Reviewerly/exHarmony huggingface dataset:
Document Liveness Challenge Dataset (DLC-2021)

Various government and commercial services, including, but not limited to, e-government, fintech, banking, and sharing economy services, widely use smartphones to simplify service access and user authorization. Many organizations involved in these areas use identity document analysis systems in order to improve user personal-data-input processes. The tasks of such systems are not only ID document data recognition and extraction but also fraud prevention by detecting document forgery or by checking whether the document is genuine. Modern systems of this kind are often expected to operate in unconstrained environments. A significant amount of research has been published on the topic of mobile ID document analysis, but the main difficulty for such research is the lack of public datasets due to the fact that the subject is protected by security requirements. In this paper, we present the DLC-2021 dataset, which consists of 1424 video clips captured in a wide range of real-world conditions, focused on tasks relating to ID document forensics. The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. For the presented dataset, benchmark baselines are provided for tasks such as screen recapture detection and glare detection. The data presented are openly available in Zenodo.
GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond

We show that pre-trained Generative Adversarial Networks (GANs) such as StyleGAN and BigGAN can be used as a latent bank to improve the performance of image super-resolution. While most existing perceptual-oriented approaches attempt to generate realistic outputs through learning with adversarial loss, our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN. But unlike prevalent GAN inversion methods that require expensive image-specific optimization at runtime, our approach only needs a single forward pass for restoration. GLEAN can be easily incorporated in a simple encoder-bank-decoder architecture with multi-resolution skip connections. Employing priors from different generative models allows GLEAN to be applied to diverse categories (e.g., human faces, cats, buildings, and cars). We further present a lightweight version of GLEAN, named LightGLEAN, which retains only the critical components in GLEAN. Notably, LightGLEAN consists of only 21% of parameters and 35% of FLOPs while achieving comparable image quality. We extend our method to different tasks including image colorization and blind image restoration, and extensive experiments show that our proposed models perform favorably in comparison to existing methods. Codes and models are available at https://github.com/open-mmlab/mmediting.
The contribution of object identity and configuration to scene representation in convolutional neural networks

Scene perception involves extracting the identities of the objects comprising a scene in conjunction with their configuration (the spatial layout of the objects in the scene). How object identity and configuration information is weighted during scene processing and how this weighting evolves over the course of scene processing however, is not fully understood. Recent developments in convolutional neural networks (CNNs) have demonstrated their aptitude at scene processing tasks and identified correlations between processing in CNNs and in the human brain. Here we examined four CNN architectures (Alexnet, Resnet18, Resnet50, Densenet161) and their sensitivity to changes in object and configuration information over the course of scene processing. Despite differences among the four CNN architectures, across all CNNs, we observed a common pattern in the CNN's response to object identity and configuration changes. Each CNN demonstrated greater sensitivity to configuration changes in early stages of processing and stronger sensitivity to object identity changes in later stages. This pattern persists regardless of the spatial structure present in the image background, the accuracy of the CNN in classifying the scene, and even the task used to train the CNN. Importantly, CNNs' sensitivity to a configuration change is not the same as their sensitivity to any type of position change, such as that induced by a uniform translation of the objects without a configuration change. These results provide one of the first documentations of how object identity and configuration information are weighted in CNNs during scene processing.
TESN: Transformers enhanced segmentation network for accurate nanoparticle size measurement of TEM images

• Propose a deep learning-based method Transformers Enhanced Segmentation Network. • Adopt multi-stage architecture to provide high-quality target detection. • Introduce Transformer into the mask head to provide high-quality segmentation. • Proposed method is fully automated and provides humanoid particle size measurement. • Provide a solution for nanoparticle size measurement under complex situations. The size of nanoparticles has a great influence on the properties of nanomaterials. Transmission electron microscope (TEM) is the most reliable and intuitive way to observe the size of nanoparticles but is only suitable for separated particles under existing traditional algorithms and deep learning methods. Therefore, we proposed a Transformers Enhanced Segmentation Network (TESN) to accurately segment nanoparticles. Based on Mask R-CNN, TESN introduces the multi-stage architecture and adopts hybrid CNN-Transformers architecture as the mask head. TESN shows excellent performance in the segmentation even under complex situations such as multiple nanoparticles overlap or nanoparticle edge fuzzy, the AP 50:5:95 reaches 0.910 on the test set. We applied TESN to the size measurement of nanospheres and compared with other measurement methods. The results of TESN are closest to the manual annotation, the error of size measurement for the four gold nanospheres with different sizes ranges from 0.38% to 3.52%.
PostCog: A tool for interdisciplinary research into underground forums at scale

