Reviewerly/exHarmony huggingface dataset:
Interactive Healthcare Robot Using Attention-Based Question-Answer Retrieval and Medical Entity Extraction Models

In healthcare facilities, answering the questions from the patients and their companions about the health problems is regarded as an essential task. With the current shortage of medical personnel resources and an increase in the patient-to-clinician ratio, staff in the medical field have consequently devoted less time to answering questions for each patient. However, studies have shown that correct healthcare information can positively improve patients' knowledge, attitudes, and behaviors. Therefore, delivering correct healthcare knowledge through a question-answering system is crucial. In this article, we develop an interactive healthcare question-answering system that uses attention-based models to answer healthcare-related questions. Attention-based transformer models are utilized to efficiently encode semantic meanings and extract the medical entities inside the user query individually. These two features are integrated through our designed fusion module to match against the pre-collected healthcare knowledge set, so that our system will finally give the most accurate response to the user in real-time. To improve the interactivity, we further introduce a recommendation module and an online web search module to provide potential questions and out-of-scope answers. Experimental results for question-answer retrieval show that the proposed method has the ability to retrieve the correct answer from the FAQ pairs in the healthcare domain. Thus, we believe that this application can bring more benefits to human beings.
SF6 Pointer Pressure Meter Reading Method Based on Fusion Attention Feature UNet

Aiming at the problem of decreasing the accuracy of meter reading caused by manual reading, an SF6 pointer-type pressure meter reading method based on fusion attention feature UNet is proposed to improve the accuracy of pointer-type pressure meter reading. Firstly, design the fusion attention feature UNet neural network to segment the pointer and dense scale of the SF6 pressure meter. The Ghost convolutional module used in the coding layer of the neural network can reasonably utilize redundant features to strengthen the inference ability of the network. Deep semantic information feature fusion module built to extract deep potential feature information. Also, introduce the pyramid split attention mechanism to strengthen the information interaction between the network coding and decoding layers. Then use the minimum outer rectangle algorithm and K-means clustering algorithm to determine the circle’s center of the SF6 pressure meter for the segmented data. Finally, using the circle’s center to fit the initial scale and the pointer in two straight lines. It calculates the angle between the two straight lines. The angle conversion formula obtains the accurate reading of the SF6 manometer. It is proved by experiment that the proposed intelligent reading algorithm will not be affected by environmental factors and can better divide the pointer and dense scale in the SF6 pointer pressure meter to complete the accurate reading of the meter.
Nonparametric Spatio-Temporal Joint Probabilistic Data Association Coupled Filter and Interfering Extended Target Tracking

Extended target tracking estimates the centroid and shape of the target in space and time. In various situations where extended target tracking is applicable, the presence of multiple targets can lead to interference, particularly when they maneuver behind one another in a sensor like a camera. Nonetheless, when dealing with multiple extended targets, there’s a tendency for them to share similar shapes within a group, which can enhance their detectability. For instance, the coordinated movement of a cluster of aerial vehicles might cause radar misdetections during their convergence or divergence. Similarly, in the context of a self-driving car, lane markings might split or converge, resulting in inaccurate lane tracking detections. A well-known joint probabilistic data association coupled (JPDAC) filter can address this problem in only a single-point target tracking. A variation of JPDACF was developed by introducing a nonparametric Spatio-Temporal Joint Probabilistic Data Association Coupled Filter (ST-JPDACF) to address the problem for extended targets. Using different kernel functions, we manage the dependency of measurements in space (inside a frame) and time (between frames). Kernel functions are able to be learned using a limited number of training data. This extension can be used for tracking the shape and dynamics of nonparametric dependent extended targets in clutter when targets share measurements. The proposed algorithm was compared with other well-known supervised methods in the interfering case and achieved promising results.
guitARhero: Interactive Augmented Reality Guitar Tutorials

This paper presents guitARhero, an Augmented Reality application for interactively teaching guitar playing to beginners through responsive visualizations overlaid on the guitar neck. We support two types of visual guidance, a highlighting of the frets that need to be pressed and a 3D hand overlay, as well as two display scenarios, one using a desktop magic mirror and one using a video see-through head-mounted display. We conducted a user study with 20 participants to evaluate how well users could follow instructions presented with different guidance and display combinations and compare these to a baseline where users had to follow video instructions. Our study highlights the trade-off between the provided information and visual clarity affecting the user's ability to interpret and follow instructions for fine-grained tasks. We show that the perceived usefulness of instruction integration into an HMD view highly depends on the hardware capabilities and instruction details.
Context and Spatial Feature Calibration for Real-Time Semantic Segmentation

