Reviewerly/exHarmony huggingface dataset:
Few-Shot Font Generation by Learning Fine-Grained Local Styles

Few-shot font generation (FFG), which aims to generate a new font with a few examples, is gaining increasing attention due to the significant reduction in labor cost. A typical FFG pipeline considers characters in a standard font library as content glyphs and transfers them to a new target font by extracting style information from the reference glyphs. Most existing solutions explicitly disentangle content and style of reference glyphs globally or component-wisely. However, the style of glyphs mainly lies in the local details, i.e. the styles of radicals, components, and strokes together depict the style of a glyph. Therefore, even a single character can contain different styles distributed over spatial locations. In this paper, we propose a new font generation approach by learning 1) the fine-grained local styles from references, and 2) the spatial correspondence between the content and reference glyphs. Therefore, each spatial location in the content glyph can be assigned with the right fine-grained style. To this end, we adopt cross-attention over the representation of the content glyphs as the queries and the representations of the reference glyphs as the keys and values. Instead of explicitly disentangling global or component-wise modeling, the cross-attention mechanism can attend to the right local styles in the reference glyphs and aggregate the reference styles into a fine-grained style representation for the given content glyphs. The experiments show that the proposed method outperforms the state-of-the-art methods in FFG. In particular, the user studies also demonstrate the style consistency of our approach significantly outperforms previous methods.
Cross-Modality Transformer for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging task due to the large cross-modality discrepancies and intra-class variations. Existing works mainly focus on learning modality-shared representations by embedding different modalities into the same feature space. However, these methods usually damage the modality-specific information and identification information contained in the features. To alleviate the above issues, we propose a novel Cross-Modality Transformer (CMT) to jointly explore a modality-level alignment module and an instance-level module for VI-ReID. The proposed CMT enjoys several merits. First, the modality-level alignment module is designed to compensate for the missing modality-specific information via a Transformer encoder-decoder architecture. Second, we propose an instance-level alignment module to adaptively adjust the sample features, which is achieved by a query-adaptive feature modulation. To the best of our knowledge, this is the first work to exploit a cross-modality transformer to achieve the modality compensation for VI-ReID. Extensive experimental results on two standard benchmarks demonstrate that our CMT performs favorably against the state-of-the-art methods.
Multi-Dimensional Resource Allocation in Distributed Data Centers Using Deep Reinforcement Learning

With the development of edge-cloud computing technologies, distributed data centers (DCs) have been extensively deployed across the global Internet. Since different users/applications have heterogeneous requirements on specific types of ICT resources in distributed DCs, how to optimize such heterogeneous resources under dynamic and even uncertain environments becomes a challenging issue. Traditional approaches are not able to provide effective solutions for multi-dimensional resource allocation that involves the balanced utilization across different resource types in distributed DC environments. This paper presents a reinforcement learning based approach for multi-dimensional resource allocation (termed as NESRL-MRM) that is able to achieve balanced utilization and availability of resources in dynamic environments. To train NESRL-MRM’s agent with sufficiently quick wall-clock time but without the loss of exploration diversity in the search space, a natural evolution strategy (NES) is employed to approximate the gradient of the reward function. To realistically evaluate the performance of NESRL-MRM, our simulation evaluations are based on real-world workload traces from Amazon EC2 and Google datacenters. Our results show that NESRL-MRM is able to achieve significant improvement over the existing approaches in balancing the utilization of multi-dimensional DC resources, which leads to substantially reduced blocking probability of future incoming workload demands.
FCDT-IWBOA-LSSVR: An innovative hybrid machine learning approach for efficient prediction of short-to-mid-term photovoltaic generation