Underground forums provide useful insights into cybercrime, where researchers analyse underlying economies, key actors, their discussions and interactions, as well as different types of cybercrime. This interdisciplinary topic of study incorporates expertise from diverse areas, including computer science, criminology, economics, psychol-ogy, and other social sciences. Historically, there were sig-nificant challenges around access to data, but there are now research datasets of millions of messages scraped from underground forums. The problems now stem from the size of these datasets and the technical nature of methods and tools available for data sampling and analysis at scale, which make data exploration difficult for non-technical users. Postcoghas been developed to solve this problem. We first provide a survey of prior work into underground forums; this was used to understand the requirements and functionalities valued by researchers, and to inform the design of a data exploration tool. We then describe Postcog,a web application developed to support users from both technical and non-technical backgrounds in forum analyses, such as search, information extraction and cross-forum comparison. The prototype's usability is then evaluated through two user studies with expert users of the Crimebbdataset. Postcogis made available for academic research upon signing an agreement with the Cambridge Cybercrime Centre.
Qauxi: Cooperative multi-agent reinforcement learning with knowledge transferred from auxiliary task

Deep multi-agent reinforcement learning (MARL) can efficiently learn decentralized policies for real-world applications. However, current MARL methods suffer from the difficulty of transferring knowledge from already learned tasks to improve its exploration. In this paper, we propose a novel MARL method called Qauxi, which forms coordinated exploration scheme to improve the traditional MARL algorithms by reusing the meta-experience transferred from auxiliary task. We also use the weighting function to weight the importance of the joint action in monotonic loss function in order to focus on more important joint actions and thus avoid yielding suboptimal policies. Furthermore, we prove the convergence of Qauxi based on contraction mapping theorem. Qauxi is evaluated on the widely adopted StarCraft benchmarks (SMAC) across easy, hard, and super hard scenarios. Experimental results show that the proposed method outperforms the state-of-the-art MARL methods by a large margin in the most challenging super hard scenarios.
The role of board dynamics in explaining payout policy and shareholders’ wealth: Evidence from the banking sector in Africa

The paper investigates the role of board dynamics in explaining the effect of payout policy on shareholders’ wealth creation at the market and bank levels. The study employs the 2SLS estimation of a panel dataset of 528 banks from 29 African countries from the year 2006 to 2018. The study finds that board dynamics enhance payout policy. The study shows that board dynamics create market and bank-level wealth to shareholders. Further, payout policy reduces shareholders’ wealth creation in the market but increases bank-level wealth of shareholders. The study finds that payout policy can be utilized as a substitute control device in the presence of board governance mechanism in order to protect shareholders’ wealth. In general, the marginal effect of payout policy on shareholders’ wealth creation conditioned on board dynamics is relatively stronger at the market level compared to the bank level.
From Semantic Vectors to Analogical Mapping

Human reasoning goes beyond knowledge about individual entities, extending to inferences based on relations between entities. Here we focus on the use of relations in verbal analogical mapping, sketching a general approach based on assessing similarity between patterns of semantic relations between words. This approach combines research in artificial intelligence with work in psychology and cognitive science, with the aim of minimizing hand coding of text inputs for reasoning tasks. The computational framework takes as inputs vector representations of individual word meanings, coupled with semantic representations of the relations between words, and uses these inputs to form semantic-relation networks for individual analogues. Analogical mapping is operationalized as graph matching under cognitive and computational constraints. The approach highlights the central role of semantics in analogical mapping.
Theoretical analysis and experimental investigation of the Floyd-Steinberg-based fringe binary method with offset compensation for accurate 3D measurement