Context modeling or multi-level feature fusion methods have been proved to be effective in improving semantic segmentation performance. However, they are not specialized to deal with the problems of pixel-context mismatch and spatial feature misalignment, and the high computational complexity hinders their widespread application in real-time scenarios. In this work, we propose a lightweight Context and Spatial Feature Calibration Network (CSFCN) to address the above issues with pooling-based and sampling-based attention mechanisms. CSFCN contains two core modules: Context Feature Calibration (CFC) module and Spatial Feature Calibration (SFC) module. CFC adopts a cascaded pyramid pooling module to efficiently capture nested contexts, and then aggregates private contexts for each pixel based on pixel-context similarity to realize context feature calibration. SFC splits features into multiple groups of sub-features along the channel dimension and propagates sub-features therein by the learnable sampling to achieve spatial feature calibration. Extensive experiments on the Cityscapes and CamVid datasets illustrate that our method achieves a state-of-the-art trade-off between speed and accuracy. Concretely, our method achieves 78.7% mIoU with 70.0 FPS and 77.8% mIoU with 179.2 FPS on the Cityscapes and CamVid test sets, respectively. The code is available at https://nave.vr3i.com/ and https://github.com/kaigelee/CSFCN.
A Survey on the Deep Learning-Based Mismatch Removal: Principles and Methods

Due to the inherent limitations of matching algorithms and the complexities associated with image contents, mismatches are inevitable and can have detrimental effects on downstream tasks in computer vision and remote sensing. Researchers have published numerous reviews on mismatch removal, which may suffer from two primary deficiencies. Firstly, these reviews are often embedded within studies that primarily focus on image matching, thereby limiting the detailed and comprehensive analysis of mismatch removal methods. Secondly, reviews of deep learning (DL)-based methods, despite their numerous existence and interconnection, tend to be fragmentary and lack a systematic approach. To address these two shortcomings, this paper presents a comprehensive survey of DL-based mismatch removal principles and methods. We provide a summary of network architectures, techniques for extracting geometrical information, and various training modes. Specifically, we highlight the importance of permutation invariance in mining operations, enumerate a majority of existing mining methods, and provide an explanation of their permutation invariant properties. Furthermore, we present both the intuitive motivation and mathematical analysis of commonly used methods, elucidating their underlying principles and efficacy. In the conclusion, we predict upcoming trends based on the findings of our review, aiming to provide valuable insights into mismatch removal techniques and guide their practical applications.
Physically-Interpretable Data Augmentation for Multi-Range Hand Gesture Recognition Using FMCW Radar Time Series

In this paper, a robust hand gesture recognition (HGR) system using a frequency modulated continuous wave (FMCW) radar and InceptionTime networks on data augmented time series is implemented. The paper proposes multiple data augmentation techniques for radar-based HGR. Since a realistic manipulation of raw radar data frames or even range-Doppler maps is a very complex challenge, we instead propose data manipulation on physically interpretable time series of range, azimuth and elevation angles extracted from the data. Due to working on physically interpretable time series data, we can on the one hand make use of well explored existing augmentation techniques for time series and on the other hand do usecase specific interpretable augmentations, such as simulating a different aspect angle or range. To investigate the system, a data recording process covering multiple ranges, angles and types of gestures is carried out. The gain in accuracy from the proposed data augmentation scheme amounts to more than 4 percentage points to reach a global prediction accuracy higher than 95% for a very diverse dataset.
A Novel Energy Efficient Multi-Dimensional Virtual Machines Allocation and Migration at the Cloud Data Center

Due to the rapid utilization of cloud services, the energy consumption of cloud data centres is increasing dramatically. These cloud services are provided by Virtual Machines (VMs) through the cloud data center. Therefore, energy-aware VMs allocation and migration are essential tasks in the cloud environment. This paper proposes a Branch-and-Price based energy-efficient VMs allocation algorithm and a Multi-Dimensional Virtual Machine Migration (MDVMM) algorithm at the cloud data center. The Branch-and-Price based VMs allocation algorithm reduces energy consumption and wastage of resources by selecting the optimal number of energy-efficient PMs at the cloud data center. The proposed MDVMM algorithm saves energy consumption and avoids the Service Level Agreement (SLA) violation by performing an optimal number of VMs migrations. The experimental results demonstrate that our proposed Branch-and-Price based VMs allocation with VMs migration algorithms saves more than 31% energy consumption and improves 21.7% average resource utilization over existing state-of-the-art techniques with a 95% confidence interval. The performance of the proposed approaches outperforms in terms of SLA violation, VMs migration, and Energy SLA Violation (ESV) combined metrics over existing state-of-the-art VMs allocation and migration algorithms.
Extended Depth-of-Field Lensless Imaging Using an Optimized Radial Mask

