Simplicity in joy and detail in anger: Intertwining effect of cognitive and affective review disposition on review helpfulness

Abstract: Review length and readability, are cognitive dispositions of reviews supposed to reflect diagnostic content and lead to favorable evaluation of review helpfulness. However, underlying these two cognitive dispositions of reviews, are discrepancies that make it difficult for a helpful review to simultaneously satisfy both of them. To resolve the discrepancies, this present study, drawn on cognitive tuning theory, proposes and validates the intertwining effect of cognitive dispositions (length and readability) and affective dispositions (joy and anger expression) in judging review helpfulness. Based on analysis of heterogeneous hotel review data, our findings indicate joy expression in reviews, decreased the positive impact of review length on review helpfulness, while enhancing the positive impact of review readability on helpfulness. Anger expression in reviews, shows reverse effects, enhancing the positive impact of review length on helpfulness. Our findings contribute to an in-depth understanding of review characteristics and review helpfulness judgment.


Cross-scale Vision Transformer for crowd localization

Abstract: Crowd localization can provide the positions of individuals and the total number of people, which has great application value for security monitoring and public management, meanwhile it meets the challenges of lighting, occlusion and perspective effect. In recent times, Transformer has been applied in crowd localization to overcome these challenges. Yet such kind of methods only consider to integrate the multi-scale information once, which results in incomplete multi-scale information fusion. In this paper, we propose a novel Transformer network named Cross-scale Vision Transformer (CsViT) for crowd localization, which simultaneously fuses multi-scale information during both the encoder and decoder stages and meanwhile building the long-range context dependencies on the combined feature maps. To this end, we design the multi-scale encoder to fuse the feature maps of multiple scales at corresponding positions so as to obtain the combined feature maps, and meanwhile design the multi-scale decoder to integrate the tokens at multiple scales when modeling the long-range context dependencies. Furthermore, we propose Multi-scale SSIM (MsSSIM) loss to adaptively compute head regions and optimize the similarity at multiple scales. Specifically, we set the adaptive windows with different scales for each head and compute the loss values within these windows so as to enhance the accuracy of the predicted distance transform map. We perform comprehensive experiments on five public datasets, and the results obtained validate the effectiveness of our method.


A Needs Assessment for Promoting Transformational Change Towards Accessibility in Education and Competence Mobilization for eLearning

Abstract: During the COVID-19 pandemic, higher education institutions worldwide encountered issues related to student dissatisfaction with online education. Several nations are poised to migrate to distance learning for technological and economic reasons. It becomes clear that current online learning should be applied under certain circumstances. The deployment of e-learning resources (ELR) has emerged as a strategy for addressing problems related to instructional design, technological packages, and easy access to education. In this context, Rwanda has used open and distance learning (ODL) to thrive in a continuously evolving educational environment. However, learners still have limited information about the use of e-learning platforms. To keep up with the changing nature of education, today's educators and students require ELR tools and information and communication technology (ICT) competencies. This research aimed to evaluate the needs, preparedness, and willingness of ODL professionals and learners at the University of Rwanda (UR) to enhance the use of ELR in the education and learning environment. In this research, a systematic questionnaire was considered for data collection purposes. The analyses were descriptive and consisted of either determining the proportion of answers to items in different segments of the questionnaire or determining the percentage of answers using various Likert scales and calculating the differences between scales. The results highlighted that several participants admitted to the University of Rwanda to having moderate knowledge of e-learning resources, and most of them regularly used text-based documents on e-learning platforms. Moreover, the participants demonstrated a need to avail internet connectivity and updated IT infrastructure, organize workshops and training, and conduct an awareness campaign on ELR and learning management system (LMS), It was also demonstrated that the ICT basic skills are needed to effectively implement e-learning at the University of Rwanda.


Data Analytics for Imbalanced Dataset

Abstract: The primary issue in real-time big data classification is imbalanced datasets. Even though we have many balancing techniques to reduce imbalance ratio which is not suitable for big data that has scalability issues. This study is envisioned to explore different balancing techniques with experimental study. We tried comparing the effectiveness of various balancing strategies, including cutting-edge approaches for severely unbalanced data from online repositories. Here we apply SMOTE, SMOTE ENN and SMOTE Tomek balancing algorithms for dermatology, wine quality and diabetes datasets. After balancing the dataset, the balanced dataset is classified with AdaBoost and random forest algorithms. On three datasets, the outcomes show that the classification algorithm with the balancing technique improves the classification performance for imbalanced datasets. Experiment results showed that the SMOTE ENN technique produces higher classification with accuracy than the SMOTE and SMOTE Tomek techniques. The findings are analyzed with other factors like execution time and scalability. Though SMOTE Tomek produces 1.0 for a few datasets, its execution time is longer than SMOTE ENN. Therefore, SMOTE ENN with random forest classification produces 1.0 accuracy for all three datasets with less execution time. This experimental study analyses to create a novel ensemble technique for balancing highly imbalanced data.


Deepfake Detection using Deep Feature Stacking and Meta-learning

Abstract:
Abstract
 Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to detect deepfake has become a pressing need. This paper aims to address the problem of deepfake detection by identifying deepfake forgeries in video sequences. In this paper, a solution to the said problem is presented, which at first uses a stacking based ensemble approach, where features obtained from two popular deep learning models, namely Xception and EfficientNet-B7, are combined. Then by selecting a near-optimal subset of features using a ranking based approach, the final classification is performed to classify real and fake videos using a meta-learner, called multi-layer perceptron. In our experimentation, we have achieved an accuracy of 96.33% on Celeb-DF (V2) dataset and 98.00% on the FaceForensics++ dataset using the meta-learning model both of which are higher than the individual base models. Various types of experiments have been conducted to validate the robustness of the current method.


Fuzzy Logic Controller for UAV With Gains Optimized Via Genetic Algorithm

Abstract: A gains optimizer of a fuzzy controller system for an Unmanned Aerial Vehicle (UAV) based on a metaheuristic algorithm is developed in the present investigation. The contribution of the work is the adjustment by the Genetic Algorithm (GA) to tune the gains at the input of a fuzzy controller. First, a typical fuzzy controller was modeled, designed, and implemented in a mathematical model obtained by Newton-Euler methodology. Subsequently, the control gains were optimized using a metaheuristic algorithm. The control objective is that the UAV consumes the least amount of energy. With this basis, the Genetic Algorithm finds the necessary gains to meet the design parameters. The tests were performed using the Matlab-Simulink environment. The results indicate an improvement, reducing the error in tracking trajectories from 30% in some tasks and following trajectories that could not be completed without a tuned controller in other tasks.


