Reviewerly/exHarmony huggingface dataset:
Dual-Aligned Oriented Detector

In the past few years, object detection in remote sensing images has achieved remarkable progress. However, the detection of oriented and densely packed objects are still unsatisfactory due to the following spatial and feature misalignments. 1) Most two-stage oriented detectors only introduce an orientation regression branch in the detection head, while still leverage horizontal proposals for classification and regression. This inevitably results in the spatial misalignment problem between horizontal proposals and oriented objects. 2) The features used for classification are in fact extracted from the region proposals which have shifted to the final predictions via the regression branch. This leads to the feature misalignment problem between the classification and the localization tasks. In this article, we present a two-stage oriented object detection method, termed dual-aligned oriented detector (DODet), toward evading the aforementioned problems of spatial and feature misalignments. In DODet, the first stage is an oriented proposal network (OPN), which generates high-quality oriented proposals via a novel representation scheme of oriented objects. The second stage is a localization-guided detection head (LDH) that aims at alleviating the feature misalignment between classification and localization. Comprehensive and extensive evaluations on three benchmarks, including DIOR-R, DOTA, and HRSC2016, indicate that our method could obtain consistent and substantial gains compared with the baseline method. The source code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yanqingyao1994/DODet</uri> .
Initial Coin Offering (ICO) Success and Post-ICO Performance

We compile a comprehensive data set of initial coin offerings (ICOs) from 19 data sources including 11 ICO aggregators. We alleviate severe limitations of available ICO data by performing the first systematic analysis of ICO data quality. We use our data set to study determinants of ICO funding success as well as post-ICO operating performance and provide evidence on some novel determinants of initial and longer-term ICO success. Finally, we show that post-ICO operating performance is positively associated with contemporaneous token returns and is generally negatively associated with token return volatility, suggesting that post-ICO operating performance has financial ramifications. This paper was accepted by Kay Giesecke, finance. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2022.4312 .
Scalable Vision Transformers with Hierarchical Pooling

The recently proposed Visual image Transformers (ViT) with pure attention have achieved promising performance on image recognition tasks, such as image classification. However, the routine of the current ViT model is to maintain a full-length patch sequence during inference, which is redundant and lacks hierarchical representation. To this end, we propose a Hierarchical Visual Transformer (HVT) which progressively pools visual tokens to shrink the sequence length and hence reduces the computational cost, analogous to the feature maps downsampling in Convolutional Neural Networks (CNNs). It brings a great benefit that we can increase the model capacity by scaling dimensions of depth/width/resolution/patch size without introducing extra computational complexity due to the reduced sequence length. Moreover, we empirically find that the average pooled visual tokens contain more discriminative information than the single class token. To demonstrate the improved scalability of our HVT, we conduct extensive experiments on the image classification task. With comparable FLOPs, our HVT outperforms the competitive baselines on ImageNet and CIFAR-100 datasets. Code is available at https://github.com/MonashAI/HVT.
The algorithm of stereo vision and shape from shading based on endoscope imaging

With medical endoscopic equipment development, minimally invasive surgery (MIS) has gradually become an essential technical means in daily medical practice. In recent years, minimally invasive surgery has been widely used because of its small incision and quick recovery. However, at the same time, minimally invasive surgery has put forward higher requirements for the operator. A 3D reconstruction framework combined with stereo vision and Shape from Shading (SFS) was proposed to improve endoscopic imaging accuracy and reduce the difficulty of minimally invasive surgery. This paper constructs a joint objective function based on the improved SFS and the classical stereo matching method. The optimization algorithm of the depth map under the joint objective function is given. Finally, the experimental verification of the joint reconstruction algorithm is carried out. The joint reconstruction framework's effectiveness is verified by qualitative and quantitative comparison and analysis based on the silica-gel-heart model and real-heart image datasets. The experimental results show that the joint reconstruction framework can restore the heart surface shape as a whole and retain the local details. Compared with the classical stereo vision and SFS methods, the proposed joint reconstruction method has better robustness and reconstruction accuracy.
MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA methods are far from meeting the needs of predicting accurate quality scores on GAN-based distortion images. To this end, we propose Multi-dimension Attention Network for no-reference Image Quality Assessment (MANIQA) to improve the performance on GAN-based distortion. We firstly extract features via ViT, then to strengthen global and local interactions, we propose the Transposed Attention Block (TAB) and the Scale Swin Transformer Block (SSTB). These two modules apply attention mechanisms across the channel and spatial dimension, respectively. In this multi-dimensional manner, the modules cooperatively increase the interaction among different regions of images globally and locally. Finally, a dual branch structure for patch-weighted quality prediction is applied to predict the final score depending on the weight of each patch’s score. Experimental results demonstrate that MANIQA outperforms state-of-the-art methods on four standard datasets (LIVE, TID2013, CSIQ, and KADID-10K) by a large margin. Besides, our method ranked first place in the final testing phase of the NTIRE 2022 Perceptual Image Quality Assessment Challenge Track 2: No-Reference. Codes and models are available at https://github.com/IIGROUP/MANIQA.
Trusted Multi-View Classification With Dynamic Evidential Fusion

