Reviewerly/exHarmony huggingface dataset:
Coresets over multiple tables for feature-rich and data-efficient machine learning

Successful machine learning (ML) needs to learn from good data. However, one common issue about train data for ML practitioners is the lack of good features. To mitigate this problem, feature augmentation is often employed by joining with (or enriching features from) multiple tables, so as to become feature-rich ML. A consequent problem is that the enriched train data may contain too many tuples, especially if the feature augmentation is obtained through 1 (or many)-to-many or fuzzy joins. Training an ML model with a very large train dataset is data-inefficient. Coreset is often used to achieve data-efficient ML training, which selects a small subset of train data that can theoretically and practically perform similarly as using the full dataset. However, coreset selection over a large train dataset is also known to be time-consuming. In this paper, we aim at achieving both feature-rich ML through feature augmentation and data-efficient ML through coreset selection. In order to avoid time-consuming coreset selection over a feature augmented (or fully materialized) table, we propose to efficiently select the coreset without materializing the augmented table. Note that coreset selection typically uses weighted gradients of the subset to approximate the full gradient of the entire train dataset. Our key idea is that the gradient computation for coreset selection of the augmented table can be pushed down to partial feature similarity of tuples within each individual table, without join materialization. These partial feature similarity values can be aggregated to estimate the gradient of the augmented table, which is upper bounded with provable theoretical guarantees. Extensive experiments show that our method can improve the efficiency by nearly 2 orders of magnitudes, while keeping almost the same accuracy as training with the fully augmented train data.
Using Elastic Bands for Collision Avoidance in Collaborative Robotics

The paper presents a new version of the existing elastic band algorithm used for path finding, with application in the field of collaborative robotics and point-to-point movements.The algorithm places control points on the path and dynamically modifies the position of these control points in reaction to any obstacles located or moving in the workspace.The control points are updated in the robot space (TCP space), obstacles are represented by a set of grid-aligned voxels acquired by a camera system.Repulsive forces are created between the obstacles and the robot body (represented by a set of points covering evenly the surface of individual links) and transferred to the locations of the elastic band control points.The method is computationally effective and provides a smooth and length-optimal path while considering collisions in a more accurate manner than traditional usage of simple bounding volumes.The dynamic iterative updating of elastic band provides a clear principle for modification of the trajectory even close to the actual location of the robot as it is following the trajectory.The algorithm is verified on a set of practical experiments made on a physical UR3 robot with simulated obstacles, and the results are also compared to other commonly used methods for dynamic obstacle avoidance.
Data Augmentation to Address Various Rotation Errors of Wearable Sensors for Robust Pre-impact Fall Detection

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> This paper proposes a novel application of data augmentation to address various rotation errors of wearable sensors for robust pre-impact fall detection. In such systems, sensor rotation errors are inevitable because of loose attachment and body movement during long deployment. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> Two augmented models with uniform and normal strategies were compared with a non-augmented model on the original dataset (no rotation error) and a validation dataset (with rotation error). The validation dataset was constructed with three types of rotation errors, namely, pitch, roll, and compound roll and pitch (CRP) at three levels of range (low: 15°, medium: 30°, and high: 45°). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Five-fold cross validation showed the two augmented models maintained accuracy (>98.5%) as high as the non-augmented model on the original dataset but showed considerable improvements of 6.11% and 6.50% on the validation dataset, respectively. CRP error negatively affected the model accuracy the most, followed by pitch and then roll errors. In addition, the normal model had advantages over the uniform model in the low-to-medium range of error, which is expected to be the typical error range in practical applications. As for lead time, similarly, the augmented models achieved performance similar to the non-augmented model on the original dataset but showed significant improvements on the validation dataset. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion and significance:</i> Data augmentation had notable capacities to address sensor rotation errors for practical applications and augmented models especially the normal model showed good potential to be embedded in a wearable system for robust pre-impact fall detection and injury prevention.
Multi-View Scheduling of Onboard Live Video Analytics to Minimize Frame Processing Latency

