Reviewerly/exHarmony huggingface dataset:
Adaptive Semantic Matching in a Multilingual Context

In an increasingly multilingual digital world, information management tools must support the simultaneous use and matching of multiple natural languages. A prerequisite for this is that the underlying database engine seamlessly processes multilingual data across languages. However, most natural language processing-based techniques have focused on developing monolingual matching algorithms, often ignoring context knowledge and external domain-based sources, which lead to incomplete and inaccurate matching results in a multilingual environment. The purpose of this study is to propose an adaptive semantic matching method with context knowledge and user involvement as two new dimensions for matching the semantically related entities ontologies. We present a comprehensive evaluation of our solution by applying it in a multilingual e-commerce platform case study, which performed well on matching accuracy.
An Efficient Noisy Label Learning Method with Semi-supervised Learning

Even though deep learning models make success in many application areas, it is well-known that they are vulnerable to data noise. Therefore, researches on a model that detects and removes noisy data or the one that operates robustly against noisy data have been actively conducted. However, most existing approaches have limitations in either that important information could be left out while noisy data are cleaned up or that prior information on the dataset is required while such information may not be easily available. In this paper, we propose an effective semi-supervised learning method with model ensemble and parameter scheduling techniques. Our experiment results show that the proposed method achieves the best accuracy under 20% and 40% noise-ratio conditions. The proposed model is robust to data noise, suffering from only 2.08% of accuracy degradation when the noise ratio increases from 20% to 60% on CIFAR-10. We additionally perform an ablation study to verify net accuracy enhancement by applying one technique after another.
Comparison of Face Recognition on RGB and Grayscale Color with Deep Learning in Forensic Science

In forensic science face recognition, we cannot request high-quality face images from sources, but we have face images from CCTV grayscale on the crime scene at night, face images in RGB mode from Web Cameras, etc. This research needs to find a satisfying method of face recognition in forensic science to identify the “Who's face?” at the request of a police investigator. The experiment uses Siamese neural network face recognition of both RGB and GRAY color modes to compare and show the performance of both color modes. The evaluation shows a confusion matrix, F1-score ROC/AUC, and a strong recommend with Likelihood ratio (LR) that supports court in evidence identification recommended by NIST and ENFSI.
Integrating User Gaze with Verbal Instruction to Reliably Estimate Robotic Task Parameters in a Human-Robot Collaborative Environment

As robots become more ubiquitous in our daily life, it has become very important to extract task and environmental information through more natural, meaningful, and easy-to-use interaction interfaces. Not only this helps the user to adapt to (thus trust) a robot in a collaborative environment, it can supplement the core sensory information, helping the robot make reliable decisions. This paper presents a framework that combines two natural interaction interfaces: speech and gaze to reliably infer the object of interest and the robotic task parameters. The gaze estimation module utilizes pre-defined 3D facial points and matches them to a set of extracted estimated 3D facial landmarks of the users from 2D images to infer the gaze direction. Subsequently, the verbal instructions are passed through a deep learning model to extract the information relevant to a robotic task. These extracted task parameters from verbal instructions along with the estimated gaze directions are combined to detect and/or disambiguate objects in the scene to generate the final task configurations. The proposed framework shows very promising results in integrating the relevant task parameters for the intended robotic tasks in different real-world interaction scenarios.
Object-Based Vehicle Color Recognition in Uncontrolled Environment

The demand for vehicle recognition significantly increases with impact on many businesses in recent decades. This paper focuses on a vehicle color attribute. A novel method for vehicle color recognition is introduced to overcome three challenges of vehicle color recognition. The first challenge is an uncontrolled environment such as shadow, brightness, and reflection. Second, similar color is hard to be taken into account. Third, few research works dedicate to multi-color vehicle recognition. Previous works can provide only color information of the whole vehicle, but not at vehicle part level. In this study, a new approach for recognizing the colors of vehicles at the part level is introduced. It utilizes object detection techniques to identify the colors based on the different objects (e.g. parts of a vehicle in this research). In addition, a novel generic post-processing is proposed to improve robustness in the uncontrolled environment and support not only single-color but also multi-color vehicles. Experimental results show that it can effectively identify the color under the three challenges addressed above with 99 % accuracy for single-color vehicle and outperforms the other seven baseline models, and 76 % accuracy for multi-color vehicle.
Discrete Radial-Harmonic-Fourier Moments for Image Description

