Reviewerly/exHarmony huggingface dataset:
SIESTA: A Scalable Infrastructure of Sequential Pattern Analysis

Sequential pattern analysis has become a mature topic with a lot of techniques for a variety of sequential pattern mining-related problems. Moreover, tailored solutions for specific domains, such as business process mining, have been developed. However, there is a gap in the literature for advanced techniques for efficient detection of arbitrary sequences in large collections of activity logs. In this work, we introduce the SIESTA ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> calable <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</u> nfrastructur <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> of <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> equential pa <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> tern <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> nalysis) solution making a threefold contribution: (i) we employ a novel architecture that relies on inverted indices during preprocessing and we introduce an advanced query processor that can detect and explore arbitrary patterns efficiently; (ii) we discuss and evaluate different configurations to optimize both the preprocessing and the querying phase; and (iii) we present evaluation results competing against representatives of the state-of-the-art with a focus on Big Data. The experimental results are particularly encouraging, e.g., when all methods are deployed in a cluster and the volume of the data is increased,SIESTA creates the indices in almost half the time compared to the state-of-the-art Elasticsearch-based solution, while also yielding faster query responses than all its competitors by up to 1 order of magnitude.
LTR*: Rapid Replanning in Executing Consecutive Tasks with Lazy Experience Graph

In an environment where a manipulator needs to execute multiple consecutive tasks, the act of object manoeuvre will change the underlying configuration space, affecting all subsequent tasks. Previously free configurations might now be occupied by the manoeuvred objects, and previously occupied space might now open up new paths. We propose Lazy Tree-based Replanner (LTR *)-a novel hybrid planner that inherits the rapid planning nature of existing anytime incremental sampling-based planners. At the same time, it allows subsequent tasks to leverage prior experience via a lazy experience graph. Previous experience is summarised in a lazy graph structure, and LTR * is formulated to be robust and beneficial regard-less of the extent of changes in the workspace. Our hybrid approach attains a faster speed in obtaining an initial solution than existing roadmap-based planners and often with a lower cost in trajectory length. Subsequent tasks can utilise the lazy experience graph to speed up finding a solution and take advant-age of the optimised graph to minimise the cost objective. We provide proofs of probabilistic completeness and almost-surely asymptotic optimal guarantees. Experimentally, we show that in repeated pick-and-place tasks, L T R * attains a high gain in performance when planning for subsequent tasks.
Generalized Adversarial and Hierarchical Co-occurrence Network based Synthetic Skeleton Generation and Human Identity Recognition

Human skeleton data provides a compact, low noise representation of relative joint locations that may be used in human identity and activity recognition. Hierarchical Co-occurrence Network (HCN) has been used for human activity recognition because of its ability to consider correlation between joints in convolutional operations in the network. HCN shows good identification accuracy but requires a large number of samples to train. Acquisition of this large-scale data can be time consuming and expensive, motivating synthetic skeleton data generation for data augmentation in HCN. We propose a novel method that integrates an Auxiliary Classifier Generative Adversarial Network (AC-GAN) and HCN hybrid framework for Assessment and Augmented Identity Recognition for Skeletons (AAIRS). The proposed AAIRS method performs generation and evaluation of synthetic 3-dimensional motion capture skeleton videos followed by human identity recognition. Synthetic skeleton data produced by the generator component of the AC-GAN is evaluated using an Inception Score-inspired realism metric computed from the HCN classifier outputs. We study the effect of increasing the percentage of synthetic samples in the training set on HCN performance. Before synthetic data augmentation, we achieve 74.49% HCN performance in 10-fold cross validation for 9-class human identification. With a synthetic-real mixture of 50%-50%, we achieve 78.22% mean accuracy, significantly <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{p} &lt; 0.05)$</tex> outperforming the baseline HCN performance. The proposed framework demonstrates the feasibility of combining a synthetic data generation architecture with hierarchical co-occurrence feature learning for human identity recognition.
Domain-Agnostic Domain Adaption for Building Footprint Extraction

For global range satellite imaging mission, images captured from different areas may have large distribution biases due to different illuminations, shooting angles and atmospheric conditions.A straightforward idea to mitigate this problem is to categorize the images into different domains according the cities they belong to, and apply domain adaptation approaches.However, categorization by cities becomes unreasonable with the increase of the city number, and the emergence of inter-city similarity and intra-city discrepancy.With such consideration, this paper proposes a novel domain adaptation method named domain-agnostic domain adaptation (DADA) to reduce the distribution biases without explicitly defining the domain each image belongs to.To implement this, we augment the images to the styles of different domains by Generative Adversarial Networks (GAN) and contrastive learning to increase the generalizability of downstream tasks.Experiments on Planetscope building footprint extraction datasets verify the effectiveness of our method.
FedAP: Adaptive Personalization in Federated Learning for Non-IID Data

Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patients. Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number of patients, and devices, lead to distinctive data distributions. Data heterogeneity poses a challenge for FL and the personalization of the local models. In this work, we investigate an adaptive hierarchical clustering method for FL to produce intermediate semi-global models, so clients with similar data distribution have the chance of forming a more specialized model. Our method forms several clusters consisting of clients with the most similar data distributions; then, each cluster continues to train separately. Inside the cluster, we use meta-learning to improve the personalization of the participants’ models. We compare the clustering approach with classical FedAvg and centralized training by evaluating our proposed methods on the HAM10k dataset for skin lesion classification with extreme heterogeneous data distribution. Our experiments demonstrate significant performance gain in heterogeneous distribution compared to standard FL methods in classification accuracy. Moreover, we show that the models converge faster if applied in clusters and outperform centralized training while using only a small subset of data.
Adaptive stabilization of quantum circuits executed on unstable devices

Conventional computers have evolved to device components that demonstrate failure rates of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−17</sup> or less, while current quantum computing devices typically exhibit error rates of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> or greater. This raises concerns about the reliability and reproducibility of the results obtained from quantum computers. The problem is highlighted by experimental observation that today’s NISQ devices are inherently unstable. Remote quantum cloud servers typically do not provide users with an ability to calibrate the device themselves. Using inaccurate characterization data for error mitigation can have devastating impact on reproducibility. In this study, we investigate if one can infer the critical channel parameters dynamically from the noisy binary output of the executed quantum circuit and use it to improve program stability. An open question however is how well does this methodology scale. We discuss the efficacy and efficiency of our adaptive algorithm using canonical quantum circuits such as the uniform superposition circuit. Our metric of performance is the Hellinger distance between the post-stabilization observations and the reference (ideal) distribution.
OptiPauli: An algorithm to find a near-optimal Pauli Feature Map for Quantum Support Vector Classifiers

Quantum Support Vector Classifiers have been gaining traction in solving classification problems as it enables efficient kernel estimation and potential improvement in accuracy. To estimate the kernel matrices, a quantum kernel needs a Pauli Feature Map which encodes the classical data into the quantum state space. For a given dataset and dimension, we can have an exorbitant number of Pauli Feature Maps. Hence, finding a Pauli Feature Map that maximizes model accuracy is a research challenge. To address this optimization problem, we propose an algorithm that finds a near-optimal Pauli Feature Map by solving several sub-problems with varying numbers of decision variables and their constraints. Each sub-problem aims to find the Pauli Feature Map that maximizes the model accuracy. We use genetic algorithm to solve each sub-problem, and then select the best out of all the near-optimal Pauli Feature Maps. Instead of formulating a single optimization problem, we use this divide-and-conquer approach (of breaking down the problem into multiple sub-problems) to reduce the overall search space. To evaluate the efficiency of our algorithm, we compare it against solving each sub-problem through exhaustive approach. The latter yields the optimal Pauli Feature Map by exploring the full state space. For the scikit-learn Breast Cancer dataset with dimension 5, our algorithm converges to the optimal Pauli Feature Map in about 804 seconds which is over 4 times faster than the exhaustive approach.
Multi-Objective Energy-Efficient Virtual Machine Consolidation Using Dynamic Double Threshold-Enhanced Search and Rescue-Based Optimization

The popularization of the cloud and its need to solve complex engineering application have alarmed energy and environmental concerns among the researchers. Achieving energy efficiency has become one of the most essential aims of the data center, offering more services with minimal energy consumption (EC). VM consolidation aims at adjusting the VMs to fewer PMs by live migration of VMs and then switching off the inactive servers, achieving energy efficiency. However, uncontrolled consolidation could violate the SLA. The paper contributes by considering the optimization problem targeting the EC and the number of VM migrations. Dynamic double threshold with enhanced search and rescue (DDT-ESAR) optimization has been introduced utilizing two thresholds; the first value defines the upper and lower bound for host classification, whereas the other is used to make migration decision. For migration, ESAR has been adopted for the most appropriate PM- VM mapping. The experimental analysis proves the efficiency where EC is computed to be 0.384kWh, SLA violations to be 6.33% and 64 number of migrations.
HKE-GCN: Heatmaps-guided Keypoints Encoder and Graph Convolutional Network for Human Pose Estimation

