Reviewerly/exHarmony huggingface dataset:
Ancient Egyptian Hieroglyphs Segmentation and Classification with Convolutional Neural Networks

Deep Learning is expanding in every domain of knowledge, allowing specialists to build tools to support their work in fields apparently unrelated to information technology. In this study, we exploit this opportunity by focusing on ancient Egyptian hieroglyphic texts and inscriptions. We investigate the ability of several convolutional neural networks (CNNs) to segment glyphs and classify images of ancient Egyptian hieroglyphs derived from various image datasets. Three well-known CNN architectures (ResNet-50, Inception-v3, and Xception) were considered for classification and trained on the supplied pictures using both the transfer learning and training from scratch paradigms. Furthermore, we constructed a specifically devoted CNN, termed Glyphnet, by changing the architecture of one of the prior networks and customizing its complexity to our classification goal. The suggested Glyphnet outperformed the others in terms of performance, ease of training, and computational savings, as judged by established measures. The ancient hieroglyphs segmentation was faced in parallel, using a deep neural network architecture known as Mask-RCNN. This network was trained to segment the glyphs, identifying the bounding box, which will be the input to a network for classification. Even though we focus here on single hieroglyph segmentation and classification tasks, the application of Deep Learning techniques in the Egyptological field opens up new and beneficial opportunities. In this light, the proposed work can be viewed as a jumping-off point for much more complex goals such as hieroglyphic sign coding, recognition, and transliteration; toposyntax of hieroglyphic signs combined to form words; linguistics analysis of hieroglyphic texts; recognition of corrupt, rewritten, and erased signs, and even identification of the scribe’s “hand” or sculptor’s school. This work shows how the ancient Egyptian hieroglyphs identification task can be supported by the Deep. Learning paradigm, laying the foundation for developing novel information tools for automatic documents recognition, classification and, most importantly, the language translation task.
VOSA: Verifiable and Oblivious Secure Aggregation for Privacy-Preserving Federated Learning

Federated learning has emerged as a promising paradigm by collaboratively training a global model through sharing local gradients without exposing raw data. However, the shared gradients pose a threat to privacy leakage of local data. The central server may forge the aggregated results. Besides, it is common that resource-constrained devices drop out in federated learning. To solve these problems, the existing solutions consider either only efficiency, or privacy preservation. It is still a challenge to design a verifiable and lightweight secure aggregation with drop-out resilience for large-scale federated learning. In this article, we propose VOSA, an efficient verifiable and oblivious secure aggregation protocol for privacy-preserving federated learning. We exploit aggregator oblivious encryption to efficiently mask users’ local gradients. The central server performs aggregation on the obscured gradients without revealing the privacy of local data. Meanwhile, each user can efficiently verify the correctness of the aggregated results. Moreover, VOSA adopts a dynamic group management mechanism to tolerate users’ dropping out with no impact on their participation in future learning process. Security analysis shows that the VOSA can guarantee the security requirements of privacy-preserving federated learning. The extensive experimental evaluations conducted on real-world datasets demonstrate the practical performance of the proposed VOSA with high efficiency.
Approximate Differentiable Rendering with Algebraic Surfaces

Differentiable renderers provide a direct mathematical link between an object's 3D representation and images of that object. In this work, we develop an approximate differentiable renderer for a compact, interpretable representation, which we call Fuzzy Metaballs. Our approximate renderer focuses on rendering shapes via depth maps and silhouettes. It sacrifices fidelity for utility, producing fast runtimes and high-quality gradient information that can be used to solve vision tasks. Compared to mesh-based differentiable renderers, our method has forward passes that are 5x faster and backwards passes that are 30x faster. The depth maps and silhouette images generated by our method are smooth and defined everywhere. In our evaluation of differentiable renderers for pose estimation, we show that our method is the only one comparable to classic techniques. In shape from silhouette, our method performs well using only gradient descent and a per-pixel loss, without any surrogate losses or regularization. These reconstructions work well even on natural video sequences with segmentation artifacts. Project page: https://leonidk.github.io/fuzzy-metaballs
Acknowledging the Unknown for Multi-label Learning with Single Positive Labels

Due to the difficulty of collecting exhaustive multi-label annotations, multi-label datasets often contain partial labels. We consider an extreme of this weakly supervised learning problem, called single positive multi-label learning (SPML), where each multi-label training image has only one positive label. Traditionally, all unannotated labels are assumed as negative labels in SPML, which introduces false negative labels and causes model training to be dominated by assumed negative labels. In this work, we choose to treat all unannotated labels from an alternative perspective, i.e. acknowledging they are unknown. Hence, we propose entropy-maximization (EM) loss to attain a special gradient regime for providing proper supervision signals. Moreover, we propose asymmetric pseudo-labeling (APL), which adopts asymmetric-tolerance strategies and a self-paced procedure, to cooperate with EM loss and then provide more precise supervision. Experiments show that our method significantly improves performance and achieves state-of-the-art results on all four benchmarks. Code is available at https://github.com/Correr-Zhou/SPML-AckTheUnknown .
Holocurtains: Programming Light Curtains via Binary Holography

