Reviewerly/exHarmony huggingface dataset:
Salient Object Detection for Point Clouds

This paper researches the unexplored task—point cloud salient object detection (SOD). Differing from SOD for images, we find the attention shift of point clouds may provoke saliency conflict, i.e., an object paradoxically belongs to salient and non-salient categories. To eschew this issue, we present a novel view-dependent perspective of salient objects, reasonably reflecting the most eye-catching objects in point cloud scenarios. Following this formulation, we introduce PCSOD, the first dataset proposed for point cloud SOD consisting of 2,872 in-/out-door 3D views. The samples in our dataset are labeled with hierarchical annotations, e.g., super-/sub-class, bounding box, and segmentation map, which endows the brilliant generalizability and broad applicability of our dataset verifying various conjectures. To evidence the feasibility of our solution, we further contribute a baseline model and benchmark five representative models for a comprehensive comparison. The proposed model can effectively analyze irregular and unordered points for detecting salient objects. Thanks to incorporating the task-tailored designs, our method shows visible superiority over other baselines, producing more satisfactory results. Extensive experiments and discussions reveal the promising potential of this research field, paving the way for further study.
POCS-based Clustering Algorithm

A novel clustering technique based on the projection onto convex set (POCS) method, called POCS-based clustering algorithm, is proposed in this paper. The proposed POCS-based clustering algorithm exploits a parallel projection method of POCS to find appropriate cluster prototypes in the feature space. The algorithm considers each data point as a convex set and projects the cluster prototypes parallelly to the member data points. The projections are convexly combined to minimize the objective function for data clustering purpose. The performance of the proposed POCS-based clustering algorithm is verified through experiments on various synthetic datasets. The experimental results show that the proposed POCS-based clustering algorithm is competitive and efficient in terms of clustering error and execution speed when compared with other conventional clustering methods including Fuzzy C-Means (FCM) and K-Means clustering algorithms.
A Conservative Approach for Unbiased Learning on Unknown Biases

Although convolutional neural networks (CNNs) achieve state-of-the-art in image classification, recent works address their unreliable predictions due to their excessive dependence on biased training data. Existing unbiased modeling postulates that the bias in the dataset is obvious to know, but it is actually unsuited for image datasets including countless sensory attributes. To mitigate this issue, we present a new scenario that does not necessitate a predefined bias. Under the observation that CNNs do have multi-variant and unbiased representations in the model, we propose a conservative framework that employs this internal information for unbiased learning. Specifically, this mechanism is implemented via hierarchical features captured along the multiple layers and orthogonal regularization. Extensive evaluations on public benchmarks demonstrate our method is effective for unbiased learning. <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> Source code: https://github.com/aandyjeon/UBNet
Innovative Variational AutoEncoder for an End-to-end Communication System

Powered by deep learning (DL), autoencoders (AE) end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in traditional communication systems and have achieved great success.In this paper, a new probabilistic model, based on the variational autoencoders (VAE), is proposed for short packet wireless communication systems.Using this new approach, the information messages are represented by the so called packet hot vectors (PHV) which are inferred by the VAE latent random variables (LRVs).Then only LRVs' parameters can be transmitted through the physical wireless channel.This results in a significant improvement in spectral efficiency when compared with the pure AE approach where a longer hot vectors are to be transmitted.Specific VAE models have been developed for both binary (BPSK) as well as Quadrature phase shift keying (QPSK) systems.Simulation and numerical results are given to demonstrate the performance of the proposed method in different real scenarios, including Rayleigh and Rician fading channels with Shadowing and Doppler effects.Our simulation and numerical results show that the new proposed VAE with a DL classifier can provide an improved symbol error rate (SER) performance than both the baseline AE and the classical Hamming code with hard decision decoding.Furthermore, as far as the spectral efficiency of the proposed method is concerned, we show that using two channels in the proposed VAE performance exceeds the 7 channels baseline AE.
Estimating Uncertainty of Deep Learning Multi-Label Classifications Using Laplace Approximation

