Reviewerly/exHarmony huggingface dataset:
Prediction and analysis of illegal accounts on Ethereum based on Catboost algorithm

Increasingly frequent illegal transactions hinder the security of Ethereum transactions, and the anonymity of electronic money makes it difficult to track and analyze problems. In this paper, the transaction data of the Ethereum trading platform is used as the data source, and the marked illegal account and the unmarked normal account data set are used as the training set. Based on the CatBoost algorithm, the overall prediction of the various types of illegal accounts is made. The process adopts multiple cross-validation, the accuracy of the established algorithm model prediction reached 94.07%, and the evaluation metric of the area under the curve of the receiver reached 0.9846. The proposed scheme accurately predicts illegal behaviors on the Ethereum trading platform and effectively improves the blockchain-based trading environment.
Cross-Boosted Multi-Target Domain Adaptation for Multi-Modality Histopathology Image Translation and Segmentation

Recent digital pathology workflows mainly focus on mono-modality histopathology image analysis. However, they ignore the complementarity between Haematoxylin & Eosin (H&E) and Immunohistochemically (IHC) stained images, which can provide comprehensive gold standard for cancer diagnosis. To resolve this issue, we propose a cross-boosted multi-target domain adaptation pipeline for multi-modality histopathology images, which contains Cross-frequency Style-auxiliary Translation Network (CSTN) and Dual Cross-boosted Segmentation Network (DCSN). Firstly, CSTN achieves the one-to-many translation from fluorescence microscopy images to H&E and IHC images for providing source domain training data. To generate images with realistic color and texture, Cross-frequency Feature Transfer Module (CFTM) is developed to pertinently restructure and normalize high-frequency content and low-frequency style features from different domains. Then, DCSN fulfills multi-target domain adaptive segmentation, where a dual-branch encoder is introduced, and Bidirectional Cross-domain Boosting Module (BCBM) is designed to implement cross-modality information complementation through bidirectional inter-domain collaboration. Finally, we establish Multi-modality Thymus Histopathology (MThH) dataset, which is the largest publicly available H&E and IHC image benchmark. Experiments on MThH dataset and several public datasets show that the proposed pipeline outperforms state-of-the-art methods on both histopathology image translation and segmentation.
New query optimization techniques in the Spark engine of Azure synapse

The cost of big-data query execution is dominated by stateful operators. These include sort and hash-aggregate that typically materialize intermediate data in memory, and exchange that materializes data to disk and transfers data over the network. In this paper we focus on several query optimization techniques that reduce the cost of these operators. First, we introduce a novel exchange placement algorithm that improves the state-of-the-art and significantly reduces the amount of data exchanged. The algorithm simultaneously minimizes the number of exchanges required and maximizes computation reuse via multi-consumer exchanges. Second, we introduce three partial push-down optimizations that push down partial computation derived from existing operators ( group-bys , intersections and joins ) below these stateful operators. While these optimizations are generically applicable we find that two of these optimizations ( partial aggregate and partial semi-join push-down ) are only beneficial in the scale-out setting where exchanges are a bottleneck. We propose novel extensions to existing literature to perform more aggressive partial push-downs than the state-of-the-art and also specialize them to the big-data setting. Finally we propose peephole optimizations that specialize the implementation of stateful operators to their input parameters. All our optimizations are implemented in the spark engine that powers azure synapse. We evaluate their impact on TPCDS and demonstrate that they make our engine 1.8X faster than Apache Spark 3.0.1.
Verification of agent navigation in partially-known environments

This paper establishes a framework based on logic and automata theory in which to model and automatically verify systems of multiple mobile agents moving in environments with partially-known topologies, i.e., ones which are not completely known at design time. Examples include physical agents designed to be used in many spatial environments and not tailored for a specific one, robots in environments not reachable by humans, and software exploring partially-mapped networks. We model spatial environments as graphs whose edges are labelled with directions. We model agents as finite-state machines that move on the graphs by issuing commands of the form "go in direction X", that can communicate their internal state to other agents, and that can sense agent positions (including current and visited positions). We treat the incomplete information about the spatial environment by studying the decision problem that asks whether a given collection of agents achieve their tasks on all graphs from a class of graphs — this is called the parameterised verification problem. The framework also introduces a new logical language based on Linear Temporal Logic that is tailored for expressing agent navigation tasks in such environments. Although the parameterised verification problem is undecidable, we identify two key dimensions that need to be limited in order to regain decidability, namely, the set of graph-environments and the amount of sensing and communication between agents. In particular, one should limit the families of graphs to exclude grids, and there should be a bound on the number of times an agent senses the position of another agent or communicates its own state to another agent. We prove that dropping either of these assumptions results in undecidability, even for agents with severe restrictions on their abilities (e.g., with very limited sensing abilities and no communication abilities). The importance of this work is that a) it provides a general computational model for mobile multi-agent systems in environments with partially-known topologies, b) it identifies, for the first time, the precise causes of undecidability of these systems and presents minimal restrictions to alleviate this problem, and c) it provides a generic sound and complete procedure for solving the parameterised verification problem over a broad range of spatial-environments and for agents with very powerful sensing and communication abilities.
Validating Mobile Forensic Metamodel Using Tracing Method

