Reviewerly/exHarmony huggingface dataset:
LiSiam: Localization Invariance Siamese Network for Deepfake Detection

Advances in facial manipulation technology have led to increasing indistinguishable and realistic face swap videos, which raises growing concerns about the security risk of deepfakes in the community. Although current deepfake detectors can gain promising performance when handling high-quality faces under within-database settings, most detectors suffer from performance degradation in cross-database evaluation. Moreover, when test faces&#x2019; quality is different from training faces, the performance degrades even under within-database settings. To this end, we propose a novel Localization invariance Siamese Network (LiSiam) to enforce localization invariance against different image degradation for deepfake detection. Specifically, our Siamese network-based feature extractor takes the original image and the corresponding quality-degraded image as pairwise inputs and outputs two segmentation maps. A localization invariance loss is further proposed to impose localization consistency between the two segmentation maps. In addition, we design a Mask-guided Transformer to capture the co-occurrence between the forgery region and its surroundings. Finally, a multi-task learning strategy is utilized to obtain a robust and discriminative feature representation and jointly optimize multiple objective functions (i.e., segmentation, classification, and localization invariance losses) in an end-to-end manner. Experimental results on two public datasets, i.e., FaceForensics&#x002B;&#x002B; and Celeb-DF, demonstrate the superior performance of our proposed method to state-of-the-art methods.
Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams

Time series anomaly detection remains one of the most active areas of research in data mining. In spite of the dozens of creative solutions proposed for this problem, recent empirical evidence suggests that time series discords, a relatively simple twenty-year old distance-based technique, remains among the state-of-art techniques. While there are many algorithms for computing the time series discords, they all have limitations. First, they are limited to the batch case, whereas the online case is more actionable. Second, these algorithms exhibit poor scalability beyond tens of thousands of datapoints. In this work we introduce DAMP, a novel algorithm that addresses both these issues. DAMP computes exact left-discords on fast arriving streams, at up to 300,000 Hz using a commodity desktop. This allows us to find time series discords in datasets with trillions of datapoints for the first time. We will demonstrate the utility of our algorithm with the most ambitious set of time series anomaly detection experiments ever conducted.
Invariant Preference Learning for General Debiasing in Recommendation

Current recommender systems have achieved great successes in online services, such as E-commerce and social media. However, they still suffer from the performance degradation in real scenarios, because various biases always occur in the generation process of user behaviors. Despite the recent development of addressing some specific type of bias, a variety of data bias, some of which are even unknown, are often mixed up in real applications. Although the uniform (or unbiased) data may help for the purpose of general debiasing, such data can either be hardly available or induce high experimental cost. In this paper, we consider a more practical setting where we aim to conduct general debiasing with the biased observational data alone. We assume that the observational user behaviors are determined by invariant preference (i.e. a user's true preference) and the variant preference (affected by some unobserved confounders). We propose a novel recommendation framework called InvPref which iteratively decomposes the invariant preference and variant preference from biased observational user behaviors by estimating heterogeneous environments corresponding to different types of latent bias. Extensive experiments, including the settings of general debiasing and specific debiasing, verify the advantages of our method.
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?

Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations. Recently there has been remarkable progress on large-scale contrastive learning models which require substantial computing resources, yet such models are not practically optimal for small-scale tasks. To fill the gap, we aim to study contrastive learning on the wearable-based activity recognition task. Specifically, we conduct an in-depth study of contrastive learning from both algorithmic-level and task-level perspectives. For algorithmic-level analysis, we decompose contrastive models into several key components and conduct rigorous experimental evaluations to better understand the efficacy and rationale behind contrastive learning. More importantly, for task-level analysis, we show that the wearable-based signals bring unique challenges and opportunities to existing contrastive models, which cannot be readily solved by existing algorithms. Our thorough empirical studies suggest important practices and shed light on future research challenges. In the meantime, this paper presents an open-source PyTorch library CL-HAR, which can serve as a practical tool for researchers. The library is highly modularized and easy to use, which opens up avenues for exploring novel contrastive models quickly in the future.
What drives volatility in Bitcoin market?

