Reviewerly/exHarmony huggingface dataset:
Student’s t-Based Robust Poisson Multi-Bernoulli Mixture Filter under Heavy-Tailed Process and Measurement Noises

A novel Student’s t-based robust Poisson multi-Bernoulli mixture (PMBM) filter is proposed to effectively perform multi-target tracking under heavy-tailed process and measurement noises. To cope with the common scenario where the process and measurement noises possess different heavy-tailed degrees, the proposed filter models this noise as two Student’s t-distributions with different degrees of freedom. Furthermore, this method considers that the scale matrix of the one-step predictive probability density function is unknown and models it as an inverse-Wishart distribution to mitigate the influence of heavy-tailed process noise. A closed-form recursion of the PMBM filter for propagating the approximated Gaussian-based PMBM posterior density is derived by introducing the variational Bayesian approach and a hierarchical Gaussian state-space model. The overall performance improvement is demonstrated through three simulations.
Responding to change over time: A longitudinal case study on changes in coordination mechanisms in large-scale agile

Abstract Context Responding to change and continuously improving processes, practices, and products are core to agile software development. It is no different in large-scale agile, where multiple software development teams need to respond both to changes in their external environments and to changes within the organization. Objective With this study, we aim to advance knowledge on coordination in large-scale agile by developing a model of the types of organizational changes that influence coordination mechanisms. Method We conducted a longitudinal case study in a growing large-scale agile organization, focusing on how external and internal changes impact coordination over time. We collected our data through 62 days of fieldwork across one and a half years. We conducted 37 interviews, observed 118 meetings at all organizational levels, collected supplementary material such as chat logs and presentations, and analyzed the data using thematic analysis. Results Our findings demonstrate how external events, such as onboarding new clients, and internal events, such as changes in the team organization, influence coordination mechanisms in the large-scale software development program. We find that external and internal change events lead to the introduction of new coordination mechanisms, or the adjustment of existing ones. Further, we find that continuous scaling requires continuous change and adjustment. Finally, we find that having the right mechanisms in place at the right time strengthens resilience and the ability to cope with change in coordination needs in complex large-scale environments. Conclusions Our findings are summarized in an empirically based model that provides a practical approach to analyzing change, aimed at supporting both researchers and practitioners dealing with change in coordination mechanisms in large-scale agile development contexts.
Design of a competition specifically for spoken dialogue with a humanoid robot

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Predicting human poses via recurrent attention network

Abstract Human motion prediction is a challenging task due to the diversity and randomness of future poses. Due to the inherent topology of pose data, most recent work has used graph convolution networks (GCNs) to accomplish the task of human motion prediction. However, the GCN-based method has a major shortcoming in that the modeling of temporal information is insufficient. In this paper, we propose a simple approach that combines recurrent neural networks (RNNs) and attention mechanism for motion prediction, which considers both spatial relations between different joints and temporal correlation. The query, key and value of the attention mechanism allow us to select the information we need for the subsequent prediction. To solve the difficult problem of RNN training, we utilize uncertainty and strong short-term constraints to optimize the training process. We evaluate our method on several standard benchmark datasets for human motion prediction, i.e., the Human3.6M dataset and the CMU MoCap dataset. The experimental results show that our approach outperforms previous approaches.
Tight Cramér-Rao type bounds for multiparameter quantum metrology through conic programming

In the quest to unlock the maximum potential of quantum sensors, it is of paramount importance to have practical measurement strategies that can estimate incompatible parameters with best precisions possible. However, it is still not known how to find practical measurements with optimal precisions, even for uncorrelated measurements over probe states. Here, we give a concrete way to find uncorrelated measurement strategies with optimal precisions. We solve this fundamental problem by introducing a framework of conic programming that unifies the theory of precision bounds for multiparameter estimates for uncorrelated and correlated measurement strategies under a common umbrella. Namely, we give precision bounds that arise from linear programs on various cones defined on a tensor product space of matrices, including a particular cone of separable matrices. Subsequently, our theory allows us to develop an efficient algorithm that calculates both upper and lower bounds for the ultimate precision bound for uncorrelated measurement strategies, where these bounds can be tight. In particular, the uncorrelated measurement strategy that arises from our theory saturates the upper bound to the ultimate precision bound. Also, we show numerically that there is a strict gap between the previous efficiently computable bounds and the ultimate precision bound.
Classification of Arabic Software Requirements Using Machine Learning Techniques

