Reviewerly/exHarmony huggingface dataset:
Clustering Document based on Semantic Similarity Using Graph Base Spectral Algorithm

The Internet’s continued growth has resulted in a significant rise in the amount of electronic text documents. Grouping these materials into meaningful collections has become crucial. The old approach of document compilation based on statistical characteristics and categorization relied on syntactic rather than semantic information. This article introduces a unique approach for classifying texts based on their semantic similarity. The graph-based approach is depended an efficient technique been utilized for clustering. This is performed by extracting document summaries called synopses from the Wikipedia and IMDB databases and grouping thus downloaded documents, then utilizing the NLTK dictionary to generate them by making some important preprocessing to make it more convenient to use. Following that, a vector space is modelled using TFIDF and converted to TFIDF matrix as numeric form, and clustering is accomplished using Spectral methods. The results are compared with previews work.
Verification of Sitter Identity Across Historical Portrait Paintings by Confidence-aware Face Recognition

Verifying the identity of a person (sitter) portrayed in a historical painting is often a challenging but critical task in art historian research. In many cases, this information has been lost due to time or other circumstances and today there are only speculations of art historians about which person it could be. Art historians often use subjective factors for this purpose and then infer from the identity information about the person depicted in terms of his or her life, status, and era. On the other hand, automated face recognition has achieved a high level of accuracy, especially on photographs, and considers objective factors to determine the identity or verify a suspected identity. The limited amount of data, as well as the domain-specific challenges, make the use of automated face recognition methods in the domain of historic paintings difficult. We propose a specialized, likelihood-based fusion method to enable deep learning-based face recognition on historic portrait paintings. We additionally propose a method to accurately determine the confidence of the made decision to assist art historians in their research. For this purpose, we used a model trained on common photographs and adapted it to the domain of historical paintings through transfer learning. By using an underlying challenge dataset, we compute the likelihood for the assumed identity against reference images of the identity and fuse them to utilize as much information as possible. From these results of the likelihoods fusion, we then derive decision confidence to make statements to determine the certainty of the model’s decision. The experiments were carried out in a leave-one-out evaluation scenario on our created database, the largest authentic database of historic portrait paintings to date, consisting of over 760 portrait paintings of 210 different sitters by over 250 different artists. The experiments demonstrated, that a) the proposed approach outperforms pure face recognition solutions, b) the fusion approach effectively combines the sitter information towards a higher verification accuracy, and c) the proposed confidence estimation approach is highly successful in capturing the estimated accuracy of the decision. The meta-information of the used historic face images can be found at https://github.com/marcohuber/HistoricalFaces.
TropeTwist: Trope-based Narrative Structure Generation

Games are complex, multi-faceted systems that share common elements and underlying narratives, such as the conflict between a hero and a big bad enemy or pursuing a goal that requires overcoming challenges. However, identifying and describing these elements together is non-trivial as they might differ in certain properties and how players might encounter the narratives. Likewise, generating narratives also pose difficulties when encoding, interpreting, and evaluating them. To address this, we present TropeTwist, a trope-based system that can describe narrative structures in games in a more abstract and generic level, allowing the definition of games' narrative structures and their generation using interconnected tropes, called narrative graphs. To demonstrate the system, we represent the narrative structure of three different games. We use MAP-Elites to generate and evaluate novel quality-diverse narrative graphs encoded as graph grammars, using these three hand-made narrative structures as targets. Both hand-made and generated narrative graphs are evaluated based on their coherence and interestingness, which are improved through evolution.
High-Performance Transformer Tracking

Correlation has a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion method that considers the similarity between the template and the search region. However, the correlation operation is a local linear matching process, losing semantic information and easily falling into a local optimum, which may be the bottleneck in designing high-accuracy tracking algorithms. In this work, to determine whether a better feature fusion method exists than correlation, a novel attention-based feature fusion network, inspired by the transformer, is presented. This network effectively combines the template and search region features using attention mechanism. Specifically, the proposed method includes an ego-context augment module based on self-attention and a cross-feature augment module based on cross-attention. First, we present a transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression heads. Based on the TransT baseline, we also design a segmentation branch to generate the accurate mask. Finally, we propose a stronger version of TransT by extending it with a multi-template scheme and an IoU prediction head, named TransT-M. Experiments show that our TransT and TransT-M methods achieve promising results on seven popular benchmarks. Code and models are available at https://github.com/chenxin-dlut/TransT-M.
Efficient Fake News Detection using Bagging Ensembles of Bidirectional Echo State Networks