The freedom of design of coded masks used by mask-based lensless cameras is an advantage these systems have when compared to lens-based ones. We leverage this freedom of design to propose a shape-preserving optimization scheme for a radial-type amplitude coded mask. Due to the depth-independency of the radial mask's point spread function, they can be used for extending the effective depth of field (DOF) of a lensless imaging system. In this paper we optimized a coded mask for improved frequency response, while retaining its radial characteristics and therefore extended-DOF capabilities. We show that our optimized radial mask achieved better overall frequency response when compared to naive implementations of a radial mask. We also quantitatively and qualitatively demonstrated the extended DOF imaging achieved by our optimized radial mask in simulations by comparing it to different non-radial coded masks. Finally, we built a prototype camera to validate the extended DOF capabilities of our coded mask in real scenarios.
Autonomous Parking Path Tracking Control Based on Interference Suppression

Owing to the increased demand for autonomous driving and advanced driver assistant systems, autonomous parking configurations have been widely researched. In order to solve the problem of path tracking accuracy degradation, caused by ignoring the external interference in the actual parking process and the uncertainty of vehicle steering modeling, a parking path tracking algorithm, combining Sliding Model Control (SMC) and Extended State Observer (ESO),is proposed. First, the vehicle kinematics model is established, which includes many external interference factors, such as lateral position, speed, heading angle, uncertainty parameters and time delay of steering mechanism. Considering the vehicle physical constraints and boundary condition constraints, during the parking process, the parking reference path is designed according to reverse outbound driving, while the parking collision analysis and path smoothing are further carried out. Next, an ESO is designed to observe and compensate the external disturbances and model uncertainties, treating these as the total disturbances of the system. On this basis, a SMC for parallel parking path tracking is designed. The observed value of ESO is used as the compensation in the SMC, to weaken the influence of external interference. Finally, using Matlab simulation, the feasibility and effectiveness of the proposed path planning and tracking control method are verified. The simulation results show that, the maximum tracking error of lateral position is less than 0.01m, whereas the maximum tracking error of heading angle is less than 2.5°.Comparing two control strategies, the control effect of the designed SMC with ESO is better than that of the traditional SMC controller, while the ability of resisting external interference is stronger. In addition, real vehicle tests are carried out, to verify the effectiveness of the proposed control method. The test results show that, the vehicle can safely park in the parking space, quickly and accurately, even if there is external interference. The proposed method can produce the parking trajectory according to the given constraints and control the vehicle to complete the parking operation accurately along the planned path.
Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling

The advent of quantum computing can potentially revolutionize how complex problems are solved. This paper proposes a two-loop quantum-classical solution algorithm for generation scheduling by infusing quantum computing, machine learning, and distributed optimization. The aim is to facilitate employing noisy near-term quantum machines with a limited number of qubits to solve practical power system optimization problems such as generation scheduling. The outer loop is a 3-block quantum alternative direction method of multipliers (QADMM) algorithm that decomposes the generation scheduling problem into three subproblems, including one quadratically unconstrained binary optimization (QUBO) and two non-QUBOs. The inner loop is a trainable quantum approximate optimization algorithm (T-QAOA) for solving QUBO on a quantum computer. The proposed T-QAOA translates interactions of quantum classical machines as sequential information and uses a recurrent neural network to estimate variational parameters of the quantum circuit with a proper sampling technique. T-QAOA determines the QUBO solution in a few quantum-learner iterations instead of hundreds of iterations needed for a quantum-classical solver. The outer 3-block ADMM coordinates QUBO and non-QUBO solutions to obtain the solution to the original problem. The conditions under which the proposed QADMM is guaranteed to converge are discussed. Two mathematical and three generation scheduling cases are studied. Analyses performed on quantum simulators and classical computers show the effectiveness of the proposed algorithm. The advantages of T-QAOA are discussed and numerically compared with QAOA which uses a stochastic gradient descent-based optimizer.
Covert surveillance applications

Hidden spy applications are categorized as spy software. There are two categories of hidden spy applications: bossware and stalkerware . Bossware finds its usage in the business domain, for the tracking of employee productivity. On the other hand, stalkerware is used for partner surveillance, as well as the monitoring of children by the parents. Stalkerware are dangerous applications because they provide full control over the life of a victim, due to the large number of advanced options which allow their users to secretly record phone calls, view photos and videos, access users' contact history, listen in on the phone environment live, etc. "Hidden" stalkerware applications are especially dangerous to their users. A large number of popular applications, which possess certain functions that allow them to gather data on their users and surveil them, are considered "hidden" stalkerware applications. A particular problem with these applications is the fact that they are not visible in the application menu or the title screen, making the odds of the device user realizing they are being surveilled minimal. The paper introduces stalkerware applications, "hidden" stalkerware applications, the signs that indicate the existence of stalkerware applications on the device and Google's politics and attitude towards stalkerware.
Information security In the function of business continuity management