Deep learning methods have become valuable tools in remote sensing for tasks like aerial scene classification or land cover analysis. Dealing with noisy and very varying data, the need for reliable confidence statements becomes apparent. While deep learning models are known to yield overconfident predictions, quantifying the model uncertainty of those classifiers can help mitigating that effect. Although uncertainty estimation methods for multi-class classification have been published, multi-label classification - the task of labelling data with multiple class labels simultaneously - has hardly been considered yet. In this study, we use multi-label Laplace Approximation to estimate the model uncertainty of deep multi-label classifiers and show how this method can improve calibration and out-of-distribution detection in the remote sensing domain.
A Comprehensive Review on Sign Language Recognition Using Machine Learning

The people of the dumb and deaf community adopt sign language to talk with one another. Sign language is an effective method for the interaction between speech and hearing-impaired individuals and a normal person without any requirement of an interpreter. Every nation has its own sign language, which has been used by the impaired residents of that particular country to communicate either with normal individuals or with a computer system in real-time. The recognition of sign language becomes an emerging research domain in computer vision. The researchers find several difficulties to recognize them correctly and adequately as the sign instance varies with appearance as well as motion. This paper explained all the challenges faced by the researchers while developing or designing a real-time system for sign language recognition. The main intent of this paper is to review the numerous machine learning methodologies that are utilized by various researchers to present a real- time system for the recognition of sign language. The performances of the existing system are also compared with each other to identify which machine learning methodology is generating accurate as well as an effective result. Based on this review paper, further future research work can be conducted as this paper also presented the directions of present future research.
Augmented Random Oracles

We propose a new paradigm for justifying the security of random oracle-based protocols, which we call the Augmented Random Oracle Model (AROM). We show that the AROM captures a wide range of important random oracle impossibility results. Thus a proof in the AROM implies some resiliency to such impossibilities. We then consider three ROM transforms which are subject to impossibilities: Fiat-Shamir (FS), Fujisaki-Okamoto (FO), and Encrypt-with-Hash (EwH). We show in each case how to obtain security in the AROM by strengthening the building blocks or modifying the transform. Along the way, we give a couple other results. We improve the assumptions needed for the FO and EwH impossibilities from indistinguishability obfuscation to circularly secure LWE; we argue that our AROM still captures this improved impossibility. We also demonstrate that there is no “best possible” hash function, by giving a pair of security properties, both of which can be instantiated in the standard model separately, which cannot be simultaneously satisfied by a single hash function.
Lightweight Midrange Arm-Gesture Recognition System From mmWave Radar Point Clouds

This article considers arm-gesture recognition in the form of a point cloud in mid-range scenarios. A 2-D steer vector is established according to the structure of the radar array antenna, and the average power of the array is added to the point cloud image as a feature. In the data processing stage, the proposed system aggregates multiple measurements on all frames, overcoming the issues of the spatial sparsity and ambiguous temporal variation of the point cloud image of the millimeter-wave (mmWave) radar, as a new arm-gesture point cloud image. In the arm-gesture recognition stage, a lightweight PointNet-based classifier is custom-designed to recognize and classify arm-gestures point cloud images. Extensive experiments are carried out on custom-built radar gesture datasets with eight human subjects performing 19 arm-gestures. The experimental results demonstrate that the proposed arm-gesture recognition system can accurately distinguish 19 different arm-gestures with an average accuracy of 99.69% in two indoor environments and outperform five related arm-gesture classifiers. We further analyze the effect of the scenarios and distance on recognition performance.
Online Search-Based Collision-Inclusive Motion Planning and Control for Impact-Resilient Mobile Robots

This article focuses on the emerging paradigm shift of collision-inclusive motion planning and control for impact-resilient mobile robots, and develops a unified hierarchical framework for navigation in unknown and partially observable cluttered spaces. At the lower level, we develop a deformation recovery control and trajectory replanning strategy that handles collisions that may occur at run time, locally. The low-level system actively detects collisions (via embedded Hall effect sensors on a mobile robot built in-house), enables the robot to recover from them, and locally adjusts the postimpact trajectory. Then, at the higher level, we propose a search-based planning algorithm to determine how to best utilize potential collisions to improve certain metrics, such as control energy and computational time. Our method builds upon A* with jump points. We generate a novel heuristic function, and a collision checking and adjustment technique, thus making the A* algorithm converge faster to reach the goal by exploiting and utilizing possible collisions. The overall hierarchical framework generated by combining the global A* algorithm and the local deformation recovery and replanning strategy, as well as individual components of this framework, are tested extensively both in simulation and experimentally. An ablation study draws links to related state-of-the-art search-based collision-avoidance planners (for the overall framework), as well as search-based collision-avoidance and sampling-based collision-inclusive global planners (for the higher level). Results demonstrate our method's efficacy for collision-inclusive motion planning and control in unknown environments with isolated obstacles for a class of impact-resilient robots operating in 2-D.
Engineering Compact Data Structures for Rank and Select Queries on Bit Vectors

