshore. In this work, we formulate the novel problem of a marine locomotive quadrotor UAV, which manipulates the surge velocity of a floating buoy by means of a cable. The proposed robotic system can have a variety of novel applications for UAVs where their high speed and maneuverability, as well as their ease of deployment and wide field of vision, give them a superior advantage. In addition, the major limitation of limited flight time of quadrotor UAVs is typically addressed through an umbilical power cable, which naturally integrates with the proposed system. A detailed high-fidelity dynamic model is presented for the buoy, UAV, and water environment. In addition, a stable control system design is proposed to manipulate the surge velocity of the buoy within certain constraints that keep the buoy in contact with the water surface. Polar coordinates are used in the controller design process since they outperform traditional Cartesian-based velocity controllers when it comes to ensuring correlated effects on the tracking performance, where each control channel independently affects one control parameter. The system model and controller design are validated in numerical simulation under different wave scenarios.
Interleaving & Reconfigurable Interaction: Separating Choice from Scheduling using Glue
Venue: arXiv cs.LO 2021
Year: 2021

Reconfigurable interaction induces another dimension of nondeterminism in concurrent systems which makes it hard to reason about the different choices of the system from a global perspective. Namely, (1) choices that correspond to concurrent execution of independent events; and (2) forced interleaving (or scheduling) due to reconfiguration. Unlike linear order semantics of computations, partial order semantics recovers information about the interdependence among the different events for fixed interaction, but still is unable to handle reconfiguration. We introduce glued partial orders as a way to capture reconfiguration. Much like partial orders capture all possible choices for fixed systems, glued partial orders capture all possible choices alongside reconfiguration. We show that a glued partial order is sufficient to correctly capture all partial order computations that differ in forced interleaving due to reconfiguration. Furthermore, we show that computations belonging to different glued partial orders are only different due to non-determinism.
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

High sample complexity remains a barrier to the application of reinforcement learning (RL), particularly in multi-agent systems. A large body of work has demonstrated that exploration mechanisms based on the principle of optimism under uncertainty can significantly improve the sample efficiency of RL in single agent tasks. This work seeks to understand the role of optimistic exploration in non-cooperative multi-agent settings. We will show that, in zero-sum games, optimistic exploration can cause the learner to waste time sampling parts of the state space that are irrelevant to strategic play, as they can only be reached through cooperation between both players. To address this issue, we introduce a formal notion of strategically efficient exploration in Markov games, and use this to develop two strategically efficient learning algorithms for finite Markov games. We demonstrate that these methods can be significantly more sample efficient than their optimistic counterparts.
Towards Universality in Multilingual Text Rewriting
Venue: arXiv cs.CL 2021
Year: 2021

In this work, we take the first steps towards building a universal rewriter: a model capable of rewriting text in any language to exhibit a wide variety of attributes, including styles and languages, while preserving as much of the original semantics as possible. In addition to obtaining state-of-the-art results on unsupervised translation, we also demonstrate the ability to do zero-shot sentiment transfer in non-English languages using only English exemplars for sentiment. We then show that our model is able to modify multiple attributes at once, for example adjusting both language and sentiment jointly. Finally, we show that our model is capable of performing zero-shot formality-sensitive translation.
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges
Venue: arXiv cs.CR 2021
Year: 2021

Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This article focuses on three such functions: how to privately retrieve information from cyberspace (privacy in information retrieval), how to privately leverage large-scale distributed/parallel processing (privacy in distributed computing), and how to learn/train machine learning models from private data spread across multiple users (privacy in distributed (federated) learning). The article motivates each privacy setting, describes the problem formulation, summarizes breakthrough results in the history of each problem, and gives recent results and discusses some of the major ideas that emerged in each field. In addition, the cross-cutting techniques and interconnections between the three topics are discussed along with a set of open problems and challenges.