In the business environment, information as a means of understanding the world and communication, has taken on the role of a corporate resource that deserves at least as much attention as any other resource that any organization has. When talking about information as a corporate resource, it is indispensable to deal with the issue of its protection. Therefore, the attention of the academic and professional public is already largely occupied by the issue of information security, including the implementation of security and other information resources that act as support for the use of information. Information security is a function that must be continuous due to its importance. On the other hand, every organization is faced with the task of ensuring business continuity due to and after disruptions, regardless of the nature of those disruptions. In this regard, the paper starts from the assumption that one of the prerequisites for the business continuity is the (continuity) of information security. The author will try to present how information security contributes to business continuity management .
Utilizing the Full Potential of Norms for the Agent’s Decision Process

Norms are a crucial part of human behavior that received a lot of attention within the social simulation community. However, some aspects—up until now—have not been addressed in existing agent architectures, such as their motivational aspects and their importance and impact in planning and action selection. In this paper we present an agent architecture capable of grasping this potential of norms. We use perspectives to reflect how different people engage with a norm, and how it effects their long-term goals, their planning, and course of action. Our architecture is capable of having fast habitual-like behavior, as well as more complex deliberation if necessary.
Extending Partial-Order Planning to Account for Norms in Agent Behavior

Following a couple of models aiming to assess the effectiveness of norms in Madagascar on the MIMOSA platform, Müller et al., have noticed that the current architecture was not expressive enough to deal with all relevant norms, their different aspects, and how they interfere with the agent’s behavior for such complex systems. In response, this paper proposes a new agent architecture and its dedicated language to enhance the expressiveness of norms in agent-based modeling. The architecture has to (1) identify all the applicable norms given a temporal, spatial, and social context, and (2) generate an agent behavior to account for these norms. We propose to extend automated planning and use a Model-Driven Engineering approach to build the abstract and concrete syntaxes of the language and its semantics. The resulting architecture will allow modelers to express a wider spectrum of norms and provides a normative decision tool that will ease further discussions and interpretations.
Machine Learning-Based Phishing Detection Using URL Features: A Comprehensive Review

Phishing is a social engineering attack in which an attacker sends a fraudulent message to a user in the hope of obtaining sensitive confidential information. Machine learning appears to be a promising technique for phishing detection. Typically, website content and Unified Resource Locator (URL) based features are used. However, gathering website content features requires visiting malicious sites, and preparing the data is labor-intensive. Towards this end, researchers are investigating if URL-only information can be used for phishing detection. This approach is lightweight and can be installed at the client's end, they do not require data collection from malicious sites and can identify zero-day attacks. We conduct a systematic literature review on URL-based phishing detection. We selected recent papers (2018 –) or if they had a high citation count (50+ in Google Scholar) that appeared in top conferences and journals in cybersecurity. This survey will provide researchers and practitioners with information on the current state of research on URL-based website phishing attack detection methodologies. In this survey, we have seen that even though there is a lack of a centralized dataset, algorithms like Random Forest, and Long Short-Term Memory with appropriate lexical features can detect phishing URLs effectively.
Hierarchical Identity-Based Inner Product Functional Encryption for Unbounded Hierarchical Depth

Cloud computing is becoming popular with emerging applications in big data analysis, online storage, e-commerce, social network, accounting, and management. As industries, organizations and individuals prefer to use the cloud for high speed, scalability, and easy accessibility, it is essential to protect data privacy and security. Inner product functional encryption (IPFE) with access control is a promising technique for protecting privacy of original data by specifying who can obtain inner product on encrypted data in the cloud. Hierarchical identity-based IPFE (HID-IPFE), proposed by Song et al. (Information Sciences 2021) is a variant in hierarchical environment that specifies which hierarchical identities are allowed to obtain the inner product on encrypted data. We have observed that the existing works on HID-IPFE fixes the maximum hierarchical identity depth at the Setup phase. It is not practical as the maximum hierarchy depth may increase. To resolve this issue, we introduce the technique of unbounded hierarchical identity-based inner product functional encryption (UHID-IPFE) that does not fix maximum hierarchy depth at the Setup phase to support unbounded hierarchy depth. We provide an instantiation of selective chosen plaintext attack (CPA) secure UHID-IPFE protocol based on hardness of the q-RW2 problem. We prove the security of our protocol by detailed security analysis in the existing security model. More interestingly, our scheme outperforms the previous HID-IPFE schemes in terms of storage and is the first scheme supporting unbounded hierarchy depth.