Light curtain systems are designed for detecting the presence of objects within a user-defined 3D region of space, which has many applications across vision and robotics. However, the shape of light curtains have so far been limited to ruled surfaces, i.e., surfaces composed of straight lines. In this work, we propose Holocurtains: a light-efficient approach to producing light curtains of arbitrary shape. The key idea is to synchronize a rolling-shutter camera with a 2D holographic projector, which steers (rather than block) light to generate bright structured light patterns. Our prototype projector uses a binary digital micromirror device (DMD) to generate the holographic interference patterns at high speeds. Our system produces 3D light curtains that cannot be achieved with traditional light curtain setups and thus enables all-new applications, including the ability to simultaneously capture multiple light curtains in a single frame, detect subtle changes in scene geometry, and transform any 3D surface into an optical touch interface.
Smart Money Wasting: Analyzing Gas Cost Drivers of Ethereum Smart Contracts

Users must pay a fee depending on resource consumption when using smart contracts on the Ethereum blockchain. As even the most basic operations cost several dollars under moderate network load, developers may actively reduce user-paid fees by optimizing the smart contract resource consumption (’gas costs’). Previous works suggested patterns and tools supporting developers in gas cost optimization, but up to now a comprehensive analysis of their real-world impact is missing. Another gap is the maintenance and evolution support for smart contracts leveraging the publicly available usage data. We propose high-level gas cost profiles and review which profiles are considered in the existing literature. Additionally, we sampled around 68,000 smart contract interactions from three years, analyzed them using the gas cost profiles, and compare the findings to the current focus in literature. In our data set, external code, storage, and the transaction base fee are first-level cost drivers in terms of absolute gas usage, but contract deployment becomes also costly when considering the average gas usage per transaction. Our analysis also shows that plenty of previous work focused cost categories barely influencing resource consumption.
Hybrid Ensemble Learning With Feature Selection for Sentiment Classification in Social Media

This article presents a study on ensemble learning and an empirical evaluation of various ensemble classifiers and ensemble features for sentiment classification of social media data. The data was collected from Twitter in real-time using Twitter API and text pre-processing and ranking-based feature selection is applied to textual data. A framework for a hybrid ensemble learning model is presented where a combination of ensemble features (Information Gain and CHI-Squared) and ensemble classifier that includes Ada Boost with SMO-SVM and Logistic Regression has been implemented. The classification of Twitter data is performed where sentiment analysis is used as a feature. The proposed model has shown improvements as compared to the state-of-the-art methods with an accuracy of 88.2% with a low error rate.
Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes

Most hard attention models initially observe a complete scene to locate and sense informative glimpses, and predict class-label of a scene based on glimpses. However, in many applications (e.g., aerial imaging), observing an entire scene is not always feasible due to the limited time and resources available for acquisition. In this paper, we develop a Sequential Transformers Attention Model (STAM) that only partially observes a complete image and predicts informative glimpse locations solely based on past glimpses. We design our agent using DeiT-distilled [44] and train it with a one-step actorcritic algorithm. Furthermore, to improve classification performance, we introduce a novel training objective, which enforces consistency between the class distribution predicted by a teacher model from a complete image and the class distribution predicted by our agent using glimpses. When the agent senses only 4% of the total image area, the inclusion of the proposed consistency loss in our training objective yields 3% and 8% higher accuracy on ImageNet and fMoW datasets, respectively. Moreover, our agent outperforms previous state-of-the-art by observing nearly 27% and 42% fewer pixels in glimpses on ImageNet and fMoW.
Intelligent Typography: Artistic Text Style Transfer for Complex Texture and Structure

Text style transfer is an important task to render artistic texts from a reference image or style, and is widely desired in many visual creations. Previous works have brought some efficient methods for text style transfer, which facilitate users to design various artistic texts automatically. However, these works mainly focus on relatively simple text effects, and do not perform well on complex reference styles. In this paper, we propose a coarse-to-fine framework to generate exquisite texts with complex texture and structure in an unsupervised way, achieving real-time control of style scales (i.e., text stylistic degree or deformation degree). The key idea is to decouple the overall task into two steps, prototype generation and detail refinement, and explore delicate networks for each step to imitate the features at different levels. Based on this idea, in the first step, we present a novel pro-gen GAN to generate prototypes of artistic texts using the reference style, and develop a deformable module to empower the pro-gen GAN to continuously characterize the multi-scale shape features without network retraining. Furthermore, we propose a mix-attention training scheme for text style transfer, which can avoid artifacts and retain a clear text background. In the second step, we introduce two optimized networks for detail refinements. Experimental results show that the proposed method can synthesize exquisite stylized texts with complex reference styles, and surpass the state of the arts in texture reconstruction, contour imitation, and text image quality drastically.
Empirical Similarity Measure for Metaheuristics

