Reviewerly/exHarmony huggingface dataset:
Research and Technologies for next-generation high-temperature data centers – State-of-the-arts and future perspectives

Data centers have attracted increasing attention worldwide over the last decades due to their high energy consumption. Cooling accounts for about 30–40% of the total energy consumption of data centers. High-temperature data centers could save large amounts of cooling energy by changing their cooling mechanism. More effective use of “free cooling” is the basic and effective means for high-temperature data centers to reduce cooling energy consumption. It is possible to build chiller-less or even chiller-free data centers. They require less capital investment for cooling and allow more hours of “free cooling”. However, a few essential concerns need to be addressed before the wide application of high-temperature data centers, particularly the technical bottlenecks and the reliability and performance of servers and IT equipment. Though many reviews on data centers exist in the existing research, a systematic review of high-temperature data centers, particularly on the above essential concerns, is still unavailable. This paper is intended to fill in these gaps and provide a comprehensive review of these critical aspects. The main benefits and the major bottlenecks for implementing high-temperature data centers as well as the existing efforts and latest technologies to tackle the bottlenecks are categorized and analyzed systematically. In addition, a through review of the main temperature-sensitive IT components (e.g., hard disk drives and CPU) is done, and their current states and potential solutions are analyzed. Finally, the paper elaborates on future perspectives for the development and applications of the high-temperature data center. • A comprehensive review of high-temperature data center development is done. • Bottlenecks and benefits for implementing high-temperature data centers are analyzed. • Existing efforts and latest technologies to tackle the bottlenecks are analyzed. • A review of temperature-sensitive IT components & technology development is given. • Future perspectives for high-temperature data center development are elaborated.
Human-level play in the game of <i>Diplomacy</i> by combining language models with strategic reasoning

Despite much progress in training artificial intelligence (AI) systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. We introduce Cicero, the first AI agent to achieve human-level performance in Diplomacy, a strategy game involving both cooperation and competition that emphasizes natural language negotiation and tactical coordination between seven players. Cicero integrates a language model with planning and reinforcement learning algorithms by inferring players' beliefs and intentions from its conversations and generating dialogue in pursuit of its plans. Across 40 games of an anonymous online Diplomacy league, Cicero achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game.
Sketch2Photo: Synthesizing photo-realistic images from sketches via global contexts

Sketch-to-image synthesis aims to generate realistic images that match the input sketches or edge maps exactly. Most known sketch-to-image synthesis methods use various generative adversarial networks (GANs) that are trained with numerous pairs of sketches and real images. Because of the convolution locality, the low-level layers of the generators in these GANs lack global perception ability, causing feature maps derived from them easily to overlook global cues. Since the global receptive field is crucial for acquiring the non-local structures and features of sketches, the absence of global contexts will impact the generation of high-quality images. Some recent models turn to self-attention to construct global dependencies. However, they are not viable for large feature maps for the quadratic computational complexity concerning the size of feature maps. To address these problems, in this work, we propose Sketch2Photo — a new image synthesis approach that can capture global contexts as well as local features to generate photo-realistic images from weak or partial sketches or edge maps. We employ fast Fourier convolution (FFC) residual blocks to create global receptive fields in the bottom layers of the network and incorporate Swin Transformer block (STB) units to obtain long-range global contexts for large-size feature maps efficiently. We also present an improved spatial attention pooling (ISAP) module to relax the strict alignment requirements between incomplete sketches and generated images. Quantitative and qualitative experiments on multiple public datasets demonstrate the superiority of the proposed approach over many other sketch-to-image synthesis methods. The project code is available at https://github.com/hengliusky/Skecth2Photo.
TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers

In this paper, we present TransMVSNet, based on our exploration of feature matching in multi-view stereo (MVS). We analogize MVS back to its nature of a feature matching task and therefore propose a powerful Feature Matching Transformer (FMT) to leverage intra- (self-) and inter-(cross-) attention to aggregate long-range context information within and across images. To facilitate a better adaptation of the FMT, we leverage an Adaptive Receptive Field (ARF) module to ensure a smooth transit in scopes of features and bridge different stages with a feature pathway to pass transformed features and gradients across different scales. In addition, we apply pair-wise feature correlation to measure similarity between features, and adopt ambiguity-reducing focal loss to strengthen the supervision. To the best of our knowledge, TransMVSNet is the first attempt to leverage Transformer into the task of MVS. As a result, our method achieves state-of-the-art performance on DTU dataset, Tanks and Temples benchmark, and BlendedMVS dataset. Code is available at https://github.com/MegviiRobot/TransMVSNet.
Video Frame Interpolation Transformer