Digital fringe projection (DFP) with defocused binary fringe patterns has the ability to overcome the projector nonlinearity and achieve a high-speed 3D measurement. The Floyd-Steinberg (FS) dithering technique is one of the most commonly adopted binary fringe coding methods due to its relatively high measurement accuracy. Nevertheless, we found that the FS binary fringe would cause a fixed error in the recovered phase, which is proven to be invariable for various defocusing levels and various phase-shift steps according to the analysis of the phase error based on noise model of phase-shifting profilometry. It means that FS binary fringe would have a certain offset in space, compared with standard sinusoidal fringe, which is verified to be essentially constant for different fringe pitches through simulation and experiment. This offset would distort the 3D geometry of the tested target for monocular systems relying on triangulation, which needs to be compensated to improve 3D measurement accuracy. Experiments are presented to demonstrate the enhanced 3D result after compensation.
Divide-and-Attention Network for HE-Stained Pathological Image Classification

Since pathological images have some distinct characteristics that are different from natural images, the direct application of a general convolutional neural network cannot achieve good classification performance, especially for fine-grained classification problems (such as pathological image grading). Inspired by the clinical experience that decomposing a pathological image into different components is beneficial for diagnosis, in this paper, we propose a Divide-and-Attention Network (DANet) for Hematoxylin-and-Eosin (HE)-stained pathological image classification. The DANet utilizes a deep-learning method to decompose a pathological image into nuclei and non-nuclei parts. With such decomposed pathological images, the DANet first performs feature learning independently in each branch, and then focuses on the most important feature representation through the branch selection attention module. In this way, the DANet can learn representative features with respect to different tissue structures and adaptively focus on the most important ones, thereby improving classification performance. In addition, we introduce deep canonical correlation analysis (DCCA) constraints in the feature fusion process of different branches. The DCCA constraints play the role of branch fusion attention, so as to maximize the correlation of different branches and ensure that the fused branches emphasize specific tissue structures. The experimental results of three datasets demonstrate the superiority of the DANet, with an average classification accuracy of 92.5% on breast cancer classification, 95.33% on colorectal cancer grading, and 91.6% on breast cancer grading tasks.
Extraction from Relative Clauses in Icelandic and Swedish: A Parallel Investigation

Extraction from relative clauses is generally taken to be unacceptable in Icelandic, unlike in the Mainland Scandinavian languages. Recent studies on Mainland Scandinavian show that the type of dependency as well as the embedding predicate matters for the acceptability of such extractions, and the study of spontaneously produced examples has improved our ability to create felicitous extraction contexts. The studies of Icelandic extraction predate these findings, and there is to date no study which systematically compares parallel sentences in Icelandic and Mainland Scandinavian. This article presents such a study, using two acceptability judgment experiments, one in Icelandic and one in Swedish, drawing on newly gained insights about fronting conditions in the two languages to create plausible contexts. The Icelandic participants rated extraction from relative clauses as unnatural, with a very large acceptability cost compared to in situ versions and good fillers. Extraction from að-clauses received mixed ratings, and local fronting was rated on a par with the in situ versions. In Swedish, extraction from relative clauses was rated as natural a majority of the time. There was no extraction cost in local fronting, extraction from att-clauses, or extraction from relative clauses in existential sentences, while extraction with other embedding predicates incurred some cost. No differences relating to the embedding predicate were seen in Icelandic. The study corroborates the view that extraction from relative clauses is unacceptable in Icelandic.
Tiramisu: Layering Consensus Protocols for Scalable and Secure Blockchains

Cryptocurrencies are poised to revolutionize the modern economy by democratizing commerce. These currencies operate on top of blockchain-based distributed ledgers. Existing permissionless blockchain-based protocols offer unparalleled benefits like decentralization, anonymity, and transparency. However, these protocols suffer in performance which hinders their widespread adoption. In particular, high time-to-finality and low transaction rates keep them from replacing centralized payment systems such as the Visa network. Permissioned blockchain protocols offer attractive performance guarantees, but they are not considered suitable for deploying decentralized cryptocurrencies due to their centralized nature. Researchers have developed several multi-layered blockchain protocols that combine both permissioned and permissionless blockchain protocols to achieve high performance along with decentralization. The key idea with existing layered blockchain protocols in literature is to divide blockchain operations into two layers and use different types of consensus to manage each layer. However, many such works come with the assumptions of honest majority which may not accurately reflect the real world where the participants may be self-interested or rational. These assumptions may render the protocols susceptible to security threats in the real world, as highlighted by the literature focused on exploring game-theoretic attacks on these protocols. We generalize the “layered” approach taken by existing protocols in the literature and present a framework to analyze the system in the BAR Model and provide a generalized game-theoretic analysis of such protocols. Using our analysis, we identify the critical system parameters required for a distributed ledger’s secure operation in a more realistic setting.
GID: Global information distillation for medical semantic segmentation