A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

Abstract: Fake profile identification on social media platforms is essential for preserving a reliable online community. Previous studies have primarily used conventional classifiers for fake account identification on social networking sites, neglecting feature selection and class balancing to enhance performance. This study introduces a novel multistage stacked ensemble classification model to enhance fake profile detection accuracy, especially in imbalanced datasets. The model comprises three phases: feature selection, base learning, and meta-learning for classification. The novelty of the work lies in utilizing chi-squared feature-class association-based feature selection, combining stacked ensemble and cost-sensitive learning. The research findings indicate that the proposed model significantly enhances fake profile detection efficiency. Employing cost-sensitive learning enhances accuracy on the Facebook, Instagram, and Twitter spam datasets with 95%, 98.20%, and 81% precision, outperforming conventional and advanced classifiers. It is demonstrated that the proposed model has the potential to enhance the security and reliability of online social networks, compared with existing models.


Using deep learning for effective simulation of ghost reflections

Abstract: Stray light (SL) control is an important aspect in the development of optical instruments. Iterations are necessary between design and analysis phases, where ray tracing simulations are performed for performance prediction. This process involves trial and error, requiring to be able to perform rapid evaluation of SL properties. The limitation is that accurate SL simulations require sending many rays, which can be time consuming. In this paper, we use deep learning to improve the accuracy of SL maps even when obtained with very few rays. Two different deep learning methods are used, an autoencoder and an EAM. The training process is performed by generating a large database of artificial SL maps, with different noise levels reproduced with a Poisson distribution. Once the training completed, we show that the autoencoder performs the best and improves significantly the accuracy of SL maps. Even with extremely small number of rays, it recovers complex SL patterns which are not visible on raw ray traced maps. This method thus enables more efficient iterations between design and analysis. It is also useful for developing SL correction algorithms, as it requires tracing SL maps under large number of illumination conditions in a reasonable amount of time.


Constructions of correlation immnue S-boxes with high nonlinearity

Abstract: S-boxes play a central role role in the design of symmetric cipher schemes. For stream cipher applications, a S-box should satisfy several criteria such as high nonlinearity, balanceness, correlation immunity, and so on. In this paper, by using disjoint linear codes, a class of S-boxes possessing high nonlinearity and 1st-order correlation immunity is given. It is shown that the constructed correlation immune S-boxes can possess currently best known nonlinearity, which is confirmed by the example 1st-order correlation immune (12, 3) S-box with nonlinearity 2000. In addition, two other frameworks concerning the criteria of balanced and resiliency are obtained respectively.


A bibliometric review of Internet of Things (IoT) on cybersecurity issues

Abstract: Purpose In response to the growing interest in Internet of Things (IoT) technology and its profound implications for businesses and individuals, this bibliometric study focuses on a critical yet understudied aspect, i.e. cybersecurity. As IoT adoption grows, so do concerns regarding user privacy and data security. This study aims to provide a comprehensive understanding of the current research in this vital area, shedding light on research trends, gaps and emerging themes. Design/methodology/approach The study conducted a bibliometric analysis and systematic review of literature spanning over two decades (2013–2023). Bibliometric analysis is conducted using Biblioshiny which is R-software-based advanced analytical tool. Further, VOSviewer is used to conduct network analysis. The study highlights the evolving landscape of IoT cybersecurity, emphasizing interdisciplinary intersections and the ethical dimensions of IoT technologies. Findings The study uncovers crucial concerns related to IoT adoption, emphasizing the urgent need for comprehensive cybersecurity protocols. It identifies emerging themes such as artificial intelligence and blockchain integration, indicating a shift toward interdisciplinary solutions. Furthermore, the research highlights ethical gaps in current IoT discussions, emphasizing the importance of responsible innovation. Research limitations/implications Businesses can bolster their cybersecurity strategies, policymakers can craft informed regulations and researchers are encouraged to explore IoT’s ethical dimensions. Originality/value This study pioneers a nuanced analysis of IoT cybersecurity, filling a crucial gap in the existing business and management literature. By synthesizing a decade of scholarly work, it provides foundational insights for researchers, businesses and policymakers. The research not only informs academic discourse but also offers practical guidance for enhancing IoT security measures and fostering ethical innovation.


The design and use of a digital PMS, and the emergence of gaming logic: the experience of the Italian Ministry of Culture

Abstract: The analysis of digital transformation processes by governments has increasingly attracted attention from academics and practitioners, but it has been less frequently investigated from an accounting perspective. This chapter investigates the complex process of designing and using a digital performance measurement system (PMS) in a central government, empirically exploring the case of the Italian Ministry of Culture, which enacted a reform to provide central and peripheral units with a system to monitor the reputation of state museums, leveraging real-time user-generated data (UGD). The study shows that the design and use of a digital PMS is characterised by the complexity of choosing and interpreting the algorithm to analyse UGD, which may stimulate gaming logic by the actors who are held accountable through the digital PMS. These findings contribute to a broader discussion on the accounting and accountability implications of adopting a digital PMS in the public sector.


Copyright and COVID

Abstract: The rise of the COVID-19 pandemic revived a focus of scholars and policy makers on the issue of patents and access to medicines (and vaccines). But the COVID-19 pandemic displayed as well how other intellectual property rights, including copyright and trade secrets, may also complicate public health measures. This chapter documents many instances where non-patent forms of intellectual property created barriers to the global scale-up of access to vaccines, treatments, and the ability to contain the COVID-19 virus. It concludes by explaining some of the existing flexibilities in the multilateral intellectual property system that may be used to respond to these problems.


Cryptography in cloud: An in-depth investigation into encryption mechanisms for safeguarding cloud-based data

Abstract: Cloud computing is one of the most significant computing models, which provides easy access of resources. It is an alternative for costlier data framework and networking models. The services are provided to the data users based on their requirements. More industries are depending on the cloud, because of its fast computations and robust nature. However, there are several security concerns and threats in cloud storages. Moreover, large amounts of data are shared between people and organizations through the cloud. The data owner doesn’t have control over the data in the cloud. To address the security and privacy issues in the cloud, this thesis aims at overcoming this trade-off, while considering security.


