Reviewerly/exHarmony huggingface dataset:
Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation

Seq2seq language generation models that are trained offline with multiple domains in a sequential fashion often suffer from catastrophic forgetting.Lifelong learning has been proposed to handle this problem.However, existing work such as experience replay or elastic weighted consolidation requires incremental memory space.In this work, we propose an innovative framework, RMR_DSE that leverages a recall optimization mechanism to selectively memorize important parameters of previous tasks via regularization, and uses a domain drift estimation algorithm to compensate for the drift between different domains in the embedding space.These designs enable the model to be trained on the current task while keeping the memory of previous tasks, and avoid much additional data storage.Furthermore, RMR_DSE can be combined with existing lifelong learning approaches.Our experiments on two seq2seq language generation tasks, paraphrase and dialog response generation, show that RMR_DSE outperforms stateof-the-art models by a considerable margin and greatly reduces forgetting.
PromptGen: Automatically Generate Prompts using Generative Models

Recently, prompt learning has received significant attention, where the downstream tasks are reformulated to the mask-filling task with the help of a textual prompt.The key point of prompt learning is finding the most appropriate prompt.This paper proposes a novel model PromptGen, which can automatically generate prompts conditional on the input sentence.PromptGen is the first work considering dynamic prompt generation for knowledge probing, based on a pre-trained generative model.To mitigate any label information leaking from the pre-trained generative model, when given a generated prompt, we replace the query input with "None".We pursue that this perturbed context-free prompt cannot trigger the correct label.We evaluate our model on the knowledge probing LAMA benchmark, and show that PromptGen significantly outperforms other baselines.
A new self-paced learning method for privilege-based positive and unlabeled learning

Positive and unlabeled learning (PU learning) is a kind of problem whose goal is learning a two-classes classifier with little proportion of positive samples and numerous unlabeled samples. A series of studies focus on how to extract most likely negative samples from the unlabeled samples, and then train a classifier with the labeled samples as supervised learning. Previous PU learning methods always ignore the additional information called privileged information, which is just provided during the training process while unavailable during testing. In this paper, we propose a novel self-paced algorithm for PU learning with privileged information (SPUPI). The proposed SPUPI extracts some reliable negative samples from unlabeled samples at first, and then generates weights for the unlabeled samples according to the similarity with each class. After that, it builds a more accurate classifier based on privileged information and similarity weights by self-paced learning. By taking the self-paced learning into training, we can build the model with a few labeled samples from easy to complex. We also solve the problem by transforming the primal problem of the proposed model into its dual problem and achieving the PU classifier. Various experiments on the practical datasets indicate that the SPUPI has a better performance compared with previous methods.
Single-shot 3D shape reconstruction for complex surface objects with colour texture based on deep learning

Three-dimensional (3D) shape reconstruction based on structured light technique is one of the most crucial techniques in the field of optical measurement due to the nature of non-contact and high-precision. In recent years, researchers found that the deep-learning method can significantly improve the quality and efficiency of 3D reconstruction. So far, the research object of structured light 3D reconstruction technology related to deep-learning methods usually adopts plaster models. However, the surface of the reconstructed objects always contains rich colour texture information in a real-world scenario, which affects the accuracy of the reconstruction, especially for some single-shot applications. To solve the above problem, we propose a deep-learning-based method single-shot 3D reconstruction method (SSR) for colour textured objects, and it can reconstruct colour textured objects with high precision. The experimental results show that the proposed method provides good guidance for the practical application and scientific research of fringe projection profilometry based on colour textured objects.
DeFakePro: Decentralized Deepfake Attacks Detection Using ENF Authentication

Advancements in generative models, like Deepfake allows users to imitate a targeted person and manipulate online interactions. It has been recognized that disinformation may cause disturbance in society and ruin the foundation of trust. This article presents DeFakePro, a decentralized consensus mechanism-based Deepfake detection technique in online video conferencing tools. Leveraging Electrical Network Frequency (ENF), an environmental fingerprint embedded in digital media recording, affords a consensus mechanism design called Proof-of-ENF (PoENF) algorithm. The similarity in ENF signal fluctuations is utilized in the PoENF algorithm to authenticate the media broadcasted in conferencing tools. By utilizing the video conferencing setup with malicious participants to broadcast deep fake video recordings to other participants, the DeFakePro system verifies the authenticity of the incoming media in both audio and video channels.
On the validity of using ground-based observations to validate geostationary-satellite-derived direct and diffuse surface solar irradiance: Quantifying the spatial mismatch and temporal averaging issues