Mobile Forensic (MF) is a branch of digital forensic used to collect and analyze mobile device crimes. Several forensic models and frameworks have been proposed in the literature for the MF domain to identify, acquire, and investigate MF crimes. However, these models are redundant and developed for specific purposes. Therefore, the authors developed a metamodel to solve the redundancy and heterogeneity of the MF domain called Mobile Forensic Metamodel (MFM). However, the MFM has not been evaluated from the modeling perspective to evaluate the effectiveness of the MFM in terms of instantiation solution models for the MF domain. Thus, this paper aims to evaluate the effectiveness of the MFM using a tracing method. The tracing method is a common way of validating metamodels through reasonable reliability on the domain application of the metamodel to assess the logical consistency of metamodels against domain models. For this purpose, two real scenarios were selected to confirm the capabilities of developed MFM in instantiate solution models for problems in hands. From real scenarios, the developed MFM was found to be scalable, logical, complete, interoperable, coherent, and useful for the MFM domain.
Dance In the Wild: Monocular Human Animation with Neural Dynamic Appearance Synthesis

Synthesizing dynamic appearances of humans in motion plays a central role in applications such as ARWR and video editing. While many recent methods have been proposed to tackle this problem, handling loose garments with complex textures and high dynamic motion still remains challenging. In this paper, we propose a video based appearance synthesis method that tackles such challenges and demonstrates high quality results for in-the-wild videos that have not been shown before. Specifically, we adopt a StyleGAN based architecture to the task of person specific video based motion retargeting. We introduce a novel motion signature that is used to modulate the generator weights to capture dynamic appearance changes as well as regularizing the single frame based pose estimates to improve temporal coherency. We evaluate our method on a set of challenging videos and show that our approach achieves state-of-the-art performance both qualitatively and quantitatively.
Let's Supercharge the Workflows: An Empirical Study of GitHub Actions

Automation has become a norm in software development practices, especially in CI/CD practices. Recently, GitHub introduced GitHub Actions (GA) to provide automated workflows for software maintainers. However, few researches have been proposed to evaluate its capability and impact, even though several GA have been built by practitioners. In this paper, we conduct a large-scale empirical study of GitHub projects, to help practitioners gain deep insights into the GA. We quantitatively investigate the basic adoption of GA and its potential correlation with project properties. We also analyze the usage details of GA, including its component scale and action sequences. Finally, using regression modeling, we investigate the impact of GA on the commit frequency, pull request and issue's resolution efficiency. Our findings suggest a nuanced picture of how practitioners are adapting to, and benefiting from GA.
Optical-to-SAR Image Matching Using Multiscale Masked Structure Features

Automatic and precise matching between optical and synthetic aperture radar (SAR) images is still a challenging task because of significant radiation and texture differences between such images. Recently, structure feature-based methods are popular for the matching of SAR and optical images. However, current structure descriptors include many noninformative features, which degrade their matching performance. To address that, we present a robust matching method by a multiscale masked structure feature representation. We first extract pixelwise gradient structure features on multiple scales of images. Then, a mask is constructed according to large contours of an image, which is used to increase the contribution of the main structure region and alleviate the influence of noninformative regions. Finally, a fast template scheme based on fast Fourier transform (FFT) is employed to obtain correspondences. The proposed method is tested using the optical and SAR images from the Sentinel and GaoFen sensors. Experiment results show that the proposed method significantly improves the matching performance compared with the state-of-the-art methods, especially for the images with poor structure features.
Data Integrity Audit Based on Data Blinding for Cloud and Fog Environment