Cloud-Driven Transformation: Ensuring Security and Privacy in Data and Application Modernization

Abstract: Today, businesses are advancing their IT strategies to leverage cloud environments providing greater scalability and efficiency in the changing digital environment. This indicates that it will be the modernization of data and apps that will lead the way as enterprises try to adapt themselves to the change brought about by the cloud. Even though they are not intentionally constructing a new entry medium that may be readily stolen, information providers in both the public and commercial sectors are also facing critical challenges regarding data security. At the same time as it satisfies the requirements of commercial customers as well as those of public sector organizations, this article provides the many methods that may be used to build cloud computing systems that protect the data of customers, guaranteeing that it is kept private. Also included in this are the methods for safely transferring client information from servers housed in your office building to the cloud, as well as the best practices that are prevalent within a certain industry and particulars that are applicable exclusively to a particular sort of company activity


AI-Assisted Programming Tasks Using Code Embeddings and Transformers

Abstract: This review article provides an in-depth analysis of the growing field of AI-assisted programming tasks, specifically focusing on the use of code embeddings and transformers. With the increasing complexity and scale of software development, traditional programming methods are becoming more time-consuming and error-prone. As a result, researchers have turned to the application of artificial intelligence to assist with various programming tasks, including code completion, bug detection, and code summarization. The utilization of artificial intelligence for programming tasks has garnered significant attention in recent times, with numerous approaches adopting code embeddings or transformer technologies as their foundation. While these technologies are popular in this field today, a rigorous discussion, analysis, and comparison of their abilities to cover AI-assisted programming tasks is still lacking. This article discusses the role of code embeddings and transformers in enhancing the performance of AI-assisted programming tasks, highlighting their capabilities, limitations, and future potential in an attempt to outline a future roadmap for these specific technologies.


Ecological ICMS: environmental policy for the sustainable development of Rondônia

Abstract: Emotional competence in teaching is a topic of great interest today, since it is extremely important for teachers to feel good about the tasks assigned to them in terms of work, and it is also very important for them to be emotionally successful in carrying them out efficiently. Due to the context of the COVID-19 Pandemic that we are all experiencing there have been several modifications to which the teachers had to adjust to provide good learning to the students and this is the reason for the conduct of this study, so the objective of this research is to know the emotional competence profile of the teachers of the Schools Groupings of the capital of Bragança district in the context of the COVID-19 Pandemic, making use of an anonymous and confidential questionnaire applied to the teachers of the Groupings of the capital of Bragança, divided into two parts: the first part, contemplating variables of sociodemographic and professional characterization and a second part consisting of "Scaling" the Veiga de Competência Emocional (EVCE) (Veiga-Branco, 2005, 2011, 2015, 2016, 2017, 2018). The results obtained show that there is no influence of sociodemographic or professional variables on the emotional competence profile of the teachers or on the dimensions of it.


A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8

Abstract: Aiming at the characteristics of remote sensing images such as a complex background, a large number of small targets, and various target scales, this paper presents a remote sensing image target detection algorithm based on improved YOLOv8. First, in order to extract more information about small targets in images, we add an extra detection layer for small targets in the backbone network; second, we propose a C2f-E structure based on the Efficient Multi-Scale Attention Module (EMA) to enhance the network’s ability to detect targets of different sizes; and lastly, Wise-IoU is used to replace the CIoU loss function in the original algorithm to improve the robustness of the model. Using our improved algorithm for the detection of multiple target categories in the DOTAv1.0 dataset, the mAP@0.5 value is 82.7%, which is 1.3% higher than that of the original YOLOv8 algorithm. It is proven that the algorithm proposed in this paper can effectively improve target detection accuracy in remote sensing images.


Assessment of Student Satisfaction With the Quality of Education Using the Example of One of the Departments of the Kyrgyz-Russian Slavic University

Abstract: To improve the quality of education at a university, analysis of the educational process and organization of effective interaction between teachers and students are crucial. The professional activity of a university teacher is multifaceted and covers many types of work, which are assessed during periodic certification. However, evaluation of a teacher’s performance is incomplete without taking into account the opinions of students, which can be identified using a survey. Therefore, the teacher should be an interesting person; hence, his subject will become an interesting science area. This article presents a comparative analysis of the students’ satisfaction at the Faculty of Medicine with the quality of taught disciplines, such as biology, physics, medical informatics and evidence-based medicine. These subjects are given in junior and senior courses at the Department of Physics, Medical Informatics and Biology at the Kyrgyz-Russian Slavic University. This analysis was carried out for the first time at the department in 2020 (297 students), again in 2023 (504 students), and showed that in general the average score of the department increased slightly, as well as the pedagogical qualities of the teacher in the rating of lecture courses. Nevertheless, the ratings for practical classes remained unchanged. An analysis of students’ feedback expressed in free form to the teacher showed the majority of students are grateful for the teachers’ work.


Abstract: Previous articleNext article No AccessDo We Have a Duty to Mitigate the Deterioration of Democratic Communication?Corrado FumagalliCorrado FumagalliPDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by The Journal of Politics Just Accepted Sponsored by the Southern Political Science Association Article DOIhttps://doi.org/10.1086/729957 HistoryAccepted October 16, 2023 © 2024 Southern Political Science Association. All Rights reserved.PDF download Crossref reports no articles citing this article.


Using Graphs for Network Analysis in ArcMap Environment

Abstract: The emergence of powerful and fast computer technologies has led to the massive adoption of GIS in various fields. Modern GIS make it possible to quickly solve such problems in the mathematical analysis of network data. This work aims to limit the use of GIS modules in this area. The relevance of the article is the use of graphs to develop network analysis in the ArcMap environment.


A Comprehensive Review of Techniques Enabling OHGP Completions in Low Frac Window Environment

Abstract: Abstract Openhole gravel packing has proven to be one of the best sand control completion methods in the industry with high reliability and low skin. As more and more wells are being drilled in challenging environment (long drain lengths, depleted formation, wellbore stability concerns, presence of faults, etc.) with low fracture window, the ability to fully pack the openhole sections constrains the wider use of the method. Many techniques have been used to overcome the low fracture window challenge both in alpha beta and shunted gravel packing. This paper will review the techniques used in both gravel packing approaches, highlight their working mechanisms, and provide a comparison of these methods. Techniques such as packing with friction reducer, BOP open, use of lighter carrier fluids, running diverter valves, wash-pipe less Bottom Hole Assembly (BHA), managed pressure gravel packing etc. have been successfully used depending on the available fracture margin, well configuration and gravel packing technique. The paper will briefly touch on the challenges leading to low fracture margin and the necessity to utilize one or a combination of the techniques to fully pack wells. It will then describe the working principle of each of the techniques, provide the advantages and disadvantages of the techniques. This will be complemented with several case histories showing how these wells were successfully completed despite the low fracture pressure window.


An efficient quantum circuit implementation of Shor’s algorithm for GPU accelerated simulation

Abstract: In this study, we introduce a novel implementation of Shor’s algorithm specifically designed for the Graphics Processing Unit (GPU) acceleration framework. Our focus lies on achieving efficient execution of the modular multiplication circuit through GPU simulation. To seamlessly integrate our design into the PyQPanda library framework, we made necessary modifications, making a deliberate trade-off by sacrificing a small number of quantum resources to leverage the advantages of GPU acceleration. Subsequently, we conducted simulations and rigorously validated the functionality of our circuit using the PyQPanda library, resulting in a significant speedup compared to a central processing unit-only mode.


