Reviewerly/exHarmony huggingface dataset:
Career Mobility Analysis With Uncertainty-Aware Graph Autoencoders: A Job Title Transition Perspective

Career mobility analysis aims at discovering the movement patterns of employees across different job positions or grades, which can benefit various human resource-related applications. Indeed, recent studies in this direction mainly focus on modeling individual career trajectories, while the macroguidance for labor market assessment has been largely ignored. To this end, in this article, we propose to study career mobility from a market-driven perspective based on large-scale online professional networks (OPNs). Specifically, we propose an uncertainty-aware graph autoencoders (UnGAEs) framework, which can simultaneously discover potential job title transition patterns and predict job durations. In this phase, we first construct a job title transition graph based on massive career trajectory data from OPNs. Then, considering the inherent uncertainty in career mobility, we introduce a novel uncertainty-aware graph encoder (UnGE) to represent job titles as Gaussian embeddings. Furthermore, we design two task-specific decoders that can preserve the asymmetric relationships between job titles, namely the gravity-inspired decoder (GID) and the energy-inspired decoder (EID), for predicting potential transition patterns and corresponding duration, respectively. In particular, both tasks are modeled through a specially designed multitask learning approach. Finally, extensive experiments on a real-world dataset clearly demonstrate the effectiveness of UnGAE compared with state-of-the-art baselines, as well as some potential applications such as job title benchmarking and career path planning.
CSC-Net: Cross-Color Spatial Co-Occurrence Matrix Network for Detecting Synthesized Fake Images

Recently, the Generative Adversarial Networks (GAN) generated images have been spread over the social networks, which brings the new challenge in the the community of media forensics. Although some reliable forensic tools have advanced the study of detecting GAN generated images, while the detection accuracy cannot be guaranteed when facing the malicious post-processing attacks, especially in the practical social network scenario. Thus, in this context, we propose a novel well-designed deep neural network equipped with handed-crafted features for dealing with this problem. In particular, relying on the Cross-color Spatial Co-occurrence Matrix (CSCM), the discriminative features are extracted after carefully analyzing and selecting the most effective color channels. Next, the fused features are fed into the deep neutral network for training a high-efficient forensic detector. Extensive experimental results empirically verify that in most detection scenarios, our proposed detector performs superiorly to the prior arts, especially in the case of post-processing attacks. Moreover, we also highlight the relevance of the proposed detector over the realistic social network platforms, and its generalization capability in three different scenarios.
An intelligent active probing and trace-back scheme for IoT anomaly detection

Due to their simple hardware, sensor nodes in IoT are vulnerable to attack, leading to data routing blockages or malicious tampering, which significantly disrupts secure data collection. An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection (APTAD) is proposed to collect integrated IoT data by recruiting Mobile Edge Users (MEUs). (a) An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes. (b) Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes. (c) The last, the number of active detection packets and detection paths are designed, so as to accurately identify the trust of nodes in IoT at the minimum cost of the network. A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33% respectively, while the accuracy of trust identification is improved by 20%.
Consensus Synergizes With Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes

Anomaly segmentation is a critical task for safety-critical applications, such as autonomous driving in urban environments. Its objective is to detect out-of-distribution (OOD) samples with unseen categories, given a pre-trained segmentation model. The core challenge of this task is how to distinguish hard in-distribution samples from OOD samples, which has not been explicitly discussed in previous research. In this paper, we propose a simple yet effective approach named CosMe (Consensus Synergizes with Memory) to address this challenge. CosMe consists of two key components: 1) building a memory bank comprising seen prototypes extracted from multiple layers of the given segmentation model, and 2) training an auxiliary model that mimics the behavior of the given model and using the consensus of their mid-level features as complementary cues that synergize with the memory bank. The former serves as a baseline that can detect all potential outliers, including both OOD and hard in-distribution samples; the latter assists in distinguishing between these two types of outliers. Experimental results on several urban scene anomaly segmentation datasets demonstrate that CosMe outperforms previous approaches by a significant margin.
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.
Training GAN with predictive centripetal acceleration

