OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Face Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.
GWO-FI: A novel machine learning framework by combining Gray Wolf Optimizer and Frequent Itemsets to diagnose and investigate effective factors on In-Hospital Mortality and Length of Stay among Kermanshahian Cardiovascular Disease patients
Venue: arXiv cs.LG 2022
Year: 2022

Investigation and analysis of patient outcomes, including in-hospital mortality and length of stay, are crucial for assisting clinicians in determining a patient's result at the outset of their hospitalization and for assisting hospitals in allocating their resources. This paper proposes an approach based on combining the well-known gray wolf algorithm with frequent items extracted by association rule mining algorithms. First, original features are combined with the discriminative extracted frequent items. The best subset of these features is then chosen, and the parameters of the used classification algorithms are also adjusted, using the gray wolf algorithm. This framework was evaluated using a real dataset made up of 2816 patients from the Imam Ali Kermanshah Hospital in Iran. The study's findings indicate that low Ejection Fraction, old age, high CPK values, and high Creatinine levels are the main contributors to patients' mortality. Several significant and interesting rules related to mortality in hospitals and length of stay have also been extracted and presented. Additionally, the accuracy, sensitivity, specificity, and auroc of the proposed framework for the diagnosis of mortality in the hospital using the SVM classifier were 0.9961, 0.9477, 0.9992, and 0.9734, respectively. According to the framework's findings, adding frequent items as features considerably improves classification accuracy.
Learning-based Predictive Path Following Control for Nonlinear Systems Under Uncertain Disturbances
Venue: arXiv cs.RO 2022
Year: 2022

Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial for a nonlinear robotic system to achieve high-performance path following control. In this paper, we propose a novel learning-based predictive control scheme that couples a high-level model predictive path following controller (MPFC) with a low-level learning-based feedback linearization controller (LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental disturbances online and tracks the reference state accurately with a probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the linearized system model augmented with a virtual linear path dynamics model to optimize the evolution of path reference targets, and provides the reference states and controls for the low-level LB-FBLC. Simulation results illustrate the effectiveness of the proposed control strategy on a quadrotor path following task under unknown wind disturbances.
"Just a Normal Day in the Metaverse" -- Distraction Conflicts of Knowledge Work in Virtual Environments
Venue: arXiv cs.SI 2022
Year: 2022

The changing nature of knowledge work creates demands for emerging technologies as enablers for workplace innovation. One emerging technology to potentially remedy drawbacks of remote work arrangements are meta-verses that merge physical reality with digital virtuality. In the literature, such innovations in the knowledge work sector have been primarily examined against the backdrop of collaboration as a dependent variable. In this paper, however, we investigate knowledge work in metaverses from a distraction-conflict perspective because independent, uninterrupted activities are as much characteristic of knowledge work as collaboration. Preliminary findings show that knowledge workers in meta-verses experience arousal from the 1) presence, appearance, and behaviour of other avatars, 2) realism, novelty, and affordances of the virtual environment, and 3) technological friction and navigation. This work has the theoretical implication that distraction-conflict theory must be extended to incorporate additional sources of arousal when applied to the context of knowledge work in metaverses.
Toward Efficient Automated Feature Engineering
Venue: arXiv cs.LG 2022
Year: 2022

Automated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applications. Current AFE methods mainly focus on improving the effectiveness of the produced features, but ignoring the low-efficiency issue for large-scale deployment. Therefore, in this work, we propose a generic framework to improve the efficiency of AFE. Specifically, we construct the AFE pipeline based on reinforcement learning setting, where each feature is assigned an agent to perform feature transformation \com{and} selection, and the evaluation score of the produced features in downstream tasks serve as the reward to update the policy. We improve the efficiency of AFE in two perspectives. On the one hand, we develop a Feature Pre-Evaluation (FPE) Model to reduce the sample size and feature size that are two main factors on undermining the efficiency of feature evaluation. On the other hand, we devise a two-stage policy training strategy by running FPE on the pre-evaluation task as the initialization of the policy to avoid training policy from scratch. We conduct comprehensive experiments on 36 datasets in terms of both classification and regression tasks. The results show $2.9\%$ higher performance in average and 2x higher computational efficiency comparing to state-of-the-art AFE methods.
Gaussian Process Classification Bandits
Venue: arXiv cs.LG 2022
Year: 2022