Disorder enhanced relative intrinsic detection efficiency in NbTiN superconducting nanowire single photon detectors at high temperature

Abstract: The intrinsic detection performance of superconducting nanowire single photon detectors (SNSPDs) is highly dependent on the superconducting properties of underlying thin films. This report outlines the enhancement of detection performance for single telecom wavelength photons in disordered NbTiN SNSPD at 4.2 K. By increasing the nitrogen content and deposition pressure, the NbTiN films show suppression in critical temperature and an increase in sheet resistance. Notably, the resulting SNSPDs display a broader saturation plateau at 2.2 K, leading to superior detection performance at 4.2 K. With the disordered 7-nm-thick NbTiN films, we fabricated SNSPDs with system detection efficiency up to 83% for 1550 nm photons at 4.2 K. Moreover, these devices also show saturated intrinsic detection efficiency for 2000 nm photons. With the features outlined, the devices can be integrated into the idle 4.2 K stage of the dilution refrigerator for applications in optical quantum information processing or utilize for detecting laser radar signals in airborne platforms.


Model selection over partially ordered sets

Abstract: In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as the presence or absence of a variable or an edge. Consequently, false-positive error or false-negative error can be specified as the number of variables/edges that are incorrectly included or excluded in an estimated model. However, there are several other problems such as ranking, clustering, and causal inference in which the associated model classes do not admit transparent notions of false-positive and false-negative errors due to the lack of an underlying Boolean logical structure. In this paper, we present a generic approach to endow a collection of models with partial order structure, which leads to a hierarchical organization of model classes as well as natural analogs of false-positive and false-negative errors. We describe model selection procedures that provide false-positive error control in our general setting, and we illustrate their utility with numerical experiments.


Generative Adversarial Networks for Brain MRI Synthesis: Impact of Training Set Size on Clinical Application

Abstract: We evaluated the impact of training set size on generative adversarial networks (GANs) to synthesize brain MRI sequences. We compared three sets of GANs trained to generate pre-contrast T1 (gT1) from post-contrast T1 and FLAIR (gFLAIR) from T2. The baseline models were trained on 135 cases; for this study, we used the same model architecture but a larger cohort of 1251 cases and two stopping rules, an early checkpoint (early models) and one after 50 epochs (late models). We tested all models on an independent dataset of 485 newly diagnosed gliomas. We compared the generated MRIs with the original ones using the structural similarity index (SSI) and mean squared error (MSE). We simulated scenarios where either the original T1, FLAIR, or both were missing and used their synthesized version as inputs for a segmentation model with the original post-contrast T1 and T2. We compared the segmentations using the dice similarity coefficient (DSC) for the contrast-enhancing area, non-enhancing area, and the whole lesion. For the baseline, early, and late models on the test set, for the gT1, median SSI was .957, .918, and .947; median MSE was .006, .014, and .008. For the gFLAIR, median SSI was .924, .908, and .915; median MSE was .016, .016, and .019. The range DSC was .625-.955, .420-.952, and .610-.954. Overall, GANs trained on a relatively small cohort performed similarly to those trained on a cohort ten times larger, making them a viable option for rare diseases or institutions with limited resources.


Multimodal Sensor Fusion Frameworks With Application to Human Action Recognition

Abstract:
Human Action Recognition (HAR) is a progressive research area in the field of computer vision and machine learning. Earlier methods on HAR were based on single sensor modality, either vision-based sensor or wearable inertial sensor. Both of these modalities have some limitations that prevent widespread adoption of HAR; e.g. visual sensors typically require elaborate hardware setup and are limited to a small operating area, where inertial sensors are prone to drift. The solution is to fuse the information from different modalities. In this dissertation, we present novel multimodal sensor fusion frameworks that overcome the limitations of single sensor modality. In these frameworks, we convert all data streams to images through innovative signal to image conversion schemes and feed them to Convolutional Neural Networks (CNN), thus enabling extraction of higher-level features that CNNs are proven to be capable of especially from images. Moreover, we propose fast and robust multilevel fusion schemes by extracting features from multiple layers of the CNNs and employing statistical methods such as Canonical Correlation Analysis and gated fusion, instead of the more popular single stage fusion. We applied these fusion frameworks for HAR using depth and inertial sensors. At the input of each fusion framework, we transform depth and inertial sensor data into images called Sequential Front view Images (SFI) and Signal Images (SI). The SFI and SI images are then fused through our proposed multilevel frameworks for more accurate HAR while maintaining computational speed. We evaluate the proposed frameworks on three public multimodal HAR datasets, namely, UTD Multimodal Human Action Dataset (MHAD), Berkeley MHAD, and UTDMHAD Kinect V2 and achieved accuracies of 99.3%, 99.85% and 99.8% respectively.

While the proposed frameworks were developed with HAR as a target application area, they can be applied to other fusion problems as well. We show the generalizability of frameworks by applying them to a different domain, where ECG (1D time series) data is converted to multimodal images and fed through our fusion frameworks for arrythmia classification and stress assessment. Preliminary results in these applications are encouraging, further strengthening the significance of the proposed frameworks.


Abstract: Implementation of high-fidelity gate operations on integrated-qubit systems is vital for fault-tolerant quantum computation. Qubit-frequency allocation is an essential part of improving control fidelity. A metric for qubit-frequency allocation, frequency collision, has been proposed on simple systems of only a few qubits driven by a monomodal microwave drive. However, frequency allocation for quantum processors for more advanced purposes, such as quantum error correction, needs further investigation. In this study, we propose a Floquet analysis of frequency collisions. The key to our proposed method is a reinterpretation of frequency collisions as an unintended degeneracy of Floquet states, which allows a collision analysis on more complex systems with many qubits driven by multimodal microwave drives. Although the Floquet state is defined in an infinite-dimensional Hilbert space, we develop algorithms, based on operation perturbation theory, to truncate the Hilbert space down to the optimal computational complexity. In particular, we show that the computational complexity of the collision analysis for a sparse qubit lattice is linear with the number of qubits. Finally, we demonstrate our proposed method on cross-resonance-based experimental protocols. We first study the cross-resonance gate in an isolated three-qubit system, where the effectiveness of our method is verified by comparing it with previous studies. We next consider the more complex problem of syndrome extraction in the heavy-hexagon code [Christopher et al., Phys. Rev. X 10, 011022 (2020). doi:10.1103/PhysRevX.10.011022]. Our proposed method advances our understanding of quantum control for quantum processors and contributes to their improved design and control.


