Reviewerly/exHarmony huggingface dataset:
Classification of Solar Radio Spectrum Based on Swin Transformer

Solar radio observation is a method used to study the Sun. It is very important for space weather early warning and solar physics research to automatically classify solar radio spectrums in real time and judge whether there is a solar radio burst. As the number of solar radio burst spectrums is small and uneven, this paper proposes a classification method for solar radio spectrums based on the Swin transformer. First, the method transfers the parameters of the pretrained model to the Swin transformer model. Then, the hidden layer weights of the Swin transformer are frozen, and the fully connected layer of the Swin transformer is trained on the target dataset. Finally, parameter tuning is performed. The experimental results show that the method can achieve a true positive rate of 100%, which is more accurate than previous methods. Moreover, the number of our model parameters is only 20 million, which is 80% lower than that of the traditional VGG16 convolutional neural network with more than 130 million parameters.
Polyp Detection in Colonoscopy Images using Improved Deformable DETR

Early detection of colorectal cancer (CRC) can lead to a better prognosis and clinical management. This can be achieved by detecting polyps, the precursor to such cancer. Therefore, in this work, we have proposed a deep learning based polyp detection approach using Deformable DETR. We introduced a loss function, replacing the conventional loss function used in the aforementioned model. We further experimented with the different layers in the model's architecture and reported the optimal number of layers for our work. Exhaustive experiments and analyses are done with different loss functions, augmentations, and TTA (Test Time Augmentation). The results show improvement over the state-of-the-art methods and provide more precise and compact bounding boxes that are close to the clinically validated ground truth.
An MPI-Parallel Algorithm for Static and Dynamic Top-k Harmonic Centrality

Analyzing large graphs in parallel has received considerable attention recently due to ever increasing data set sizes. Centrality measures indicate the importance of vertices (or edges) and belong to the most widely used analytic kernels. Harmonic centrality is a popular vertex centrality measure with many desirable properties. Since most of the applications of vertex centrality rely only on the ranking of vertices and not on their exact centrality scores, previous research has considered various algorithms that can quickly determine a ranking of the top-k vertices with highest harmonic centrality. Such algorithms are available for many types of real-world graphs, including dynamic graphs. Yet, no attempts have been made to efficiently parallelize these top-k algorithms (besides naive implementations based on a global lock). In this paper, we propose an MPI-distributed algorithm for (dynamic) top-k harmonic centrality. Our algorithm exploits an algebraic BFS technique and batching to parallelize the approximation of centrality scores of multiple vertices. Likewise, we use algebraic techniques to compute various bounds and heuristics that are necessary to obtain a fast top-k algorithm. Experiments demonstrate that our MPI-parallel algorithm outperforms existing implementations. Consequently, our new approach allows the computation of top-k harmonic centrality on graphs that are substantially larger.
LMENet: A lightweight multiscale efficient convolutional neural network for image denoising

Various convolutional neural network (CNN) based image denoising methods have been proposed and achieved their attractive denoising performances in past decades. These methods benefit from carefully designed network structures via their increasing network depth and width. Since enlarged networks suffer from numerous parameters and cause heavy computational burden, their deployment application is limited on portable devices. To tackle this problem, we propose a lightweight multiscale efficient convolutional neural network (LMENet). It uses a set of cascaded residual networks (RNs) as its backbone network, where multiscale features are exported from these RNs. By fusing these features with a dual attention mechanism, denoised images are finally reconstructed. The experiments show that, compared with some state-of-the-art methods, our LMENet has a competitive performance for denoising in both additive white Gaussian noise (AWGN) and real noise tests. More importantly, it has approximate one-eighth of the parameter size compared with the existing methods. It demonstrates the computational efficiency of the LMENet, while achieving similar PSNR results.
CNN-based Baybayin Character Recognition on Android System

Baybayin is an old Philippine script that has become part of the Filipino heritage; however, due to the difficulty of the writing system, most people nowadays do not use it anymore. That is why this paper proposed an improved CNN model for classifying the Philippine Script Baybayin in order to help people to learn this old script. The model was systematically developed through experimentation and hyperparameter tuning with MobileNetV2 as the baseline. Sample handwritten Baybayin characters were obtained from Kaggle and Mendeley Data for training. It was evaluated that the proposed model has training and validation accuracies of 97.04% and 96.02%, respectively, in contrast to the MobileNetV2, which has slightly lower accuracies and larger fluctuations in its validation accuracy. Aside from that, this model only has total parameters of 892,255 and a size of 3575.944 kB. Lastly, through mobile deployment testing, it was concluded that only the proposed model is working, as the MobileNetV2 was not able to accurately detect the diacritics.
Admissible rules for six intuitionistic modal logics

