Reviewerly/exHarmony huggingface dataset:
End-to-end learning for weakly supervised video anomaly detection using Absorbing Markov Chain

We propose a principled deep neural network framework with Absorbing Markov Chain (AMC) for weakly supervised anomaly detection in surveillance videos. Our model consists of both a weakly supervised binary classification network and a Graph Convolutional Network (GCN), which are jointly optimized by backpropagation. Unlike the previous works that employ AMC for label noise filtering in a post-processing step, the proposed framework migrates the component inside the GCN part of our model and realizes an end-to-end learning network. The integration of the AMC module into our deep neural network model enables us to learn the associated parameters automatically, which is helpful to improve the quality of segment-wise label estimation via tightly-coupled processing between the main network and the AMC module. In addition, we introduce a pseudo-labeling strategy based on Gaussian mixture model to fully utilize examples in abnormal videos. Our algorithm achieves outstanding performance compared to the state-of-the-art weakly supervised anomaly detection methods on UCF-Crime and ShanghaiTech datasets.
Converge: QoE-driven Multipath Video Conferencing over WebRTC

Video conferencing has become a daily necessity, but protocols to support video conferencing have yet to keep pace despite the innovation in next-generation networks. As video resolutions increase and mobile applications using multiple cameras for photos and videos become popular, the need to meet the Quality of Experience (QoE) requirements is growing. Multipath protocols could be a possible solution.
Global, Passive Detection of Connection Tampering

In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywords, and it affects billions of users worldwide with little-to-no transparency. To detect, diagnose, and measure connection-level blocking, "active" measurement techniques originate queries with domains or keywords believed to be blocked and send them from vantage points within networks of interest. Active measurement efforts have been critical to understanding how traffic tampering occurs, but they inherently are unable to capture critical parts of the picture. For instance, knowing the set of domains in a block-list (i.e., what could get blocked) is not the same as knowing what real users are actively experiencing (i.e., what is actively getting blocked).
Poster: Traffic Shaping and YouTube Performance Interaction in GEO Satellite Networks

Geosynchronous satellite (GEO) networks are a crucial option for users beyond terrestrial connectivity. However, unlike terrestrial networks, GEO networks exhibit high latency and deploy TCP proxies and traffic shapers. The deployment of proxies mitigates the impact of high network latency, while traffic shapers help realize customer-controlled data-saver options that optimize data usage. It is unclear how the interplay between GEO networks' high latency, TCP proxies, and traffic-shaping policies affects the quality of experience (QoE) for commonly used video applications. In our study, we examine this relationship through a series of video streaming experiments at a shaped rate of 900kbps. Our preliminary analysis reveals that 28% of TCP sessions (with TCP proxies) and 18% of gQUIC sessions (without TCP proxies) experience rebuffering events, while the median average resolution is only 380p for TCP and 299p for gQUIC. Additionally, we identify two key factors contributing to sub-optimal performance: (i) unlike TCP, gQUIC only utilizes 63% of network capacity; and (ii) YouTube's chunk request pipelining is imperfect. To avoid potential degradation in video quality, the satellite provider subsequently discontinued providing data saver options that shape video traffic to US residential customers.
Multi-constraint point set registration with redundant point removal for the registration of coronary arteries

Coronary artery disease (CAD) is the leading cause of death worldwide. The registration of the coronary artery at different phases can help radiologists explore the motion patterns of the coronary artery and assist in the diagnosis of CAD. However, there is no automatic and easy-to-execute method to solve the missing data problem that occurs at the endpoints of the coronary artery tree. This paper proposed a non-rigid multi-constraint point set registration with redundant point removal (MPSR-RPR) algorithm to tackle this challenge.Firstly, the MPSR-RPR algorithm roughly registered two coronary artery point sets with the pre-set smoothness regularization parameter and Gaussian filter width value. The moving coherent, local feature, and the corresponding relationship between bifurcation point pairs were exploited as the constraints. Next, the spatial geometry information of the coronary artery was utilized to automatically recognize the vessel endpoints and to delete the redundant points of the coronary artery. Finally, the algorithm continued carrying out the multi-constraint registration with another group of the pre-set parameters to improve the alignment performance.The experimental results demonstrated that the MPSR-RPR algorithm achieved a significantly lower mean value of the modified Hausdorff distance (MHD) compared to the other state-of-the-art methods for addressing the serious missing data in the left and right coronary arteries.This study demonstrated the effectiveness of the proposed algorithm in aligning coronary arteries, providing significant value in assisting in the diagnosis of coronary artery and myocardial lesions.
AdaInf: Data Drift Adaptive Scheduling for Accurate and SLO-guaranteed Multiple-Model Inference Serving at Edge Servers