A new type of multi-distorted invariant discrete orthogonal moments, discrete Radial-Harmonic-Fourier moments was proposed. The kernel function of the moments was composed of radial discrete orthogonal triangular function and angular Fourier complex componential factor. The relationship between discrete Radial-Harmonic-Fourier moments and Radial-Harmonic-Fourier moments was also analyzed. The experimental results indicate that the discrete Radial-Harmonic-Fourier moments have excellent image description ability and can be effectively used as invariant image features in image analysis and pattern recognition.
Research on Compact Quantum Classifier Based on Kernel Method

Kernel method is widely used in machine learning. At present, the connection between kernel methods and quantum computing has been gradually established, which provides a new algorithm idea for the field of quantum machine learning. Research shows that the construction of minimized quantum circuits can be reliably performed on Noisy Intermediate-Scale Quantum (NISQ) devices. This paper proposes a compact quantum classifier based on kernel method. By introducing the compact amplitude encoding, the data label of the phase corresponding to the quantum state is encoded. Compared with the proposed classifier based on quantum kernel method, it can reduce 2 quantum registers, further reduce the circuit depth, and thus reduce the algorithm complexity. The double qubit measurement is simplified to single qubit measurement. In addition, this model achieves the optimal variance in quantum circuit parameters, which can effectively save computational resources. Experimental simulation shows that the expected value measurement in the proposed classifier model is closer to the theoretical value, and the classification accuracy is more accurate. At the same time, the system model has low entanglement, which can effectively reduce the cost of the whole preparation.
Detection of Conversational Health in a Multimodal Conversation Graph by Measuring Emotional Concordance

With the advent of social media and technology, the increased connections between individuals and organizations have led to a similar increase in the number of conversations. These conversations, in most cases are bimodal in nature, consisting of both images and text. Existing work in multimodal conversation typically focuses on individual utterances rather than the overall dialogue. The aspect of conversational health is important in many real world conversational uses cases including the emerging world of Metaverse. The work described in this paper investigates conversational health from the viewpoint of emotional concordance in bimodal conversations modelled as graphs. Using this framework, an existing multimodal dialogue dataset has been reformatted as a graph dataset that is labelled with the emotional concordance score. In this work, determination of conversational health has been framed as a graph classification problem. A graph neural network based model using algorithms such as Graph Convolution Network and Graph Attention Network is then used to detect the emotional concordance or discordance based upon the multimodal conversation that is provided. The model proposed in this paper achieves an overall F1 Score of 0.71 for equally sized class training and testing size, which offers improved results compared to previous models using the same benchmark dataset.
Named entities as key features for detecting semantically similar news articles

The focus of this work is detecting semantically similar news articles for search engines and recommender systems which is an important step towards processing and understanding natural language. Search engines and recommender systems typically filter out near-duplicate articles which are often just a paraphrasing of a previous article and therefore irrelevant for the users. Articles with a high level of overlapping content are not interesting to the reader and should be avoided. Here, we focus on named entities, such as people, organizations and places, and their role as a key feature for identifying near-duplicate articles. Since our dataset from the energy business contains a significant amount of paraphrased articles, standard techniques, e.g. based on the Jaccard coefficient, already serve quite well. A fine-tuned BERT model evaluated on named entities achieves best model results with more than 97% accuracy and highest True Positive Rates. The importance of individual words for the model decisions is evaluated by computing their Shapley values. It was found that the explanations are in overall good agreement with the human intuitive interpretation.
PrimitivePose: Generic Model and Representation for 3D Bounding Box Prediction of Unseen Objects

A considerable amount of research is concerned with the challenging task of estimating three-dimensional (3D) pose and size for multi-object indoor scene configurations. Many existing models rely on a priori known object models, such as 3D CAD models and are therefore limited to a predefined set of object categories. This closed-set constraint limits the range of applications for robots interacting in dynamic environments where previously unseen objects may appear. This paper addresses this problem with a highly generic 3D bounding box detection method that relies entirely on geometric cues obtained from depth data percepts. While the generation of synthetic data, e.g. synthetic depth maps, is commonly used for this task, the well-known synth-to-real gap often emerges, which prohibits transition of models trained solely on synthetic data to the real world. To ameliorate this problem, we use stereo depth computation on synthetic data to obtain pseudo-realistic disparity maps. We then propose an intermediate representation, namely disparity-scaled surface normal (SN) images, which encodes geometry and at the same time preserves depth/scale information unlike the commonly used standard SNs. In a series of experiments, we demonstrate the usefulness of our approach, detecting everyday objects on a captured data set of tabletop scenes, and compare it to the popular PoseCNN model. We quantitatively show that standard SNs are less adequate for challenging 3D detection tasks by comparing predictions from the model trained on disparity alone, SNs and disparity-scaled SNs. Additionally, in an ablation study we investigate the minimal number of training samples required for such a learning task. Lastly, we make the tool used for 3D object annotation publicly available at: https://preview.tinyurl.com/3ycn8v5k . A video showcasing our results can be found at: https://preview.tinyurl.com/dzdzabek .
Developing a model that supports the evolution of legacy systems into an enterprise