This paper characterizes the admissible rules for six interesting intuitionistic modal logics: iCK4, iCS4≡IPC, strong Löb logic iSL, modalized Heyting calculus mHC, Kuznetsov-Muravitsky logic KM, and propositional lax logic PLL. Admissible rules are rules that can be added to a logic without changing the set of theorems of the logic. We provide a Gentzen-style proof theory for admissibility that combines methods known for intuitionistic propositional logic and classical modal logic. From this proof theory, we extract bases for the admissible rules, i.e., sets of admissible rules that derive all other admissible rules. In addition, we show that admissibility is decidable for these logics.
Non-Graph Data Clustering via $\mathcal {O}(n)$ Bipartite Graph Convolution

Since the representative capacity of graph-based clustering methods is usually limited by the graph constructed on the original features, it is attractive to find whether graph neural networks (GNNs) can be applied to augment the capacity. The core problems mainly come from two aspects: (1) the graph is unavailable in the most clustering scenes so that how to construct high-quality graphs on the non-graph data is usually the most important part; (2) given n samples, the graph-based clustering methods usually consume at least $\mathcal O(n^2)$ time to build graphs and the graph convolution requires nearly $\mathcal O(n^2)$ for a dense graph and $\mathcal O(|\mathcal{E}|)$ for a sparse one with $|\mathcal{E}|$ edges. Accordingly, both graph-based clustering and GNNs suffer from the severe inefficiency problem. To tackle these problems, we propose a novel clustering method, AnchorGAE, with the self-supervised estimation of graph and efficient graph convolution. We first show how to convert a non-graph dataset into a graph dataset, by introducing the generative graph model and anchors. We then show that the constructed bipartite graph can reduce the computational complexity of graph convolution from $\mathcal O(n^2)$ and $\mathcal O(|\mathcal{E}|)$ to $\mathcal O(n)$. The succeeding steps for clustering can be easily designed as $\mathcal O(n)$ operations. Interestingly, the anchors naturally lead to siamese architecture with the help of the Markov process. Furthermore, the estimated bipartite graph is updated dynamically according to the features extracted by GNN, to promote the quality of the graph. However, we theoretically prove that the self-supervised paradigm frequently results in a collapse that often occurs after 2-3 update iterations in experiments, especially when the model is well-trained. A specific strategy is accordingly designed to prevent the collapse.
Articulating Institutionalization: How U.S. Academic Faculty Organize Work to Deposit Data and the Impacts on Long-Term Research Data Sustainability

With the ‘data deluge’ leading to an institutionalized research environment for data management, U.S. academic faculty have increasingly faced pressure to make data suitable for deposit into research data repositories, which, in turn, is engendering a new set of practices to adapt federal policy to local circumstances. When these practices involve reorganizing workflows to align the goals of local and institutional stakeholders, we might call them ‘data articulations.’ This dissertation uses interviews to establish a grounded understanding of the data articulations behind deposit in 3 essays: (1) a phenomenological study of genomics faculty data management practices; (2) a grounded theory study developing a theory of data deposit as articulation work; (3) a comparative case study identifying the factors associated with ‘articulating institutionalization’ in high-institutionalization and low-institutionalization of data deposit contexts. Expected findings include a framework of datarticulations and empirical insight into how faculty organize data management work.
The Futures of Hybrid Work - Participatory Futuring in Practice

Throughout the last years, but especially with the outbreak of the Covid-19 pandemic, our workplaces and way of 'going to work' has changed a lot. One might even claim that the nature of work is 'in limbo', a transitional phase between the traditional on-site workplace and hybrid and remote work conditions. This overview of my doctoral research starts with a number of future oriented questions regarding collaboration and human interactions, embodied presence and socio-materiality in hybrid and remote work environments. The hypothesis is that a participatory futuring approach can engage stakeholders from different industries in a collective development of alternative futures and thereby inform the re-thinking process around remote and hybrid work conditions within and across industries. Hence, several empirical studies are proposed in order to (1) understand hybrid work settings and its challenges (2) create knowledge around possible futures of hybrid work and (3) investigate the strategic value of participatory futuring methods in industrial settings.
Transfer learning supported accurate assessment of multiclass cervix type images