The dissemination of fake news is one of the most concerning issues in current digital media platforms, originating from the quick and easy spread of unverified information therethrough. Consequently, intense research efforts have been invested towards automating the process of identifying fake news from textual data by means of Artificial Intelligence methods. Among the manifold approaches proposed for this purpose to date, a large fraction of studies have examined the performance of modern deep neural network architectures, mostly relying on pretrained word embeddings and neural processing modules of diverse kind. Unfortunately, such sophisticated Deep Learning methods often require intense computational efforts for training. In this work we explore a novel approach based on randomization-based recurrent neural networks. Specifically, our proposal consists of a weighted ensemble of bidirectional Echo State Networks learned from word sequences processed through pretrained embeddings. Experiments over two fake news detection datasets reveal that competitive detection statistics are obtained by our proposed approach when compared to shallow learning and avant-garde Deep Learning models, but at a dramatically less computational complexity in their training phase.
Data-Driven Interval Type-2 Fuzzy Inference System Based on the Interval Type-2 Distending Function

Fuzzy type-2 modeling techniques are increasingly being used to model uncertain dynamical systems.However, some challenges arise when applying the existing techniques.These are the following: 1) a large number of rules are required to complete cover the whole input space; 2) a large number of parameters associated with type-2 membership functions have to be determined; 3) the identified fuzzy model is usually difficult to interpret due to the large number of rules; and 4) designing a fuzzy type-2 controller using these models is a computationally expensive task.To overcome these limitations, a procedure is proposed here to identify the fuzzy type-2 model directly from the data.This model is called the distending-function-based fuzzy inference system (DFIS).This model consists of rules and interval type-2 distending functions.First, a few key rules are identified from the data, and later, more rules are added until the error is less than the threshold.The proposed procedure is used to model the altitude controller of a quadcopter.The performance of the DFIS model is compared with that of various fuzzy models.Furthermore, a simplified procedure based on the rules is presented to design a computationally low-cost type-2 controller.The effectiveness of the controller is shown by regulating the height of a quadcopter in the presence of noisy sensory data.The performance of this controller is compared with that of various other controllers.Finally, the proposed type-2 controller was implemented on a Parrot Mambo quadcopter to demonstrate its real-time performance. Index Terms-Arithmetic-based
Self-Supervised Feature Learning for Long-Term Metric Visual Localization

Visual localization is the task of estimating camera pose in a known scene, which is an essential problem in robotics and computer vision. However, long-term visual localization is still a challenge due to the environmental appearance changes caused by lighting and seasons. While techniques exist to address appearance changes using neural networks, these methods typically require ground-truth pose information to generate accurate image correspondences or act as a supervisory signal during training. In this paper, we present a novel self-supervised feature learning framework for metric visual localization. We use a sequence-based image matching algorithm across different sequences of images (i.e., experiences) to generate image correspondences without ground-truth labels. We can then sample image pairs to train a deep neural network that learns sparse features with associated descriptors and scores without ground-truth pose supervision. The learned features can be used together with a classical pose estimator for visual stereo localization. We validate the learned features by integrating with an existing Visual Teach & Repeat pipeline to perform closed-loop localization experiments under different lighting conditions for a total of 22.4 km.
Two-Stage Underwater Image Restoration Algorithm Based on Physical Model and Causal Intervention

Underwater images often suffer from severe color degradation, haze and local blur, which are caused by the scattering and absorption effects of light in water. Firstly, to address the lack of annotations in underwater images, we propose a multi-degradation rate underwater image degradation model. Additionally, we use the generative adversarial network (GAN) to guide the underwater image degradation model and generate paired images that can be used for underwater image restoration (UIR) network training. Also, given the problems of poor restoration quality, serious loss of detail, and slow inference speed of existing deep learning algorithms, we construct an underwater image restoration network that can reference in real-time. Moreover, through causal interventions in the image generation process, spurious correlations between global features and detailed features are eliminated. As a result, the detail generation ability of the image is improved. Experiments on real underwater image datasets demonstrate that compared with existing methods, our proposed method more effectively solves the problem of underwater image degradation.
Improved Multiclass AdaBoost Using Sparse Oblique Decision Trees