Metaheuristic Search is a successful strategy for solving optimization problems, leading to over two hundred published metaheuristic algorithms. Consequently, there is an interest in understanding the similarities between metaheuristics. Previous studies have done theoretical analyses based on components and search strategies, providing insights into the relationship between different algorithms. In this paper, we argue that it is also important to consider the classes of optimization problems that the algorithms are capable of solving. To this end, we propose a method to measure the similarity between metaheuristics based on their performance on a set of optimization functions. We then use the proposed method to analyze the similarity between different algorithms as well as the similarity between the same algorithm but with different parameter settings. Our method can show if parameter settings of the same algorithm are more similar between themselves than to other algorithms and suggest a clustering based on the performance profile.
Selective TransHDR: Transformer-Based Selective HDR Imaging Using Ghost Region Mask

The primary issue in high dynamic range (HDR) imaging is the removal of ghost artifacts afforded when merging multi-exposure low dynamic range images. In the weakly misaligned region, ghost artifacts can be suppressed using convolutional neural network (CNN)-based methods. However, in highly misaligned regions, it is necessary to extract features from the global region because the necessary information does not exist in the local region. Therefore, the CNN-based methods specialized for local features extraction cannot obtain satisfactory results. To address this issue, we propose a transformer-based selective HDR image reconstruction network that uses a ghost region mask. The proposed method separates a given image into ghost and non-ghost regions, and then, selectively applies either the CNN or the transformer. The proposed selective transformer module divides an entire image into several regions to effectively extract the features of each region for HDR image reconstruction, thereby extracting the whole information required for HDR reconstruction in the ghost regions from the entire image. Extensive experiments conducted on several benchmark datasets demonstrate the superiority of the proposed method over existing state-of-the-art methods in terms of the mitigation of ghost artifacts.
Insightful Mining Equilibria

The selfish mining attack, arguably the most famous game-theoretic attack in blockchain, indicates that the Bitcoin protocol is not incentive-compatible. Most subsequent works mainly focus on strengthening the selfish mining strategy, thus enabling a single strategic agent more likely to deviate. In sharp contrast, little attention has been paid to the resistant behavior against the selfish mining attack, let alone further equilibrium analysis for miners and mining pools in the blockchain as a multi-agent system. In this paper, first, we propose a novel strategy called insightful mining to counteract the selfish mining attack. By infiltrating an undercover miner into the selfish pool, the insightful pool could acquire the number of its hidden blocks. We prove that, with this extra insight, the utility of the insightful pool is strictly greater than the selfish pool's when they have the same mining power. Then we investigate the mining game where all pools can choose to be honest or take the insightful mining strategy. We characterize the Nash equilibrium of such a game and derive three corollaries: (a) each mining game has a pure Nash equilibrium; (b) there are at most two insightful pools under some equilibrium no matter how the mining power is distributed; (c) honest mining is a Nash equilibrium if the largest mining pool has a fraction of mining power no more than 1/3. Our work explores, for the first time, the idea of spying in the selfish mining attack, which might shed new light on researchers in the field.
The Capacity Region of Entanglement Switching: Stability and Zero Latency

Quantum switches distribute entangled pairs among end nodes by entanglement swapping and are critical components in quantum networks. In this work, we design protocols that schedule entanglement swapping in quantum switches. In contrast to most existing studies, we consider that entanglement requests randomly arrive at the switch, and determine the capacity region of rate vectors that the switch can support stably. For a rate vector inside the capacity region, we develop protocols that not only stabilize the switch, but also achieve zero average latency. Among these protocols, the on-demand protocols are computationally efficient and achieve high fidelity and low latency demonstrated by results obtained using a quantum network discrete event simulator.
Cyberbullying Detection and Hate Speech Identification using Machine Learning Techniques