Cervical cancer predominately affects women compared to lung, breast and endometrial cancer. Premature stage identification and proper treatment of this cancer may lead to 100% survival rate. The cervix type is very prominent in the detailed diagnosis of cervical cancer. High expertise and experienced gynecologist are required for an accurate diagnosis of cervical cancer. To reduce their burden, a model is proposed, based on deep learning that automatically classifies the cervix types. This paper presents Modified Deep Convolutional Neural Networks namely Modified VGG16 (MVGG16), Modified VGG19 (MVGG19), Modified ResNet50 (MRN50), Modified InceptionV3 (MIV3), and Modified InceptionResNetV2 (MIRNV2) for the classification of cervix type images. These modified networks are implemented using a Multiclass Support Vector Machine classifier. The performance metrics are tabulated and compared with pre-trained models. The simulation results show that MIRNV2 achieves the best performance compared to other models with an overall Accuracy of 92.91% and a Kappa score of 0.88. MIRNV2 model also gives better classification accuracy of 96.62% for type 1, 93.58% for type 2, and 95.61% for type 3 cervix images. Hence, this facilitates the application of MIRNV2 as a diagnostic tool to assist the gynecologist in the classification of cervix type images.
Fault tolerance in qudit circuit design

The efficient decomposition of multi-controlled gates is a significant factor in quantum compiling, both in circuit depth and T-gate count. Recent work has demonstrated that qudits have the potential to reduce resource requirements from linear to logarithmic depth and to avoid fractional phase rotations. Here we argue, based on the scaling of decoherence in high-index states, that circuit depth is not the primary factor, and that both the choice of entangling gate and interaction network topology act together to determine the spread of errors and ultimate failure rate in a circuit. We further show that for certain linear-depth circuits, additional error mitigation is possible via selective application of resources.
Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications

Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including the algorithm’s high computation cost. In other words, the algorithm may be impractical for real-time or near-real-time implementation. Furthermore, clustering algorithms necessitate the tuning of hyperparameters in order to fit the dataset. In this paper, we approach clustering moving points using our proposed Adaptive Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, which is an implementation of an adaptive approach to building the minimum spanning tree. We switch between the Boruvka and the Prim algorithms as a means to build the minimum spanning tree, which is one of the most expensive components of the HDBSCAN. The Adaptive HDBSCAN yields an improvement in execution time by 5.31% without depreciating the accuracy of the algorithm. The motivation for this research stems from the desire to cluster moving points on video. Cameras are used to monitor crowds and improve public safety. We can identify potential risks due to overcrowding and movements of groups of people by understanding the movements and flow of crowds. Surveillance equipment combined with deep learning algorithms can assist in addressing this issue by detecting people or objects, and the Adaptive HDBSCAN is used to cluster these items in real time to generate information about the clusters.
Analysis of Scale Sensitivity of Ship Detection in an Anchor-Free Deep Learning Framework

Ship detection is an important task in sea surveillance. In the past decade, deep learning-based methods have been proposed for ship detection from images and videos. Convolutional features are observed to be very effective in representing ship objects. However, the scales of convolution often lead to different capacities of feature representation. It is unclear how the scale influences the performance of deep learning methods in ship detection. To this end, this paper studies the scale sensitivity of ship detection in an anchor-free deep learning framework. Specifically, we employ the classical CenterNet as the base and analyze the influence of the size, the depth, and the fusion strategy of convolution features on multi-scale ship target detection. Experiments show that, for small targets, the features obtained from the top-down path fusion can improve the detection performance more significantly than that from the bottom-up path fusion; on the contrary, the bottom-up path fusion achieves better detection performance on larger targets.
360BroadView

360-degree video is becoming an integral part of our content consumption through both video on demand and live broadcast services. However, live broadcast is still challenging due to the huge network bandwidth cost if all 360-degree views are delivered to a large viewer population over diverse networks. In this paper, we present 360BroadView, a viewer management approach to viewport prediction in 360-degree video live broadcast. We make some high-bandwidth network viewers be leading viewers to help the others (lagging viewers) predict viewports during 360-degree video viewing and save bandwidth. Our viewer management maintains the leading viewer population despite viewer churns during live broadcast, so that the system keeps functioning properly. Our evaluation shows that 360BroadView maintains the leading viewer population at a minimal yet necessary level for 97 percent of the time.
Two-Layer Learning-Based P-Frame Coding with Super-Resolution and Content-Adaptive Conditional ANF