Existing methods for video interpolation heavily rely on deep convolution neural networks, and thus suffer from their intrinsic limitations, such as content-agnostic kernel weights and restricted receptive field. To address these issues, we propose a Transformer-based video interpolation framework that allows content-aware aggregation weights and considers long-range dependencies with the self-attention operations. To avoid the high computational cost of global self-attention, we introduce the concept of local attention into video interpolation and extend it to the spatial-temporal domain. Furthermore, we propose a space-time separation strategy to save memory usage, which also improves performance. In addition, we develop a multi-scale frame synthesis scheme to fully realize the potential of Transformers. Extensive experiments demonstrate the proposed model performs favorably against the state-of-the-art methods both quantitatively and qualitatively on a variety of benchmark datasets. The code and models are released at https://github.com/zhshi0816/Video-Frame-Interpolation-Transformer.
All-In-One Image Restoration for Unknown Corruption

In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels. To this end, we propose an All-in-one Image Restoration Network (AirNet) consisting of two neural modules, named Contrastive-Based Degraded Encoder (CBDE) and Degradation-Guided Restoration Network (DGRN). The major advantages of AirNet are two-fold. First, it is an all-in-one solution which could recover various degraded images in one network. Second, AirNet is free from the prior of the corruption types and levels, which just uses the observed corrupted image to perform inference. These two advantages enable AirNet to enjoy better flexibility and higher economy in real world scenarios wherein the priors on the corruptions are hard to know and the degradation will change with space and time. Extensive experimental results show the proposed method outperforms 17 image restoration baselines on four challenging datasets. The code is available at https://github.com/XLearning-SCU/2022-CVPR-AirNet.
Multiscale Feature Enhancement Network for Salient Object Detection in Optical Remote Sensing Images

Aircraft detection in synthetic aperture radar (SAR) images plays an essential role in satellite observation and military decisions. Due to discrete scattering properties, speckle noise interference, and various aircraft types, many existing methods struggle to achieve the desired detection performance. In this article, we propose an innovative semantic condition constraint guided feature aware network (SCFNet) for detecting different aircraft categories in SAR images. First, considering the discrete scattering properties of aircraft, we design a local-global feature aware module (LGA-M) and morphological-semantic feature aware module (MSF-M), which can effectively extract the fine-grained feature information contained in SAR images. Second, to effectively fuse different feature information, we construct a feature fusion pyramid (FFP), which uses different branches and paths to reasonably merge multiple feature information types and suppresses background information interference. Third, according to the structure characteristics of aircraft, the global coordinate attention mechanism (G-CAT) is presented to highlight foreground target features and suppress speckle noise interference. Finally, we construct semantic condition constraints, including constraint condition setting, semantic information calculation, and template matching, to improve aircraft localization and recognition accuracy. Extensive experiments demonstrate that the proposed SCFNet can obtain state-of-the-art performance on the SAR aircraft detection dataset, which achieves AP and F1 Score of 94.83% and 95.58%, respectively. The related implementation codes will be made publicly available at https://github.com/darkseid-arch/AirDetection.
An empirical evaluation of GitHub copilot's code suggestions

GitHub and OpenAI recently launched Copilot, an "AI pair programmer" that utilizes the power of Natural Language Processing, Static Analysis, Code Synthesis, and Artificial Intelligence. Given a natural language description of the target functionality, Copilot can generate corresponding code in several programming languages. In this paper, we perform an empirical study to evaluate the correctness and understandability of Copilot's suggested code. We use 33 LeetCode questions to create queries for Copilot in four different programming languages. We evaluate the correctness of the corresponding 132 Copilot solutions by running LeetCode's provided tests, and evaluate understandability using SonarQube's cyclomatic complexity and cognitive complexity metrics. We find that Copilot's Java suggestions have the highest correctness score (57%) while JavaScript is the lowest (27%). Overall, Copilot's suggestions have low complexity with no notable differences between the programming languages. We also find some potential Copilot shortcomings, such as generating code that can be further simplified and code that relies on undefined helper methods.
Cross-view Transformers for real-time Map-view Semantic Segmentation