This paper presents a real-time multi-view scheduling framework for DNN-based live video analytics at the edge to minimize frame processing latency. The work is motivated by applications where a higher frame rate is important, not to miss actions of interest. Examples include defense, border security, and intruder detection applications where sensors (in this paper, cameras) are deployed to monitor key roads, chokepoints, or passageways to identify events of interest (and intervene in real-time). Supporting a higher frame rate entails lowering frame processing latency. We assume that multiple cameras are deployed with partially overlapping views. Each camera has access to limited onboard computing capacity. Many targets cross the field of view of these cameras (but the great majority do not require action). We take advantage of the spatial-temporal correlations among multi-camera video streams to perform target-to-camera assignment such that the maximum frame processing time across cameras is minimized. Specifically, we use a data-driven approach to identify objects seen by multiple cameras, and propose a batch-aware latency-balanced (BALB) scheduling algorithm to drive the object-to-camera assignment. We empirically evaluate the proposed system with a real-world surveillance dataset on a testbed consisting of multiple NVIDIA Jetson boards. The results show that our system substantially improves the video processing speed, attaining multiplicative speedups of 2.45× to 6.85×, and consistently outperforms the competitive static region partitioning strategy.
Partial Equivalence Checking of Quantum Circuits

Equivalence checking of quantum circuits is an essential element in quantum program compilation, in which a quantum program can be synthesized into different quantum circuits that may vary in the number of qubits, initialization requirements, and output states. Verifying the equivalences among the implementation variants requires proper generality. Although different notions of quantum circuit equivalence have been defined, prior methods cannot check observational equivalence between two quantum circuits whose qubits are partially initialized, which is referred to as partial equivalence. In this work, we prove a necessary and sufficient condition for two circuits to be partially equivalent. Based on the condition, we devise algorithms for checking quantum circuits whose partial equivalence cannot be verified by prior approaches. Experiment results confirm the generality and demonstrate the efficiency and effectiveness of our method. Our result may unleash the optimization power of quantum program compilation to take more aggressive steps.
Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification

To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role. However, such cross-view training samples could be unavailable under the ISo-lated Camera Supervised (ISCS) setting, e.g., a surveillance system deployed across distant scenes. To handle this challenging problem, a new pipeline is introduced by synthesizing the cross-camera samples in the feature space for model training. Specifically, the feature encoder and generator are end-to-end optimized under a novel method, Camera-Conditioned Stable Feature Generation (CCSFG). Its joint learning procedure raises concern on the stability of generative model training. Therefore, a new feature generator, σ-Regularized Conditional Variational Autoencoder (σ-Reg. CVAE), is proposed with theoretical and experimental analysis on its robustness. Extensive experiments on two ISCS person Re-ID datasets demonstrate the superiority of our CCSFG to the competitors. <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> https://github.com/ftd-Wuchao/CCSFG
Vision, status, and research topics of Natural Language Processing

The field of Natural Language Processing (NLP) has evolved with, and as well as influenced, recent advances in Artificial Intelligence (AI) and computing technologies, opening up new applications and novel interactions with humans. Modern NLP involves machines' interaction with human languages for the study of patterns and obtaining meaningful insights. NLP is increasingly receiving attention across academia and industry and demonstrates extraordinary opportunities and across AI applications (e.g., question answering, information retrieval, sentiment analysis, and recommender systems) and helps to deal with new tasks such as machine translation and reading comprehension, with real world performance improving all the time. This editorial first provides an overview of the field of NLP in terms of research grants, publication venues, and research topics. We then introduce the mission of Natural Language Processing Journal, a new NLP-focused Elsevier journal intended as a forum for researchers and practitioners to publish theoretical, practical, and methodological achievements related to trustworthy AI development and applications for analyzing, processing, and modeling human languages.
Multi-label Iterated Learning for Image Classification with Label Ambiguity

Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that datasets like ImageNet are weakly labeled since images with multiple object classes present are assigned a single label. This ambiguity biases models towards a single prediction, which could result in the suppression of classes that tend to co-occur in the data. Inspired by language emergence literature, we propose multi-label iterated learning (MILe) to incorporate the inductive biases of multi-label learning from single labels using the framework of iterated learning. MILe is a simple yet effective procedure that builds a multi-label description of the image by propagating binary predictions through successive generations of teacher and student networks with a learning bottleneck. Experiments show that our approach exhibits systematic benefits on ImageNet accuracy as well as ReaL F1 score, which indicates that MILe deals better with label ambiguity than the standard training procedure, even when fine-tuning from self-supervised weights. We also show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision. Furthermore, MILe improves performance in class incremental settings such as IIRC and it is robust to distribution shifts. Code: https://github.com/rajeswar18/MILe
Recognizing British Sign Language Using Deep Learning: A Contactless and Privacy-Preserving Approach

Sign language is utilized by deaf-mute to communicate through hand movements, body postures, and facial emotions. The motions in sign language comprise a range of distinct hand and finger articulations that are occasionally synchronized with the head, face, and body. Automatic sign language recognition (SLR) is a highly challenging area and still remains in its infancy compared with speech recognition after almost three decades of research. Current wearable and vision-based systems for SLR are intrusive and suffer from the limitations of ambient lighting and privacy concerns. To the best of our knowledge, our work proposes the first contactless British sign language (BSL) recognition system using radar and deep learning (DL) algorithms. Our proposed system extracts the 2-D spatiotemporal features from the radar data and applies the state-of-the-art DL models to classify spatiotemporal features from BSL signs to different verbs and emotions, such as Help, Drink, Eat, Happy, Hate, and Sad. We collected and annotated a large-scale benchmark BSL dataset covering 15 different types of BSL signs. Our proposed system demonstrates highest classification performance with a multiclass accuracy of up to 90.07% at a distance of 141 cm from the subject using the VGGNet model.
Understanding Collapse in Non-contrastive Siamese Representation Learning

Contrastive methods have led a recent surge in the performance of self-supervised representation learning (SSL). Recent methods like BYOL or SimSiam purportedly distill these contrastive methods down to their essence, removing bells and whistles, including the negative examples, that do not contribute to downstream performance. These “non-contrastive” methods surprisingly work well without using negatives even though the global minimum lies at trivial collapse. We empirically analyze these non-contrastive methods and find that SimSiam is extraordinarily sensitive to model size. In particular, SimSiam representations undergo partial dimensional collapse if the model is too small relative to the dataset size. We propose a metric to measure the degree of this collapse and show that it can be used to forecast the downstream task performance without any fine-tuning or labels. We further analyze architectural design choices and their effect on the downstream performance. Finally, we demonstrate that shifting to a continual learning setting acts as a regularizer and prevents collapse, and a hybrid between continual and multi-epoch training can improve linear probe accuracy by as many as 18% points using ResNet-18 on ImageNet.
Building a cybersecurity culture of public administration system in Poland

The security of the state and its citizens depends on the security of information and IT systems. Cybercrime incidents are recorded every day in public administration units of all countries. The article presents the level of use of information technologies in public administration units in Poland. The principles of building a cybersecurity culture that can help ensure cyber resilience were discussed, especially in situations of dynamic changes in the functioning of public administration, such as the Covid-19 pandemic. Selected aspects of the Cybersecurity Strategy of the Republic of Poland for 2019-2024, which are to contribute to building a security culture in Poland, were also presented.
Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval

As an increasingly popular task in multimedia information retrieval, video moment retrieval (VMR) aims to localize the target moment from an untrimmed video according to a given language query. Most previous methods depend heavily on numerous manual annotations (i.e., moment boundaries), which are extremely expensive to acquire in practice. In addition, due to the domain gap between different datasets, directly applying these pre-trained models to an unseen domain leads to a significant performance drop. In this paper, we focus on a novel task: cross-domain VMR, where fully-annotated datasets are available in one domain (“source domain”), but the domain of interest (“target domain”) only contains unannotated datasets. As far as we know, we present the first study on cross-domain VMR. To address this new task, we propose a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> odal <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ross- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> omain <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> lignment (MMCDA) network to transfer the annotation knowledge from the source domain to the target domain. However, due to the domain discrepancy between the source and target domains and the semantic gap between videos and queries, directly applying trained models to the target domain generally leads to a performance drop. To solve this problem, we develop three novel modules: (i) a domain alignment module is designed to align the feature distributions between different domains of each modality; (ii) a cross-modal alignment module aims to map both video and query features into a joint embedding space and to align the feature distributions between different modalities in the target domain; and (iii) a specific alignment module tries to obtain the fine-grained similarity between a specific frame and the given query for optimal localization. By jointly training these three modules, our MMCDA can learn domain-invariant and semantic-aligned cross-modal representations. Extensive experiments on three challenging benchmarks (ActivityNet Captions, Charades-STA and TACoS) illustrate that our cross-domain method MMCDA outperforms all state-of-the-art single-domain methods. Impressively, MMCDA raises the performance by more than 7% in representative cases, which demonstrates its effectiveness.
Silent Speech Eyewear Interface: Silent Speech Recognition Method using Eyewear with Infrared Distance Sensors

As eyewear devices such as smart glasses become more common, it is important to provide input methods that can be used at all times for such situations and people. Silent speech interaction (SSI) has the potential to be useful as a hands-free input method for various situations and people, including those who have difficulty with voiced speech. However, previous methods have involved sensor devices that are difficult to use anytime and anywhere. We propose a method for SSI that involves using an eyewear device equipped with infrared distance sensors. The proposed method measures facial skin movements associated with a speech from the infrared distance sensor mounted on an eyewear device and recognizes silent speech commands by applying machine learning to the time-series sensor data. The proposed method was applied to a prototype system including a sensor device consisting of eyewear and ear-mounted microphones to measure the movements of the cheek, jaw joint, and jaw. From the evaluation results, F-measure was 0.90 with five speech commands and 0.83 with ten longer speech commands, which indicates the feasibility of the proposed method as a simple hands-free input interface. Our study provides the first wearable sensing method that can easily apply SSI functions to eyewear devices.
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination

In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal. Large domain discrepancy makes HFR a difficult problem. Recent methods attempting to fill the gap via synthesis have achieved promising results, but their performance is still limited by the scarcity of paired training data. In practice, large-scale heterogeneous face data are often inaccessible due to the high cost of acquisition and annotation process as well as privacy regulations. In this paper, we propose a new face hallucination paradigm for HFR, which not only enables data-efficient synthesis but also allows to scale up model training without breaking any privacy policy. Unlike existing methods that learn face synthesis entirely from scratch, our approach is particularly designed to take advantage of rich and diverse facial priors from visible domain for more faithful hallucination. On the other hand, large-scale training is enabled by introducing a new federated learning scheme to allow institution-wise collaborations while avoiding explicit data sharing. Extensive experiments demonstrate the advantages of our approach in tackling HFR under current data limitations. In a unified framework, our method yields the state-of-the-art hallucination results on multiple HFR datasets.
The SPN Network for Digital Audio Data Based on Elliptic Curve Over a Finite Field

The mathematical operation of the Elliptic Curve over the prime finite field is wildly used for secure data communication as it provides high security while utilizing the same size as the secret key.This manuscript presents a novel approach the SPN (Substitution Permutation Network) for a new digital audio encryption scheme based on Mordell elliptic curve (MEC) over a finite prime field.The proposed scheme consists of a confusion and diffusion module.For the confusion module, the scheme initially generates 5 × 5 bijective S-boxes, which have never been applied in the present literature with good cryptographic properties.The generated S-box is then used parallel in the substitution module, which provides optimum confusion in the cipher data.For the diffusion property, the scheme generates pseudorandom number sequences used for block permutation and achieves the property of diffusion.The scheme has been thoroughly securitized against various attacks.The result shows the efficiency of the proposed algorithm over the existing schemes.In addition, the framework that is used to generate MEC points is based on the searching technique.Consequently, it significantly reduces the computational cost and enhances the scheme's performance compared to the schemes presented in the literature.