Ambiguity is a crucial problem in Software Requirements Specification (SRS) documents since most SRS documents are written in natural language, which is generally ambiguous. A variety of strategies have been employed to detect ambiguity in SRS papers. This paper will present techniques to classify Arabic language requirements through machine learning techniques. The proposed framework could help streamline the classification process for software development in Arabic-speaking countries by automating tasks and reducing the time taken.
Create and implement a new method for robust video face recognition using convolutional neural network algorithm

Biometric applications have massive demand in today's era. The areas of applications are mostly linked with the security of the system. Biometric features are regarded as the primary resource for security purposes due to their own distinctiveness and non-volatile essence. System authentication using biometrics is considered to be a sophisticated technology. Noise effect inducts variation in the biometric subject that causes an adverse impact on establishing the recognition. The proposed model supported the development of an effective method for performing facial biometric feature recognition. The model's goal is to reduce the number of false approvals and refusals. The proposed algorithm has been applied over a video dataset containing surveillance video frames that captures facial subjects dynamically. The first step is pre-processing of the video frames that have been carried out in the proposed model. Then, the Viola-Jones algorithm was applied to detect the facial subjects in the video frames. Feature extraction from the facial subject has been accomplished by applying a deep reinforcement learning algorithm. Further, the proposed model applied a convolutional neural network (CNN) algorithm to perform feature recognition of facial identity accurately. The proposed technique aims to maintain a huge recognition rate of dynamic facial subjects under various unprecedented noise variations. In the classification algorithm, the recognition accuracy is found to be 98.85%.
Multilingual hope speech detection: A Robust framework using transfer learning of fine-tuning RoBERTa model

Hope Speech Detection (HSD) from social media is a new direction for promoting and supporting positive content to encourage harmony and positivity in society. As users of social media belong to different linguistic communities, hope speech detection is rarely studied as a multilingual task considering low-resource languages. Moreover, prior studies explored only monolingual techniques, and the Russian language is not addressed. This study tackles the issue of Multi-lingual Hope Speech Detection (MHSD) in English and Russian languages using the transfer learning paradigm with fine-tuning approach. We explore joint multi-lingual and translation-based approaches to tackle the task of multilingualism, where the latter approach adopts the translation mechanism to transform all content into one language and then classify them. The joint multi-lingual method handles it by designing a universal classifier for various languages. We explore the strengths of the Robustly Optimized BERT Pre-Training Approach (RoBERTa) that showed a benchmark in capturing the semantics and contextual information within the content. The proposed framework consists of several stages: 1) data preprocessing, 2) representation of data using RoBERTa models, 3) fine-tuning phase, and 4) classification of hope speech into two labels. A new Russian corpus for hope speech detection is built, containing YouTube comments. Several experiments are conducted in English and Russian languages by using semi-supervised bilingual English and Russian datasets. The findings show that the proposed framework demonstrated benchmark performance and outperformed the baselines. Furthermore, the translation-based approach (Russian-RoBERTa) offered the best performance by achieving 94% accuracy and 80.24% f1-score.
Human Detection Based Yolo Backbones-Transformer in UAVs

This study presents a new method for human detection in UAVs using Yolo backbones transformer. The proposed framework utilizes backbones YoloV8s, SC3T (Based Transformer), with RGB inputs to accurately perceive human detection. Experimental results demonstrate that the proposed method achieves an average accuracy of around 90.0% mAP@0.5 for human detection in the Human UAVs dataset, surpassing the performance of competitive baselines. The superior performance of our Deep Neural Network (DNN) can provide context awareness to UAVs. Furthermore, the proposed method can be easily adapted to detect UAVs in various applications. This work highlights the potential of the Yolo backbones transformer for enhancing human detection in UAVs, demonstrating its superiority over conventional methods. Overall, the proposed framework can pave the way for future research in UAV detection applications. Training code and self-collected Human detection dataset are released in https://github.com/Tyler-Do/Yolov8-Transformer.
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a useful global model. Feature selection (FS) is important to VFL. It is still an open research problem as existing FS works designed for VFL either assumes prior knowledge on the number of noisy features or prior knowledge on the post-training threshold of useful features to be selected, making them unsuitable for practical applications. To bridge this gap, we propose the Federated Stochastic Dual-Gate based Feature Selection (FedSDG-FS) approach. It consists of a Gaussian stochastic dual-gate to efficiently approximate the probability of a feature being selected, with privacy protection through Partially Homomorphic Encryption without a trusted third-party. To reduce overhead, we propose a feature importance initialization method based on Gini impurity, which can accomplish its goals with only two parameter transmissions between the server and the clients. Extensive experiments on both synthetic and real-world datasets show that FedSDG-FS significantly outperforms existing approaches in terms of achieving accurate selection of high-quality features as well as building global models with improved performance.
Biomedical Image Analysis for Colon and Lung Cancer Detection Using Tuna Swarm Algorithm With Deep Learning Model