We present cross-view transformers, an efficient attention-based model for map-view semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping from individual camera views into a canonical map-view representation using a camera-aware cross-view attention mechanism. Each camera uses positional embeddings that depend on its intrinsic and extrinsic calibration. These embeddings allow a transformer to learn the mapping across different views without ever explicitly modeling it geometrically. The architecture consists of a convolutional image encoder for each view and cross-view transformer layers to infer a map-view semantic segmentation. Our model is simple, easily parallelizable, and runs in realtime. The presented architecture performs at state-of-the-art on the nuScenes dataset, with 4x faster inference speeds. Code is available at https://github.com/bradyz/cross_view_transformers.
EDTER: Edge Detection with Transformer

Convolutional neural networks have made significant progresses in edge detection by progressively exploring the context and semantic features. However, local details are gradually suppressed with the enlarging of receptive fields. Recently, vision transformer has shown excellent capability in capturing long-range dependencies. Inspired by this, we propose a novel transformer-based edge detector, Edge Detection TransformER (EDTER), to extract clear and crisp object boundaries and meaningful edges by exploiting the full image context information and detailed local cues simultaneously. EDTER works in two stages. In Stage I, a global transformer encoder is used to capture long-range global context on coarse-grained image patches. Then in Stage II, a local transformer encoder works on fine-grained patches to excavate the short-range local cues. Each transformer encoder is followed by an elaborately designed Bi-directional Multi-Level Aggregation decoder to achieve high-resolution features. Finally, the global context and local cues are combined by a Feature Fusion Module and fed into a decision head for edge prediction. Extensive experiments on BSDS500, NYUDv2, and Multicue demonstrate the superiority of EDTER in comparison with state-of-the-arts. The source code is available at https://github.com/MengyangPu/EDTER.
A Survey of Blockchain and Intelligent Networking for the Metaverse

The virtual world created by the development of the Internet, computers, artificial intelligence (AI), and hardware technologies have brought various degrees of digital transformation to people’s lives. With multiple demands for virtual reality increasing, the metaverse, a new type of social ecology that can connect the physical and virtual worlds, is booming. However, with the rapid growth of data volume and value, the continuous evolution of the metaverse faces the demands and challenges of privacy, security, high synchronization, and low latency. Fortunately, the ever-evolving blockchain and intelligent networking technologies can be used to satisfy the trusted construction, continuous data interaction, and computing demands of the metaverse. Therefore, it is necessary to conduct an in-depth review of the role and gains of blockchain, intelligent networking, and the combination of both in providing the immersive experiences of the metaverse. In this survey, we first discuss the development trend, characteristics, and architecture of the metaverse. Then, the existing work on blockchain, networking, and the combination of the two technologies are reviewed, including overviews, applications, and challenges. Next, applications of the metaverse are summarized, emphasizing the importance of the metaverse and the fields of development. Finally, we discuss some open issues, challenges, and future research directions.
EHDHE: Enhancing security of healthcare documents in IoT-enabled digital healthcare ecosystems using blockchain

Nowadays, blockchain technology is one of the advanced technologies to ensure the security of users’ sensitive or confidential data. Blockchain technology plays a vital role in various applications like artificial intelligence, supply chain, cloud computing, the healthcare sector, and many more. It helps the healthcare domain to get benefitted from its many advanced features, such as confidentiality, decentralization, security, and privacy. Also, the Internet of Things (IoT) devices connect with the healthcare systems, and the healthcare sector application software further communicates with the IT industry. The blockchain-based IoT systems have significantly impacted the healthcare sector by enhancing security, privacy, transparency, and efficiency, providing better business opportunities. Moreover, traditional healthcare systems face severe security and privacy problems, such as phishing, masquerades, identity theft, and many others. Thus, a secure blockchain-based Proposed Application (PA) is designed to generate, maintain, and validate healthcare certificates. The PA acts as a communication medium between the backend blockchain network and application entities like hospitals, patients, doctors, and IoT devices to create and verify medical certificates. It also ensures various security features, namely confidentiality, authentication, and access control, using the concept of smart contracts. The comparative and performance analysis of the proposed work shows that it provides a more effective solution than the existing schemes.
Nonparametric Estimation of Local Bandwidth from Level Crossings Sampling

We study the problem of a local bandwidth recovery for nonstationary stochastic signals when the measured information is given in terms of level crossings. We propose a kernel estimate of the local bandwidth from samples generated from level crossings of stochastic signals being the time-warped version of stationary Gaussian processes. The positivity and bandlimited nature of the local intensity is captured by the properly selected class of bandlimited and positive kernel functions. The asymptotic properties of the estimator are derived.
Towards accelerating model parallelism in distributed deep learning systems

