Reviewerly/exHarmony huggingface dataset:
Crafting Better Contrastive Views for Siamese Representation Learning

Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims at minimizing distances between positive pairs. For high performance Siamese representation learning, one of the keys is to design good contrastive pairs. Most previous works simply apply random sampling to make different crops of the same image, which overlooks the semantic information that may degrade the quality of views. In this work, we propose ContrastiveCrop, which could effectively generate better crops for Siamese representation learning. Firstly, a semantic-aware object localization strategy is proposed within the training process in a fully unsupervised manner. This guides us to generate contrastive views which could avoid most false positives (i.e., object vs. background). Moreover, we empirically find that views with similar appearances are trivial for the Siamese model training. Thus, a center-suppressed sampling is further designed to enlarge the variance of crops. Remarkably, our method takes a careful consideration of positive pairs for contrastive learning with negligible extra training overhead. As a plug-and-play and framework-agnostic module, ContrastiveCrop consistently improves SimCLR, MoCo, BYOL, SimSiam by 0.4% ∼ 2.0% classification accuracy on CIFAR-10, CIFAR-100, Tiny ImageNet and STL-10. Superior results are also achieved on downstream detection and segmentation tasks when pre-trained on ImageNet-1K.
Neural 3D Video Synthesis from Multi-view Video

We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation. Our approach takes the high quality and compactness of static neural radiance fields in a new direction: to a model-free, dynamic setting. At the core of our approach is a novel time-conditioned neural radiance field that represents scene dynamics using a set of compact latent codes. We are able to significantly boost the training speed and perceptual quality of the generated imagery by a novel hierarchical training scheme in combination with ray importance sampling. Our learned representation is highly compact and able to represent a 10 second 30 FPS multi-view video recording by 18 cameras with a model size of only 28MB. We demonstrate that our method can render high-fidelity wide-angle novel views at over 1K resolution, even for complex and dynamic scenes. We perform an extensive qualitative and quantitative evaluation that shows that our approach outperforms the state of the art. Project website: https://neural-3d-video.github.io/.
A new one-dimensional chaotic map for image encryption scheme based on random DNA coding

In this paper, we propose a novel one-dimensional sine - cosine chaotic map (SCCM) to address the shortcomings of existing one-dimensional chaotic maps. And demonstrating that SCCM has better chaotic performance than existing more advanced one-dimensional chaotic maps using a series of performance metrics. Such as bifurcation diagram, Lyapunov exponent, approximate entropy. In addition, we propose an image encryption algorithm based on SCCM and random DNA operations. In confusion process, the chaotic sequence generated by the SCCM is used to achieve simultaneous row and column indexing dislocation of the plaintext image, and this phase can be performed more times to achieve better confusion results. Random DNA coding is designed in the encryption scheme, in which the coding, decoding and calculating rules are randomly selected by chaotic sequences based on the principle of one pixel for one rule, making the algorithm achieve greater security and sophistication. Furthermore, the initial conditions of the chaotic map are generated by the SHA-512 value associated with the plaintext, which improves the ability of the algorithm to resist chosen-plaintext attacks while expanding the key space of the algorithm. Simulation results and security analysis prove that the algorithm is resistant to various attacks and has good encryption capability.
An innovative deep anomaly detection of building energy consumption using energy time-series images

Deep anomaly detection (DAD) is essential in optimizing building energy management. Nonetheless, most existing works concerning this field consider unsupervised learning and involve the analysis of sensor readings through a one-dimensional (1D) energy time series, which limits the options for detecting anomalies within the building’s energy consumption. To the best of the authors’ knowledge, this paper presents the first study that explores using two-dimensional (2D) image representations as features of a supervised deep transfer learning (DTL) approach. Specifically, using 2D image representations allows taking advantage of any underlying data within the feature set, providing more possibilities to encode data and detect pertinent features which may not be considered in standard 1D time-series. Furthermore, the effects of using CNN activations as machine learning (ML) model features are also investigated to combine the advantages of both techniques. Additionally, the concept of layer and hyperparameter variation for the CNN model is also studied, with the objective of reducing the overall time computation and resource requirements of the proposed system. Hence, this makes our approach a candidate for edge-based applications. As per the conducted experiments, the top methodology rests at the F1-scores of 93.63% and 99.89% under simulated and real-world energy datasets, respectively. This involves using grayscale 2D image representations that combine CNN activations extracted from AlexNet and GoogleNet pre-trained models as features to a linear support vector machine (SVM) classifier. Finally, the comparison analysis with the state-of-the-art has shown the superiority of the proposed method in various assessment criteria.
A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts

In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation criteria have been developed within the research field of explainable artificial intelligence (XAI). With the amount of XAI methods vastly growing, a taxonomy of methods is needed by researchers as well as practitioners: To grasp the breadth of the topic, compare methods, and to select the right XAI method based on traits required by a specific use-case context. Many taxonomies for XAI methods of varying level of detail and depth can be found in the literature. While they often have a different focus, they also exhibit many points of overlap. This paper unifies these efforts and provides a complete taxonomy of XAI methods with respect to notions present in the current state of research. In a structured literature analysis and meta-study, we identified and reviewed more than 50 of the most cited and current surveys on XAI methods, metrics, and method traits. After summarizing them in a survey of surveys, we merge terminologies and concepts of the articles into a unified structured taxonomy. Single concepts therein are illustrated by more than 50 diverse selected example methods in total, which we categorize accordingly. The taxonomy may serve both beginners, researchers, and practitioners as a reference and wide-ranging overview of XAI method traits and aspects. Hence, it provides foundations for targeted, use-case-oriented, and context-sensitive future research.
Environmental Risk Assessment of Wetland Ecosystems Using Bayesian Belief Networks

Wetlands are valuable natural capital and sensitive ecosystems facing significant risks from anthropogenic and climatic stressors. An assessment of the environmental risk levels for wetlands’ dynamic ecosystems can provide a better understanding of their current ecosystem health and functions. Different levels of environmental risk are defined by considering the categories of risk and the probability and severity of each in the environment. Determining environmental risk levels provides a general overview of ecosystem function. This mechanism increases the visibility of risk levels and their values in three distinct states (i.e., low, moderate, and high) associated with ecosystem function. The Bayesian belief network (BBN) is a novel tool for determining environmental risk levels and monitoring the effectiveness of environmental planning and management measures in reducing the levels of risk. This study develops a robust methodological framework for determining the overall level of risks based on a combination of varied environmental risk factors using the BBN model. The proposed model is adopted for a case study of Shadegan International Wetlands (SIWs), which consist of a series of Ramsar wetlands in the southwest of Iran with international ecological significance. A comprehensive list of parameters and variables contributing to the environmental risk for the wetlands and their relationships were identified through a review of literature and expert judgment to develop an influence diagram. The BBN model is adopted for the case study location by determining the states of variables in the network and filling the probability distribution tables. The environmental risk levels for the SIWs are determined based on the results obtained at the output node of the BBN. A sensitivity analysis is performed for the BBN model. We proposed model-informed management strategies for wetland risk control. According to the BBN model results, the SIWs ecosystems are under threat from a high level of environmental risk. Prolonged drought has been identified as the primary contributor to the SIWs’ environmental risk levels.
Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms

Metaheuristics are popularly used in various fields, and they have attracted much attention in the scientific and industrial communities. In recent years, the number of new metaheuristic names has been continuously growing. Generally, the inventors attribute the novelties of these new algorithms to inspirations from either biology, human behaviors, physics, or other phenomena. In addition, these new algorithms, compared against basic versions of other metaheuristics using classical benchmark problems, show competitive performances. However, many new metaheuristics are not rigorously tested on challenging benchmark suites and are not compared with state-of-the-art metaheuristic variants. Therefore, in this study, we exhaustively tabulate more than 500 metaheuristics. In particular, several representative metaheuristics are introduced from two aspects, namely, the inspirational source and the essential operators for generating solutions. To comparatively evaluate the performance of the state-of-the-art and newly proposed metaheuristics, 11 newly proposed metaheuristics (generally with high numbers of citations) and 4 state-of-the-art metaheuristics are comprehensively compared on the CEC2017 benchmark suite. For fair comparisons, a parameter tuning tool named irace is used to automatically configure the parameters of all 15 algorithms. In addition, whether these algorithms have a search bias to the origin (i.e., the center of the search space) is investigated. All the experimental results are analyzed by several nonparametric statistical methods, including the Bayesian rank-sum test, Friedman test, Wilcoxon signed-rank test, critical difference plot and Bayesian signed-rank test. Moreover, the convergence, diversity, and the trade-off between exploration and exploitation of these 15 algorithms are also analyzed. The results show that the performance of the newly proposed EBCM algorithm performs similarly to the 4 compared algorithms and has the same properties and behaviors, such as convergence, diversity, exploration and exploitation trade-offs, in many aspects. However, the other 10 recent metaheuristics are less efficient and robust than the 4 state-of-the-art metaheuristics. The performance of all 15 of the algorithms is likely to deteriorate due to certain transformations, while the 4 state-of-the-art metaheuristics are less affected by transformations such as the shifting of the global optimal point away from the center of the search space. It should be noted that, except EBCM, the other 10 new algorithms are inferior to the 4 state-of-the-art algorithms in terms of convergence speed and global search ability on CEC 2017 functions. Moreover, the other 10 new algorithms are rougher (i.e., present in their behavior with high oscillations) in terms of the trade-off between exploitation and exploration and population diversity compared with the 4 state-of-the-art algorithms. Finally, several important issues relevant to the metaheuristic research area are discussed and some potential research directions are suggested.
Defining the Metaverse: A Systematic Literature Review

The term Metaverse is emerging as a result of the late push by multinational technology conglomerates and a recent surge of interest in Web 3.0, Blockchain, NFT, and Cryptocurrencies. From a scientific point of view, there is no definite consensus on what the Metaverse will be like. This paper collects, analyzes, and synthesizes scientific definitions and the accompanying major characteristics of the Metaverse using the methodology of a Systematic Literature Review (SLR). Two revised definitions for the Metaverse are presented, both condensing the key attributes, where the first one is rather simplistic holistic describing “a three-dimensional online environment in which users represented by avatars interact with each other in virtual spaces decoupled from the real physical world”. In contrast, the second definition is specified in a more detailed manner in the paper and further discussed. These comprehensive definitions offer specialized and general scholars an application within and beyond the scientific context of the system science, information system science, computer science, and business informatics, by also introducing open research challenges. Furthermore, an outlook on the social, economic, and technical implications is given, and the preconditions that are necessary for a successful implementation are discussed.
Adaptive cylinder vector particle swarm optimization with differential evolution for UAV path planning

Particle swarm optimization (PSO) algorithm has a potential to solve route planning problem for unmanned aerial vehicle (UAV). However, the traditional PSO algorithm is easy to fall into local optimum under the complicated environments with multiple threats. In order to improve the performance in different complicated environments, a novel and effective PSO algorithm with adaptive adjustment of the parameters, cylinder vector and different evolution operator, named ACVDEPSO, is proposed and demonstrated to be effective for route planning problem for UAV. In the proposed ACVDEPSO, the velocity of the particle is converted to its cylinder vector for the convenience of the path search. It is worth highlighting that the parameters of ACVDEPSO algorithm are automatically chosen by the time and the fitness values of the particles. Furthermore, a challenger based on differential evolution operator is introduced to reduce the probability of falling into local optimum and accelerate the algorithm convergence speed. The simulation experiments have been conducted in real digital elevation model (DEM) maps to test the performance of the ACVDEPSO. The experiment results validate that the optimization performance of the ACVDEPSO outperforms the other comparison methods, which can efficiently generate a higher quality path for UAV under the complicated 3D environments.
A Survey of Explainable Artificial Intelligence for Smart Cities

The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities.
Artificial intelligence empowered conversational agents: A systematic literature review and research agenda