Boosting, one of the most effective machine learning frameworks, has attracted an enduring interest since its introduction 30 years ago. The majority of boosting methods use trees as base learner and, while much work has focused on theoretical and empirical variations of boosting, there has been surprisingly little progress on the tree learning procedure itself. To this day, each individual tree is typically axis-aligned (which is ill-suited to model correlations and results in relatively weak classifiers), and is learned using a greedy divide-and-conquer approach such as CART or C5.0, which produces suboptimal trees. We show we can improve boosted forests drastically by making each tree a much stronger classifier. We do this by using sparse oblique trees, which are far more powerful than axis-aligned ones, and by optimizing them using “tree alternating optimization” (TAO), suitably modified to handle the base learner optimization problem dictated by the boosting framework. Focusing on two versions of AdaBoost, we show that the resulting forests not only are consistently and considerably more accurate than random forests or gradient boosting, but that they use a very small number of trees and a comparable number of parameters.
Identifying Erroneous Software Changes through Self-Supervised Contrastive Learning on Time Series Data

Software changes are frequent and inevitable. How-ever, erroneous software changes may cause failures and incidents, degrading user experience and system stability. Thus, it is critical to distinguish erroneous software changes from normal ones. Our empirical study from a global data center reveals that erroneous software changes have caused nearly one-third of the critical incidents in the last two years. Some quantitative results also imply that the number of software changes and that of the Key Performance Indicator (KPI) time series related to a software change are relatively large. Based on the observations, we propose Kontrast, a self-supervised, generic and adaptive approach using contrastive learning, aiming to identify erroneous software changes on time. Its key idea is to compare pre-change and post-change KPI time series related to the software change, assuring the time series is still in a normal state after the software change. Since contrastive learning approaches need a fully-labeled dataset, we propose a novel data augmentation technique inspired by self-supervised learning to generate data with pseudo labels. Our model significantly outperforms all the compared approaches on two datasets with a millisecond-level speed for each KPI and is proven to obtain cross-dataset adaptability. To better certify our contribution, we also exhibit some success cases of Kontrast from its deployment.
Unifying Event Detection and Captioning as Sequence Generation via Pre-training

Dense video captioning aims to generate corresponding text descriptions for a series of events in the untrimmed video, which can be divided into two sub-tasks, event detection and event captioning. Unlike previous works that tackle the two sub-tasks separately, recent works have focused on enhancing the inter-task association between the two sub-tasks. However, designing inter-task interactions for event detection and captioning is not trivial due to the large differences in their task specific solutions. Besides, previous event detection methods normally ignore temporal dependencies between events, leading to event redundancy or inconsistency problems. To tackle above the two defects, in this paper, we define event detection as a sequence generation task and propose a unified pre-training and fine-tuning framework to naturally enhance the inter-task association between event detection and captioning. Since the model predicts each event with previous events as context, the inter-dependency between events is fully exploited and thus our model can detect more diverse and consistent events in the video. Experiments on the ActivityNet dataset show that our model outperforms the state-of-the-art methods, and can be further boosted when pre-trained on extra large-scale video-text data. Code is available at https://github.com/QiQAng/UEDVC .
Automatically prioritizing and assigning tasks from code repositories in puzzle driven development

Automatically prioritizing software development tasks extracted from codes could provide significant technical and organizational advantages. Tools exist for the automatic extraction of tasks, but they still lack the ability to capture their mutual dependencies; hence, the capability to prioritize them. Solving this important puzzle is the goal of the presented industrial challenge.
FastHare: Fast Hamiltonian Reduction for Large-scale Quantum Annealing

Quantum annealing (QA) that encodes optimization problems into Hamiltonians remains the only near-term quantum computing paradigm that provides sufficient many qubits for real-world applications. To fit larger optimization instances on existing quantum annealers, reducing Hamiltonians into smaller equivalent Hamiltonians provides a promising approach. Unfortunately, existing reduction techniques are either computationally expensive or ineffective in practice. To this end, we introduce a novel notion of non-separable group, defined as a subset of qubits in a Hamiltonian that obtains the same value in optimal solutions. We develop non-separability theory accordingly and propose FastHare, a highly efficient reduction method. FastHare, iteratively, detects and merges non-separable groups into single qubits. It does so within a provable worst-case time complexity of only O(αn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), for some user-defined parameter α. Our extensive benchmarks for the feasibility of the reduction are done on both synthetic Hamiltonians and 3000+ instances from the MQLIB library. The results show FastHare outperforms the roof duality, the implemented reduction in D-Wave’s library. It demonstrates a high level of effectiveness with an average of 62% qubits saving and 0.3s processing time, advocating for Hamiltonian reduction as an inexpensive necessity for QA.
A Survey on Efficient Methods for Adversarial Robustness