The domain of Artificial Intelligence (AI) is made important strides recently, leading to developments in several domains comprising biomedical diagnostics and research. The procedure of AI-based systems in biomedical analytics takes opened up novel avenues for the progress of disease analysis, drug discovery, and treatment. Cancer is the second major reason of death worldwide; around one in every six people pass away suffering from it. Among several kinds of cancers, the colon and lung variations are the most frequent and deadliest ones. Initial detection of conditions on both fronts significantly reduces the probability of mortality. Deep learning (DL) and Machine learning (ML) systems are exploited to speed up such cancer detection, permitting researchers to analyze a huge count of patients in a lesser time count and at a minimal cost. This study develops a new Biomedical Image Analysis for Colon and Lung Cancer Detection using Tuna Swarm Algorithm with Deep Learning (BICLCD-TSADL) model. The presented BICLCD-TSADL technique examines the biomedical images for the identification and classification of colon and lung cancer. To accomplish this, the BICLCD-TSADL technique applies Gabor filtering (GF) to preprocess the input images. In addition, the BICLCD-TSADL technique employs a GhostNet feature extractor to create a collection of feature vectors. Moreover, AFAO was executed to adjust the hyperparameters of the GhostNet technique. Furthermore, the TSA with echo state network (ESN) classifier is utilized for detecting lung and colon cancer. To demonstrate the more incredible outcome of the BICLCD-TSADL system, an extensive experimental outcome is carried out. The comprehensive comparative analysis highlighted the greater efficiency of the BICLCD-TSADL technique with other approaches with maximum accuracy of 99.33%.
K-Means Clustering with Local Distance Privacy

With the development of information technology, a mass of data are generated every day. Collecting and analysing these data help service providers improve their services and gain an advantage in the fierce market competition. K-means clustering has been widely used for cluster analysis in real life. However, these analyses are based on users' data, which disclose users' privacy. Local differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering analysis. However, existing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy guarantee. To solve this problem, we propose a novel method that provides local distance privacy for users who participate in the clustering analysis. Instead of making the users' records in-distinguish from each other in high-dimensional space, we map the user's record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each other. To be specific, we generate a noisy distance first and then synthesize the high-dimensional data record. We propose a Bounded Laplace Method (BLM) and a Cluster Indistinguishable Method (CIM) to sample such a noisy distance, which satisfies the local differential privacy guarantee and local d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</inf> -privacy guarantee, respectively. Furthermore, we introduce a way to generate synthetic data records in high-dimensional space. Our experimental evaluation results show that our methods outperform the traditional methods significantly.
Remote Sensing Image Dehazing Using Adaptive Region-Based Diffusion Models

Remote sensing (RS) dehazing is a long-standing topic, as the contrast and clarity of images are seriously reduced in the severe environment. Recent learning-based ways achieve great success in RS dehazing, while these models usually suffer from unusual artifacts, mode collapse and size limitation. To address these problems, we propose an Adaptive Region-Based Diffusion Dehazing Net (ARDD-Net), which is a novel diffusion-based model toward free-form RS dehazing. To be specific, a guided diffusion process is employed to transform clear RS region parts by adding Gaussian noise repeatedly, and then restore the clear ones through a reverse generative procedure. In addition, Region-Based model is employed to address the free-size images for diffusion model. Further, a cyclic shift strategy is applied to the inference process, in order to avoid the inconsistent effect of certain regions and increase the image harmony. With extensive experiments, we state that our model gets outstanding performances than the comparing methods on both synthetic and real-world RS images.
Automated Detection of Students’ Gaze Interactions in Collaborative Learning Videos: A Novel Approach