Study on cluster centers optimization of max-min distance k-means clustering algorithm based on inter-cluster separation measure

Abstract: Clustering is an essential unsupervised technique when category information is not available. Although K-means and Max-min distance K-means clustering algorithms are widely used, they have some disadvantages such as dependence on the initial centers, sensitivity to outliers caused by using only distance as the clustering criterion. To overcome the problems, this paper proposes SMM-K-means algorithm which overcomes the dependence on the initial cluster centers and the initial number of clusters and the sensitivity to the outliers. First, the initial value K of the optimal cluster number is determined by the elbow method, and K-means is used for initial clustering. A new inter-cluster separation measure is then constructed based on the idea of q-nearest neighbors, which is constructed by comprehensive considering the separation between clusters and the distribution compactness of clusters themselves. Finally, the two sample points with highest degree of separation are brought into Max-min distance K-means algorithm as new initial centers for clustering. The definite determining method of cluster centers eliminates the complicated iterative calculation, and the construction of inter-cluster separation measure overcomes the sensitivity of clustering results to noise points and isolated points, and has good applicability and generalization. In addition, this algorithm is not limited by the shape and size of the clusters and has better flexibility. The experimental results show that the SMM-K-means algorithm has higher CH values, resulting in a better clustering effect and stability.


Profiling with trust: system monitoring from trusted execution environments

Abstract: Abstract Large-scale attacks on IoT and edge computing devices pose a significant threat. As a prominent example, Mirai is an IoT botnet with 600,000 infected devices around the globe, capable of conducting effective and targeted DDoS attacks on (critical) infrastructure. Driven by the substantial impacts of attacks, manufacturers and system integrators propose Trusted Execution Environments (TEEs) that have gained significant importance recently. TEEs offer an execution environment to run small portions of code isolated from the rest of the system, even if the operating system is compromised. In this publication, we examine TEEs in the context of system monitoring and introduce the Trusted Monitor (TM), a novel anomaly detection system that runs within a TEE. The TM continuously profiles the system using hardware performance counters and utilizes an application-specific machine-learning model for anomaly detection. In our evaluation, we demonstrate that the TM accurately classifies 86% of 183 tested workloads, with an overhead of less than 2%. Notably, we show that a real-world kernel-level rootkit has observable effects on performance counters, allowing the TM to detect it. Major parts of the TM are implemented in the Rust programming language, eliminating common security-critical programming errors.


Fundamental limits on quantum cloning from the no-signaling principle

Abstract: The no-cloning theorem is a cornerstone of quantum cryptography. Here we generalize and rederive in a unified framework various upper bounds on the maximum achievable fidelity of probabilistic and deterministic cloning machines. Building on ideas by Gisin [Phys. Lett. A 242, 1 (1998)], our result follows from the fact that remote state preparation is possible and the no-signaling principle holds. We apply our general theorem to several subsets of states that are of interest in quantum cryptography.


A planar tracking strategy based on multiple-interpretable improved PPO algorithm with few-shot technique

Abstract: Abstract Facing to a planar tracking problem, a multiple-interpretable improved Proximal Policy Optimization (PPO) algorithm with few-shot technique is proposed, namely F-GBQ-PPO. Compared with the normal PPO, the main improvements of F-GBQ-PPO are to increase the interpretability, and reduce the consumption for real interaction samples. Considering to increase incomprehensibility of a tracking policy, three levels of interpretabilities has been studied, including the perceptual, logical and mathematical interpretabilities. Detailly speaking, it is realized through introducing a guided policy based on Apollonius circle, a hybrid exploration policy based on biological motions, and the update of external parameters based on quantum genetic algorithm. Besides, to deal with the potential lack of real interaction samples in real applications, a few-shot technique is contained in the algorithm, which mainly generate fake samples through a multi-dimension Gaussian process. By mixing fake samples with real ones in a certain proportion, the demand for real samples can be reduced.


Entwicklung eines neuen Ansatzes für eine digitale, kartographische Fels- und Reliefdarstellung

Abstract: Abstract This paper presents a new approach to digital rock and relief representation. A prerequisite for the method was its applicability for different scales and thus different resolutions of the underlying terrain models. A further requirement was its suitability for different rock types. It must also take into account the rock surfaces that have become ice-free due to glacier retreat and be easy to integrate into the map in the event of an update. The relief representation follows the principle that the terrain should be reproduced as naturally as possible to provide the map user with the best possible orientation in the terrain. The approach requires a very good knowledge of the terrain as well as a certain artistic talent to achieve an appealing, natural color scheme. Through the process of color manipulation from the four-color set, it is also possible, in addition to the summer representation from the data collected with remote sensing methods, to generate winter representations for e.g. ski tourers. The use of established, automatic procedures considerably reduces the workload for the cartographer and also enables simple integration of corrections and changes to the landscape, e.g. due to glacial retreat and landslides, and can be applied to large map series due to its reproducibility.


Deep photonic network platform enabling arbitrary and broadband optical functionality

Abstract: Expanding applications in optical communications, computing, and sensing continue to drive the need for high-performance integrated photonic components. Designing these on-chip systems with arbitrary functionality requires beyond what is possible with physical intuition, for which machine learning-based methods have recently become popular. However, computational demands for physically accurate device simulations present critical challenges, significantly limiting scalability and design flexibility of these methods. Here, we present a highly-scalable, physics-informed design platform for on-chip optical systems with arbitrary functionality, based on deep photonic networks of custom-designed Mach-Zehnder interferometers. Leveraging this platform, we demonstrate ultra-broadband power splitters and a spectral duplexer, each designed within two minutes. The devices exhibit state-of-the-art experimental performance with insertion losses below 0.66 dB, and 1-dB bandwidths exceeding 120 nm. This platform provides a tractable path towards systematic, large-scale photonic system design, enabling custom power, phase, and dispersion profiles for high-throughput communications, quantum information processing, and medical/biological sensing applications.


Deep Learning based Effective Watermarking Technique for IoT Systems Signal Authentication

Abstract: In order to identify cyber-attacks, this research suggests a special watermarking technique for dynamic IoT System signal validation. IoT Systems (IoTSs) can extract a group of randomly generated characteristics from their produced signal and then periodically watermark these attributes into the transmission owing to the proposed efficient watermarking technique. Using dynamic watermarking for IoT signal authentication, a potent deep learning technique is used to detect cyber-attacks. Based on an LSTM structure, the proposed learning system enables IoT devices to extract a set of random features from the signal they release, hence enabling dynamic watermarking of the signal.