Deep-learning-based video compression technique has been rapidly growing in recent years. This paper adopts the Conditional Augmented Normalizing Flow video codec (CANF-VC) [8] as our basic system. To improve the quality of the condition signal (image) for CANF, we propose a two-layer structure learning-based video codec. At low cost of extra bit rate, the low-resolution base layer provides side information to improve the quality of motion-compensated reference frame through a super-resolution module with a merge-net. In addition, the base layer also provides information to the skip-mask generator. The skip-mask guides the coding mechanism to reduce the transmitted samples for the high-resolution enhancement layer. The experiment results indicate that the proposed two-layer coding scheme can provide 22.19% PSNR BD-Rate saving and 49.59% MS-SSIM BD-Rate saving over H.265 (HM 16.20) on the UVG test sequences.
Institutional environment quality and the longevity of billionaire entrepreneurs

Using self-made billionaire entrepreneurs in China as a proxy for large entrepreneurs, we examine the impact of institutional environment quality on the longevity of such entrepreneurs. While property rights institutions and contracting institutions are important in explaining macroeconomic growth and small firm growth, we find no evidence that they matter to the longevity of billionaire entrepreneurs. However, we find access to finance to be important to the longevity of billionaire entrepreneurs. Our results help better understand the relative importance of various institutional environment forces to the continuous success of billionaire entrepreneurs.
Thermodynamic optimization of quantum algorithms: On-the-go erasure of qubit registers

We consider two bottlenecks in quantum computing: limited memory size and noise caused by heat dissipation. Trying to optimize both, we investigate "on-the-go erasure" of quantum registers that are no longer needed for a given algorithm: freeing up auxiliary qubits as they stop being useful would facilitate the parallelization of computations. We study the minimal thermodynamic cost of erasure in these scenarios, applying results on the Landauer erasure of entangled quantum registers. For the class of algorithms solving the Abelian hidden subgroup problem, we find optimal on-the-go erasure protocols. We conclude that there is a trade-off: if we have enough partial information about a problem to build efficient on-the-go erasure, we can use it to instead simplify the algorithm, so that fewer qubits are needed to run the computation in the first place. We provide explicit protocols for these two approaches.
ESPnet-ONNX: Bridging a Gap Between Research and Production

In the field of deep learning, researchers often focus on inventing novel neural network models and improving benchmarks. In contrast, application developers are interested in making models suitable for actual products, which involves optimizing a model for faster inference and adapting a model to various platforms (e.g., C++ and Python). In this work, to fill the gap between the two, we establish an effective procedure for optimizing a PyTorch-based research-oriented model for deployment, taking ESPnet, a widely used toolkit for speech processing, as an instance. We introduce different techniques to ESPnet, including converting a model into an ONNX format, fusing nodes in a graph, and quantizing parameters, which lead to approximately 1.3-2x speedup in various tasks (i.e., ASR, TTS, speech translation, and spoken language understanding) while keeping its performance without any additional training. Our ESPnet-ONNX will be publicly available at https://github.com/espnet/espnet_onnx.
Proposal of Associative Watermarking Method

We propose a novel type of zero-watermarking method that incorporates associative memory models. Because the zero-watermarking method does not embed a watermark directly in an image, it avoids degrading the original image. However, as a watermark is associated with each image, it is difficult to manage the associations when dealing with a large number of images. Moreover, the conventional zero-watermarking method cannot correct the watermark when an image is degraded, and the watermark's length must be equal to that of the feature vector extracted from an image. Hence, we propose a novel management scheme that uses both a hetero-associative memory model and an auto-associative memory model. The proposed method can manage a large number of mappings between images and watermarks via the hetero-associative memory model, while also eliminating the watermark length restriction. Furthermore, even if an image is heavily degraded, the auto-associative memory model can correct watermark errors. The proposed method was evaluated in the case of JPEG compression, and we found that it can sufficiently reduce errors in watermarking.
BEAM - An Algorithm for Detecting Phishing Link

This paper aims to develop an attention-based phishing detector by performing sub-word tokenization and fme-tuning the Bidirectional Encoder Representation from Transformers (BERT) model. It is called BERT embedding attention model (BEAM). Our proposed BEAM method contains five building blocks: a data pre-processing block to extract components according to the URL structure, a tokenization block to tokenize the individual URL components into a number of sub-words, an embedding block to produce a numerical sequence representation, an encoding block to give a context feature vector and a classification block for phishing URL detection. The subword tokenization allows us to characterize the relationship among connecting subwords, while the attention mechanism in the BERT allows the proposed model to focus selectively on important parts contributing to phishing behavior. We have compared our proposed BEAM method with other existing state-of-the-art phishing detection methods such as CNN, Bi-LSTM, and machine learning models (random forest and XGBoost). Experimental results confirm that our proposed BEAM method effectively detects phishing links and outperforms other existing methods.