Existing multi-view classification algorithms focus on promoting accuracy by exploiting different views, typically integrating them into common representations for follow-up tasks. Although effective, it is also crucial to ensure the reliability of both the multi-view integration and the final decision, especially for noisy, corrupted and out-of-distribution data. Dynamically assessing the trustworthiness of each view for different samples could provide reliable integration. This can be achieved through uncertainty estimation. With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed TMC can promote classification reliability by considering evidence from each view. Specifically, we introduce the variational Dirichlet to characterize the distribution of the class probabilities, parameterized with evidence from different views and integrated with the Dempster-Shafer theory. The unified learning framework induces accurate uncertainty and accordingly endows the model with both reliability and robustness against possible noise or corruption. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.
Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition

In this paper, we present Vision Permutator, a conceptually simple and data efficient MLP-like architecture for visual recognition. By realizing the importance of the positional information carried by 2D feature representations, unlike recent MLP-like models that encode the spatial information along the flattened spatial dimensions, Vision Permutator separately encodes the feature representations along the height and width dimensions with linear projections. This allows Vision Permutator to capture long-range dependencies and meanwhile avoid the attention building process in transformers. The outputs are then aggregated in a mutually complementing manner to form expressive representations. We show that our Vision Permutators are formidable competitors to convolutional neural networks (CNNs) and vision transformers. Without the dependence on spatial convolutions or attention mechanisms, Vision Permutator achieves 81.5% top-1 accuracy on ImageNet without extra large-scale training data (e.g., ImageNet-22k) using only 25M learnable parameters, which is much better than most CNNs and vision transformers under the same model size constraint. When scaling up to 88M, it attains 83.2% top-1 accuracy, greatly improving the performance of recent state-of-the-art MLP-like networks for visual recognition. We hope this work could encourage research on rethinking the way of encoding spatial information and facilitate the development of MLP-like models. PyTorch/MindSpore/Jittor code is available at https://github.com/Andrew-Qibin/VisionPermutator.
Convolutional Prototype Network for Open Set Recognition

Despite the success of convolutional neural network (CNN) in conventional closed-set recognition (CSR), it still lacks robustness for dealing with unknowns (those out of known classes) in open environment. To improve the robustness of CNN in open-set recognition (OSR) and meanwhile maintain its high accuracy in CSR, we propose an alternative deep framework called convolutional prototype network (CPN), which keeps CNN for representation learning but replaces the closed-world assumed softmax with an open-world oriented and human-like prototype model. To equip CPN with discriminative ability for classifying known samples, we design several discriminative losses for training. Moreover, to increase the robustness of CPN for unknowns, we interpret CPN from the perspective of generative model and further propose a generative loss, which is essentially maximizing the log-likelihood of known samples and serves as a latent regularization for discriminative learning. The combination of discriminative and generative losses makes CPN a hybrid model with advantages for both CSR and OSR. Under the designed losses, the CPN is trained end-to-end for learning the convolutional network and prototypes jointly. For application of CPN in OSR, we propose two rejection rules for detecting different types of unknowns. Experiments on several datasets demonstrate the efficiency and effectiveness of CPN for both CSR and OSR tasks.
Challenges of Cloud Computing Adoption From the TOE Framework Perspective

