Reviewerly/exHarmony huggingface dataset:
Adaptive Navigation Method for Mobile Robots in Various Environments using Multiple Control Policies

In recent years, to achieve safe and efficient navigation for autonomous mobile robots, several methods have been proposed to switch between multiple policies (action decision methods), including deep reinforcement learning, depending on the situation. We have also proposed methods to introduce new policy-switching criteria and to add new policies to avoid freezing conditions. Compared to existing methods, we have shown that the method improves safety metric values (e.g., collision rate) even in narrow corridors; however, there are still challenges in achieving sufficient performance because safety and efficiency metric values fluctuate depending on the environment. In this paper, we propose an adaptive navigation method that uses sensing results to classify robot deployment environments into several groups and adaptively changes the policy-switching algorithm according to the environment. Specifically, we use collision risk and congestion level for the environment classification and associate the environment classes with appropriate control parameter values (i.e., parameter tuning) to achieve adaptive navigation. Furthermore, we verify the effectiveness of the proposed method by conducting simulation experiments.
Trust Management System for Hybrid Industrial Blockchains

As industrial networks continue to expand and connect more devices and users, they face growing security challenges such as unauthorized access and data breaches. This paper delves into the crucial role of security and trust in industrial networks and how trust management systems (TMS) can mitigate malicious access to these networks.The TMS presented in this paper leverages distributed ledger technology (blockchain) to evaluate the trustworthiness of blockchain nodes, including devices and users, and make access decisions accordingly. While this approach is applicable to blockchain, it can also be extended to other areas. This approach can help prevent malicious actors from penetrating industrial networks and causing harm. The paper also presents the results of a simulation to demonstrate the behavior of the TMS and provide insights into its effectiveness.
Learning Attention as Disentangler for Compositional Zero-Shot Learning

Compositional zero-shot learning (CZSL) aims at learning visual concepts (i.e., attributes and objects) from seen compositions and combining concept knowledge into unseen compositions. The key to CZSL is learning the disentanglement of the attribute-object composition. To this end, we propose to exploit cross-attentions as compositional disentanglers to learn disentangled concept embeddings. For example, if we want to recognize an unseen composition “yellow flower”, we can learn the attribute concept “yellow” and object concept “flower” from different yellow objects and different flowers respectively. To further constrain the disentanglers to learn the concept of interest, we employ a regularization at the attention level. Specifically, we adapt the earth mover's distance (EMD) as a feature similarity metric in the cross-attention module. Moreover, benefiting from concept disentanglement, we improve the inference process and tune the prediction score by combining multiple concept probabilities. Comprehensive experiments on three CZSL benchmark datasets demonstrate that our method significantly outperforms previous works in both closed- and open-world settings, establishing a new state-of-the-art. Project page: https://haoosz.github.io/ade-czsl/
Understanding and Constructing Latent Modality Structures in Multi-Modal Representation Learning

Contrastive loss has been increasingly used in learning representations from multiple modalities. In the limit, the nature of the contrastive loss encourages modalities to exactly match each other in the latent space. Yet it remains an open question how the modality alignment affects the downstream task performance. In this paper, based on an information-theoretic argument, we first prove that exact modality alignment is sub-optimal in general for down-stream prediction tasks. Hence we advocate that the key of better performance lies in meaningful latent modality structures instead of perfect modality alignment. To this end, we propose three general approaches to construct latent modality structures. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and intermodality regularization. Extensive experiments are conducted on two popular multi-modal representation learning frameworks: the CLIP-based two-tower model and the ALBEF-based fusion model. We test our model on a variety of tasks including zero/few-shot image classification, image-text retrieval, visual question answering, visual reasoning, and visual entailment. Our method achieves consistent improvements over existing methods, demonstrating the effectiveness and generalizability of our proposed approach on latent modality structure regularization.
Progressive Transformation Learning for Leveraging Virtual Images in Training

To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime.
Learning Procedure-aware Video Representation from Instructional Videos and Their Narrations

