Reviewerly/exHarmony huggingface dataset:
The Effect of Out-of-Body Experience and Tactile Feedback on the Sense of Embodiment in Virtual Reality

The out-of-body experience (OBE) is generated from sensory disintegration. In virtual reality (VR), we can provide OBE to people by switching the first-person perspective (1PP) to third-person perspective (3PP). Generally, 1PP is the choice for high body ownership and presence. In addition, it is known from prior research that tactile feedback under 1PP can also provide a better immersive experience. However, it is underexplored whether the combination of 3PP and tactile feedback could affect the sense of embodiment in immersive environments. Thus, we plan to conduct a 2 (OBE: 1PP vs. 3PP) x 2 (Tactile Feedback: with vs. without tactile feedback) VR study to discover the effect of OBE in terms of perspectives and Tactile Feedback (TF). Out study plans to examine the six dimensions of the sense of embodiment: body ownership, agency, tactile sensations, location of the body, external appearance, and response to external stimuli. We will also discuss directions for future studies regarding different perspectives in VR.
Towards Powerful Graph Contrastive Learning without Negative Examples

Contrastive learning gains remarkable success in graph representation learning through imposing two properties to node representations, i.e., aligning representations of similar nodes in different augmented graphs and uniformly distributing node representations on the hypersphere. Existing graph contrastive learning methods capture these two key properties by extensive negative examples, resulting in unbearable computational costs. To overcome this issue, we propose a novel graph contrastive learning method, i.e., GraphALU, to capture these two properties without negative examples. For alignment, we leverage both self-alignment, i.e., pulling the representations of the same node in different augmented graphs together, and neighbor-alignment, pulling the representations of neighboring nodes in different augmented graphs together to capture the structure proximity among nodes. For uniformity, we innovatively design a star-shaped distance comparison, i.e., convert the distance comparison of any pair of nodes to the distance comparison of any nodes with a virtual center node, reducing the quadratic computational cost to a linear one with respect to the number of nodes. Extensive experiments show both the effectiveness and efficiency of GraphALU on various established benchmark datasets and downstream tasks.
Image Art Innovation based on Extended Reality Technology

Image art creation based on XR technology has developed rapidly in the past years. Relevant technologies have been continuously improved, so that corresponding works have also received extensive attention in different media. This paper first discusses the creative characteristics of image art based on XR technology. In terms of production process, technical modules and artistic language, XR images fully reflect the artistic characteristics which integrate virtual and real. It is embodied in the combination of multi technology modules, multi space narration, interactive, real-time, on-site creation, Pre-post production, etc. Secondly, from the perspective of art and technology integration and innovation, XR image art is a new form of narrative art creation. It is a new stage of the development of traditional image narrative art, which embodies the characteristics of real-time, virtual, interactive and so on. It can better adapt to the demands of social and cultural development. Finally, the paper considers and analyzes the practice of XR image in the inheritance and development of traditional culture and art. Explore the innovative significance of XR image art in the current era in terms of social cultural demands and aesthetic thoughts.
Unsupervised Keyphrase Generation by Utilizing Masked Words Prediction and Pseudo-label BART Finetuning

A keyphrase is a short phrase of one or a few words that summarizes the key idea discussed in the document. Keyphrase generation is the process of predicting both present and absent keyphrases from a given document. Recent studies based on sequence-to-sequence (Seq2Seq) deep learning framework have been widely used in keyphrase generation. However, the excellent performance of these models on the keyphrase generation task is acquired at the expense of a large quantity of annotated documents. In this paper, we propose an unsupervised method called MLMPBKG, based on masked language model (MLM) and pseudo-label BART finetuning. We mask noun phrases in the article, and apply MLM to predict replaceable words. We observe that absent keyphrases can be found in these words. Based on the observation, we first propose MLMKPG, which utilizes MLM to generate keyphrase candidates and use a sentence embedding model to rank the candidate phrases. Furthermore, we use these top-ranked phrases as pseudo-labels to finetune BART for obtaining more absent keyphrases. Experimental results show that our method achieves remarkable results on both present and abstract keyphrase predictions, even surpassing supervised baselines in certain cases.
Overview of Interactive Visualization Methods