Cloud-fog computing is a novel computing model that expands the functionality of cloud computing, which provides various services through fog nodes. The issue of traditional data integrity auditing are low data security, slow data processing speed and low communication efficiency. To solve these problems, this paper proposes a data integrity audit scheme based on data blinding. This scheme uses the edge devices in the transmission node to establish a fog computing layer between the cloud service provider and the data owner to reduce transmission delay. The subordinate distribution relationship and weight between fog nodes dynamically allocate the optimal path and transmit the data to reduce transmission delay. At the same time, a blind factor is added to the integrity audit in the evidence generation process to avoid data leakage. This paper gives a security model and security proof based on computational Diffie-Hellman (CDH) assumptions. The experimental results show that the fog computing layer and blind factor are introduced into the data integrity audit process, which can reduce the data communication delay effectively and improve the security of data audit.
FPX-NIC: An FPGA-Accelerated 4K Ultra-High-Definition Neural Video Coding System

The recent trend in neural image compression (NIC) research could be generally grounded into two categories: analysis-synthesis transform network improvements and entropy estimation optimization. They promote the compression efficiency of NIC by leveraging more expressive network structures and advanced entropy models respectively. From a different but more systematic viewpoint, we extend the horizon of NIC from software- to hardware-based lossy compression using more resource-constrained platforms, such as field programmable gate array (FPGA) or deep-learning processor unit (DPU). In this paper, we propose a novel hardware-oriented NIC system for real-time edge-computing video services. We for the first time present FPX-NIC, an FPGA-accelerated NIC framework designed for hardware encoding, which consists of a novel NIC scheme and an energy-efficient neural network (NN) deployment method. The former contribution is a block-based adaptive NIC approach based on local content characteristics. Essential side-information is also signalled to realize adaptive patch representation. The critical advantage of our latter contribution lies in the network-reconfigurable framework plus fixed-precision weights quantization method that takes advantage of quantization-aware post training procedure to compensate the performance degradation caused by quantization error. Therefore it is able to improve both processing speed and energy efficiency. We finally establish an intelligent video coding system using the proposed scheme, enabling visual capturing, neural encoding, decoding, and display, realizing 4K ultra-high-definition (UHD) all intra neural video coding on edge-computing devices.
A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks

A Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA) is used in web systems to secure authentication purposes; it may break using Optical Character Recognition (OCR) type methods. CAPTCHA breakers make web systems highly insecure. However, several techniques to break CAPTCHA suggest CAPTCHA designers about their designed CAPTCHA's need improvement to prevent computer vision-based malicious attacks. This research primarily used deep learning methods to break state-of-the-art CAPTCHA codes; however, the validation scheme and conventional Convolutional Neural Network (CNN) design still need more confident validation and multi-aspect covering feature schemes. Several public datasets are available of text-based CAPTCHa, including Kaggle and other dataset repositories where self-generation of CAPTCHA datasets are available. The previous studies are dataset-specific only and cannot perform well on other CAPTCHA's. Therefore, the proposed study uses two publicly available datasets of 4- and 5-character text-based CAPTCHA images to propose a CAPTCHA solver. Furthermore, the proposed study used a skip-connection-based CNN model to solve a CAPTCHA. The proposed research employed 5-folds on data that delivers 10 different CNN models on two datasets with promising results compared to the other studies.
Object Detection in Video by Detecting Vehicles Using Machine Learning and Deep Learning Approaches

In recent, the field of object vehicles detection in video is very interested and had became applicable with methods of deep learning and machine learning. The main objective for these applications is to display the targeted object from the videos. This field still facing low detection accuracy problems. The paper goal is to develop an approach able to classify vehicles in videos by using HOG features, Linear SVM classifier as machine learning method and YOLOv3 (You Only Look Once, Version 3) algorithm as deep learning method. In the present work, we used the two famous public datasets for vehicles detection from videos, which are KITTI and GTI datasets. The result of this study addresses the problems of vehicle detection and improved the accuracy of the training.
Does Financial Development Increase Education Level? Empirical Evidence from Sub-Saharan Africa

This study analyzes the effect of financial development on education in a sample of 37 sub-Saharan African countries with data covering the period from 2000 to 2018. We use the nine measures of financial development proposed by the International Monetary Fund (IMF) and the three levels of education, including primary, secondary, and tertiary education. Applying the two-stage system Generalized Method of Moment (GMM), we find that financial development increases school enrollment in each level in sub-Saharan Africa. The gender results also show that financial development improves primary and secondary education for both male and female, except at the tertiary level where the effect does not appear to be robust for male. Based on these results, we suggest that the financial market and financial institution in SSA should be improved given its beneficial effects on the education system.
Detection of Generative Linguistic Steganography Based on Explicit and Latent Text Word Relation Mining Using Deep Learning