Classification bandits are multi-armed bandit problems whose task is to classify a given set of arms into either positive or negative class depending on whether the rate of the arms with the expected reward of at least h is not less than w for given thresholds h and w. We study a special classification bandit problem in which arms correspond to points x in d-dimensional real space with expected rewards f(x) which are generated according to a Gaussian process prior. We develop a framework algorithm for the problem using various arm selection policies and propose policies called FCB and FTSV. We show a smaller sample complexity upper bound for FCB than that for the existing algorithm of the level set estimation, in which whether f(x) is at least h or not must be decided for every arm's x. Arm selection policies depending on an estimated rate of arms with rewards of at least h are also proposed and shown to improve empirical sample complexity. According to our experimental results, the rate-estimation versions of FCB and FTSV, together with that of the popular active learning policy that selects the point with the maximum variance, outperform other policies for synthetic functions, and the version of FTSV is also the best performer for our real-world dataset.
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN
Venue: arXiv cs.CV 2022
Year: 2022

Human Activity Recognition (HAR) is an emerging technology with several applications in surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the quick growth of ubiquitous Wi-Fi technologies, and the correlation between CSI dynamics and body motions. In this paper, we propose Principal Component-based Wavelet Convolutional Neural Network (or PCWCNN) -- a novel approach that offers robustness and efficiency for practical real-time applications. Our proposed method incorporates two efficient preprocessing algorithms -- the Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT). We employ an adaptive activity segmentation algorithm that is accurate and computationally light. Additionally, we used the Wavelet CNN for classification, which is a deep convolutional network analogous to the well-studied ResNet and DenseNet networks. We empirically show that our proposed PCWCNN model performs very well on a real dataset, outperforming existing approaches.
MRTNet: Multi-Resolution Temporal Network for Video Sentence Grounding
Venue: arXiv cs.CV 2022
Year: 2022

Given an untrimmed video and natural language query, video sentence grounding aims to localize the target temporal moment in the video. Existing methods mainly tackle this task by matching and aligning semantics of the descriptive sentence and video segments on a single temporal resolution, while neglecting the temporal consistency of video content in different resolutions. In this work, we propose a novel multi-resolution temporal video sentence grounding network: MRTNet, which consists of a multi-modal feature encoder, a Multi-Resolution Temporal (MRT) module, and a predictor module. MRT module is an encoder-decoder network, and output features in the decoder part are in conjunction with Transformers to predict the final start and end timestamps. Particularly, our MRT module is hot-pluggable, which means it can be seamlessly incorporated into any anchor-free models. Besides, we utilize a hybrid loss to supervise cross-modal features in MRT module for more accurate grounding in three scales: frame-level, clip-level and sequence-level. Extensive experiments on three prevalent datasets have shown the effectiveness of MRTNet.
Twisted skew $G$-codes
Venue: arXiv cs.IT 2022
Year: 2022

In this paper we investigate left ideals as codes in twisted skew group rings. The considered rings, which are often algebras over a finite field, allows us to detect many of the well-known codes. The presentation, given here, unifies the concept of group codes, twisted group codes and skew group codes.
On Ranges and Partitions in Optimal TCAMs
Venue: arXiv cs.NI 2022
Year: 2022

Traffic splitting is a required functionality in networks, for example for load balancing over paths or servers, or by the source's access restrictions. The capacities of the servers (or the number of users with particular access restrictions) determine the sizes of the parts into which traffic should be split. A recent approach implements traffic splitting within the ternary content addressable memory (TCAM), which is often available in switches. It is important to reduce the amount of memory allocated for this task since TCAMs are power consuming and are often also required for other tasks such as classification and routing. In the longest-prefix model (LPM), Draves et al. (INFOCOM 1999) find a minimal representation of a function, and Sadeh et al. (INFOCOM 2019) find a minimal representation of a partition. In certain situations, range-functions are of special interest, that is, all the addresses with the same target, or action, are consecutive. In this paper we show that minimizing the amount of TCAM entries to represent a partition comes at the cost of fragmentation, such that for some partitions some actions must have multiple ranges. Then, we also study the case where each target must have a single segment of addresses.
Leveraging Collaboration for Multifaceted Design and Product Teams: A Financial Perspective
Venue: arXiv cs.HC 2022
Year: 2022