The short-term photovoltaic (PV) power prediction is mainly used to prevent photovoltaic power generation fluctuations, grid overvoltage, reverse current, and islanding effects. At the same time, the mid-term PV power forecast is of great significance to improving the availability of PV power generation and the benefit of PV grid connection. Because of the significant differences in time scales between mid-term and short-term power prediction, the short-term power prediction model or method cannot be directly applied to mid-term power prediction. Therefore, few scholars have developed effective short-to-mid-term power forecasting strategies for photovoltaics. As a result, this research proposes a prediction model based on a fast cull outlier algorithm (FC), a decision tree (DT), least squares support vector regression (LSSVR), and an improved whale bat optimization algorithm (IWBOA), which is used for the prediction of the short-to-mid-term photovoltaic power on different time scales, one day and one month in advance. The FC algorithm is used to analyze the difference between the overall data and the abnormal data in the local density and distance to remove the outliers in the original data set. The revised DT model differentiates the data according to climate characteristics to obtain different input data subsets with the best correlation and constructs an independent LSSVR-based model corresponding to the data subsets. The WOA algorithm is improved by independently designing the Chebyshev chaotic initialization strategy, chaotic adaptive inertia weight and BOA algorithm, and the kernel function width and penalty factor of each short-to-mid-term independent sub-PV power prediction model are optimized by IWBOA, thereby improving the computational speed and prediction accuracy of the LSSVR model. Finally, the effectiveness of the proposed strategy is evaluated using actual data from photovoltaic power plants in China. The regression coefficient (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) of short-term forecasting ability of the FCDT-IWBOA-LSSVR model established in this paper are 0.99657, 0.22009, 0.32515, and 0.20959, respectively, while the R2, MSE, RMSE, and MAE of medium-term forecasting ability are 0.98374, 1.91371, 1.38336, and 0.6256. The calculation results show that the prediction accuracy of the FCDT-IWBOA-LSSVR model is higher than that of the WOA-LSSVR, PSO-LSSVR, PSO-BP, I-ACO-SVR, ANN, EMS-DSM-RT, 5 ML, and ELM models.
How blockchain technology improves sustainable supply chain processes: a practical guide

Blockchain technology has rapidly grown in the last decade and supply chain management has started to emerge as one of its possible fields of application. Blockchain is estimated to have a transformative impact and potentially transform and disrupt supply chains. However, despite recognizing its enormous opportunity, there is still an incomplete, dispersed, and fragmented knowledge of blockchain applications beyond cryptocurrencies. Very few business people still deeply understand how blockchain works and how it can concretely benefit supply chains. This paper contributes to closing this gap, providing a holistic approach to analyzing blockchain technology's applications and benefits in supply chain management. This study aims to support academics and practitioners in adopting a holistic perspective and realizing the potential this technology can offer in different areas of the supply chain, its managerial impact, challenges, and limitations. A "closed-loop" value chain perspective is adopted to analyze the benefits at each core process of the supply chain, from sourcing to final customer and reverse logistics. This paper conducts qualitative research using case studies analysis based on secondary data. Further, it builds a theoretical framework for blockchain-enhanced supply chain performance based on the results of the qualitative research. Finally, the paper discusses the major drawbacks and barriers to blockchain implementation, which help managers evaluate its real net benefits.
Generalized Pose Decoupled Network for Unsupervised 3D Skeleton Sequence-Based Action Representation Learning

Human action representation is derived from the description of human shape and motion. The traditional unsupervised 3-dimensional (3D) human action representation learning method uses a recurrent neural network (RNN)-based autoencoder to reconstruct the input pose sequence and then takes the midlevel feature of the autoencoder as representation. Although RNN can implicitly learn a certain amount of motion information, the extracted representation mainly describes the human shape and is insufficient to describe motion information. Therefore, we first present a handcrafted motion feature called pose flow to guide the reconstruction of the autoencoder, whose midlevel feature is expected to describe motion information. The performance is limited as we observe that actions can be distinctive in either motion direction or motion norm. For example, we can distinguish "sitting down" and "standing up" from motion direction yet distinguish "running" and "jogging" from motion norm. In these cases, it is difficult to learn distinctive features from pose flow where direction and norm are mixed. To this end, we present an explicit pose decoupled flow network (PDF-E) to learn from direction and norm in a multi-task learning framework, where 1 encoder is used to generate representation and 2 decoders are used to generating direction and norm, respectively. Further, we use reconstructing the input pose sequence as an additional constraint and present a generalized PDF network (PDF-G) to learn both motion and shape information, which achieves state-of-the-art performances on large-scale and challenging 3D action recognition datasets including the NTU RGB+D 60 dataset and NTU RGB+D 120 dataset.
Coordinated optimal scheduling of integrated energy system for data center based on computing load shifting