In recent years, interactive visualization has become a research hotspot in data visualization. It is also an important part and one of the core research contents of the visual analysis system. In this paper, the advantages and application fields of interactive visualization are firstly introduced; Secondly, the visualization methods based on application scenarios are summarized from two aspects of text analysis and user behavior analysis; Then it discusses the advantages and disadvantages and main development trends of four methods based on data feature visualization: time event sequence visualization, multidimensional data visualization, spatial point pattern visualization, and hierarchical data visualization; Finally, three research directions of interactive visualization in the future are pointed out and its application fields have prospected.
Robust outlier detection by de-biasing VAE likelihoods

Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that DGM likelihoods are unreliable and can be easily biased by simple transformations to input data. Here, we examine outlier detection with variational autoencoders (VAEs), among the simplest of DGMs. We propose novel analytical and algorithmic approaches to ameliorate key biases with VAE likelihoods. Our bias corrections are sample-specific, computationally inexpensive, and readily computed for various decoder visible distributions. Next, we show that a well-known image pre-processing technique – contrast stretching – extends the effectiveness of bias correction to further improve outlier detection. Our approach achieves state-of-the-art accuracies with nine grayscale and natural image datasets, and demonstrates significant advantages – both with speed and performance – over four recent, competing approaches. In summary, lightweight remedies suffice to achieve robust outlier detection with VAEs. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at https://github.com/google-research/google-research/tree/master/vae_ood.
Multi-View Guided Multi-View Stereo

This paper introduces a novel deep framework for dense 3D reconstruction from multiple image frames, leveraging a sparse set of depth measurements gathered jointly with image acquisition.Given a deep multi-view stereo network, our framework uses sparse depth hints to guide the neural network by modulating the plane-sweep cost volume built during the forward step, enabling us to infer constantly much more accurate depth maps.Moreover, since multiple viewpoints can provide additional depth measurements, we propose a multi-view guidance strategy that increases the density of the sparse points used to guide the network, thus leading to even more accurate results.We evaluate our Multi-View Guided framework within a variety of state-of-the-art deep multi-view stereo networks, demonstrating its effectiveness at improving the results achieved by each of them on BlendedMVG and DTU datasets.
Dynamic Security Assessment Framework for Steel Casting Workshops in Smart Factory

Security assessment (SA) system is crucial to ensure the production safety of a smart factory with rapid development of artificial intelligence. In this article, we propose a novel SA framework. Different from the conventional static monitoring systems based on traditional sensing technologies, the proposed framework can automatically detect objects via visual sensing. We use a skeleton-based graph convolutional network to generate action vocabulary for the intermediate representations of action-to-action cooccurrence relations. These representations are encoded into the sequential interaction models to form the interaction representations. Integrating the states of molten steel levels as the reference labels, the sequential representations are fed into a recurrent neural network model with multilayer gated recurrent units (GRUs) to capture the key interactions leading to the accidents, in which an attention mechanism is used to reweight the actions and eliminate the invalid interactions. The predicted labels and the hidden states of the scenes are passing among multilayer GRUs. Finally, we optimize the global output to dynamically assess the security by calculating a joint objective function with a regularized cross-entropy loss. On the self-collected dataset from our partner Iron and Steel company and on-line video clips, the proposed framework performs better than the existing SA schemes.
Regulating Balance Degree for More Reasonable Visual Question Answering Benchmark

Superficial linguistic correlations is a critical issue for Visual Question Answering (VQA), where models can achieve high performance by exploiting the connection between question and answer, but fail to obtain better generalization ability for out-of-domain data. To ease such issue, VQA-CP v2.0 greedily re-partitions the distribution of VQA v2.0's training and test divides, it suppresses the performance improvement acquired by superficial linguistic correlations. However, some opportunistic methods (such as inverse supervision) can take advantage of the dataset's distribution characteristics to obtain high performance, which is incompatible with academic efforts to increase the model's visual reasoning and modal fusion abilities. To address this problem, we propose a more reasonable dataset in which we attempt to make the training split conform to the long-tailed distribution and the test split more balanced, so that inverse supervision does not result in performance gains and superficial linguistic correlations still can not assist the model in achieving high accuracy. Besides, we propose a decoupled training schema which can obtain better representation and visual reasoning modules to compensate for the shortcomings of ensemble-based methods that selectively learn some samples. Without any further annotations, such schema achieves state-of-the-art performance. In VQA-CP v2.0, it outperforms the simple baseline model UpDn by 15.54%. And its accuracy on VQA v2.0 has almost no drop compared to UpDn. Code is available at https://github.com/asklvd/new-benchmark-for-robust-VQA.
Anomaly detection in Sonar images: application of saliency filters