Rethinking Conversation Styles of Chatbots from the Customer Perspective: Relationships between Conversation Styles of Chatbots, Chatbot Acceptance, and Perceived Tie Strength and Perceived Risk

Abstract: Grounded in the Stereotype Content Model, Risk Perception Theory, Technology Acceptance Model, and Relational Embeddedness Theory, this research delves into the relationship between chatbot conversation styles, customer risk, and the mediating role of chatbot acceptance and tie strength in online shopping. A 2 (warm vs. cold) * 2 (competent vs. incompetent) between-subjects experiment is conducted on 320 participants and the results obtained from two-way ANOVA and PROCESS macro revealed that: (a) customer-perceived risk decreases with conversation warmth rather than conversation competence; (b) customer acceptance of chatbots improves with conversation competence rather than conversation warmth, while not acting as an intermediary factor between the conversation styles and customer-perceived risk; (c) customer perceived tie strength increases with both conversation warmth and conversation competence. The findings contribute to the existing literature about the impact of chatbot anthropomorphism on customer cognitive processes and offer executives insights into the design of customer-friendly chatbots.


Geology, fluid inclusions, mineral and (S-O) isotope chemistry of the Badran orogenic Au deposit, Yana-Kolyma belt, eastern Siberia: implications for ore genesis

Abstract: The Badran orogenic gold deposit is located in the Yana-Kolyma belt, Eastern Siberia; it has proven reserves of ∼9.3 t of gold and an average grade of 7.8 ppm. The total gold production at the Badran deposit since 1984 amounts to ∼34 t. Despite many years of study, the origin of the gold deposits of the Yana-Kolyma metallogenic belt, one of the world’s largest belts, and the Badran deposit is controversial. Synthesis of regional geology and geology of the Badran deposit, fluid inclusion analysis, mineral and (S-O) isotope chemistry defines the genetic model, origin of fluids, and source of metals in the evolution of the ore-forming system, equivalent to other orogenic gold deposits on the margin of the Siberian craton. The deposit is localized in the Upper Triassic clastic rocks and is controlled by the NW-trending thrust. Polyphase mineralization occurs as disseminated arsenian pyrite and arsenopyrite ores with invisible gold, quartz veins with native gold and Fe, Pb, Zn, Cu sulfides and sulfosalts of orogenic type, and locally post-ore Ag, Sb-bearing minerals and Hg epithermal features. The quartz veins with native gold were formed from low-medium saline (1.5–10 wt% NaCl eq.) aqueous-carbonic fluids boiling at temperatures of 290°C to 210 °C and pressures of 300–250 to 125 bar. The δ 34 S values of pyrite and arsenopyrite vary from −1.1‰ to +2.4‰, with an average of +0.4‰; the δ 18 О of quartz from +15.1‰ to +17.5‰ at constant δ 18 О H2O about +7.5‰ (±1.0‰). High contents of As (up to 2.4 wt%) and Co/Ni ∼ from 0.3 to 9.9 in pyrite of proximal alteration are typical for hydrothermal systems. The results obtained confirm that the ore-forming fluids did not have a single origin, but were formed from a mixture of subcontinental lithospheric mantle and metamorphic sources. The subcrustal lithospheric mantle was fertilized in the time preceding mineralization (Late Jurassic) and was derived directly from the down-going subduction slab and overlying sediment wedge at the closure of the Oymyakon Ocean.


Consumer’s acceptance of retail service robots: mediating role of pleasure and arousal

Abstract: This study explores the influence of consumer emotions, specifically pleasure and arousal, on the intention to use retail service robots. Through a survey of 323 people using an online snowball sampling technique and analysis via the PLS-SEM method, it finds that subjective norms, technological self-efficacy, habitual behavior, and attitudes significantly affect users’ intentions. The research notably reveals that pleasure plays a more crucial role than arousal in mediating how consumers’ perceptions impact their willingness to interact with these robots. By integrating the Theory of Reasoned Action, self-efficacy theory, and the PAD model (pleasure, arousal, dominance), the study enhances the understanding of human-robot interaction. It provides actionable insights for businesses, suggesting that emphasizing emotional aspects, particularly pleasure, can be a strategic approach to encourage the adoption of service robots. These insights are valuable for designing marketing strategies and service experiences that effectively increase consumer engagement with robotic technology. Top of Form.


The Implementation of Canva Through Poster Creation on Fourth Graders

Abstract:
This community service program introduced Canva, a graphic design platform, to elementary school students as part of the University of Al-Azhar Indonesia’s Gerakan Nasional Revolusi Mental, under the theme of “Digital Literacy and Ethics”. The program was carried out at Al-Fityan School Tangerang on August 21, 2023. Students, in 4th grade, were trained to create a poster using Canva. They were asked to choose one of their five favorite things: mom, dad, hobby, food, or animal. The results showed that the popular choice was between “hobby” and “animal” while the least popular was “dad”. All 28 students produced their own posters and even six of them produced two posters. In this regard, the training of Canva seemed to be effective. There should be a continuation of training using the similar theme to Al-Fityan students for the future program given students’ progress and enthusiasm.

Keywords – Canva, Gerakan Nasional Revolusi Mental, Poster Creation, Fourth Graders, Digital Literacy


Coordinated ifs and theories of conditionals

Abstract: Abstract This paper concerns the semantics of coordinated if -clauses, as in (1)-(2). It is argued that the meanings of such sentences are explained straightforwardly on theories of conditionals that tie their non- monotonic behaviour to the if -clause itself (e.g. Schlenker 2004, but not theories that tie it to a (covert) modal operator (e.g. Kratzer 1981; 1991). Coordinated if -clauses are revealing of the fine-grained compositional semantics of conditionals.


Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models

Abstract: Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based models achieving better performance. However, manual annotation of training data is time-consuming and expensive. The aim of this study was to develop an automatic de-identification pipeline for all kinds of clinical documents based on a distant supervised method to significantly reduce the cost of manual annotations and to facilitate the transfer of the de-identification pipeline to other clinical centers.We proposed an automated annotation process for French clinical de-identification, exploiting data from the eHOP clinical data warehouse (CDW) of the CHU de Rennes and national knowledge bases, as well as other features. In addition, this paper proposes an assisted data annotation solution using the Prodigy annotation tool. This approach aims to reduce the cost required to create a reference corpus for the evaluation of state-of-the-art NER models. Finally, we evaluated and compared the effectiveness of different NER methods.A French de-identification dataset was developed in this work, based on EHRs provided by the eHOP CDW at Rennes University Hospital, France. The dataset was rich in terms of personal information, and the distribution of entities was quite similar in the training and test datasets. We evaluated a Bi-LSTM + CRF sequence labeling architecture, combined with Flair + FastText word embeddings, on a test set of manually annotated clinical reports. The model outperformed the other tested models with a significant F1 score of 96,96%, demonstrating the effectiveness of our automatic approach for deidentifying sensitive information.This study provides an automatic de-identification pipeline for clinical notes, which can facilitate the reuse of EHRs for secondary purposes such as clinical research. Our study highlights the importance of using advanced NLP techniques for effective de-identification, as well as the need for innovative solutions such as distant supervision to overcome the challenge of limited annotated data in the medical domain.