This study aims to identify the main drivers of Bitcoin volatility. The empirical analysis is based on a dynamic Bayesian model averaging approach for twenty-two potential determinants. The results reveal that the most important factors for Bitcoin volatility are Google trends, total circulation of Bitcoins, US consumer confidence and the S&P500 index.
The Application of Blockchain Technology to Smart City Infrastructure

A smart city can be defined as an integration of systems comprising a plethora of task-oriented technologies that aim to evolve and advance with city and infrastructure needs while providing services to citizens and resolving urban challenges through intersystem and data-driven analytical means, with minimal human intervention. Applications of technology include management, operations, and finance. One such technology is Blockchain. A main advantage of Blockchain is the simplification of processes that are costly and time-consuming. This is accomplished by simplifying operations to minimize costs resulting from the decentralization of assets. Blockchain has been proven to facilitate transparency, security, and the elimination of data fragmentation. However, as a relatively new technology, it poses regulatory obstacles. This issue can be attributed to the fact that many infrastructural governing organizations have incomplete knowledge of their infrastructure, which can lead to confusion when attempting to comprehend the different elements of the infrastructure, resulting in a lack of direction when trying to solve a problem. This paper explores the different applications of Blockchain technology in the sectors of energy, transportation, water, construction, and government, and provides a mechanism for implementing this technology in smart cities. As a present component of infrastructure management systems, Blockchain may potentially serve as the initial step toward upgrading infrastructure technology.
Speculative bubbles and herding in cryptocurrencies

Abstract This study investigates speculative bubbles in the cryptocurrency market and factors affecting bubbles during the COVID-19 pandemic. Our results indicate that each cryptocurrency covered in the study presented bubbles. Moreover, we found that explosive behavior in one currency leads to explosivity in other cryptocurrencies. During the pandemic, herd behavior was evident among investors; however, this diminishes during bubbles, indicating that bubbles are not explained by herd behavior. Regarding cryptocurrency and market-specific factors, we found that Google Trends and volume are positively associated with predicting speculative bubbles in time-series and panel probit regressions. Hence, investors should exercise caution when investing in cryptocurrencies and follow both crypto currency and market-related factors to estimate bubbles. Alternative liquidity, volatility, and Google Trends measures are used for robustness analysis and yield similar results. Overall, our results suggest that bubble behavior is common in the cryptocurrency market, contradicting the efficient market hypothesis.
NTIRE 2022 Spectral Recovery Challenge and Data Set

This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the "ARAD_1K" data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyper-spectral information from synthetically generated JPEG-compressed RGB images simulating capture by a known calibrated camera, operating under partially known parameters, in a setting which includes acquisition noise. The challenge was attended by 241 teams, with 60 teams com-peting in the final testing phase, 12 of which provided de-tailed descriptions of their methodology which are included in this report. The performance of these submissions is re-viewed and provided here as a gauge for the current state-of-the-art in spectral reconstruction from natural RGB images.
Collaborative annealing power <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e9859" altimg="si10.svg"><mml:mi>k</mml:mi></mml:math>-means++ clustering

Clustering is the most fundamental technique for data processing. This paper presents a collaborative annealing power k-means++ clustering algorithm by integrating the k-means++ and power k-means algorithms in a collaborative neurodynamic optimization framework. The proposed algorithm starts with k-means++ to select initial cluster centers, then leverages the power k-means to find multiple sets of centers as alternatives and a particle swarm optimization rule to reinitialize the centers in the subsequential iterations for improving clustering performance. Experimental results on twelve benchmark datasets are elaborated to demonstrate the superior performance of the proposed algorithm to seven mainstream clustering algorithms in terms of 21 internal and external indices.
Regulating the Metaverse, a Blueprint for the Future

The core Immersive Media (IM) technologies of Virtual Reality (VR) and Augmented Reality (AR) have steadily advanced over the last thirty years, enabling high fidelity experiences at consumer prices. Over the same period, networking speeds have increased dramatically, culminating in the deployment of 5G networks. Combined, these advancements greatly increase the prospects for widespread adoption of virtual and augmented worlds. Recently branded "the metaverse" by Meta and other large platforms, major corporations are currently investing billions to deploy immersive worlds that target mainstream activities from socializing and shopping to education and business. With the prospect that corporate-controlled metaverse environments proliferate society over the next decade, it is important to consider the risks to consumers and plan for meaningful regulation. This is especially true in light of the unexpected negative impact that social media platforms have had on society in recent years. The dangers of the metaverse are outlined herein along with proposals for sensible regulation.
Hybrid Transformer-CNN for Real Image Denoising