Covert communication channels can be easily constructed using text steganography based on social media. Offenders can easily utilize these channels to engage in various criminal activities, which brings great challenges in maintaining the security of cyberspace. Among the text information hiding methods, generative linguistic steganography poses the biggest threat to network security because it does not need the original carrier and has high embedding efficiency. The existing generative linguistic steganalysis methods fail to deeply mine text word relation, hence the detection performance is relatively unsatisfactory. In this article, we prove that there is explicit and latent steganography-sensitive text word relation. Based on this, we propose a generative linguistic steganalysis method based on <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> xplicit and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> atent text word relation <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ining, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ELM</i> . First, we employ a distributed readin module to convert words into real number vectors. Then, MRA (Mining Relation by Attentions) is proposed to mine the explicit and latent text word relation. Finally, global adaptive classification module is presented to exploit the mined relation feature to predict whether secret information is embedded in the current text segment. Experimental results demonstrate that the detection performance of ELM is better than the existing generative linguistic steganalysis methods.
Fine-Grained Feature Enhancement for Object Detection in Remote Sensing Images

Recently, object detection in aerial images has ushered in a new challenge—a new benchmark for fine-grained object recognition in high-resolution remote sensing imagery called FAIR1M has been proposed. Fine-grained categories usually have smaller inter class differences and intra-class similarities, which is more difficult to classify with existing object detectors. To address this problem, we propose two enhanced strategies on the current two-stage object detection algorithm. The first strategy uses attention-based group feature enhancement called group enhance module (GEM). By extending and grouping feature channels, the model can improve the ability to extract various discriminative features. The second strategy is to emphasize the sub-saliency feature learning, avoiding the network only focusing on the most significant part of the feature and ignoring the other parts. Our method is easy to implement and effective, and experiments show that our method can improve the Oriented regions with convolutional neural networks features (R-CNN) by about 1.45 mAP on the FAIR1M benchmark.
Stealthy and Flexible Trojan in Deep Learning Framework

Deep neural networks (DNNs) are increasingly used as the critical component of applications, bringing high computational costs. Many practitioners host their models on third-party platforms. This practice exposes DNNs to risks: A third party hosting the model may use a malicious deep learning framework to implement a backdoor attack. Our goal is to develop the realistic potential for backdoor attacks in third-party hosting platforms. We introduce a threatening and realistically implementable backdoor attack that is highly stealthy and flexible. We inject trojans by hijacking the built-in functions of the deep learning framework. Existing backdoor attacks rely on poisoning; its trigger is a special pattern superimposed on the input. Unlike existing backdoor attacks, the proposed sequential trigger is a specific sequence of clean image sets. Moreover, our attack is model agnostic and does not require retraining the model or modifying the parameters. Its stealthy is that injecting trojans will not change the model’s prediction for a clean image, so existing backdoor defenses cannot detect it. Its flexibility lies in that adversary can remodify the trojan behavior at any time. Extensive experiments on multiple benchmarks with different frameworks demonstrate that our attack achieves a perfect success rate (up to 100%) with minimal damage to model performance. And we can inject multiple trojans which do not affect each other at the same time, trojans hidden in the framework make a universal backdoor attack possible. Analysis and experiments further show that state-of-the-art defenses are ineffective against our attacks. Our work suggests that backdoor attacks in the supply chain need to be urgently explored.
Lessons Learned from Industrial Augmented Reality Applications

Smart production concepts are on the way to revolutionize industrial processes. A good example is the application of Extended Reality techniques that have emerged in recent years. This paper presents six different use cases where Extended Reality is realized utilizing tablets and digital projectors. The use cases reach from virtual sticky notes to artificial intelligence guided projections. The main aim of this work is to not only tell the success stories, but also give a sight behind the curtain and highlight challenges and lessons learned for each application such that the community can profit from the presentations.
Few-Shot Object Detection via Context-Aware Aggregation for Remote Sensing Images

Few-shot object detection methods have made prodigious progress in recent years. However, these methods are designed for optical images at a single scale, which leads to significantly degraded detection performance due to object scale variation of remote sensing images. In this letter, we propose a few-shot object detection method for the problem of scale variation in remote sensing images. More specifically, our model contains two main components: a context-aware pixel aggregation (CPA) that allows the model to adapt to objects at different scales through different scale convolution and a context-aware feature aggregation (CFA) that enhances context awareness to obtain more semantic information through a graph convolution network (GCN). Experiments on the DIOR dataset demonstrate that our model can achieve a satisfying detection performance on remote sensing images, and our model performs significantly better than the state-of-the-art model.