To alleviate the issue of limit cycle behavior in training generative adversarial networks (GAN), in this paper, we draw inspiration from the centripetal acceleration algorithm and the modified predictive method (MPM) by Liang and Stokes (2019). Building upon geometric observation of uniform circular motion, we propose the predictive centripetal acceleration algorithm (PCA). First and foremost, we prove the last-iterate convergence of the PCA on the bilinear game, which is a special case of the GAN. Besides, by combining PCA with the stochastic gradient descent (SGD) algorithm and adaptive moment estimation (Adam) algorithm, we propose two variants, which are called stochastic PCA (SPCA) and PCA-Adam, for the practical training GAN. Last but not least, experiments conducted on the bilinear game, multivariate Gaussian distribution, and CIFAR10 and CelebA datasets validate the effectiveness of the proposed algorithm.
Application Prioritization Engine for Enhancing Real-Time Performance in Smartphones

Smartphone consumers use various applications (apps), including online gaming, chat, streaming, video calling, and social networking. The smartphone relies on the network backhaul, such as a Wi-Fi access point, to provide the required Quality of Service (QoS). In its send-and-receive queues, the smartphone processes the packets in a first-in-first-out (FIFO) fashion. Many people worldwide started using video calling apps daily during the pandemic and post-pandemic periods. On the other hand, online gaming apps skyrocketed and continue to engage people. When real-time (RT) video calling and gaming apps race with non-real-time (NRT) traffic, we found a severe degradation in the Quality of Experience (QoE). In this work, we propose an Application Prioritization Engine (APE) framework that will improve user experience by dynamically allocating bandwidth to the different apps in the smartphone. APE helps improve the end-user experience by detecting and prioritizing real-time traffic over concurrent best-effort traffic. We introduced an eBPF (extended Berkeley Packet filter) that can control the NRT traffic to the extent that it does not affect the RT traffic. We evaluate the performance of APE with the top-chart video calling and gaming apps in a live-air scenario. APE enhances video calling performance in poor network conditions by improving the bit rate to 110%. Furthermore, it provides a four-fold gaming latency reduction despite NRT traffic. APE is a tech-transferred, app-agnostic, server-independent solution enabled in the latest Samsung flagship Smartphones with Android 13 OS.
See and Learn More: Dense Caption-Aware Representation for Visual Question Answering

With the rapid development of deep learning models, great improvements have been achieved in the Visual Question Answering (VQA) field. However, modern VQA models are easily affected by language priors, which ignore image information and learn the superficial relationship between questions and answers, even in the optimal pre-training model. The main reason is that visual information is not fully extracted and utilized, which results in a domain gap between vision and language modalities to a certain extent. In order to mitigate the circumstances, we propose to extract dense captions (auxiliary semantic information) from images to enhance the visual information for reasoning and utilize them to release the gap between vision and language since the dense captions and the questions are from the same language modality (i.e., phrase or sentence). In this paper, we propose a novel dense caption-aware visual question answering model called DenseCapBert to enhance visual reasoning. Specifically, we generate dense captions for the images and propose a multimodal interaction mechanism to fuse dense captions, images, and questions in a unified framework, which makes the VQA models more robust. The experimental results on GQA, GQA-OOD, VQA v2, and VQA-CP v2 datasets show that dense captions are beneficial to improving the model generalization and our model effectively mitigates the language bias problem.
ViChaser: Chase Your Viewpoint for Live Video Streaming With Block-Oriented Super-Resolution

The usage of live streaming services has led to a substantial increase in live video traffic. However, the perceived quality of experience of users is frequently limited by variations in the upstream bandwidth of streamers. To address this issue, several adaptive bitrate (ABR) algorithms have been developed to mitigate bandwidth variations. Nevertheless, the ability of users to enjoy high-quality live streams remains limited. While neural-enhanced approaches, such as super-resolution, offer significant quality improvements, frame-oriented super-resolution leads to excessive inference delay that violates the real-time feature of live streaming. In response, we propose ViChaser, which examines block-oriented super-resolution for live streaming. ViChaser performs neural super-resolution on potential blocks of interest in the media server, corresponding to the user’s viewpoint, and uses online learning to adapt to the dynamic content of the video. Additionally, ViChaser utilizes the Lyapunov framework to efficiently allocate uplink bandwidth for original low-quality live video and high-quality labels. The experimental results demonstrate that ViChaser achieves 1.2–1.5 dB higher video quality in Peak-Signal-to-Noise-Ratio than WebRTC and increases processing speed by 11–16 fps relative to LiveNAS.
Adversarial Attacks for Neural Network-Based Industrial Soft Sensors: Mirror Output Attack and Translation Mirror Output Attack