Collaboration is a key driving force for a team's success. In this case study, we discuss the collaboration practices of the Design Team and a subset of product teams at Chatham Financial -- a financial risk management advisory and technology firm. The Design Team's collaboration workflow has four key elements surrounding the structure, cross-team communication, onboarding, and feedback that occurs both in team and cross-team collaborative partnerships. Each of the key elements leads to a unique set of challenges and opportunities for the Design Team. We analyze the current state of each element, their value proposition, challenges, and initiatives undertaken to make the collaboration practice more robust
Measuring an artificial intelligence agent's trust in humans using machine incentives
Venue: arXiv cs.AI 2022
Year: 2022

Scientists and philosophers have debated whether humans can trust advanced artificial intelligence (AI) agents to respect humanity's best interests. Yet what about the reverse? Will advanced AI agents trust humans? Gauging an AI agent's trust in humans is challenging because--absent costs for dishonesty--such agents might respond falsely about their trust in humans. Here we present a method for incentivizing machine decisions without altering an AI agent's underlying algorithms or goal orientation. In two separate experiments, we then employ this method in hundreds of trust games between an AI agent (a Large Language Model (LLM) from OpenAI) and a human experimenter (author TJ). In our first experiment, we find that the AI agent decides to trust humans at higher rates when facing actual incentives than when making hypothetical decisions. Our second experiment replicates and extends these findings by automating game play and by homogenizing question wording. We again observe higher rates of trust when the AI agent faces real incentives. Across both experiments, the AI agent's trust decisions appear unrelated to the magnitude of stakes. Furthermore, to address the possibility that the AI agent's trust decisions reflect a preference for uncertainty, the experiments include two conditions that present the AI agent with a non-social decision task that provides the opportunity to choose a certain or uncertain option; in those conditions, the AI agent consistently chooses the certain option. Our experiments suggest that one of the most advanced AI language models to date alters its social behavior in response to incentives and displays behavior consistent with trust toward a human interlocutor when incentivized.
Poseidon: Non-server WEB Forms Off-line Processing System
Venue: arXiv cs.NI 2022
Year: 2022

The proposed Poseidon system is based on email services of filled forms instead of WEB server based services. This approach is convenient especially for small applications or small-medium companies. It is based on PDF forms that are available on a WEB page. PDF forms can be downloaded, off-line filled in, printed and finally sent by email for final processing. Data are actually stored in the local outbox waiting for a connection to a mail server. This follows an idea of the standard "paper" letter sending. Filled in data are actually sent when a user is on-line, therefore a user is "free" of being on-line when filling the forms. When the PDF form is processed on the recipient side, answer is sent back via email as well. Typical application is e.g. in conference management systems, systems for submission to journals etc. The great advantage of the PDF forms use is that they can be easily made or modified by a non-specialized administrative person easily.
The most general manner to injectively align true and predicted segments
Venue: arXiv cs.CV 2022
Year: 2022

Kirilov et al (2019) develop a metric, called Panoptic Quality (PQ), to evaluate image segmentation methods. The metric is based on a confusion table, and compares a predicted to a ground truth segmentation. The only non straightforward part in this comparison is to align the segments in the two segmentations. A metric only works well if that alignment is a partial bijection. Kirilov et al (2019) list 3 desirable properties for a definition of alignment: it should be simple, interpretable and effectively computable. There are many definitions guaranteeing a partial bijection and these 3 properties. We present the weakest: one that is both sufficient and necessary to guarantee that the alignment is a partial bijection. This new condition is effectively computable and natural. It simply says that the number of correctly predicted elements (in image segmentation, the pixels) should be larger than the number of missed, and larger than the number of spurious elements. This is strictly weaker than the proposal in Kirilov et al (2019). In formulas, instead of |TP|> |FN\textbar| + |FP|, the weaker condition requires that |TP|> |FN| and |TP| > |FP|. We evaluate the new alignment condition theoretically and empirically.
Financial Crimes in Web3-empowered Metaverse: Taxonomy, Countermeasures, and Opportunities
Venue: arXiv cs.CR 2022
Year: 2022