The abundance of instructional videos and their narrations over the Internet offers an exciting avenue for understanding procedural activities. In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a large-scale dataset of web instructional videos and their narrations, without using human annotations. Our method jointly learns a video representation to encode individual step concepts, and a deep probabilistic model to capture both temporal dependencies and immense individual variations in the step ordering. We empirically demonstrate that learning temporal ordering not only enables new capabilities for procedure reasoning, but also reinforces the recognition of individual steps. Our model significantly advances the state-of-the-art results on step classification (+2.8%/+3.3% on COIN / EPIC-Kitchens) and step forecasting (+7.4% on COIN). Moreover, our model attains promising results in zero-shot inference for step classification and fore-casting, as well as in predicting diverse and plausible steps for incomplete procedures. Our code is available at https://github.com/facebookresearch/ProcedureVRL.
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation

Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary categories of text-based descriptions, which popularizes the segmentation system to more general-purpose application scenarios. However, existing methods devote to designing specialized architectures or parameters for specific segmentation tasks. These customized design paradigms lead to fragmentation between various segmentation tasks, thus hindering the uniformity of segmentation models. Hence in this paper, we propose FreeSeg, a generic framework to accomplish Unified, Universal and Open-Vocabulary Image Segmentation. FreeSeg optimizes an all-in-one network via one-shot training and employs the same architecture and parameters to handle diverse segmentation tasks seamlessly in the inference procedure. Additionally, adaptive prompt learning facilitates the unified model to capture task-aware and category-sensitive concepts, improving model robustness in multi-task and varied scenarios. Extensive experimental results demonstrate that FreeSeg establishes new state-of-the-art results in performance and generalization on three segmentation tasks, which outperforms the best task-specific architectures by a large margin: 5.5% mIoU on semantic segmentation, 17.6% mAP on instance segmentation, 20.1% PQ on panoptic segmentation for the unseen class on COCO. Project page: https://FreeSeg.github.io.
Disentangled Representation Learning for Unsupervised Neural Quantization

The inverted index is a widely used data structure to avoid the infeasible exhaustive search. It accelerates retrieval significantly by splitting the database into multiple disjoint sets and restricts distance computation to a small fraction of the database. Moreover, it even improves search quality by allowing quantizers to exploit the compact distribution of residual vector space. However, we firstly point out a problem that an existing deep learning-based quantizer hardly benefits from the residual vector space, unlike conventional shallow quantizers. To cope with this problem, we introduce a novel disentangled representation learning for unsupervised neural quantization. Similar to the concept of residual vector space, the proposed method enables more compact latent space by disentangling information of the inverted index from the vectors. Experimental results on large-scale datasets confirm that our method outperforms the state-of-the-art retrieval systems by a large margin.
Automatic Text Summarization Based on Pre-trained Models

Due to the current technological eras, increased accessibility of textual information. Numerous people search and read vast amounts of information to ascertain what is necessary, comprehensive, and pertinent to their goals. Most published documents entice readers to read the rest but frequently omit the most important details. As a result, recent years have seen several attempts to automate this process. Despite advances in deep learning and machine learning, the need for many training samples has made achieving good results on low-resource datasets difficult. Additionally, transfer learning cannot be used in conventional training methods and used the pre-trained models to address a related problem rather than creating a new model from scratch to address the same problem and improve the dataset. Promising results were obtained by assessing the text summaries created by adjusting the dataset using already-trained models based on the transformer architecture with reduced training time. Natural language processing has significantly benefited from transfer learning, where a model is initially pre-trained on a data-rich job before being fine-tuned on a downstream task (NLP). Because of its usefulness, transfer learning generated various methods, strategies, and techniques.
Clustering Algorithms Comparison for University of Anbar Researchers’ Google Scholar Profiles