This paper discusses the differences, due to different measuring mechanisms, of satellite-derived and ground measured surface solar irradiance (SSI) direct and diffuse components, and the impacts of the differences when validating satellite-derived data using ground measurements, using SSI products derived from the Advanced Himawari Imager. The issue of satellite-ground mismatch, though previously recognized, has not been given sufficient attention in the relevant scientific literature due to: (i) satellite SSI have mostly focused on relatively longer-term data, such as hourly, daily and longer; and/or (ii) most studies assessed only global (or total) irradiance. Also, studies addressing this satellite-ground mismatch issue often require a dense ground network to characterize the spatial representativeness of ground measurements, which likely limited performing such studies. With shorter-term satellite SSI data, such as 10-min or less, and the direct and diffuse components becoming increasingly available, further investigation of this satellite-ground mismatch issue is warranted. Our analysis, using direct and diffuse component SSI observations from satellite and sparse ground sites showed that a large part of apparent validation errors may have been due to the satellite-ground mismatch, often exceeding 50% in the case of 10-min direct normal irradiance. Greater appreciation and understanding of this issue should lead to more objective assessment of the real quality of satellite SSI products. Our study also showed that several factors, such as spatial averaging, temporal averaging, solar zenith angle and view zenith angle, all have often substantial impacts on the validation outcomes, over 100 W/m2 in some cases, highlighting the necessity for the ground- and satellite-based solar radiation communities to co-develop protocols for satellite SSI data validation.
Guided multi-scale refinement network for camouflaged object detection

The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications.
Perceptions of Beauty in Security Ceremonies

Abstract When we use secure computer systems, we engage with carefully orchestrated and ordered interactions called “security ceremonies”, all of which exist to assure security. A great deal of attention has been paid to improving the usability of these ceremonies over the last two decades, to make them easier for end-users to engage with. Yet, usability improvements do not seem to have endeared end users to ceremonies. As a consequence, human actors might subvert the ceremony’s processes or avoid engaging with it. Here, we consider whether beautification could be one way of making ceremonies more appealing. To explore beautification in this context, we carried out three studies. Study 1 surveyed 250 participants to derive a wide range of potential dimensions of “beautiful ceremonies”. These statements were sorted into dominant themes and converted into statements, which fed into the second study, with 309 respondents, to reveal the dominant dimensions constituting beauty. Study 3 asked 41 participants to carry out a Q-sort, which revealed the ways that people combine the identified dimensions when characterising security ceremonies as “beautiful”. These studies have allowed us to pin down the perceived dimensions of beauty in the context of security ceremonies, and also to understand how people combine these dimensions in different ways in judging security ceremonies to be beautiful, confirming the old adage of beauty being “in the eye of the beholder”. We conclude by highlighting the constraints imposed by the overarching requirement for security to be maintained in the face of any usability improvements and beautification endeavours.
Automatic segmentation of prostate MRI based on 3D pyramid pooling Unet

Automatic segmentation of prostate magnetic resonance (MR) images is crucial for the diagnosis, evaluation, and prognosis of prostate diseases (including prostate cancer). In recent years, the mainstream segmentation method for the prostate has been converted to convolutional neural networks. However, owing to the complexity of the tissue structure in MR images and the limitations of existing methods in spatial context modeling, the segmentation performance should be improved further.In this study, we proposed a novel 3D pyramid pool Unet that benefits from the pyramid pooling structure embedded in the skip connection (SC) and the deep supervision (DS) in the up-sampling of the 3D Unet. The parallel SC of the conventional 3D Unet network causes low-resolution information to be sent to the feature map repeatedly, resulting in blurred image features. To overcome the shortcomings of the conventional 3D Unet, we merge each decoder layer with the feature map of the same scale as the encoder and the smaller scale feature map of the pyramid pooling encoder. This SC combines the low-level details and high-level semantics at two different levels of feature maps. In addition, pyramid pooling performs multifaceted feature extraction on each image behind the convolutional layer, and DS learns hierarchical representations from comprehensive aggregated feature maps, which can improve the accuracy of the task.Experiments on 3D prostate MR images of 78 patients demonstrated that our results were highly correlated with expert manual segmentation. The average relative volume difference and Dice similarity coefficient of the prostate volume area were 2.32% and 91.03%, respectively.Quantitative experiments demonstrate that, compared with other methods, the results of our method are highly consistent with the expert manual segmentation.
Paint With the Sun: A Thermal-Vision Guided Robot to Harness Solar Energy for Heliography