Abstract Bit vectors are fundamental building blocks of succinct data structures used in compressed text indices, e.g., in the form of the wavelet trees. Here, two types of queries are of interest: rank and select queries. In practice, the smallest (uncompressed) rank and select data structure cs-poppy has a space overhead of $$\approx $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>≈</mml:mo> </mml:math> 3.51 % [Zhou et al. SEA 2013] [26]. Using the same overhead, we present a data structure that can answer queries up to 8 % (rank) and 16.5 % (select) faster compared with cs-poppy.
Epistemic Ensembles

Abstract An ensemble consists of a set of computing entities which collaborate to reach common goals. We introduce epistemic ensembles that use shared knowledge for collaboration between agents. Collaboration is achieved by different kinds of knowledge announcements. For specifying epistemic ensemble behaviours we use formulas of dynamic logic with compound ensemble actions. Our semantics relies on an epistemic notion of ensemble transition systems as behavioural models. These transition systems describe control flow over epistemic states for expressing knowledge-based collaboration of agents. Specifications are implemented by epistemic processes that are composed in parallel to form ensemble realisations. We give a formal operational semantics of these processes that generates an epistemic ensemble transition system. A realisation is correct w. r. t. an ensemble specification if its semantics is a model of the specification.
Tracking by Associating Clips

The tracking-by-detection paradigm today has become the dominant method for multi-object tracking and works by detecting objects in each frame and then performing data association across frames. However, its sequential frame-wise matching property fundamentally suffers from the intermediate interruptions in a video, such as object occlusions, fast camera movements, and abrupt light changes. Moreover, it typically overlooks temporal information beyond the two frames for matching. In this paper, we investigate an alternative by treating object association as clip-wise matching. Our new perspective views a single long video sequence as multiple short clips, and then the tracking is performed both within and between the clips. The benefits of this new approach are two folds. First, our method is robust to tracking error accumulation or propagation, as the video chunking allows bypassing the interrupted frames, and the short clip tracking avoids the conventional error-prone long-term track memory management. Second, the multiple frame information is aggregated during the clip-wise matching, resulting in a more accurate long-range track association than the current frame-wise matching. Given the state-of-the-art tracking-by-detection tracker, QDTrack, we showcase how the tracking performance improves with our new tracking formulation. We evaluate our proposals on two tracking benchmarks, TAO and MOT17 that have complementary characteristics and challenges each other.
HARDLESS: A Generalized Serverless Compute Architecture for Hardware Processing Accelerators

The increasing use of hardware processing accelerators tailored for specific applications, such as the Vision Processing Unit (VPU) for image recognition, further increases developers' configuration, development, and management over-head. Developers have successfully used fully automated elastic cloud services such as serverless computing to counter these additional efforts and shorten development cycles for applications running on CPUs. Unfortunately, current cloud solutions do not yet provide these simplifications for applications that require hardware acceleration. However, as the development of special-ized hardware acceleration continues to provide performance and cost improvements, it will become increasingly important to enable ease of use in the cloud. In this paper, we present an initial design and implemen-tation of Hardless, an extensible and generalized serverless computing architecture that can support workloads for arbitrary hardware accelerators. We show how Hardless can scale across different commodity hardware accelerators and support a variety of workloads using the same execution and programming model common in serverless computing today.
Model and Popularity Based Recommendation System- A Collaborative Filtering Approach

A recommendation system (RS) is a crucial application in the modern world, however it’s still unclear if recommendations can be effective and accurate. Collaborative filtering has been used for many years to improve and increase the accuracy of recommendations. Although numerous strategies have been employed, such as content-based filtering and deep learning, collaborative filtering models can identify new interests between users and items without the need for specialized knowledge. The new user problem known as cold start using user ratings can be solved by collaborative filtering, which uses item ratings to predict user preference and is effective to predict better than traditional algorithms and popularity-based approach. A product is referred to as popular by the most user ratings in one specific product. The Daraz ecommerce website, which includes four characteristics, is where we got the data for our study. For this analysis, we used Modelbased Collaborative Filtering and Popularity-based approach to solve issues like cold start and data sparsity where we use Matrix factorization and K-means clustering including Truncated SVD and TF-IDF Vectorizer as algorithm to find user and item relation for better and more effective product recommendation. This study will assist to explore how recommendation systems are employed, their many approaches, and the specific algorithms and methodologies we used in our methodology, all of which may aid in providing a thorough understanding of recommendation systems.
SoK: cross-border criminal investigations and digital evidence