Pengembangan media pembelajaran berbasis android menggunakan articulate storyline 3 materi program linear di sekolah menengah atas

Abstract: The purpose of this research to create learning media for Android using Articulate Storyline 3 (AS3) as well as identifying validity and practicality of this Android-based learning tool developed with AS3. This research uses the ADDIE model, encompassing five stages: analysis, design, development, implementation, and evaluation. The participants in this study consist of two subject matter experts, two media experts, and 32 respondents from the ninth-grade students of SMA Negeri 3 Pontianak. The focus of the research is on the Android-based linear program learning media developed during the study. The data collection instrument is a questionnaire with descriptive statistical analysis techniques. The findings indicate that the validation analysis, based on assessments from material and media experts, yields an average score of 4.18, categorizing as 'good.' Furthermore, the practicality analysis, gauged through student responses, averages at 4.33, indicating a 'very good' category. Consequently, the Android based learning media employing AS3 is deemed both valid and practical for utilization.


Mind on the battlefield: what can cognitive science add to the military lessons-learned process?

Abstract: In this paper, we demonstrate the advantages of combining the detailed military history of actual battles with contemporary decision-making cognitive theories. Specifically, we analyse two battle decisions made in Israeli-Arab wars, and interpret, through a cognitive lens, some of the decisions that were subsequently deemed “incomprehensible” in the civilian and military literature. This perspective permits analysis of multiple processes – such as risk evaluation, mental completion of unknown information, and estimation of enemy response. Moreover, it predicts that even highly experienced commanders are prone to the effect of cognitive biases, and may therefore make bad decisions with disastrous consequences.


A Hybrid Model Based on Convolutional Neural Network and Long Short-Term Memory for Multi-label Text Classification

Abstract: Abstract Multi-label text classification (MLTC) is a popular method for organizing electronic documents, which is crucial for accessing and processing data. As the number of classes increases, learning multi-label data will be challenging. The number of possible states for various labels increases exponentially, and learning algorithms in single-label data cannot be used to solve these problems. In the meantime, using single-label data algorithms could be very time-consuming. In MLTC, complexity costs should be reduced. Deep-learning neural networks that can learn intricate patterns are used in many real-world problems because of their high power and accuracy. This paper proposed a hybridization of the long short-term memory (LSTM) neural network and the convolutional neural network (CNN) method for MLTC. The proposed model uses LSTM to enhance CNN to improve the proposed model’s accuracy. Also, the competitive search algorithm (CSA) is used to improve the LSTM hyperparameters. The LSTM hyperparameters play an important role in increasing the detection accuracy. The CSA algorithm finds the best values for the hyperparameters by searching the problem space. It was tested on four different datasets of multi-label texts: Reuters-21578, RCV1-v2, EUR-Lex, and Bookmarks. The result showed that the proposed model performed better than CNN and LSTM-CSA in terms of accuracy percentage and that it has improved by an average of more than 10%. Also, the results show that the LSTM-CSA model has higher detection accuracy compared to LSTM—Gradient-based optimizer (GBO) and LSTM—whale optimization algorithm (WOA).


Study on the correlation between real-time GNSS landslide acceleration monitoring and earthquake response: a case of May 2, 2023, MW = 5.2 Baoshan earthquake, Yunnan

Abstract: Abstract Background Earthquakes and landslides pose significant threats to human safety and property, necessitating early warning systems. However, the high construction costs of earthquake early warning systems present a challenge. Purpose Landslide warnings are more prevalent, so linking them to earthquake warnings could address cost concerns. Hence, it is crucial to validate the feasibility of utilizing GNSS landslide monitoring as assistance for earthquake early warning systems. Methods This paper analyzes acceleration anomaly data from 31 GNSS landslide monitoring points near the epicenter of the May 2, 2023, MW = 5.2 Baoshan earthquake in Yunnan. The response time was determined as the time difference between an earthquake's occurrence and GNSS's acceleration anomalies. This calculation helps measure the time delay and sensitivity between these two events. Data were obtained from the geological disaster monitoring and early warning management system. Results GNSS landslide monitoring showed high sensitivity to nearby earthquakes. The fastest response time among the 31 data points was 8 seconds, while the slowest was 56 seconds, all falling within the one-minute mark. A linear correlation was found between acceleration anomaly response time and distance from the epicenter, indicating the feasibility of GNSS landslide monitoring-assisted earthquake monitoring. Conclusion A proposal is made for a GNSS landslide monitoring cluster to establish a multi-dimensional landslideearthquake disaster warning system. This approach offers new methods for combining earthquake and landslide early warning systems, leveraging existing infrastructure for cost-effectiveness and enhancing disaster preparedness.


Machine minds: Artificial intelligence in psychiatry

Abstract: ABSTRACT Diagnostic and interventional aspects of psychiatric care can be augmented by the use of digital health technologies. Recent studies have tried to explore the use of artificial intelligence-driven technologies in screening, diagnosing, and treating psychiatric disorders. This short communication presents a current perspective on using Artificial Intelligence in psychiatry.


Physics‐informed surrogates for electromagnetic dynamics using Transformers and graph neural networks

Abstract: Abstract A novel use case for two data‐driven models, namely, a Transformer and a convolutional graph neural network (CGNN) is proposed. The authors propose to use these models for emulating the dynamics of electromagnetic (EM) propagation and scattering. The Transformer translates a past sequence into a future sequence by constructing representations from the past and using it to predict the future, taking all of its own previous predictions as input at each step of prediction. The CGNN updates the current state of attribute vectors of each node by passing it information (messages) from all of its neighbouring nodes. We train these models with FDTD simulations of plane waves propagating and scattering from PEC objects. The authors demonstrate that, within the bounds of computational resources, the Transformer can be utilised as a surrogate for EM dynamics, providing 14× speed‐up, while the CGNN can be utilised as a next‐frame predictor, providing 9× speed‐up. When comparing the accuracy of these two models with the authors’ previously developed Encoder‐Recurrent‐Decoder (ERD) model, it is observed that the error for both the Transformer and the CGNN remains within the same bound for the ERD model. To the best of the authors’ knowledge, this work is the first to utilise the Transformer as a surrogate for EM dynamics.