Soft sensing using the neural network technique has been increasingly applied to industrial processes. Recently, the security and robustness of neural network based soft sensors have become primary concerns. Additionally, current studies indicated that neural networks are vulnerable to adversarial attacks. In other words, small perturbations imposed on the input can lead to significant deviations in the output. If a soft sensor for key process variables is attacked, considerable damage may be brought to industrial processes. This paper focuses on the attack methods for neural network based industrial soft sensors. Considering the characteristics of industrial soft sensors, this paper proposes two new adversarial attack methods. The first method, called the Mirror Output Attack (MOA), is a subtle attack method that flips the output curve to change the direction of outputs. The second method, called the Translation Mirror Output Attack (TMOA), is easy to make operators misoperate. TMOA translates the output curve while flipping the output curve to achieve the purpose of changing the output conditions. The effectiveness of MOA and TMOA is demonstrated in an industrial case study of the sulfur recovery unit (SRU) process. Simulation results show that the neural network based industrial soft sensors can be attacked by both of the two proposed adversarial attack methods. The study of adversarial attack methods can provide a basis for defending against attacks, thereby enhancing the security and robustness of soft sensors.
Information Transfer in Semi-Supervised Semantic Segmentation

Enhancing the accuracy of dense classification with limited labeled data and abundant unlabeled data, known as semi-supervised semantic segmentation, is an essential task in vision comprehension. Due to the lack of annotation in unlabeled data, additional pseudo-supervised signals, typically pseudo-labeling, are required to improve the performance. Although effective, these methods fail to consider the internal representation of neural networks and the inherent class-imbalance in dense samples. In this work, we propose an information transfer theory, which establishes a theoretical relationship between shallow and deep representations. We further apply this theory at both the semantic and pixel levels, referred to as IIT-SP, to align different types of information. The proposed IIT-SP optimizes shallow representations to match the target representation required for segmentation. This limits the upper bound of deep representations to enhance segmentation performance. We also propose a momentum-based Cluster-State bar that updates class status online, along with a HardClassMix augmentation and a loss weighting technique to address class imbalance issues based on it. The effectiveness of the proposed method is demonstrated through comparative experiments on PASCAL VOC and Cityscapes benchmarks, where the proposed IIT-SP achieves state-of-the-art performance, reaching mIoU of 68.34% with only 2% labeled data on PASCAL VOC and mIoU of 64.20% with only 12.5% labeled data on Cityscapes.
Leveraging Graph Neural Networks for SLA Violation Prediction in Cloud Computing

In this paper we examine different approaches for the prediction of Service Level Agreements (SLAs) violations that occur during the service provisioning between cloud customers and providers.Despite the fact that there are many network metrics that involve the server -client interaction, it is an open research question how these available metrics can be used by a SLA prediction mechanism.We study three different data representation models for the network characteristics, a time series, a content and a context representation.We see that a context approach using graph representations captures efficiently the associativity of clients and improves the performance of traditional SLA violation prediction models when it is combined with them.The prediction of the SLA violations takes place using neural networks, making us propose a composite SLA prediction model that leverages Graph Neural Networks (GNNs).In our research, we put special emphasis and try different variations on how we construct the graphs.We perform an extensive performance evaluation of 23 different SLA prediction models that can be grouped into the three representations categories, namely the vector models that are based on network features, sequential models that leverage the temporal evolution of QoS metrics and Graph models that take into consideration the associativity of the clients.The experimental results show that our proposed GNN-based model can significantly improve the accuracy of SLA violation prediction, making it a useful tool for Cloud and Service providers.
Encrypted network traffic classification based on machine learning