Abstract Digital evidence underpin the majority of crimes as their analysis is an integral part of almost every criminal investigation. Even if we temporarily disregard the numerous challenges in the collection and analysis of digital evidence, the exchange of the evidence among the different stakeholders has many thorny issues. Of specific interest are cross-border criminal investigations as the complexity is significantly high due to the heterogeneity of legal frameworks, which beyond time bottlenecks can also become prohibiting. The aim of this article is to analyse the current state of practice of cross-border investigations considering the efficacy of current collaboration protocols along with the challenges and drawbacks to be overcome. Further to performing a legally oriented research treatise, we recall all the challenges raised in the literature and discuss them from a more practical yet global perspective. Thus, this article paves the way to enabling practitioners and stakeholders to leverage horizontal strategies to fill in the identified gaps timely and accurately.
Comparative Study of Heavy Metals Analysis in Mongolian Medicines Based on High Sensitivity X-ray Fluorescence Spectroscopy and ICP-MS

The contents of Pb, Hg, As, Cr, Fe, Cu, Ba, and Cd in five traditional Mongolian medicines (Garidi-5, Susi-7, Yihe-12, Zadi- 5, and Alatanaru-5) were determined by X-ray fluorescence (XRF) spectroscopy. The results were compared with those obtained by inductively coupled plasma–mass spectrometry (ICP-MS). According to the fundamental parameter (FP) approach in XRF, the response signal value was converted into the element content value using computer software. The method was stable and fast, not requiring pretreatment processes. When the content of metal elements was below 2.0 mg/kg, the relative standard deviation (RSD) of the precision test was between 5.49–20.0%. When the content was above 2.0 mg/kg, the RSD of the precision test was less than 4.96%. The limits of quantitation (LOQ) of As, Cd, and Pb were all below 0.1 ppm, the limits of quantitation of Cr, Cu, Ba, and Hg were below 1 ppm, and the limit of quantitation of Fe was 1.525 ppm. The standard addition method was used in the accuracy test, and the recoveries of the other seven elements were all within 85–130% except Hg. Because Hg was easy to volatilize, the recoveries were low but above 68.4%.
Multiobjective Evolutionary Optimization for Prototype-Based Fuzzy Classifiers

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a “one pass” manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimize the training error of the models on historical training data or alternatively to iteratively minimize the intracluster variance of the clusters obtained via online data partitioning. This recognizes the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimizing the training error may potentially lead to overfitting while minimizing the intracluster variance does not necessarily ensure the optimized prototype-based models to attain improved classification outcomes. To achieve better classification performance while avoiding overfitting for zero-order EISs, this article presents a novel multiobjective optimization approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final nondominated set of the resulting optimized models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimization approach in improving the classification performance of zero-order EISs.
Distribution-Agnostic Few-Shot Industrial Fault Diagnosis via Adaptation-Aware Optimal Feature Transport

In complex real-world industrial systems, few-shot fault diagnosis greatly challenges model-free methods. Interest in domain adaptation methods, which enriches the diversity of accessible samples by narrowing the distance between the source and target domain distributions, has grown. However, these approaches generally rely on specific domain pairs and numerous labeled source data, which are difficult conditions to satisfy in industrial scenarios with complex/changeable working conditions and limited fault samples. Herein, we creatively propose a distribution-agnostic few-shot framework by imitating brain awareness process in unseen tasks. Our framework can generate a learnable and interpretable paradigm to learn common similarities in task embedding space, alleviating the dependence on deep supervised training and reducing the time required for conducting credible exploration from scratch. In particular, we design an adaptation-aware nonconvex matrix optimization procedure for optimal deep adaptation features transport process updating. Experimental results validate the superiority of our framework in two industrial applications, magnetic flux leakage, and bearing datasets, showing it is feasible and promising.