At present, the concept of metaverse has sparked widespread attention from the public to major industries. With the rapid development of blockchain and Web3 technologies, the decentralized metaverse ecology has attracted a large influx of users and capital. Due to the lack of industry standards and regulatory rules, the Web3-empowered metaverse ecosystem has witnessed a variety of financial crimes, such as scams, code exploit, wash trading, money laundering, and illegal services and shops. To this end, it is especially urgent and critical to summarize and classify the financial security threats on the Web3-empowered metaverse in order to maintain the long-term healthy development of its ecology. In this paper, we first outline the background, foundation, and applications of the Web3 metaverse. Then, we provide a comprehensive overview and taxonomy of the security risks and financial crimes that have emerged since the development of the decentralized metaverse. For each financial crime, we focus on three issues: a) existing definitions, b) relevant cases and analysis, and c) existing academic research on this type of crime. Next, from the perspective of academic research and government policy, we summarize the current anti-crime measurements and technologies in the metaverse. Finally, we discuss the opportunities and challenges in behavioral mining and the potential regulation of financial activities in the metaverse. The overview of this paper is expected to help readers better understand the potential security threats in this emerging ecology, and to provide insights and references for financial crime fighting.
Clock and Orientation-Robust Simultaneous Radio Localization and Mapping at Millimeter Wave Bands
Venue: arXiv cs.IT 2022
Year: 2022

This paper proposes a radio simultaneous location and mapping (radio-SLAM) scheme based on sparse multipath channel estimation. By leveraging sparse channel estimation schemes at millimeter wave bands, namely high resolution estimates of the multipath angle of arrival (AoA), time difference of arrival (TDoA), and angle of departure (AoD), we develop a radio-SLAM algorithm that operates without any requirements of clock synchronization, receiver orientation knowledge, multiple anchor points, or two-way protocols. Thanks to the AoD information obtained via compressed sensing (CS) of the channel, the proposed scheme can estimate the receiver clock offset and orientation from a single anchor transmission, achieving sub-meter accuracy in a realistic typical channel simulation.
Cross-Resolution Flow Propagation for Foveated Video Super-Resolution
Venue: arXiv cs.CV 2022
Year: 2022

The demand of high-resolution video contents has grown over the years. However, the delivery of high-resolution video is constrained by either computational resources required for rendering or network bandwidth for remote transmission. To remedy this limitation, we leverage the eye trackers found alongside existing augmented and virtual reality headsets. We propose the application of video super-resolution (VSR) technique to fuse low-resolution context with regional high-resolution context for resource-constrained consumption of high-resolution content without perceivable drop in quality. Eye trackers provide us the gaze direction of a user, aiding us in the extraction of the regional high-resolution context. As only pixels that falls within the gaze region can be resolved by the human eye, a large amount of the delivered content is redundant as we can't perceive the difference in quality of the region beyond the observed region. To generate a visually pleasing frame from the fusion of high-resolution region and low-resolution region, we study the capability of a deep neural network of transferring the context of the observed region to other regions (low-resolution) of the current and future frames. We label this task a Foveated Video Super-Resolution (FVSR), as we need to super-resolve the low-resolution regions of current and future frames through the fusion of pixels from the gaze region. We propose Cross-Resolution Flow Propagation (CRFP) for FVSR. We train and evaluate CRFP on REDS dataset on the task of 8x FVSR, i.e. a combination of 8x VSR and the fusion of foveated region. Departing from the conventional evaluation of per frame quality using SSIM or PSNR, we propose the evaluation of past foveated region, measuring the capability of a model to leverage the noise present in eye trackers during FVSR. Code is made available at https://github.com/eugenelet/CRFP.
From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis
Venue: arXiv cs.CV 2022
Year: 2022

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.