In this article, we present a novel robotic system for heliography, which literally refers to painting with the Sun. In the context of artistic creation, heliography involves the precise manipulation of a magnifying glass to burn artwork on wooden panels by concentrating solar energy. Since this painting procedure exposes the human operator to prolonged periods of intensive sunlight, it can potentially cause sunburns to the skin or damage the eyes. To avoid these issues, in this article, we propose to automate the heliography process with a robot in lieu of a human operator. The proposed thermal servoing capabilities have the potential to robotize various solar power generation technologies such as concentrated solar power (CSP) and concentrated photovoltaics (CPVs). To perform this task, our robotic system is equipped with a magnifying glass attached to the end-effector and is instrumented with vision and thermal sensors. The proposed sensor-based controls enable to automatically: 1) track the orientation of the Sun in real time to maximize the concentrated solar energy; 2) direct the solar rays toward the point of interest; and 3) control the heat power intensity at the target point to achieve the required steady-state temperature. The performance of our system is evaluated by conducting autonomous heliography experiments with various patterns.
Quantum transfer of interacting qubits

The transfer of quantum information between different locations is key to many quantum information processing tasks. Whereas, the transfer of a single qubit state has been extensively investigated, the transfer of a many-body system configuration has insofar remained elusive. We address the problem of transferring the state of n interacting qubits. Both the exponentially increasing Hilbert space dimension, and the presence of interactions significantly scale-up the complexity of achieving high-fidelity transfer. By employing tools from random matrix theory and using the formalism of quantum dynamical maps, we derive a general expression for the average and the variance of the fidelity of an arbitrary quantum state transfer protocol for n interacting qubits. Finally, by adopting a weak-coupling scheme in a spin chain, we obtain the explicit conditions for high-fidelity transfer of 3 and 4 interacting qubits.
User Behavior Pre-training for Online Fraud Detection

The outbreak of COVID-19 burgeons newborn services on online platforms and simultaneously buoys multifarious online fraud activities. Due to the rapid technological and commercial innovation that opens up an ever-expanding set of products, the insufficient labeling data renders existing supervised or semi-supervised fraud detection models ineffective in these emerging services. However, the ever accumulated user behavioral data on online platforms might be helpful in improving the performance of fraud detection on newborn services. To this end, in this paper, we propose to pre-train user behavior sequences, which consist of orderly arranged actions, from the large-scale unlabeled data sources for online fraud detection. Recent studies illustrate accurate extraction of user intentions~(formed by consecutive actions) in behavioral sequences can propel improvements in the performance of online fraud detection. By anatomizing the characteristic of online fraud activities, we devise a model named UB-PTM that learns knowledge of fraud activities by three agent tasks at different granularities, i.e., action, intention, and sequence levels, from large-scale unlabeled data. Extensive experiments on three downstream transaction and user-level online fraud detection tasks demonstrate that our UB-PTM is able to outperform the state-of-the-art designing for specific tasks.
Improving Hierarchical Short Text Clustering through Dominant Feature Learning