Various audio and video applications rely on multiple deep neural network (DNN) models deployed on edge servers to conduct inference with ms-level latency service-level-objectives (SLOs). To avoid accuracy decreases caused by data drift, continual retraining is necessary. However, this poses a challenge for GPU resource allocation to satisfy the tight SLOs while maintaining high accuracy in this scenario. There has been no research devoted to tackling this issue. In this paper, we conducted trace-based experimental analysis in this particular scenario, which shows that different models have varying degrees of impact from data drift, incremental retraining (proposed by us that retrains certain samples before inference) and early-exit model structures can help increase accuracy, and the interdependencies among tasks may lead to significant CPU-GPU memory communications. Leveraging these unique observations, we propose a data drift Adaptive scheduler for accurate and SLO-guaranteed Inference serving at edge servers (AdaInf). AdaInf uses incremental retraining and allocates GPU amount among applications based on their SLOs. For each application, it splits GPU time between retraining and inference to satisfy its SLO, and then allocates GPU time among retraining tasks based on their impact degrees. In addition, AdaInf proposes strategies that leverage the job features in this scenario to reduce the impact of CPU-GPU memory communications on latency. Our real trace-driven experimental evaluation shows that AdaInf can increase accuracy by up to 21% and reduce SLO violations by up to 54% compared to existing methods. Achieving similar accuracy as AdaInf requires 4× more GPU resources on the edge server for the existing method.
Adversarial attack for object detectors under complex conditions

In recent years, deep learning security has become a hot research topic, and attackers often use well-designed adversarial examples as input to trick the deep learning model, especially in object detection. Recently, the adversarial patch has been widely used in the attack of the object detector by adding a continuous region of noise to the image. However, current research on the adversarial patch for object detectors has certain limitations. The adversarial attack performance tends to degrade when certain regions of the adversarial patch are accidentally obscured. So the two-stage method based on Generative Adversarial Network (TS-GAN) is proposed to address this issue. To train the generator, the TS-GAN uses the occlusion rule to simulate various situations where patches are occluded in physical scenes. Then the generator's parameters are fixed, and the optimization method is adopted to select the latent variables to generate the most effective adversarial patches. The results of extensive experiments in the digital world and physical environments show that our method achieves stable attack performance under complex conditions which are different occlusion.
Personality-assisted mood modeling with historical reviews for sentiment classification

Review sentiment classification aims to predict user sentiment for given user-generated review. Most of the existing methods enhance their sentiment classifiers by incorporating user information. However, those methods normally ignore user's mood, which could influence her/his sentiment expression. Actually, users in a certain mood tend to express mood-congruent sentiment. Furthermore, the related studies may not fully utilize user's personality to model her/his mood. In this paper, we are motivated to propose a Personality-Assisted Mood for Sentiment Classification (PAMSC) model to classify user sentiment. Concretely, we first adopt the target review with global user preference to prejudge user sentiment. Meanwhile, we model the personalized mood impact on her/his sentiment expression according to the corresponding historical reviews. We finally obtain the prediction result by utilizing the personalized mood impact to adjust the prejudged sentiment distribution. Particularly, personality plays two major roles in the process of mood modeling. One is to analyze the personalized duration of user's mood, and the other is to model the personalized attention toward mood-congruent information. The experimental results demonstrate that our PAMSC model not only achieves the highest classification accuracy than the related models on three real-world datasets but also has stronger interpretability for the prediction process.
WELDP: Weighed Edge Local Directional Pattern for expression recognition from facial images

At present, the local appearance-based texture descriptors in Facial Expression Recognition have limited accuracy due to the inability to encode the discriminative edges. The major cause is the presence of distorted and weak edges due to noise. Hence, this paper proposes new Expression Descriptor called as Weighted Edge Local Directional Pattern (WELDP) which can discriminate the weak and strong edges. Unlike the conventional local descriptors, WELDP searches for the support of neighbor pixels in the determination of Facial expression attributes such as Edges, Corners, Lines, and Curved Edges. WELDP encodes only Strong edge responses and discards weaker edge responses after extracting them through edge detection masks. This work adapted two masks for edge detection: they are Robinson Compass Mask and Kirsch Compass Mask. Moreover, the WELDP aims at code redundancy and encode each pixel only with seven bits (one sign bit and six directional bits). Then the WELDP image is described by a histogram and then processed through SVM (Support Vector Machine) for expression identification. From the simulation experiments, the proposed WELDP is found as better than several existing methods.
PLM-PGHC: A novel de-biasing framework for robust question answering