High-efficiency uninterruptable power supplies and sustainable energy systems are essential to realize data centers more sustainable and environmentally friendly. Considering the coupling between computing tasks and power consumption, this paper proposes a coordinated optimization model of operational scheduling of integrated energy system (IES) for data center according to computing load transfer. An IES consisting of solar photovoltaic arrays, gas engine, and organic Rankine cycle is proposed and optimized to obtain the optimum component capacities. Then, the computing tasks of servers with task shifting are modeled to characterize the arrival time, execution time, and deadline of different tasks. The operation scheduling optimization model of components in the IES with the computing task transfer is proposed to minimize the operating costs of data center and maximize the user satisfaction level of computing tasks. The impacts of objective weights on the dispatch strategies of the IES and computing tasks are discussed. The optimization results in a case study demonstrate that the data center can effectively realize the transfer of electricity and cooling loads by transferring computing tasks to improve the IES performances. The optimum dispatch strategies with task transfer saves operational cost and fuel consumption by 2.76% and 2.56%, respectively, and the carbon dioxide emission is reduced by 6.31% in non-heating season. Their benefits in heating season are 3.47%, 4.11%, and 2.17%, respectively.
AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions

Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.
Importance of anti-money laundering regulations among prosumers for a cybersecure decentralized finance

This study displays the importance of anti-money laundering (AML) regulations among prosumers in decentralized finance (DeFi) in establishing cybersecurity. With a game theory model based on an integrated framework of crime theories, we find that beneficial claims of financial inclusivity and pseudonymity of permissionless DeFi come with a cost of vulnerability to cyberattacks due to its incompliance with AML regulations among prosumers. In contrast, cyberattackers can be deterred from attacking AML-compliant permissioned DeFi. These results have important implications for trust as a construct within the context of blockchain-based sharing economy and institutional roles played by decentralized governance and smart contract auditors in creating a “trustless” state-space without forfeiting cybersecurity of a blockchain-based sharing economy. We also provide cybersecurity policy suggestions for permissionless DeFi without sacrificing its decentralized nature.
How to Use K-means for Big Data Clustering?

K-means plays a vital role in data mining and is the simplest and most widely used algorithm under the Euclidean Minimum Sum-of-Squares Clustering (MSSC) model. However, its performance drastically drops when applied to vast amounts of data. Therefore, it is crucial to improve K-means by scaling it to big data using as few of the following computational resources as possible: data, time, and algorithmic ingredients. We propose a new parallel scheme of using K-means and K-means++ algorithms for big data clustering that satisfies the properties of a ``true big data'' algorithm and outperforms the classical and recent state-of-the-art MSSC approaches in terms of solution quality and runtime. The new approach naturally implements global search by decomposing the MSSC problem without using additional metaheuristics. This work shows that data decomposition is the basic approach to solve the big data clustering problem. The empirical success of the new algorithm allowed us to challenge the common belief that more data is required to obtain a good clustering solution. Moreover, the present work questions the established trend that more sophisticated hybrid approaches and algorithms are required to obtain a better clustering solution.
A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as they have a myriad of applications. Prior to using ANNs, the network structure needs to be determined and the ANN needs to be trained. The network structure is usually chosen based on trial and error. The training, which consists of finding the optimal connection weights and biases of the ANN, is usually done using gradient-descent algorithms. It has been found that swarm intelligence algorithms are favorable for both determining the network structure and for the training of ANNs. This is because they are able to determine the network structure in an intelligent way, and they are better at finding the most optimal connection weights and biases during the training as opposed to conventional algorithms. Recently, a number of swarm intelligence algorithms have been employed for optimizing different types of neural networks. However, there is no comprehensive survey on the swarm intelligence algorithms used for optimizing ANNs. In this paper, we present a review of the different types of ANNs optimized using swarm intelligence algorithms, the way the ANNs are optimized, the different swarm intelligence algorithms used, and the applications of the ANNs optimized by swarm intelligence algorithms.
Meta underwater camera: A smart protocol for underwater image enhancement