Multi-person pose estimation is a challenging task which aims to locate keypoints for multiple persons. Graph convolutional network can effectively capture the semantic relationship among keypoints according to the kinematic structure of the human body, which is beneficial to locate keypoints but is the lack of ability of most CNN-based models. However, existing GCN-based methods mostly flatten the 2D features directly to obtain 1D embeddings, leading to the redundant information in keypoints embeddings, large size of keypoints embeddings, and high computation cost. To address these problems, we propose a two-stage framework based on Heatmaps-guided Keypoints Encoder and graph convolutional network, called HKE-GCN. The first stage uses a heatmaps-based network to predict the heatmaps of keypoints, then the second stage refines the prediction of the first stage. The second stage consists of two modules: Heatmaps-guided Keypoints Encoder (HKE) and Graph-based Refinement Module (GRM), which are used to generate keypoints embeddings according to the guidance of heatmaps and explicitly learn the relationship among keypoints based on GCN, respectively. Experiments show our framework is model-agnostic and our proposed modules are effective and lightweight. Our best model achieves state-of-the-art 76.4AP on COCO test-dev.
Breast Cancer Image Classification Using Deep Convolutional Neural Networks

In recent years, convolutional neural network algorithms have made remarkable progress in the classification of medical images such as the classification of breast cancer tumors. Models of deep convolutional neural networks have obtained a higher accuracy rate in medical image recognition. The fine-tuning of images data and parameters are the main task of adapting a pre-trained convolution model in order to improve the classification accuracy. This paper aims to present a model for the use of deep neural networks, specifically convolutional neural network model AlexNet, for breast cancer classification. The model will be used to diagnose breast cancer using histopathological BreakHis images data set. Modifications of parameters and data are applied to increase the model ability for recognizing and classifying the input image and determine whether the image belongs to a benign or malignant tumor. It has been noticed that the training frequency and balanced training data greatly improve classification rate accuracy up to 96%. Our mission is that to achieve a higher accuracy rate than the obtained if repeated improvement of fine-tuning parameters and weights are adopted according to more accurate techniques.
Incremental Task Learning with Incremental Rank Updates

Incremental Task learning (ITL) is a category of continual learning that seeks to train a single network for multiple tasks (one after another), where training data for each task is only available during the training of that task. Neural networks tend to forget older tasks when they are trained for the newer tasks; this property is often known as catastrophic forgetting. To address this issue, ITL methods use episodic memory, parameter regularization, masking and pruning, or extensible network structures. In this paper, we propose a new incremental task learning framework based on low-rank factorization. In particular, we represent the network weights for each layer as a linear combination of several rank-1 matrices. To update the network for a new task, we learn a rank-1 (or low-rank) matrix and add that to the weights of every layer. We also introduce an additional selector vector that assigns different weights to the low-rank matrices learned for the previous tasks. We show that our approach performs better than the current state-of-the-art methods in terms of accuracy and forgetting. Our method also offers better memory efficiency compared to episodic memory- and mask-based approaches. Our code will be available at https://github.com/CSIPlab/task-increment-rank-update.git
Tackling Over-Smoothing: Graph Hollow Convolution Network with Topological Layer Fusion

In recent years, Graph Convolutional Networks (GCNs) have achieved great success in graph representations learning by incorporating features and topology information in each layer. Yet most GCNs are limited to the shallow network architecture due to over-smoothing which leads to indistinguishable nodes representations. In this paper, we provide a unique perspective on the key factor of over-smoothing which is the topological expressiveness loss when the stacked graph diffusion operators are over-densifying in deep layers. To tackle the over-smoothing issue, we propose the Graph Hollow Convolution Network (GHCN) with two key innovations. First, we design a hollow filter applied to the stacked graph diffusion operators to retain the topological expressiveness. Second, in order to further exploit the topology information, we integrate information from different layers based on graph local structures using topological layer fusion without propagating through nodes repeatedly. Extensive experiments on benchmark datasets show that our proposed method outperforms the state-of-the-art methods and effectively relieves the over-smoothing problem.
Software System Documentation: Coherent Description of Software System Properties