Reading Comprehension models have achieved superhuman performance on mainstream public datasets. However, many studies have shown that the models are likely to take advantage of biases in the datasets, which makes it difficult to efficiently reasoning when generalizing to out-of-distribution datasets with non-directional bias, resulting in serious accuracy loss. Therefore, this paper proposes a pre-trained language model based de-biasing framework with positional generalization and hierarchical combination. In this work, generalized positional embedding is proposed to replace the original word embedding to initially weaken the over-dependence of the model on answer distribution information. Secondly, in order to make up for the influence of regularization randomness on training stability, KL divergence term is introduced into the loss function to constrain the distribution difference between the two sub models. Finally, a hierarchical combination method is used to obtain classification outputs that fuse text features from different encoding layers, so as to comprehensively consider the semantic features at the multidimensional level. Experimental results show that PLM-PGHC helps learn a more robust QA model and effectively restores the F1 value on the biased distribution from 37.51% to 81.78%.
A unified framework for dataset shift diagnostics

Supervised learning techniques typically assume training data originates from the target population. Yet, in reality, dataset shift frequently arises, which, if not adequately taken into account, may decrease the performance of the supervised predictors. In this work, we propose a novel and flexible framework called DetectShift1 that quantifies and tests for multiple dataset shifts, encompassing shifts in the distributions of (X,Y), X, Y, X|Y, and Y|X. DetectShift equips practitioners with insights into data shifts, facilitating the adaptation or retraining of predictors using both source and target data. This proves extremely valuable when labeled samples in the target domain are limited. The framework utilizes test statistics with the same nature to quantify the magnitude of the various shifts, making results more interpretable. It is versatile, suitable for both regression and classification tasks, and accommodates diverse data forms—tabular, text, or image. Experimental results demonstrate the effectiveness of DetectShift in detecting dataset shifts even in higher dimensions.
VRKeyLogger: Virtual keystroke inference attack via eavesdropping controller usage pattern in WebVR

WebVR is an emerging technology that allows users to experience VR (Virtual Reality) through typical web browsers, providing an integrated environment for various VR applications. One important problem of the VR technology is how to securely interact with users, in particular, implementing secure text input. A promising approach is to use a virtual keyboard rendered as a VR object. The VR user can enter certain text by clicking a sequence of virtual keys through the VR controllers, and the input text is handled in a secure way. However, despite the sensitivity of the input text, we found that there is a critical vulnerability that the VR controllers are not properly protected. The VR controller status can be disclosed to malicious entities, imposing a severe threat that an attacker's website can infer the input text by eavesdropping and analyzing the VR controller's movements. To accurately infer the input, the attacker should address two challenges: 1) determining which clicks correspond to the virtual keyboard and 2) identifying which key is pressed. In this paper, we propose a new keystroke inference attack framework, VRKeyLogger, that addresses such challenges with two key components: key-click classifier and key-click identifier. The key-click classifier effectively distinguishes clicks on the virtual keyboard based on the SVM classifier trained by the major features of the VR controller uses. The key-click identifier then accurately identifies which key is pressed by transforming the clicked position into the local coordinate system of the virtual keyboard. We implemented a proof-of-concept prototype and conducted a user study with nine participants. In the extensive user study with three real-world WebVR applications, our VRKeyLogger results in classification and identification accuracy of 93.98 and 96.8% on average, respectively. This implies that the proposed attack poses a serious threat to WebVR security.
Multi-dimensional T-S dynamic fault tree analysis method involving failure correlation

The lack of effective failure correlation analysis is one main reason for the gap between the reliability models and the actual complex systems with mixed static and dynamic characteristics. Takagi and Sugeno (T-S) dynamic fault tree is one powerful tool to analyze the static and dynamic failure logic relationship but it assumes the failure probability of the event is independent. Therefore, this paper proposes a multi-dimensional T-S dynamic fault tree analysis method involving failure correlation. The method integrates the failure probability distribution function of basic events with multi-factors and the multi-dimensional copula function, and the important measure of this method is also deduced. The reliability model expression for systems with failure correlations, both in series and in parallel, is discussed and verified. Compare the proposed method with the assumption that the probability of a failure event is independent. This method solves the problem of a large error when ignoring the failure correlation between parts and the degree of the correlation between variables can be characterized. The reliability analysis can be conducted on complex systems affected both by multi-factors and failure correlations. The proposed method is applied to the reliability analysis of a hydraulic height adjustment system and the correctness and superiority of the method are verified.
Label correction using contrastive prototypical classifier for noisy label learning