Bullying has been prevalent since the beginning of time, It’s just the ways of bullying that have changed over the years, from physical bullying to cyberbullying. According to Williard (2004), there are eight types of cyberbullying such as harassment, denigration, impersonation, etc. It’s been around 2 decades since social media sites came into the picture, but there haven’t been a lot of effective measures to curb social bullying and it has become one of the alarming issues in recent times.Our paper presents an analytical review of cyberbullying detection approaches and assesses methods to recognize hate speech on social media. We aim to apply traditional supervised classification methods as well as some novel ensemble machine learning techniques using a manually annotated open-source dataset for this purpose. This paper does a comparative study of various Supervised algorithms, including standard, as well as ensemble methods. The evaluations of the result based upon the scores obtained by accuracy shows that Ensemble supervised methods have the potential to perform better than traditional supervised methods.
Closing the Domain Gap for Cross-modal Visible-Infrared Vehicle Re-identification

Traditional vehicle re-identification (ReID) approaches, based on visible spectrum data achieve high performance, but have limited capability of real-life applications, as they perform poorly under occluded visibility conditions, such as night-time and bad weather. In such cases, the use of infrared spectrum thermal imagery offers complementary and persistent information when the visual data contribution is inadequate. It is therefore highly beneficial to create a vehicle ReID framework that can exploit both modalities, if available, and is able to apply cross-modality matching, when ReID is required across single modal sensors. This is an extremely challenging task because the nature of the two data modalities induces high discrepancy between the two domains. In this paper we propose a robust end-to-end 2-stream vehicle ReID system that aims to solve the multi-modal and cross-modal ReID problem together by minimising the domain shift between infrared and visible distributions. Our framework consists of a shared network part, following the 2 independent streams, to extract shareable features, along with a domain alignment technique to narrow the gap between the two domains and inter-modality learning to address the cross-domain matching problem. The proposed system achieves state-of-the-art results on RGBN300 dataset, when both modalities are available at inference time. Moreover, our work is the first to explore the cross-modal settings for vehicle ReID and attempts to reduce the performance drop of the cross-modal scenario, when the query and the gallery images come from different modalities. We first measure the baseline cross-modal performance, and then prove that the proposed method improves up to 11% in mAP and 16% in Rank-1 score against the baseline.
Location-Aware Web Service QoS Prediction via Deep Collaborative Filtering

Nowadays, there is a large number of web services with similar functions, from which users choose the best according to the quality of service (QoS). Hence, QoS prediction is a primary challenge in service recommendation. Most existing approaches model the user-service interaction relationship. However, the low-dimensional linear and the high-dimensional nonlinear relationships between users and services are seldom considered simultaneously. In addition, although location information, including the local location information of users and services, is incorporated to overcome data sparsity in most approaches. The global location information is seldom considered. Aiming at the above shortcomings, we propose a new QoS prediction model that fuses local and global location information of users and services in the interaction layer of the model. The proposed model uses a multilayer perceptron (MLP) to acquire high-dimensional nonlinear relationships of users and services, where the dot product is employed in complementing the learning of low-dimensional linear relationships. Experimental results on the real world dataset WS-Dream validate the prediction performance of the proposed model.
Vision Transformer and Language Model Based Radiology Report Generation

Recent advancements in transformers exploited computer vision problems which results in state-of-the-art models. Transformer-based models in various sequence prediction tasks such as language translation, sentiment classification, and caption generation have shown remarkable performance. Auto report generation scenarios in medical imaging through caption generation models is one of the applied scenarios for language models and have strong social impact. In these models, convolution neural networks have been used as encoder to gain spatial information and recurrent neural networks are used as decoder to generate caption or medical report. However, using transformer architecture as encoder and decoder in caption or report writing task is still unexplored. In this research, we explored the effect of losing spatial biasness information in encoder by using pre-trained vanilla image transformer architecture and combine it with different pre-trained language transformers as decoder. In order to evaluate the proposed methodology, the Indiana University Chest X-Rays dataset is used where ablation study is also conducted with respect to different evaluations. The comparative analysis shows that the proposed methodology has represented remarkable performance when compared with existing techniques in terms of different performance parameters.
Automatic Color Image Stitching Using Quaternion Rank-1 Alignment

Color image stitching is a challenging task in real-world applications. This paper first proposes a quaternion rank-1 alignment (QR1A) model for high-precision color image alignment. To solve the optimization problem of QR1A, we develop a nested iterative algorithm under the framework of complex-valued alternating direction method of multipliers. To quantitatively evaluate image stitching performance, we propose a perceptual seam quality (PSQ) measure to calculate misalignments of local regions along the seamline. Using QR1A and PSQ, we further propose an automatic color image stitching (ACIS-QR1A) framework. In this frame-work, the automatic strategy and iterative learning strategy are developed to simultaneously learn the optimal seamline and local alignment. Extensive experiments on challenging datasets demonstrate that the proposed ACIS-QR1A is able to obtain high-quality stitched images under several difficult scenarios including large parallax, low textures, moving objects, large occlusions or/and their combinations.