This paper focuses on the popular problem of short text clustering. Since the short text documents typically exhibit high degrees of data sparseness and dimensionality, the problem in question is generally considered more challenging than the traditional clustering scenarios. Our proposed solution, named VEPH, is based on a novel algorithm that was published recently with the aim of optimally clustering short text documents. VEPH includes two stages: During the first stage, the original text vectors are projected on a lower dimensional space and the documents with projection vectors lying on the same dimensional space are grouped in the same cluster. The second stage is a refinement process which attempts to improve the quality of the clusters that were generated during the previous stage. The quality of a cluster is determined by its homogeneity and completeness and these are the two primary design criteria of this stage. Initially VEPH cleanses the clusters by removing all dissimilar elements, and then, it iteratively merges the similar clusters in a hierarchical agglomerative manner. The proposed algorithm has been experimentally evaluated in terms of F1 and NMI, by employing three datasets with diverse attributes. The results demonstrated its superiority over other state-of-the-art works of the relevant literature.
Spatial Focus Attention for Fine-Grained Skeleton-Based Action Tasks

Dynamic skeletal data has been widely studied for human action tasks due to its high-level semantic information and less data than RGB features. However, attention-based previous methods fail to focus on the local grouped joint dependence of the human body, which is vital to distinguishing various actions in fine-grained tasks, such as skeletal action segmentation and recognition. This work proposes spatial focus attention for the fine-grained skeleton-based action tasks. Specifically, we decouple the attention map to enhance the grouped joint dependence adaptively by the decouple probability. To further focus on local grouped dependence, the tree structural attention maps can be built by hierarchical decoupling and guide the model to focus on complementary local dependence in the different leaf nodes. Our proposed approach achieves state-of-the-art performance on fine-grained skeleton-based human action segmentation tasks (MCFS-22) and recognition tasks (FSD-10). Besides, on the coarse-grained dataset (NTU-60), the proposed spatial focus attention also achieves outstanding performance.
A Multi-color Spatio-Temporal Approach For Detecting DeepFake

The current surge in hyper-realistic faces created artificially using DeepFakes necessitates media forensics solutions suited to video streams and perform reliably with a low false alarm rate at the video level. The paper proposes a spatial and temporal aware pipeline to detect DeepFake videos automatically. Our method employed a two-stream convolutional neural network to extract local spatial and temporal features independently. These features are then fed to fully connected layers to classify whether a video has been subject to manipulation. The proposed method has been evaluated against FaceForensics++, DFTIMIT, and DFD benchmarks. Our suggested technique demonstrates encouraging performance in this task
Money Laundering Considerations in Blockchain-based Maritime Trade and Commerce

Abstract There is much to be welcomed concerning the role blockchain technology can play in modernising and enhancing international trade, creating a more level playing field and reducing costs. However, it goes without say that the technology also brings with it the risk of abuse leading to trade-based money laundering. This article explores how anti-money-laundering legislation should respond to the use of blockchain technology in shipping and trade. Maritime trade poses unique challenges because of several significant factors: the fact that it concerns large sums but many linked trading transactions over the same goods; its use of documents and involvement of numerous faceless entities; and its cross-border setting. Drawing on tried and tested forms of blockchain technology-based trade transactions, this work examines the fault lines in the current regulatory system and questions how best these gaps should be remedied. It also stresses that even states that have banned the issue and trade of cryptoassets might not be immune to these new challenges.
A novel autoencoder approach to feature extraction with linear separability for high-dimensional data

Feature extraction often needs to rely on sufficient information of the input data, however, the distribution of the data upon a high-dimensional space is too sparse to provide sufficient information for feature extraction. Furthermore, high dimensionality of the data also creates trouble for the searching of those features scattered in subspaces. As such, it is a tricky task for feature extraction from the data upon a high-dimensional space. To address this issue, this article proposes a novel autoencoder method using Mahalanobis distance metric of rescaling transformation. The key idea of the method is that by implementing Mahalanobis distance metric of rescaling transformation, the difference between the reconstructed distribution and the original distribution can be reduced, so as to improve the ability of feature extraction to the autoencoder. Results show that the proposed approach wins the state-of-the-art methods in terms of both the accuracy of feature extraction and the linear separabilities of the extracted features. We indicate that distance metric-based methods are more suitable for extracting those features with linear separabilities from high-dimensional data than feature selection-based methods. In a high-dimensional space, evaluating feature similarity is relatively easier than evaluating feature importance, so that distance metric methods by evaluating feature similarity gain advantages over feature selection methods by assessing feature importance for feature extraction, while evaluating feature importance is more computationally efficient than evaluating feature similarity.