Sea mines are still a concrete menace both for military and civilian ships and detecting them is necessary to ensure the safety of the navigation. In this work we explored the possibility of automatically detect mines by using unmanned Autonomous Underwater Vehicles equipped with Side Scan Sonar (SSS) Sensors. To accomplish the detection task, we considered saliency detection algorithms coming from RGB and radar fields to highlight the mines with respect to the background. The algorithms were tested on a valuable dataset of images collected by the Italian Navy under operational conditions during several activities conducted in the Mediterranean Sea. We evaluated the performance according to broadly used performance indices such as ROC curves and MAE scores. Furthermore, a new performance analysis score called FAR@95%Pd is presented.
Imbalanced Data Stream Classification Assisted by Prior Probability Estimation

With the processing of data streams, come inevitable challenges, such as changes in the prior (class drift) and posterior (concept drift) probability distribution over the processing time. Both these phenomena have a negative impact on the quality of the classification. Heavily imbalanced problems, which are often typical for real-world applications, bring additional processing difficulties. Classifiers are often biased towards the majority class and have difficulty identifying instances of categories described with a lower number of objects. The following article proposes a Prior Probability Assisted Classifier (2PAC), a method aiming to improve the classification quality of heavily imbalanced data streams with dynamic changes by using the estimated prior probability value and the correction of the classifier's decision for batch predictions. Presented extensive computer experiments, supported by statistical analysis, show the ability to improve the classification quality using the proposed method.
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning

Contrastive learning (or its variants) has recently become a promising direction in the self-supervised learning domain, achieving similar performance as supervised learning with minimum fine-tuning. Despite the labeling efficiency, wide and large networks are required to achieve high accuracy, which incurs a high amount of computation and hinders the pragmatic merit of self-supervised learning. To effectively reduce the computation of insignificant features or channels, recent dynamic pruning algorithms for supervised learning employed auxiliary salience predictors. However, we found that such salience predictors cannot be easily trained when they are naïvely applied to contrastive learning from scratch. To address this issue, we propose contrastive dual gating (CDG), a novel dynamic pruning algorithm that skips the uninformative features during contrastive learning without hurting the trainability of the networks. We demonstrate the superiority of CDG with ResNet models for CIFAR-10, CIFAR-100, and ImageNet-100 datasets. Compared to our implementations of state-of-the-art dynamic pruning algorithms for self-supervised learning, CDG achieves up to 15% accuracy improvement for CIFAR-10 dataset with higher computation reduction.
A Lightweight Certificateless Searchable Public Key Encryption Scheme for Medical Internet of Things

To ensure the confidentiality of medical data, the medical information is usually encrypted before outsourcing to a third party for processing. Encryption technology can ensure the privacy of data, but it limits the search for data. The problem is usually solved using public key encryption with keyword search (PEKS). Recently, a few certificateless PEKS (CPEKS) schemes have been proposed. However, they rely on the high-consuming bilinear pairing, and some of them are vulnerable to inside keyword guessing attacks (IKGA). To solve these problems, we propose a lightweight CPEKS scheme for the Medical Internet of things (IoMT), which does not contain bilinear pairing. The scheme is proved to be secure in the random oracle model. The analysis results show that it has better comprehensive performance than the existing schemes according to the security property, the computation cost, and the communication cost.
Latency-Aware Inference on Convolutional Neural Network Over Homomorphic Encryption

Homomorphic encryption enables privacy-preserving computation in convolutional neural networks (CNNs), keeping their input and output secret from the server; however, it faces long latency because of large overhead of the encryption scheme. This paper tackles shortening the inference latency on homomorphic encryption-enabled CNNs. Since the highest inference accuracy is not always needed depending on real-world applications, finding best-fit combinations of latency and accuracy is also indispensable. We propose a combination of channel-wise packing and a structured pruning technique besides changing the active functions to shorten the inference latency while allowing accuracy degradation. Our experimental evaluation shows that we successfully tune the latency from 8.1 s to 12.9 s depending on the accuracy of 66.52% to 80.96% on the CIFAR-10 dataset.
Computer-assisted Table Tennis Posture Analysis using Machine Learning