Transformer typically enjoys larger model capacity but higher computational loads than convolutional neural network (CNN) in vision tasks. In this letter, the advantages of such two networks are fused for achieving effective and efficient real image denoising. We propose a hybrid denoising model based on Transformer Encoder and Convolutional Decoder Network (TECDNet). The Transformer based on novel radial basis function (RBF) attention is used as encoder to improve the representation capability of overall model. In decoder, the residual CNN instead of Transformer is adopted to greatly reduce computational complexity of the whole denoising network. Extensive experimental results on real images show that TECDNet achieves the state-of-the-art denosing performance with relatively low computational cost.
Two-Dimensional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">Z</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:math> Lattice Gauge Theory on a Near-Term Quantum Simulator: Variational Quantum Optimization, Confinement, and Topological Order

Quantum approximate optimization algorithms can efficiently initialize the ground states of two-dimensional lattice gauge theory in different phases, showing signatures of topological order in systems sizes comparable to modern quantum computing platforms.
A Survey on Machine Learning Accelerators and Evolutionary Hardware Platforms

Advanced computing systems have long been enablers for breakthroughs in artificial intelligence (AI) and machine learning (ML) algorithms, either through sheer computational power or form-factor miniaturization. However, as AI/ML algorithms become more complex and the size of data sets increases, existing computing platforms are no longer sufficient to bridge the gap between algorithmic innovation and hardware design. This article presents a survey about various ML accelerators.
Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling

Cloud computing is a procedure of stockpiling as well as retrieval of data or computer services over the Internet that allows all its users to remotely access the data centers. Cloud computing provides all required services to the users, but every platform has its share of pros and cons, and another major problem in the cloud is task scheduling or workflow scheduling. Multiple factors are becoming a challenge for scheduling in cloud computing namely the heterogeneity of resources, tasks, and user priority. User priority has been encountered as the most challenging problem during the last decade as the number of users is increasing worldwide. This issue has been resolved by an advanced encryption standard (AES) algorithm, which decreases the response time and execution delay of the user-request. There are multifarious tasks, for instance, deploying the data on the cloud, that will be executed according to first come first serve (FCFS) and not on the payment basis, which provides an ease to the users. These investigated techniques are 30.21%, 25.20%, 25.30%, 30.25%, 24.26%, and 36.9 8% improved in comparison with the traditional FFOA, DE, ABC, PSO, GA, and ETC, respectively. Moreover, during iteration number 5, this approach is 15.20%, 20.22%, 30.56%, 26.30%, and 36.23% improved than that of the traditional techniques FFOA, DE, ABC, PSO, GA, and ETC, respectively. This investigated method is more efficient and applicable in certain arenas where user priority is the primary concern and can offer all the required services to the users without any interruption.
Blockchain in real estate: Recent developments and empirical applications

The real estate sector is often presented as an exemplary field that benefits from practical blockchain applications. The general hypothesis is that, in theory, blockchain could solve some significant challenges the real estate sector is facing, such as nontransparency, inefficiencies, fraud and corruption, high costs, and trust issues. However, the literature focuses on blockchain’s theoretical benefits, challenges, or concepts. This research aims to understand the recent developments in the blockchain literature, specifically in the real estate sector, and to understand the current real-world applications by collecting empirical evidence from blockchain studies. The systematic literature review identified 262 relevant documents, after which a thematic content analysis was performed. Conceptual blockchain literature was identified to propose blockchain benefits for the real estate sector in four categories: land administration, real estate transactions, tokenization, and real estate management. The thematic content analysis also identified 26 empirical applications, of which all except one were related to land administration. Although the conceptual and theoretical blockchain literature presents blockchain as a disruptive and transformative technology for the real estate sector, the empirical applications suggest that blockchain adoption materializes more in hybrid, smaller-scale settings, where blockchain is merely an add-on layer to existing systems. Overall, most of the conceptual blockchain benefits remain empirically unconfirmed. On the other hand, the empirical applications suggest that blockchain could, for example, increase efficiency, reduce time, and provide verifiability, transparency, and automation, even in smaller-scale, hybrid settings. In addition, the applications indicate that blockchain could, in some cases, help reduce fraud and increase security and trust compared with centralized digital solutions. Finally, the empirical insights emphasize the role of political will, regulatory framework, availability of reliable digital data, public–private partnerships, and educational aspects in blockchain applications.
Efficient Quantum Blockchain With a Consensus Mechanism QDPoS