Cloud computing can be classified as a third-generation computing platform which refers to on-demand delivery of computing infrastructure and services via a network, usually the Internet. Cloud computing promises to provide several advantages to its adopters such as: cost advantage, availability, scalability, flexibility, reduced time to market and dynamic access to computational resources. Notwithstanding the numerous advantages of cloud computing, its implementation and adoption in developing countries is still limited and surrounded by variety of issues. Hence, the main objective of this article is to identify the main challenges facing the utilization of these services in developing countries, particularly Jordan. To achieve the above-mentioned objective, six in-depth interviews with ICT officials and experts in the domain of cloud computing were used as the main data collection method. The challenges of cloud computing adoption emerged in this study are classified into technological, organizational and environmental factors.
A Data Set for Airborne Maritime Surveillance Environments

This paper presents a data set with surveillance imagery over the sea captured by a small size UAV. This data set presents the object examples ranging from cargo ships, small boats, life rafts to hydrocarbon slick. The video sequences were captured using different types of cameras, at different heights, and different perspectives. The data set also contains thousands of labels with positions of objects of interest. This was only possible to achieve with the labeling tool also described in this paper. Additionally, using standard evaluation frameworks, we establish a baseline of results using algorithms developed by the authors, which are better adapted to the maritime environment.
Privacy-Preserving Data Processing with Flexible Access Control

Cloud computing provides an efficient and convenient platform for cloud users to store, process and control their data. Cloud overcomes the bottlenecks of resource-constrained user devices and greatly releases their storage and computing burdens. However, due to the lack of full trust in cloud service providers, the cloud users generally prefer to outsource their sensitive data in an encrypted form, which, however, seriously complicates data processing, analysis, as well as access control. Homomorphic encryption (HE) as a single key system cannot flexibly control data sharing and access after encrypted data processing. How to realize various computations over encrypted data in an efficient way and at the same time flexibly control the access to data processing results has been an important challenging issue. In this paper, we propose a privacy-preserving data processing scheme with flexible access control. With the cooperation of a data service provider (DSP) and a computation party (CP), our scheme, based on Paillier's partial homomorphic encryption (PHE), realizes seven basic operations, i.e., Addition, Subtraction, Multiplication, Sign Acquisition, Absolute, Comparison, and Equality Test, over outsourced encrypted data. In addition, our scheme, based on the homomorphism of attribute-based encryption (ABE), is also designed to support flexible access control over processing results of encrypted data. We further prove the security of our scheme and demonstrate its efficiency and advantages through simulations and comparisons with existing work.
Reversible Data Hiding in Color Image With Grayscale Invariance

Different from all the previous reversible data hiding schemes, a completely novel one for the color image is proposed, which reversibly embeds messages into the color host image without modifying its corresponding gray version. The property of grayscale invariance is valuable, because many applications and image processing algorithms for color images are based on the corresponding gray versions, such as black and white printing, producing reading materials for color blind people, single-channel image processing, and so on. Thus, in terms of these applications and image processing algorithms, the presented scheme will make the generated color marked image be free for its further uses. In this paper, the unchanged gray version is utilized efficiently in both the embedding processes and the extracting processes. Messages are embedded into the red and blue channels of color image, and then the green channel is adjusted adaptively to remove the offsets from the gray version caused by modifying its red and blue channels. To return the adjusted green channel, error correcting bits guaranteeing the reversibility are regarded as one part of payloads to be recursively embedded. Therefore, the reversibility and the property of grayscale invariance are both achieved.
Thermal infrared multispectral remote sensing of lithology and mineralogy based on spectral properties of materials

The major minerals forming the Earth’s crust exhibit characteristic spectral properties in the thermal infrared (TIR; 7–14 µm) region of the spectrum, although most of them have few features in the visible and near infrared (0.4–2.5 µm) region. As there exists a good atmospheric window in the TIR spectral region, a number of multispectral TIR remote sensors have been developed by various agencies the world over for geological applications. This paper reviews such type of remote sensing systems, and especially focuses attention on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The spectroscopic basis in analyzing and interpreting the multispectral TIR remote sensing data is discussed. It includes a review of the historical studies, methodology of spectral measurements and comparison of spectra measured in laboratory/field with that derived from the remotely sensed data. Subsequently, the studies of lithology and mineralogy using TIR multispectral remote sensing data by various workers are reviewed. The various spectral indices proposed by the authors for extracting information on the mineralogy or chemistry of the target geological materials are summarized.
SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live Telepresence