Clustering is an important web mining technique that is extensively used in a variety of fields, including pattern recognition, image processing, and data analysis. K-means and K-medoids are two popular clustering algorithms that attempt to group data points that are similar into clusters. Although both algorithms have the same fundamental goal, they differ in their approaches to cluster formation and the selection of cluster centers. The K-means and K-medoids algorithms are compared in this study in terms of their strengths and weaknesses, complexity, and applicability to real-world issues. The K-Means algorithm appears to be performing better than the K-Mediods algorithm, with a higher silhouette score, Calinski-Harabasz index, and Davies-Bouldin index. The purpose of this paper is to investigate the efficacy of two popular clustering algorithms, k-means and k-medoids. In our scenario, we fetched a dataset from the Google Scholar profiles of researchers associated with the Iraqi University of Anbar. The email "uoanbar.edu.iq" was used to retrieve the dataset used in this research. The study compares the two algorithms’ performance in terms of output and measurement evaluation, to determine which algorithm is better suited to the situation at hand. Our findings show that k-means clustering outperforms k-medoids in terms of output quality as well as computational efficiency, making it the preferred algorithm for this situation. The research adds to the increasing body of knowledge about clustering algorithms and their applicability to real-world datasets.
User Log Anomaly Detection System Based on Isolation Forest

The user log anomaly detection system plays an important role in network operation and network security. By analyzing the logs generated by users, the log anomaly detection system can discover the abnormal behaviors of users in time and automatically notify administrators to handle them. Based on rule matching, traditional log anomaly detection systems reduce the maintenance cost and improves the network security while providing rapid detection. However, such methods suffer from the low accuracy, which limited the provided security for network. In this paper, we propose a user log anomaly detection system based on isolation forest, which combines the advantages of rule matching and isolation forest. In the system, we first pre-filter out the abnormal logs that are easily detected by rule matching, then use the isolation forest in unsupervised learning as the log anomaly detection algorithm, which improves the detection accuracy while ensuring the detection speed of the system. Finally, we optimize the isolation forest model to further improve the detection accuracy of the model. The obtained experiment results show that the detection accuracy of the proposed system is 1%~12% higher than that of traditional log anomaly detection systems using other classical unsupervised anomaly detection algorithms. Furthermore, we test the optimized isolation forest model. The results show that the detection accuracy of the optimized isolation forest model is 0.7% higher than that of the original model.
Deep Randomized Time Warping for Action Recognition

This paper proposes an enhanced Randomized Time Warping (RTW) using CNN features, termed Deep RTW, for motion recognition. RTW is a general extension of Dynamic Time Warping (DTW), widely used for matching and comparing sequential patterns. The basic idea of RTW is to simultaneously calculate the similarities between many pairs of various warped patterns, i.e. Time elastic (TE) features generated by randomly sampling the sequential pattern while retaining their temporal order. This mechanism enables RTW to treat the changes in motion speed flexibly. However, naive TE feature vectors generated from raw images are not expected to have high discriminative power. Besides, the dimension of TE features can increase depending on the number of concatenated images. To address the limitations, we incorporate CNN features extracted from 2D/3D CNNs into the framework of RTW as input to address this issue. Our framework is very simple but effective and applicable to various types of CNN architecture. Extensive experiment on public motion datasets, Jester and Something-Something V2, supports the advantage of our method over the original CNNs.
Pointwise Maximal Leakage on General Alphabets

Pointwise maximal leakage (PML) is an operationally meaningful privacy measure that quantifies the amount of information leaking about a secret X to a single outcome of a related random variable Y. In this paper, we extend the notion of PML to random variables on arbitrary probability spaces. We develop two new definitions: First, we extend PML to countably infinite random variables by considering adversaries who aim to guess the value of discrete (finite or countably infinite) functions of X. Then, we consider adversaries who construct estimates of X that maximize the expected value of their corresponding gain functions. We use this latter setup to introduce a highly versatile form of PML that captures many scenarios of practical interest whose definition requires no assumptions about the underlying probability spaces.
Systematic Encoding of Elliptic Codes with Dynamic Information Sets

Systematic encoding is crucial in some decoding algorithms of algebraic-geometric (AG) codes. In the context of soft decoding that requires systematic encoding, the information set changes dynamically for each decoding event, presenting a challenge for systematic encoding of AG codes. This paper addresses this challenge for elliptic codes by proposing a fast construction algorithm for both the systematic encoding polynomial (SEP) and the systematic generator matrix (SGM) based on Gröbner bases of modules and backward interpolation. The proposed algorithm has a complexity of $\mathcal{O}\left( {{k^2}} \right)$ and $\mathcal{O}(kn)$ for constructing the SEP and SGM, respectively, where k and n denote the dimension and length of the elliptic code. Additionally, its computation can be highly parallelized, enhancing its implementation efficiency.
Understanding Deep Generative Models with Generalized Empirical Likelihoods