In this work, we consider transferring global information from Transformer to Convolutional Neural Network (CNN) for medical semantic segmentation tasks. Previous network models for medical semantic segmentation tasks often suffer from difficulties in modeling global information or oversized model parameters. Here, to design a compact network with global and local information, we extract the global information modeling capability of Transformer into the CNN network and successfully apply it to the medical semantic segmentation tasks, called Global Information Distillation. In addition, the following two contributions are proposed to improve the effectiveness of distillation: i) We present an Information Transfer Module, which is based on a convolutional layer to prevent over-regularization and a Transformer layer to transfer global information; ii) For purpose of better transferring the teacher’s soft targets, a Shrinking Result-Pixel distillation method is proposed in this paper. The effectiveness of our knowledge distillation approach is demonstrated by the experiments on multi-organ and cardiac segmentation tasks.
Artificial Intelligence and Internet-of-Things Technology Application on Ideological and Political Classroom Teaching Reform

We look at the current state of ideological and political classroom teaching based on artificial intelligence and the Internet of Things. We also look at the problems with traditional ideological and political classroom teaching in order to improve the effectiveness of classroom reform. Also, this paper reforms teaching based on the real world and builds a modern, intelligent system for teaching political and ideological ideas in the classroom. The ideological and political education system is built on the Internet of Things. It has three layers: the perception layer, the network layer, and the application layer. The system collects efficient ideological and political teaching activity data in real time through the Internet of Things and wireless networks, sends the data to the data center through the Internet, and then uses the collected data as the original data for applications, data mining, and modeling simulation. Lastly, this paper proves through simulation experiments and teaching experiments that the system built in this paper can be used to reform ideological and political education.
Blockchain-Enabled Emergency Detection and Response in Mobile Healthcare System

The rapid growth of Internet of Things and communication technology has provided a lot of scope for the improvement of the current healthcare services. Mobile healthcare system can be used to monitor patients’ real-time physiological information and can also help in detecting and providing emergency services to the patients. For early detection of emergency, two associated requirements are - patients’ physiological details to be received in regular intervals and the data should be processed efficiently in an automated way. Additionally, patients sensitive healthcare details need to be protected also. For his purpose, this paper proposes a blockchain-enabled emergency detection system that detects emergency conditions from patients’ encrypted physiological information without decryption and automatically triggers quick responses to provide healthcare services to the patients’ via nearby hospitals. The support of blockchain technology increases accountability, transparency, and trust concerning the storage, safeguarding, and sharing of patients’ data. We present a proof of concept of our proposal using Hyperledger Fabric, and we perform an experimental evaluation using Caliper benchmarking tool to demonstrate the performance of the system.
MobileTrack: Siamese efficient mobile network for high‐speed UAV tracking

Recently, Siamese-based trackers have drawn amounts of attention in visual tracking field because of their excellent performance. However, visual object tracking on Unmanned Aerial Vehicles platform encounters difficulties under circumstances such as small objects and similar objects interference. Most existing tracking methods for aerial tracking adopt deeper networks or inefficient policies to promote performance, but most trackers can hardly meet real-time requirements on mobile platforms with limited computing resources. Thus, in this work, an efficient and lightweight siamese tracker (MobileTrack) is proposed for high-time Unmanned Aerial Vehicles tracking, realising the balance between performance and speed. Firstly, a lightweight convolutional network (D-MobileNet) is designed to enhance the characterisation ability of small objects. Secondly, an efficient object-aware module is proposed for local cross-channel information exchange, enhancing the feature information of the tracking object. Besides, an anchor-free region proposal network is introduced to predict the object pixel by pixel. Finally, deep and shallow feature information is fully utilised by cascading multiple anchor-free region proposal networks for accurate locating and robust tracking. Extensive experiments on the three Unmanned Aerial Vehicles benchmarks show that the proposed tracker achieves outstanding performance while keeping a beyond-real-time speed.