Modern deep neural networks cannot be often trained on a single GPU due to large model size and large data size. Model parallelism splits a model for multiple GPUs, but making it scalable and seamless is challenging due to different information sharing among GPUs with communication overhead. Specifically, we identify two key issues to make the parallelism being inefficient and inaccurate; an efficient pipelining technique is crucial to maximize GPU utilization and normalizations in deep neural networks may affect the performance due to different statistics sharing of mini-batch. In this work, we address these issues by investigating efficient pipelining for model parallelism and effective normalizations in model / data parallelisms when training a model with large mini-batch in multiple GPUs so that the model performance in accuracy can not be compromised. Firstly, we propose a novel method to search for an optimal micro-batch size considering the number of GPUs and memory size for model parallelism. For efficient pipelining, mini-batch is usually divided into smaller batches (called micro-batch). To maximize the utilization of GPU computing resources, training should be performed with the optimal micro-batch size. Our proposed micro-batch size search algorithm achieved increased image throughput by up to 12% and improved trainable mini-batch size by 25% as compared to the conventional model parallelism method. Secondly, we investigate normalizations in distributed deep learning training for different parallelisms. Our experiments using different normalization methods suggested that the performance with batch normalization can be improved by sharing the batch information among GPUs when performing data parallelism. It was also confirmed that group normalization helped minimizing accuracy degradation when performing model parallelism with pipelining and yielded consistent accuracies for diverse mini-batch sizes.
Design and implementation of adolescent health Latin dance teaching system under artificial intelligence technology

Since various dance teaching systems have attracted much attention with the development of Artificial Intelligence (AI) technology, this paper improves the recognition performance of Latin dance teaching systems by optimizing the action recognition model. Firstly, the object detection and action recognition technology under the current AI technology is analyzed, and the Two-stage object detection algorithm and One-stage object detection algorithm are evaluated. Secondly, the technologies and functions contained in the adolescent health Latin dance teaching system are described, including image acquisition, feature extraction, object detection, and action recognition. Finally, the action recognition algorithm is optimized based on object detection, and the rationality and feasibility of the proposed algorithm are verified by experiments. The experimental results show that the optimization algorithm can search the optimal feature subset after five iterations on Undefine Classes of 101 (UCF101) dataset, but it needs seven iterations on Human Motion Database 51 (HMDB51) dataset. Meanwhile, when using support vector machine classifier, the optimization algorithm can achieve the highest accuracy of motion recognition. Regressive Function, Multinomial Naive Bayes and Gaussian Naive Bayes Algorithms have lower prediction delay, as low as 0.01s. Therefore, this paper has certain reference significance for the design and implementation of adolescent health Latin dance teaching system.
Dual-Layer FL and Blockchain Empowered High Accurate Edge Training Framework

With the popularization of the smart city and the explosion of vehicles, the emergence of large amounts of data has boosted research. Distributed machine learning (DML) is an effective solution to improve the accuracy and timeliness. However, schemes based on traditional DML have the problems of high network delay, vehicle privacy leakage, and falling to provide customized models for multiple users. To solve the above problems, this paper constructs a dual-layer federated learning and blockchain-empowered high-accuracy edge training (DFLB-ET) framework for assisted driving. First of all, a dual-layer semi-asynchronous federated learning (FL) mechanism based on blockchain and local Directed Acyclic Graph are proposed to improve the efficiency and accuracy of FL and achieve high-accuracy model sharing among edge servers. Secondly, a regional node selection algorithm is proposed based on the mobility and model accuracy of vehicles, so as to better utilize the computational resources of Road-Side Units. Simulation results show that the proposed DFLB-ET framework outperforms both the local training and synchronous/asynchronous FL schemes in terms of the training delay and model accuracy.
Design and Implementation of Distributed Architecture for Test Data Analysis Platform

According to the existing business characteristics of the test data analysis platform, a distributed architecture scheme suitable for aviation, aerospace, shipbuilding and other industries is designed, and a unified description specification for test data is established to solve the problems of data format diversity and data volume under the two scenarios of non-real-time data and real-time data. The platform adopts a hierarchical design idea, and has a detailed design of the platform architecture and business functions. It realizes a high-performance and highly available test data analysis platform, provides rich analysis and drawing functions and data visualization capabilities, and has good scalability.