Abstract This article details the first step in the system dynamics analysis of the systems engineering process for the evolution of legacy systems into an enterprise. This step develops a model that depicts the interaction of the various components associated with the system of interest. This model is a collection of causal loop diagrams that will foster the development of a novel framework known as the enterprise lifecycle model, which will support system dynamics analysis through three stages: planning, development, and execution. Peer‐reviewed academic and industry sources will be utilized to understand how accepted literature defines this analysis. Specifically, this model will be based on the Vee lifecycle model as well as the Agile and Iron Triangle frameworks. Supplemental elements will be added to these diagrams to incorporate the environment within which the system of interest is planned, developed, and executed. Additional factors, such as quality management, will be added to complete the super system and system of interest views of the enterprise lifecycle model—with the goal of creating a model depicting a reductive and holistic view that aids in the reduction of complexity surrounding the systems engineering process used to prepare legacy systems for evolution into an enterprise and support the definition of the desired target system.
Exact Simulation of Poisson-Dirichlet Distribution and Generalised Gamma Process

Abstract Let $$J_1&gt;J_2&gt;\dots $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>J</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>&gt;</mml:mo> <mml:msub> <mml:mi>J</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>&gt;</mml:mo> <mml:mo>⋯</mml:mo> </mml:mrow> </mml:math> be the ranked jumps of a gamma process $$\tau _{\alpha }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>τ</mml:mi> <mml:mi>α</mml:mi> </mml:msub> </mml:math> on the time interval $$[0,\alpha ]$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>[</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mi>α</mml:mi> <mml:mo>]</mml:mo> </mml:mrow> </mml:math> , such that $$\tau _{\alpha }=\sum _{k=1}^{\infty }J_k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>τ</mml:mi> <mml:mi>α</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mo>∑</mml:mo> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:mi>∞</mml:mi> </mml:msubsup> <mml:msub> <mml:mi>J</mml:mi> <mml:mi>k</mml:mi> </mml:msub> </mml:mrow> </mml:math> . In this paper, we design an algorithm that samples from the random vector $$(J_1, \dots , J_N, \sum _{k=N+1}^{\infty }J_k)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>J</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>⋯</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>J</mml:mi> <mml:mi>N</mml:mi> </mml:msub> <mml:mo>,</mml:mo> <mml:msubsup> <mml:mo>∑</mml:mo> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>=</mml:mo> <mml:mi>N</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:mi>∞</mml:mi> </mml:msubsup> <mml:msub> <mml:mi>J</mml:mi> <mml:mi>k</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . Our algorithm provides an analog to the well-established inverse Lévy measure (ILM) algorithm by replacing the numerical inversion of exponential integral with an acceptance-rejection step. This research is motivated by the construction of Dirichlet process prior in Bayesian nonparametric statistics. The prior assigns weight to each atom according to a GEM distribution, and the simulation algorithm enables us to sample from the N largest random weights of the prior. Then we extend the simulation algorithm to a generalised gamma process. The simulation problem of inhomogeneous processes will also be considered. Numerical implementations are provided to illustrate the effectiveness of our algorithms.
‘Earlier than Early’ Detection of Breast Cancer in Israeli BRCA Mutation Carriers Applying AI-Based Analysis to Consecutive MRI Scans

Female BRCA1/BRCA2 (=BRCA) pathogenic variants (PVs) carriers are at a substantially higher risk for developing breast cancer (BC) compared with the average risk population. Detection of BC at an early stage significantly improves prognosis. To facilitate early BC detection, a surveillance scheme is offered to BRCA PV carriers from age 25-30 years that includes annual MRI based breast imaging. Indeed, adherence to the recommended scheme has been shown to be associated with earlier disease stages at BC diagnosis, more in-situ pathology, smaller tumors, and less axillary involvement. While MRI is the most sensitive modality for BC detection in BRCA PV carriers, there are a significant number of overlooked or misinterpreted radiological lesions (mostly enhancing foci), leading to a delayed BC diagnosis at a more advanced stage. In this study we developed an artificial intelligence (AI)-network, aimed at a more accurate classification of enhancing foci, in MRIs of BRCA PV carriers, thus reducing false-negative interpretations. Retrospectively identified foci in prior MRIs that were either diagnosed as BC or benign/normal in a subsequent MRI were manually segmented and served as input for a convolutional network architecture. The model was successful in classification of 65% of the cancerous foci, most of them triple-negative BC. If validated, applying this scheme routinely may facilitate 'earlier than early' BC diagnosis in BRCA PV carriers.
Living on Mount Etna Between Risk, Beauty and Need: A Field Survey on Villages Struck by 2018 Earthquake