Software systems, also those that are part of cyber-physical systems, are represented by data structures and code which are static, syntactic descriptions of complex dynamic behavior. They are complex technical entities, difficult to understand, difficult to develop, difficult to correct, to maintain, to use, to operate, to evolve, to market. For each of these tasks documentation is useful, helpful, and badly needed. In any case, when dealing with software systems, improving understanding and therefore documentation is a key issue. There is a wide range of properties of software systems that have to be described and a huge number of different documentation techniques. This defines the broad spectrum of documentation and also the wide range of documentation in use. To overcome the complexity of software systems and cyber-physical systems abstraction is a key concept. A very helpful and promising form of abstraction is by achieved by carefully selected models that can be used for documentation.
Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks

Federated learning (FL) has recently received considerable attention and is becoming a popular machine learning (ML) framework that allows clients to train machine learning models in a decentralized fashion without sharing any private dataset. In the FL framework, data for learning tasks are acquired and processed locally at the edge node, and only the updated ML parameters are transmitted to the central server for aggregation. However, because local FL parameters and the global FL model are transmitted over wireless links, wireless network performance will affect FL training performance. In particular, the number of resource blocks is limited; thus, the number of devices participating in FL is limited. Furthermore, edge nodes often have substantial constraints on their resources, such as memory, computation power, communication, and energy, severely limiting their capability to train large models locally. This paper proposes a two-hop communication protocol with a dynamic resource allocation strategy to investigate the possibility of bandwidth allocation from a limited network resource to the maximum number of clients participating in FL. In particular, we utilize an ordinary hierarchical FL with an adaptive grouping mechanism to select participating clients and elect a leader for each group based on its capability to upload the aggregated parameters to the central server. Our experimental results demonstrate that the proposed solution outperforms the baseline algorithm in terms of communication cost and model accuracy.
UniCR: Universally Approximated Certified Robustness via Randomized Smoothing

We study certified robustness of machine learning classifiers against adversarial perturbations. In particular, we propose the first universally approximated certified robustness (UniCR) framework, which can approximate the robustness certification of any input on any classifier against any $$\ell _p$$ perturbations with noise generated by any continuous probability distribution. Compared with the state-of-the-art certified defenses, UniCR provides many significant benefits: (1) the first universal robustness certification framework for the above 4 “any”s; (2) automatic robustness certification that avoids case-by-case analysis, (3) tightness validation of certified robustness, and (4) optimality validation of noise distributions used by randomized smoothing. We conduct extensive experiments to validate the above benefits of UniCR and the advantages of UniCR over state-of-the-art certified defenses against $$\ell _p$$ perturbations.
Exploiting time-series analysis to predict customers’ behavioural dynamics in social networks

In the recent years, companies have focused on ways to take advantage of the rich and endless data that flow from the social media. The effective exploitation of such data has a crucial role in businesses’ growth and development. Businesses in order to survive in a highly competitive market need to analyze and understand customers’ needs and behaviours. This research work addresses the problem of predicting the behavioural dynamics of the customers regarding a brand name, using time-series analytics in social media. While in the existing literature many researchers have focused on studying the individual analytics, in this work we propose a more coarse-grained approach that analyses the overall behavioural dynamics in social networks. In particular, a data mining model is being presented, which exploits users’ linguistic and emotional characteristics in social networks, in order to predict the future collective behavioural trends. The purpose of this work is to provide an efficient and automated tool, that will enable businesses to predict the relationships with their customers. Thus, businesses will be able to strengthen and reestablish their bonds with their customers, through appropriate social media marketing campaigns in a timely manner.
Robust Dialogue State Tracking with Weak Supervision and Sparse Data

Abstract Generalizing dialogue state tracking (DST) to new data is especially challenging due to the strong reliance on abundant and fine-grained supervision during training. Sample sparsity, distributional shift, and the occurrence of new concepts and topics frequently lead to severe performance degradation during inference. In this paper we propose a training strategy to build extractive DST models without the need for fine-grained manual span labels. Two novel input-level dropout methods mitigate the negative impact of sample sparsity. We propose a new model architecture with a unified encoder that supports value as well as slot independence by leveraging the attention mechanism. We combine the strengths of triple copy strategy DST and value matching to benefit from complementary predictions without violating the principle of ontology independence. Our experiments demonstrate that an extractive DST model can be trained without manual span labels. Our architecture and training strategies improve robustness towards sample sparsity, new concepts, and topics, leading to state-of-the-art performance on a range of benchmarks. We further highlight our model’s ability to effectively learn from non-dialogue data.