Manual assessments for table tennis players, done in person or virtually, can be tedious, inefficient, and error-prone. Existing machine learning software tries to eliminate these gaps; however, its capability is only limited to one technical skill at a time. In this study, a software was developed to help assess the key technical skills of a table tennis player: (1) upper body position (leaning and not leaning), (2) lower body position (knees bending and straight), (3) basic hand strokes (forehand and backhand), and (4) footwork (side-to-side and in-and-out); these four would be used as performance metrics for the video input. Datasets of five OpenTTGames videos depicting professional player's postures and three videos from YouTube portraying amateur player's postures were extracted into frames, resulting to 32,395 frames. Posture detection was first carried out on the extracted frames using OpenPose library, generating a total of 519,320 key points. Then, various machine learning models were trained using the key points for posture analysis, and their performances were compared and benchmarked. The models with the highest accuracies would be integrated into one assessing model. Among Backpropagation, SVM-Linear, SVM-RBF, and SVM-Polynomial models, the SVM-RBF model yielded the highest in all performance metrics: 95.03% for upper body, 95.28% for lower body, 95.72% for hand stroke, and 92.78% for footwork. These results indicated that the software successfully assessed the player's posture, providing relevant data for coach's assessment of the player's performance. This software will help coaches and players analyze and evaluate their performance for improvements in lacking areas.
THE AGE OF THE KUBAKA EPITHERMAL GOLD-SILVER DEPOSIT (OMOLON MASSIF, NORTHEAST RUSSIA): GEOLOGICAL AND ISOTOPIC-GEOCHRONOLOGICAL CONSTRAINS (U-PB AND 40AR/39AR METHODS): REPLY

Perfomed of analysis of the data, argued for few stages of mineralization at Kubaka gold deposit, included young (Jurassic-Cretaceous). We are the opinion that volume and quality of this data insufficient for controverse of Late Paleozoic age of Kubaka deposit.
An Injectivity Analysis of Crystals-Kyber and Implications on Quantum Security

The One-Way to Hiding (O2H) Lemma proposed by Bindel et al. (TCC ’19) is a central component of proofs of chosen-ciphertext attack (CCA) security of practical public-key encryption schemes in the Quantum Random Oracle Model (QROM). Recently, Kuchta et al. (EUROCRYPT ’20) introduced a new technique, called measure-rewind-measure, improving upon the O2H lemma. The latter gives a new security reduction that does not suffer from a squared security loss as in Bindel et al. (TCC ’19) but has the number of queries Q as a multiplicative factor. This result is based on an injectivity assumption that requires the probability of two different messages generating the same ciphertext being negligible. The injectivity analysis of concrete schemes was left as an open problem by Kuchta et al. (EUROCRYPT ’20). In this paper, we complement the previous work by investigating the injectivity of a particular scheme in the third round National Institute of Standards and Technology (NIST) Post-Quantum Cryptography (PQC) standardization process. More precisely, we apply the techniques and constructions by Nguyen (ASIACRYPT ’19), along with the approach of calculating decryption error by Bos et al. (EuroS &P ’18), to obtain theoretical and numerical bounds on the injectivity of Crystals-Kyber, which points out a direction of resolving injectivity assumption of concrete scheme in the previous work. Our bounds also give the tightest concrete security guarantees for the QROM CCA security of Crystals-Kyber to date, based on the Module LWE hardness assumption.
Post-quantum Insecurity from LWE

We show that for many fundamental cryptographic primitives, proving classical security under the learning-with-errors (LWE) assumption, does not imply post-quantum security. This is despite the fact that LWE is widely believed to be post-quantum secure, and our work does not give any evidence otherwise. Instead, it shows that post-quantum insecurity can arise inside cryptographic constructions, even if the assumptions are post-quantum secure. Concretely, our work provides (contrived) constructions of pseudorandom functions, CPA-secure symmetric-key encryption, message-authentication codes, signatures, and CCA-secure public-key encryption schemes, all of which are proven to be classically secure under LWE via black-box reductions, but demonstrably fail to be post-quantum secure. All of these cryptosystems are stateless and non-interactive, but their security is defined via an interactive game that allows the attacker to make oracle queries to the cryptosystem. The polynomial-time quantum attacker can break these schemes by only making a few classical queries to the cryptosystem, and in some cases, a single query suffices. Previously, we only had examples of post-quantum insecurity under post-quantum assumptions for stateful/interactive protocols. Moreover, there appears to be a folklore intuition that for stateless/non-interactive cryptosystems with black-box proofs of security, a quantum attack against the scheme should translate into a quantum attack on the assumption. This work shows otherwise. Our main technique is to carefully embed interactive protocols inside the interactive security games of the above primitives. As a result of independent interest, we also show a 3-round quantum disclosure of secrets (QDS) protocol between a classical sender and a receiver, where a quantum receiver learns a secret message in the third round but, assuming LWE, a classical receiver does not.