Deep learning has revolutionized computer vision with phenomenal success and widespread applications.Despite impressive results in complex problems, neural networks are susceptible to adversarial attacks: small and imperceptible changes in input space that lead these models to incorrect outputs.Adversarial attacks have raised serious concerns, and robustness to these attacks has become a vital issue.Adversarial training, a min-max optimization approach, has shown promise against these attacks.The computational cost of adversarial training, however, makes it prohibitively difficult to scale as well as to be useful in practice.Recently, several works have explored different approaches to make adversarial training computationally more affordable.This paper presents a comprehensive survey on efficient adversarial robustness methods with an aim to present a holistic outlook to make future exploration more systematic and exhaustive.We start by mathematically defining fundamental ideas in adversarially robust learning.We then divide these approaches into two categories based on underlying mechanisms: methods that modify initial adversarial training and techniques that leverage transfer learning to improve efficiency.Finally, based on this overview, we analyze and present an outlook of future directions.
A Hybrid Threat Model for Smart Systems

Cyber-physical systems and their smart components have a pervasive presence in all our daily activities. Unfortunately, identifying the potential threats and issues in these systems and selecting enough protection is challenging given that such environments combine human, physical and cyber aspects to the system design and implementation. Current threat models and analysis do not take into consideration all three aspects of the analyzed system, how they can introduce new vulnerabilities or protection measures to each other. In this work, we introduce a novel threat model for cyber-physical systems that combines the cyber, physical, and human aspects. Our model represents the system's components relations and security properties by taking into consideration these three aspects. Together with the threat model we also propose a threat analysis method that allows understanding the security state of the system's components. The threat model and the threat analysis have been implemented into an automatic tool, called TAMELESS, that automatically analyzes threats to the system, verifies its security properties, and generates a graphical representation, useful for security architects to identify the proper prevention/mitigation solutions. We show and prove the use of our threat model and analysis with three cases studies from different sectors.
Multiresolution Hypergraph Neural Network for Intelligent Fault Diagnosis

Intelligent fault diagnosis has made significant progress, thanks to machine learning, particularly deep-learning algorithms.However, most machine-learning algorithms treat samples as independent and ignore the correlations between samples that contain valuable information for creating discriminative features.In recent years, graph neural networks have increased diagnostic performance by capturing the correlation between samples according to establishing the inherent structure of data, but they also suffer from two shortcomings.First, a simple graph only represents pairwise relationships of samples and cannot depict complex higher-order structures.Second, the generated graph structure is insufficient to characterize the data without an explicit structure.To address the above two issues, this article proposes a multiresolution hypergraph neural network, a novel algorithm that can discover higher-order complex relationships between samples, and mine the structure hidden in data by establishing and fusing hypergraph structures at multiple resolutions.Experiments are conducted on three datasets to demonstrate the effectiveness of the proposed algorithm.
DeepMLE: A Robust Deep Maximum Likelihood Estimator for Two-view Structure from Motion

Two-view structure from motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM (vSLAM). Many existing end-to-end learning-based methods usually formulate it as a brute regression problem. However, the inadequate utilization of traditional geometry model makes the model not robust in unseen environments. To improve the generalization capability and robustness of end-to-end two-view SfM network, we formulate the two-view SfM problem as a maximum likelihood estimation (MLE) and solve it with the proposed framework, denoted as DeepMLE. First, we propose to take the deep multi-scale correlation maps to depict the visual similarities of 2D image matches decided by ego-motion. In addition, in order to increase the robustness of our framework, we formulate the likelihood function of the correlations of 2D image matches as a Gaussian and Uniform mixture distribution which takes the uncertainty caused by illumination changes, image noise and moving objects into account. Meanwhile, an uncertainty prediction module is presented to predict the pixel-wise distribution parameters. Finally, we iteratively refine the depth and relative camera pose using the gradient-like information to maximize the likelihood function of the correlations. Extensive experimental results on several datasets prove that our method significantly outperforms the state-of-the-art end-to-end two-view SfM approaches in accuracy and generalization capability.