Encrypted traffic is an essential part of maintaining the security and privacy of data transmission. It plays an important role in keeping our networks secure by preventing attackers from intercepting confidential information, which they may access without authorization; However, its effectiveness relies heavily on accurate classification techniques being applied correctly, so we can differentiate between legitimate users' activities versus those attempting malicious activity within the networks’ boundaries. Encrypted network traffic is becoming increasingly common in modern communication systems, presenting a challenge for effective network management and security. To address this challenge, machine learning models have been employed to classify encrypted traffic but with limited success due to the lack of clear visibility into packet contents and an inability to inspect their content. For the sake of tackling this issue, more effective research has begun on developing machine learning models for classifying encrypted payloads without relying on inspecting their contents directly. This research will investigate how features like packet length, time stamps or transport layer security (TLS) and encrypted payload information can be used as input features when attempting classification tasks, instead of analyzing unencrypted content directly from packets themselves which would otherwise be impossible given the current technology constraints. The evaluation process will focus on assessing different model architectures, as well as feature selection techniques that yield improved results over the existing approaches. In this paper, we proposed three approaches to identify encrypted traffic and classify different applications such as browsing, VOIP, file transfer and video streaming. The first two techniques consist of two stages: the first stage is either a neural network or a bi-directional LSTM, and the second stage is a selection of different classification techniques, namely Random Forest, Support vector machine, Linear regression, and K-nearest neighbor. The final result is achieved using an ensemble voting technique. As for the third technique, the network packets are grouped together by Source IP, destination IP and session time before feeding them into three different combinations of LSTM networks; either coupled with convolution 1D or 2D layers, or without. Like the first two techniques, the final result is achieved by means of ensemble voting. Through extensive comparison between the three approaches, The first approach yielded the highest accuracy. However, the performance of the second and third techniques in terms of time complexity was superior. The achieved accuracies were 96.8%, 95.2% and 96.5% for the proposed techniques, respectively.
RAFNet: Reparameterizable Across-Resolution Fusion Network for Real-Time Image Semantic Segmentation

The demand to implement semantic segmentation networks on mobile devices has increased dramatically. However, existing real-time semantic segmentation methods still suffer from a large number of network parameters, unsuitable for mobile devices with limited memory resources. The reason mainly arises from the fact that most existing methods take the backbone networks (e.g., ResNet-18 and MobileNet) as an encoder. To alleviate this problem, we propose a novel Reparameterizable Channel & Dilation (RCD) block and construct a considerably lightweight yet effective encoder by stacking several RCD blocks according to three guidelines. The strengths of the proposed encoder result in the abilities not only to extract discriminative feature representations via channel convolutions and dilated convolutions, but also to reduce computational burdens while maintaining segmentation accuracy with the help of re-parameterization technique. Except for encoder, we also present a simple but effective decoder that adopts an across-resolution fusion strategy to fuse multi-scale feature maps generated from the encoder instead of a bottom-up pathway fusion. With such an encoder and a decoder, we provide a Reparameterizable Across-resolution Fusion Network (RAFNet) for real-time semantic segmentation. Extensive experiments demonstrate that our RAFNet achieves a promising trade-off between segmentation accuracy, inference speed and network parameters. Specifically, our RAFNet with only 0.96M parameters obtains 75.3% mIoU at 107 FPS and 75.8% mIoU at 195 FPS on Cityscapes and CamVid test sets for full-resolution inputs, respectively. After quantization and deployment on a Xilinx ZCU104 device, our RAFNet obtains a favorable segmentation performance with only 1.4W power.
HybridAD: A Hybrid Model-Driven Anomaly Detection Approach for Multivariate Time Series

Anomaly detection, in recent years, has gained increasing attention in the research and practice of time series processing. However, the task is particularly challenging with multivariate time series which complicates the temporal dependency between observations and introduces complex inter-channel correlation. Meanwhile, in order to fit a broader range of applications, robustness during both training and detection is also a critical aspect. In this paper, we propose an unsupervised, hybrid model-driven anomaly detection scheme capable of (1) transforming sequences into a fused representation of temporal dependency embeddings and inter-channel correlation embeddings, and (2) achieving robust anomaly detection using a temporal prediction network for sample-wise posterior estimation combined with a data reconstruction network to assess the source of prediction. On this basis, we develop a probability density-based anomaly scoring mechanism for online detection in multivariate time series, where the anomaly score for each observation is rectified by the reliability of the prediction source. The results of extensive experiments on five publicly available datasets show that our proposed solution outperforms various state-of-the-art anomaly detection algorithms (including DL-based and non-DL-based), achieving a performance improvement (in F1-Score) by up to 10.42%.