Deep neural networks typically require a large number of accurately labeled images for training with cross-entropy loss, and often overfit noisy labels. Contrastive learning has proven impressive in noisy label learning because it can learn discrimination representations. However, the weak correlation between the samples and their semantic class, which ignores the correlation between the instances and labels, as well as the instance semantic divergence with the same label, may inevitably lead to class collisions, hampering the label correction. To address these problems, this study proposes a noisy label learning framework that performs label correction and constructs a contrastive prototypical classifier cooperatively. In particular, the prototypical classifier maximizes the distance between the instances and class prototypes to improve the intraclass compactness using contrastive prototypical loss. Furthermore, we provide a theoretical guarantee that the contrastive prototypical loss has a smaller Lipschitz constant and boosts the robustness. Motivated by the theoretical analysis, this framework performs label correction using the prediction of a contrastive prototypical classifier. Extensive experiments demonstrate that the proposed framework achieves superior classification accuracy on synthetic datasets with various noise patterns and levels.
Time-shift image enhancement method

This paper proposes a time-shift image enhancement method for representing constant images in spacetime, deriving the associated events in a specified time, and using them to enhance the degraded images. We assumed constant images as moving objects on a z-plane with different velocities and adopted the spacetime using the Lorentz factor to represent the movement of objects at the constant moment. The changes between the events and a reference frame are used to create adjacent z-plane events. The events provide statistical and pixel-based relationships in order to reconstruct a single frame for the common perspective of the reference image in time. Then, the gamma correction is applied in the reconstructed image based on the obtained statistics to provide enhancement of images by preserving the brightness and improving the contrast and details. In order to demonstrate the proposed methods’ general applicability, several challenging referenced and non-referenced datasets from different problem domains are considered. A comparative study has been performed, and the proposed method has been compared to state-of-the-art and recent image enhancement techniques. Both qualitative and quantitative results are analyzed for color correction, detail enhancement, and additional noise for each dataset. The results show that the Time-Shift Method could be effectively used for image enhancement for different problem domains without producing over- or under-enhancement.
A Comprehensive Analysis of the Worst Cybersecurity Vulnerabilities in Latin America

Vulnerabilities in cyber defense in the countries of the Latin American region have favored the activities of cybercriminals from different parts of the world who have carried out a growing number of cyberattacks that affect public and private services and compromise the integrity of users and organizations. This article describes the most representative vulnerabilities related to cyberattacks that have affected different sectors of countries in the Latin American region. A systematic review of repositories and the scientific literature was conducted, considering journal articles, conference proceedings, and reports from official bodies and leading brands of cybersecurity systems. The cybersecurity vulnerabilities identified in the countries of the Latin American region are low cybersecurity awareness, lack of standards and regulations, use of outdated software, security gaps in critical infrastructure, and lack of training and professional specialization.
Deep-view linguistic and inductive learning (DvLIL) based framework for Image Retrieval

The presence of abundant data over the Internet leads to complex issues when it comes to retrieving desired information from such a large volume of content. There are situations where a user needs to modify desired information across different modalities. One common example is retrieving a desired product from inventory of online commercial platforms. In such cases, a dual-modality-based Content-Based Image Retrieval (CBIR) system plays a key role in facilitating communication between the user and the agent. This research proposes a framework that is built for the retrieval of desired images with modified features. The proposed framework is based on the extraction of image and text features, followed by their combined representation through inductive learning. It learns deep insights of visual features, which are then modified by linguistic semantics. State-of-the-art deep learning techniques are employed for dense representation of both image and text features. After successfully representing the image and text queries, their combined representation is learned using a sequence of MLP (multi-layer perceptrons). The proposed approach outperformed on real-time benchmark datasets, Fashion-200K and MIT-States.
Analysis of Backchannel Inviting Cues in Dyadic Speech Communication

The paper aims to study speaker and listener behavior in dyadic speech communication. A multimodal (speech and video) corpus of dyadic face-to-face conversations on various topics was created. The corpus was manually labeled on several layers (text transcription, backchannel modality and function, POS tags, prosody, and gaze). The statistical analysis was done on the proposed corpus. We focused on backchannel inviting cues on the speaker side and backchannels on the listener side and their patterns. We aimed to study interlocutor backchannel behavior and backchannel-related signals. The results of the analysis show similar patterns in the case of backchannel inviting cues between Slovak and English data and highlight the importance of gaze direction in a face-to-face speech communication scenario. The described corpus and results of the analysis are one of the first steps leading towards natural artificial intelligence-driven human–computer speech conversation.