Sicily’s uneven geological structure is positioned within a system of tectonic plates. Its (very) active volcano, Mount Etna, has always made it prone to disasters. Nevertheless on the flanks of Etna there are many urbanized areas and the population living on the slopes of Etna has increased a lot in the last century, because there is an ancient and intimate relationship between the land and the people who live and work on the land. In this chapter I will try to investigate possible changes in the perception of the risks associated with the volcano by the resident population by conducting survey in the villages of Fleri, Pisano, Pennisi and Santa Maria la Stella. This study is part of a growing body of research on changes in the perception of risk in the Etna area.
FastFusion: Deep stereo‐LiDAR fusion for real‐time high‐precision dense depth sensing

Abstract Light detection and ranging (LiDAR) and stereo cameras are two generally used solutions for perceiving 3D information. The complementary properties of these two sensor modalities motivate a fusion to derive practicable depth sensing toward real‐world applications. Promoted by deep neural network (DNN) techniques, recent works achieve superior performance on accuracy. However, the complex architecture and the sheer number of DNN parameters often lead to poor generalization capacity and non‐real‐time computing. In this paper, we present FastFusion, a three‐stage stereo‐LiDAR deep fusion scheme, which integrates the LiDAR priors into each step of classical stereo‐matching taxonomy, gaining high‐precision dense depth sensing in a real‐time manner. We integrate stereo‐LiDAR information by taking advantage of a compact binary neural network and utilize the proposed cross‐based LiDAR trust aggregation to further fuse the sparse LiDAR measurements in the back‐end of stereo matching. To align the photometrical of the input image and the depth of the estimation, we introduce a refinement network to guarantee consistency. More importantly, we present a graphic processing unit‐based acceleration framework for providing a low‐latency implementation of FastFusion, gaining both accuracy improvement and real‐time responsiveness. In the experiments, we demonstrate the effectiveness and practicability of FastFusion, which obtains a significant speedup over state‐of‐the‐art baselines while achieving comparable accuracy on depth sensing. The video demo for real‐time depth estimation of FastFusion on the real‐world driving scenario is available at https://youtu.be/nP7cls2BA8s .
A Cloud Broker for Executing Deadline-Constrained Periodic Scientific Workflows

Scheduling workflows in cloud environments is an important issue that many types of research have been conducted in this field. However, these approaches often focus on single workflow scheduling while the need for scheduling multiple workflows is growing. This study aims at presenting a cloud broker for executing Deadline-constrained Periodic scientific Workflows (BDPW). BDPW acts as a Workflow as a Service (WaaS) broker and uses both reserved and on-demand resources in order to minimize the monetary cost of renting resources from a cloud provider. Furthermore, BDPW uses container technology by executing multiple containerized tasks on the same Virtual Machine (VM) to decrease the provisioning delay of VMs. The proposed broker uses a hybrid scheduling method, i.e., static planning and dynamic scheduling. The static planner uses resource leveling problem (RLP) to provide a scheduling plan and also recognizes the number of reserved resources that should be leased from a provider. Then, the dynamic scheduler tries to assign tasks to the reserved resources based on the primary static plan and leases on-demand instances if necessary. Also, it may make changes to the primary plan due to uncertainties in the task runtimes. The experimental results in CloudSim show that BDPW outperforms baseline algorithms in terms of monetary cost.
UAV Air Game Maneuver Decision-Making Using Dueling Double Deep Q Network with Expert Experience Storage Mechanism

Deep reinforcement learning technology applied to three-dimensional Unmanned Aerial Vehicle (UAV) air game maneuver decision-making often results in low utilization efficiency of training data and algorithm convergence difficulties. To address these issues, this study proposes an expert experience storage mechanism that improves the algorithm’s performance with less experience replay time. Based on this mechanism, a maneuver decision algorithm using the Dueling Double Deep Q Network is introduced. Simulation experiments demonstrate that the proposed mechanism significantly enhances the algorithm’s performance by reducing the experience by 81.3% compared to the prioritized experience replay mechanism, enabling the UAV agent to achieve a higher maximum average reward value. The experimental results suggest that the proposed expert experience storage mechanism improves the algorithm’s performance with less experience replay time. Additionally, the proposed maneuver decision algorithm identifies the optimal policy for attacking target UAVs using different fixed strategies.