Real-time 3D scene reconstruction from RGB-D sensor data, as well as the exploration of such data in VR/AR settings, has seen tremendous progress in recent years. The combination of both these components into telepresence systems, however, comes with significant technical challenges. All approaches proposed so far are extremely demanding on input and output devices, compute resources and transmission bandwidth, and they do not reach the level of immediacy required for applications such as remote collaboration. Here, we introduce what we believe is the first practical client-server system for real-time capture and many-user exploration of static 3D scenes. Our system is based on the observation that interactive frame rates are sufficient for capturing and reconstruction, and real-time performance is only required on the client site to achieve lag-free view updates when rendering the 3D model. Starting from this insight, we extend previous voxel block hashing frameworks by introducing a novel thread-safe GPU hash map data structure that is robust under massively concurrent retrieval, insertion and removal of entries on a thread level. We further propose a novel transmission scheme for volume data that is specifically targeted to Marching Cubes geometry reconstruction and enables a 90% reduction in bandwidth between server and exploration clients. The resulting system poses very moderate requirements on network bandwidth, latency and client-side computation, which enables it to rely entirely on consumer-grade hardware, including mobile devices. We demonstrate that our technique achieves state-of-the-art representation accuracy while providing, for any number of clients, an immersive and fluid lag-free viewing experience even during network outages.
Object Detection in a Maritime Environment: Performance Evaluation of Background Subtraction Methods

This study provides a benchmarking of the performance of 23 classical and state-of-the-art background subtraction algorithms on visible range and near infrared range videos in the Singapore-Maritime dataset [1].Importantly, our study indicates the limitations of the conventional performance evaluation criteria for maritime vision and proposes new performance evaluation criteria better suited to this problem.The study provides insight into the specific challenges of background subtraction in maritime vision.We identify four open challenges that plague background subtraction methods in maritime scenario.These include spurious dynamics of water, wakes, ghost effect, and multiple detections.Poor recall and extremely poor precision of all the 23 methods, which have been otherwise successful for other challenging background subtraction situations, allude to the need of new background subtraction methods custom-designed for maritime vision.
Topic-based knowledge mining of online student reviews for strategic planning in universities

Over the past few years, studies observe a continuous decline in university enrollment and retention rates resulting in millions of dollars in lost revenue. Past research shows that 95% of the students rely heavily on the positive word-of-mouth to select a college. Especially, with the advent of Web 2.0 tools, students are able to make informed decisions due to increased awareness through online reviews. Academic institutions can also leverage this information to understand and improve their students’ perception. The objective of this paper is to identify the current strengths, weaknesses, opportunities and threats (SWOT) of a university by analyzing online student reviews using text analytics. Our proposed approach integrates four different techniques: topic modeling, sentiment analysis, root cause and SWOT analyses. First, we introduce an ensemble of Latent Dirichlet Allocation (E-LDA) topic models to automatically identify the key features (topics) that are predominantly discussed by students and categorize each review sentence into the most related topic. We then detect the opinion associated with each sentence (positive, negative and neutral) using sentiment analysis. Finally, a topic-based opinion summary (TOS) for a university is established to identify its strengths and weaknesses from the students’ perspective, and the opportunities and threats are determined by analyzing the TOS of the competitors (or other similar institutions). A case study is used to illustrate the feasibility and application of the proposed approach. The results indicate that the proposed method provides an efficient and economic performance summary of a university and its competitors, and could help its leaders in recruitment and retention efforts.
A swarm intelligence-based algorithm for the set-union knapsack problem

Swarm intelligence-based methods offer notable opportunities in problem solving. Although they do not guarantee optimality, such methods are shown to be promising particularly for non-convex and non-differentiable problem spaces. The present study proposes a simple yet effective binary swarm intelligence technique that is based on Genetic Algorithm and Particle Swarm Optimization. The performance of the introduced method is analysed on a recently caught on binary problem, namely, the Set-union Knapsack Problem that has a wide range of real-life applications including information security systems. It is put forth in the present contribution that the effectiveness of the proposed approach indeed stems from the easiness in implementation. It does not need transfer functions and further local search procedures in the mainstream, which usually add to the required CPU time. As secondarily, an optional mutation procedure that exponentially decreases the introduced diversity to the population is developed. Thus, while local optima traps are avoided at the earlier iterations, a more intensified search is encouraged towards the end. All available benchmarking problems are solved by the proposed approach. As shown by the experimental study and statistical tests, the proposed approach can achieve significant improvements over the published results. Moreover, it is demonstrated that the developed mutation procedure, which can easily be adopted in any metaheuristic algorithm, significantly contributes to the performance of the proposed algorithm.