Understanding how well a deep generative model captures a distribution of high-dimensional data remains an important open challenge. It is especially difficult for certain model classes, such as Generative Adversarial Networks and Diffusion Models, whose models do not admit exact likelihoods. In this work, we demonstrate that generalized empirical likelihood (GEL) methods offer a family of diagnostic tools that can identify many deficiencies of deep generative models (DGMs). We show, with appropriate specification of moment conditions, that the proposed method can identify which modes have been dropped, the degree to which DGMs are mode imbalanced, and whether DGMs sufficiently capture intra-class diversity. We show how to combine techniques from Maximum Mean Discrepancy and Generalized Empirical Likelihood to create not only distribution tests that retain per-sample interpretability, but also metrics that include label information. We find that such tests predict the degree of mode dropping and mode imbalance up to 60% better than metrics such as improved precision/recall.
Safe Landing Zone Detection for UAVs using Image Segmentation and Super Resolution

Increased usage of UAVs in urban environments has led to the necessity of safe and robust emergency landing zone detection techniques. This paper presents a novel approach for detecting safe landing zones for UAVs using deep learning-based image segmentation. Our approach involves using a custom dataset to train a CNN model. To account for low-resolution input images, our approach incorporates a Super-Resolution model to upscale low-resolution images before feeding them into the segmentation model. The proposed approach achieves robust and accurate detection of safe landing zones, even on low-resolution images. Experimental results demonstrate the effectiveness of our method and show a marked improvement of upto 6.3% in accuracy over state-of-the-art safe landing zone detection methods.
Learning Attribute and Class-Specific Representation Duet for Fine-Grained Fashion Analysis

Fashion representation learning involves the analysis and understanding of various visual elements at different granularities and the interactions among them. Existing works often learn fine-grained fashion representations at the attribute level without considering their relationships and inter-dependencies across different classes. In this work, we propose to learn an attribute and class-specific fashion representation duet to better model such attribute relationships and inter-dependencies by leveraging prior knowledge about the taxonomy of fashion attributes and classes. Through two sub-networks for the attributes and classes, respectively, our proposed an embedding network progressively learns and refines the visual representation of a fashion image to improve its robustness for fashion retrieval. A multi-granularity loss consisting of attribute-level and class-level losses is proposed to introduce appropriate inductive bias to learn across different granularities of the fashion representations. Experimental results on three benchmark datasets demonstrate the effectiveness of our method, which outperforms the state-of-the-art methods by a large margin.
Neural Kaleidoscopic Space Sculpting

We introduce a method that recovers full-surround 3D reconstructions from a single kaleidoscopic image using a neural surface representation. Full-surround 3D reconstruction is critical for many applications, such as augmented and virtual reality. A kaleidoscope, which uses a single camera and multiple mirrors, is a convenient way of achieving full-surround coverage, as it redistributes light directions and thus captures multiple viewpoints in a single image. This enables single-shot and dynamic full-surround 3D reconstruction. However, using a kaleidoscopic image for multiview stereo is challenging, as we need to decompose the image into multi-view images by identifying which pixel corresponds to which virtual camera, a process we call labeling. To address this challenge, pur approach avoids the need to explicitly estimate labels, but instead “sculpts” a neural surface representation through the careful use of silhouette, background, foreground, and texture information present in the kaleidoscopic image. We demonstrate the advantages of our method in a range of simulated and real experiments, on both static and dynamic scenes.
The Optimal Rate of Second-Order Generalized-Covering Codes

The generalized-covering radius was recently proposed as a fundamental property of linear codes. We consider a natural extension of this property to general (not necessarily linear) codes, and provide an asymptotic solution to our problem by finding the optimal rate function of second-order covering codes given a fixed normalized covering radius. We also prove that the fraction of second-order covering codes among codes of sufficiently large rate tends to 1 as the code length tends to ∞.