Gaze behaviours have been considered important social signals to explore human learning. Over the past decade, previous research showed positive relationships between certain features of gaze behaviours and the quality of collaborative learning. However, most studies focus on detecting students' gaze behaviours with eye-tracking tools which are costly, logistically challenging, and can be obtrusive in real-world physical collaboration spaces. This study presents a novel approach to detecting students' gaze behaviours from videos of real-world collaborative learning activities. Pre-trained computer vision models were used to detect objects on the scenes, students' faces, and their gaze directions. Then, a rule-based approach was applied to detect gaze behaviours that are associated with peer communication and resource management aspects of collaborative learning. In order to test the accuracy of the proposed approach, twenty collaborative learning sessions, each lasting from 33 min to 67 min, from five groups in a 10-week-long higher education course were analysed. The results showed that the proposed approach achieves 66.57% overall accuracy at automatically detecting students' gaze interactions in collaborative learning videos. The implications of these findings for supporting students' collaborative learning in real-world technology-enhanced learning environments are discussed.
A Zipf's law-based text generation approach for addressing imbalance in entity extraction

Entity extraction is critical in the intelligent advancement across diverse domains. Nevertheless, a challenge to its effectiveness arises from the data imbalance, where certain entities are common while others are scarce. To address this issue, this study proposes a novel text generation approach that harnesses Zipf's law, which is a powerful tool from informetrics for studying human language. By employing characteristics of Zipf's law, words within the documents are classified as common and rare ones. Subsequently, sentences are classified into common and rare ones, and are further processed by text generation models accordingly. Rare entities within the generated sentences are then labeled using human-designed rules, serving as a supplement to the raw dataset, thereby mitigating the imbalance problem. The study presents a case of extracting entities from technical documents, and the extensive experimental results on two datasets prove the effectiveness of the proposed method. Furthermore, the significance and potential of Zipf's law in driving the progress of artificial intelligence (AI) is discussed, broadening the scope and coverage of informetrics. By incorporating the foundational principles of informetrics into text generation, this study showcases the pivotal role of informetrics in shaping the design and developmental of AI systems.
Q-learning-based unmanned aerial vehicle path planning with dynamic obstacle avoidance

Recently, unmanned aerial vehicles (UAVs) have shown promising results for autonomous sensing. UAVs have been deployed for multiple applications that include surveillance, mapping, tracking, and search operations. Finding an efficient path between a source and a goal is a critical issue that has been the focus of recent exploration. Many path-planning algorithms are utilized to find an efficient path for a UAV to navigate from a source to a goal with obstacle avoidance. Despite the extensive literature and numerous research proposals for path planning, dynamic obstacle avoidance has not been addressed with machine learning. When the obstacles are dynamic, i.e., they can change their position over time, and the constraints of the path planning algorithm become more challenging. This in turn adds a layer of complexity to the path planning algorithm. To address this challenge, a Q-learning algorithm is proposed in this work to facilitate efficient path planning for UAVs with both static and dynamic obstacle avoidance. We introduced the Shortest Distance Prioritization policy in the learning process which marginally reduces the distance that the UAV has to travel to reach the goal. Further, the proposed Q-learning algorithm adopts a grid-graph-based method to solve the path-planning problem. It learns to maximize the reward based on the agent’s behavior in the environment. Through results, the performance comparison between the proposed approach and state-of-the-art path planning approaches such as A-star, Dijkstra, and Sarsa algorithms are evaluated in terms of learning time and path length. We show through results that the proposed approach results in improved performance when compared to state-of-the-art approaches. Further, the effect of an increased number of obstacles are evaluated on the performance of the proposed approach.
Enhancing Credit Card Fraud Detection Through a Novel Ensemble Feature Selection Technique

Identifying fraudulent activities in credit card transactions is an inherent component of financial computing. The focus of our research is on the Credit Card Fraud Detection Dataset, which is widely used due to its authentic transaction data. In numerous machine learning applications, feature selection has become a crucial step. To improve the chance of discovering the globally optimal feature set, we employ ensembles of feature ranking methods. These ensemble methods merge multiple feature ranking lists through a median approach. We conduct a comprehensive empirical study that examines two different ensembles of feature ranking techniques, including an ensemble of twelve threshold-based feature selection (TBFS) techniques and an ensemble of five supervised feature selection (SFS) techniques. Additionally, we present results where all features are used. We construct classification models using two Decision Tree-based classifiers, CatBoost and XGBoost, and evaluate them using two different performance metrics, the Area Under the Receiver Operating Characteristic Curve (AUC) and the Area under the Precision-Recall Curve (AUPRC). Since AUPRC provides a more accurate representation of the number of false positives, especially for highly imbalanced datasets, evaluating models for AUPRC is a wise choice. The experimental results demonstrate that the ensemble of SFS and all features performs similarly or better than the ensemble of TBFS. Moreover, we find that XGBoost outperforms CatBoost in terms of AUPRC.