Quantum blockchain is expected to offer an alternative to classical blockchain to resist malicious attacks laughed by future quantum computers. Although a few quantum blockchain schemes have been constructed, their efficiency is low and unable to meet application requirements due to the fact that they lack of a suitable consensus mechanism. To tackle this issue, a consensus mechanism called quantum delegated proof of stake (QDPoS) is constructed by using quantum voting to provide fast decentralization for the quantum blockchain scheme at first. Then an efficient scheme is proposed for quantum blockchain based on QDPoS, where the classical information is initialized as a part of each single quantum state and these quantum states are entangled to form the chain. Compared with previous methods, the designed quantum blockchain scheme is more complete and carried out with higher efficiency, which greatly contributes to better adapting to the challenges of the quantum era.
Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

Fluctuations of the incoming solar irradiance impact the power generation from photovoltaic and concentrating solar thermal power plants. Accurate solar nowcasting becomes necessary to detect these sudden changes of generated power and to provide the desired information for optimal exploitation of solar systems. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, a benchmarking exercise has been conducted relying on a bouquet of solar nowcasting methodologies by all-sky imagers (ASI). In this paper, four ASI systems nowcast the Global Horizontal Irradiance (GHI) with a time forecast ranging from 1 to 20 min during 28 days with variable cloud conditions spanning from September to November 2019 in southern Spain. All ASIs demonstrated their ability to accurately nowcast GHI, with RMSE ranging from 6.9% to 18.1%. Under cloudy conditions, all ASIs' nowcasts outperform the persistence models. Under clear skies, three ASIs are better than persistence. Discrepancies in the observed nowcasting performance become larger at increasing forecast horizons. The findings of this study highlight the feasibility of ASIs to reliably nowcast GHI at different sky conditions, time intervals and horizons. Such nowcasts can be used either to estimate solar power at distant times or detect sudden GHI fluctuations.
Cloud calculations within the optional course Optimization Problems for 10th-11th graders

The article deals with the problem of introducing cloud calculations into 10th-11th graders’ training to solve optimization problems in the context of the STEM-education concept. After analyzing existing programmes of optional courses on optimization problems, the programme of the optional course Optimization Problems has been developed and substantiated implying solution of problems by the cloud environment CoCalc. It is a routine calculating operation and not a mathematical model that is accentuated in the programme. It allows considering more problems which are close to reality without adapting the material while training 10th-11th graders. Besides, the mathematical apparatus of the course which is partially known to students as the knowledge acquired from such mathematics sections as the theory of probability, mathematical statistics, mathematical analysis and linear algebra is enough to master the suggested course. The developed course deals with a whole class of problems of conventional optimization which vary greatly. They can be associated with designing devices and technological processes, distributing limited resources and planning business functioning as well as with everyday problems of people. Devices, processes and situations to which a model of optimization problem is applied are called optimization problems. Optimization methods enable optimal solutions for mathematical models. The developed course is noted for building mathematical models and defining a method to be applied to finding an efficient solution.
Investigating the themes in supply chain finance: the emergence of blockchain as a disruptive technology

Blockchain has gained momentum as a disruptive technology in supply chain management against its introduction as a finance-related instrument. Nevertheless, the developing academic understanding and the limited practical implications lead to insufficient insights into the use of blockchain technology, particularly in the supply chain finance (SCF) domain. Thus, the expected potential of blockchain technology remains underexplored. Accordingly, this study explicates this situation by examining the extant literature findings and web-based big data that can provide evidence about the real needs in supply chains, and investigating how blockchain emerges as a disruptive SCF-oriented technology. The study employs a web analytics method, Search Engine Results Page (SERP) analysis which considers the trends in blockchain technology use and the interactions between blockchain, supply chain and finance appearing in Google searches. The SERP method examined real-time clicks, web traffics and most commonly asked questions about blockchain. The SERP findings revealed that the interest in blockchain technology neither focused on finance nor data privacy as emphasised in the literature but mainly on the benefits of increasing digitalisation and efficiency in supply chains. The results offered practical implications for capturing recent blockchain- and supply chain-related trends and designing more digital and efficient supply chains.