Consumer research on conversational agents (CAs) has been growing. To illustrate and map out research in this field, we conducted a systematic literature review (SLR) of published work indexed in the Clarivate Web of Science and Elsevier Scopus databases. Four dominant topical areas were identified through bibliographic coupling. They are 1) consumers’ trust in CAs; 2) Natural Language Processing (NLP) in developing and designing CAs; 3) communication with CAs; 4) impact of CAs on value creation and the value of CAs for business. We leverage these findings to provide an updated synopsis of extant scientific work. Moreover, we draw a framework whereby we identify the: 1) drivers of and motivators for adoption and engagement with CAs; and 2) the outcomes of CA adoption for both users and organizations. Finally, we leverage the framework to develop an agenda for future research.
Scaling up GANs for Text-to-Image Synthesis

The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL.E 2, autoregressive and diffusion models became the new standard for large-scale generative models overnight. This rapid shift raises a fundamental question: can we scale up GANs to benefit from large datasets like LAION? We find that naïvely increasing the capacity of the StyleGan architecture quickly becomes unstable. We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis. GigaGAN offers three major advantages. First, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Second, it can synthesize high-resolution images, for example, 16-megapixel images in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations.
Microaggregation Sorting Framework for K-Anonymity Statistical Disclosure Control in Cloud Computing

In cloud computing, there have led to an increase in the capability to store and record personal data (microdata) in the cloud. In most cases, data providers have no/little control that has led to concern that the personal data may be beached. Microaggregation techniques seek to protect microdata in such a way that data can be published and mined without providing any private information that can be linked to specific individuals. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a sorting framework for Statistical Disclosure Control (SDC) to protect microdata in cloud computing. It consists of two stages. In the first stage, an algorithm sorts all records in a data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage a microaggregation method is used to create k-anonymous clusters while minimizing the information loss. The performance of the proposed techniques is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithms perform significantly better than existing associate techniques in the literature.
Construction Payment Automation through Smart Contract-based Blockchain Framework

Construction Payment Automation through Smart Contract-based Blockchain Framework Han Luo, Moumita Das, Jun Wang and Jack C.P. Cheng Pages 1254-1260 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: A construction contract facilitates payments through the supply chain by integrating people, activities, and events throughout the project period through obligations, permissions, and prohibitions in its terms and conditions. The management of payments is a manual process and is more difficult in the case of construction projects as different stakeholders at different levels of the project organizational structure are bound by different contracts. Moreover, payments are strongly affected by the lack of clarity in the definitions of the obligations, responsibilities, and liabilities of various stakeholders in construction contracts. This intensifies disputes and causes delays in construction payments leading to additional expenditure, cash flow problems, and lack of trust. Therefore, to address these problems, we propose a methodology to automate construction payments by formalizing them into smart contracts and executing on a decentralized blockchain based framework. We formalize the payment logic that binds the prohibitions and liabilities associated with financial commitments, such as interim payments on completion of tasks in a construction project, and convert it into a computer-executable code. A framework based on blockchain is used to host this smart contract and to automate actions such as the triggering of payments after achieving consensus among the relevant project stakeholders. This framework also address the conditions required for the security of information in construction projects, such as confidentiality and information integrity in a multi-party environment. The proposed framework is demonstrated through a case-based scenario. Keywords: Blockchain; Smart Contracts; Construction Projects; Interim Payments DOI: https://doi.org/10.22260/ISARC2019/0168 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
On relating explanations and adversarial examples

The importance of explanations (XP's) of machine learning (ML) model predictions and of adversarial examples (AE's) cannot be overstated, with both arguably being essential for the practical success of ML in different settings. There has been recent work on understanding and assessing the relationship between XP's and AE's. However, such work has been mostly experimental and a sound theoretical relationship has been elusive. This paper demonstrates that explanations and adversarial examples are related by a generalized form of hitting set duality, which extends earlier work on hitting set duality observed in model-based diagnosis and knowledge compilation. Furthermore, the paper proposes algorithms, which enable computing adversarial examples from explanations and vice-versa.