Underwater images captured by an underwater camera normally suffer from visual degradation issues, such as color deviations, low contrasts, and detail blurs. Existing studies tend to address these issues separately by individual techniques, such that underwater image visibility can hardly be improved in an overall, consistent manner. To address this limitation, we present a smart protocol for underwater image enhancement. The protocol comprises a comprehensive cascade of seven image enhancement techniques, i.e., attenuated channel compensation, white balance, tone mapping, hue-saturation-lightness (HSL) model-based saturation adjustment, contrast stretching, gamma correction, and high-pass fusion. The protocol is smartly configured by reinforcement learning. Specifically, a set of underwater image multi-color space features are considered as a state, a set of parameter values for the protocol as an action, and an increment of a non-reference visual preference score as a reward. By the reinforcement learning strategy, the parameter values for the seven techniques in the protocol are optimally configured as a whole, resulting in optimal underwater image enhancement results. We refer to the functionality of the smart protocol as a meta underwater camera (MUC) methodology because it operates behind an underwater camera’s operation of capturing underwater images but provides underwater images of high visibility beyond those of low visibility originally obtained from the underwater camera. Qualitative and quantitative empirical results validate that our MUC methodology outperforms state-of-the-art underwater image enhancement methods. We release the reproducible code at https://gitee.com/wanghaoupc/MUC_UnderwaterImageEnhancement for public evaluations.
Efficient mining of top-k high utility itemsets through genetic algorithms

Mining high utility itemsets is an emerging and very active research area in data mining. The goal is to mine all itemsets with a utility value, in terms of importance to the user, no less than a predefined threshold value. Setting an appropriate threshold value is not trivial, requiring not only multiple trials but also the know-how in the application field. The advantage of algorithms for mining top-k high utility itemsets is they do not require such a utility threshold, but they suffer from very long runtimes and large memory requirements when large input data is considered. We propose a new genetic algorithm for mining top-k high utility itemsets, named TKHUIM-GA (Top-K High Utility Itemset Mining through Genetic Algorithms). It guides the search process by considering the utility of each item to produce initial solutions and to combine solutions accordingly, reducing the runtime and memory consumption as a result. A highly efficient data representation is utilized to reduce memory usage and runtime. A key advantage of TKHUIM-GA is that it works on positive, negative, integer and real unit utility values unlike existing approaches. Experiments on popular benchmark datasets demonstrate the high performance of the proposal regarding the state-of-the-art algorithms.
Examining the performance of kernel methods for software defect prediction based on support vector machine

Support Vector Machine (SVM) has been widely used to build software defect prediction models. Prior studies compared the accuracy of SVM to other machine learning algorithms but arrives at contradictory conclusions due to the use of different choices of kernel functions and metrics. Such a contradictory conclusion raises an important question about the performance of kernel functions, across different experimental conditions. To this end, the present study examines the impact and stability of four kernel functions with feature selection on the performance of SVM for software defect prediction. Strictly speaking, we examine the performance of nonlinear kernel functions against linear kernel function based on different experimental parameters such as data granularity, imbalance ratio of the dataset, and feature subsets. A large-scale study has been conducted using four kernel functions, ten feature subset selection thresholds based on the Information gain algorithm, 38 public datasets and one evaluation measure. This has resulted in 1520 experiments. The findings demonstrate that: 1) Not all nonlinear kernel functions significantly outperform linear, only RBF surpasses linear and other nonlinear kernel functions. 2) We don't have significant difference between kernel functions w.r.t. data granularity, we only found significant difference between RBF and other kernel function based on 'function' data granularity. 3) we also found that RBF can work significantly better than linear and other nonlinear function over datasets with very high and high imbalance ratios. 4) The performances of all kernel functions fluctuate over different feature subsets; However, using top 40% of the features would work best with all kernel functions. To conclude, we can recommend using SVM with RBF kernel for defects datasets because the performance of other kernel functions is limited.
Automatic Code Documentation Generation Using GPT-3

Source code documentation is an important artifact for efficient software development. Code documentation could greatly benefit from automation since manual documentation is often labouring, resource and time-intensive. In this paper, we employed Codex for automatic code documentation creation. Codex is a GPT-3 based model pre-trained on both natural and programming languages. We find that Codex outperforms existing techniques even with basic settings like one-shot learning (i.e., providing only one example for training). Codex achieves an overall BLEU score of 20.6 for six different programming languages (11.2% improvement over earlier state-of-the-art techniques). Thus, Codex shows promise and warrants in-depth future studies for automatic code documentation generation to support diverse development tasks.