Exploring biometric domain adaptation in human action recognition models for unconstrained environments

Abstract: Abstract In conventional machine learning (ML), a fundamental assumption is that the training and test sets share identical feature distributions, a reasonable premise drawn from the same dataset. However, real-world scenarios often defy this assumption, as data may originate from diverse sources, causing disparities between training and test data distributions. This leads to a domain shift, where variations emerge between the source and target domains. This study delves into human action recognition (HAR) models within an unconstrained, real-world setting, scrutinizing the impact of input data variations related to contextual information and video encoding. The objective is to highlight the intricacies of model performance and interpretability in this context. Additionally, the study explores the domain adaptability of HAR models, specifically focusing on their potential for re-identifying individuals within uncontrolled environments. The experiments involve seven pre-trained backbone models and introduce a novel analytical approach by linking domain-related (HAR) and domain-unrelated (re-identification (re-ID)) tasks. Two key analyses addressing contextual information and encoding strategies reveal that maintaining the same encoding approach during training results in high task correlation while incorporating richer contextual information enhances performance. A notable outcome of this study is the comprehensive evaluation of a novel transformer-based architecture driven by a HAR backbone, which achieves a robust re-ID performance superior to state-of-the-art (SOTA). However, it faces challenges when other encoding schemes are applied, highlighting the role of the HAR classifier in performance variations.


Ontology-based security modeling in ArchiMate

Abstract: Abstract Enterprise Risk Management involves the process of identification, evaluation, treatment, and communication regarding risks throughout the enterprise. To support the tasks associated with this process, several frameworks and modeling languages have been proposed, such as the Risk and Security Overlay (RSO) of ArchiMate. An ontological investigation of this artifact would reveal its adequacy, capabilities, and limitations w.r.t. the domain of risk and security. Based on that, a language redesign can be proposed as a refinement. Such analysis and redesign have been executed for the risk elements of the RSO grounded in the Common Ontology of Value and Risk . The next step along this line of research is to address the following research problems: What would be the outcome of an ontological analysis of security-related elements of the RSO? That is, can we identify other semantic deficiencies in the RSO through an ontological analysis? Once such an analysis is provided, can we redesign the security elements of the RSO accordingly, in order to produce an improved artifact? Here, with the aid of the Reference Ontology for Security Engineering (ROSE) and the ontological theory of prevention behind it, we address the remaining gap by proceeding with an ontological analysis of the security-related constructs of the RSO. The outcome of this assessment is an ontology-based redesign of the ArchiMate language regarding security modeling. In a nutshell, we report the following contributions: (1) an ontological analysis of the RSO that identifies six limitations concerning security modeling; (2) because of the key role of the notion of prevention in security modeling, the introduction of the ontological theory of prevention in ArchiMate; (3) a well-founded redesign of security elements of ArchiMate; and (4) ontology-based security modeling patterns that are logical consequences of our proposal of redesign due to its underlying ontology of security. As a form of evaluation, we show that our proposal can describe risk treatment options, according to ISO 31000. Finally, besides presenting multiple examples, we proceed with a real-world illustrative application taken from the cybersecurity domain.


Compression of Medical images using SPIHT Algorithm for Telemedicine Application

Abstract: Image compression plays a pivotal role in the medical field for the storage and transfer of DICOM images. This research work focuses on the compression of medical images using Set Partitioning in Hierarchy Trees (SPIHT) algorithm. The CT/MR images are used as input, the images are subjected to filtering by a median filter. The CT images in general are corrupted by Gaussian noise and MR images are corrupted by rician noise. The SPIHT algorithm comprises of following phases; transformation into wavelet domain, refinement pass and sorting pass. The Haar wavelet transform is employed and the wavelet coefficients are subjected to sorting and refinement pass. The Haar wavelet transform generates LL, HL, HL and HH sub-bands. In the sorting pass, the coefficients are classified into significant and insignificant. The refinement pass creates the precision bits for the significant coefficients. The main characteristic of the SPIHT algorithm is that it does not use an entropy coder. The reconstructed image in the decoding stage was validated by performance metrics. The SPIHT algorithm generates proficient results, when compared with the classical algorithms like wavelet and embedded zero tree wavelet (EZW) algorithms.


How much freedom does an effectiveness metric really have?

Abstract: Abstract It is tempting to assume that because effectiveness metrics have free choice to assign scores to search engine result pages (SERPs) there must thus be a similar degree of freedom as to the relative order that SERP pairs can be put into. In fact that second freedom is, to a considerable degree, illusory. That is because if one SERP in a pair has been given a certain score by a metric, fundamental ordering constraints in many cases then dictate that the score for the second SERP must be either not less than, or not greater than, the score assigned to the first SERP. We refer to these fixed relationships as innate pairwise SERP orderings . Our first goal in this work is to describe and defend those pairwise SERP relationship constraints, and tabulate their relative occurrence via both exhaustive and empirical experimentation. We then consider how to employ such innate pairwise relationships in IR experiments, leading to a proposal for a new measurement paradigm. Specifically, we argue that tables of results in which many different metrics are listed for champion versus challenger system comparisons should be avoided; and that instead a single metric be argued for in principled terms, with any relationships identified by that metric then reinforced via an assessment of the innate relationship as to whether other metrics are likely to yield the same system‐versus‐system outcome.


Enhancing cervical cancer diagnosis with graph convolution network: AI-powered segmentation, feature analysis, and classification for early detection

Abstract: Abstract Cervical cancer is a prevalent disease affecting the cervix cells in women and is one of the leading causes of mortality for women globally. The Pap smear test determines the risk of cervical cancer by detecting abnormal cervix cells. Early detection and diagnosis of this cancer can effectively increase the patient’s survival rate. The advent of artificial intelligence facilitates the development of automated computer-assisted cervical cancer diagnostic systems, which are widely used to enhance cancer screening. This study emphasizes the segmentation and classification of various cervical cancer cell types. An intuitive but effective segmentation technique is used to segment the nucleus and cytoplasm from histopathological cell images. Additionally, handcrafted features include different properties of the cells generated from the distinct cervical cytoplasm and nucleus area. Two feature rankings techniques are conducted to evaluate this study’s significant feature set. Feature analysis identifies the critical pathological properties of cervical cells and then divides them into 30, 40, and 50 sets of diagnostic features. Furthermore, a graph dataset is constructed using the strongest correlated features, prioritizes the relationship between the features, and a robust graph convolution network (GCN) is introduced to efficiently predict the cervical cell types. The proposed model obtains a sublime accuracy of 99.11% for the 40-feature set of the SipakMed dataset. This study outperforms the existing study, performing both segmentation and classification simultaneously, conducting an in-depth feature analysis, attaining maximum accuracy efficiently, and ensuring the interpretability of the proposed model. To validate the model’s outcome, we tested it on the Herlev dataset and highlighted its robustness by attaining an accuracy of 98.18%. The results of this proposed methodology demonstrate the dependability of this study effectively, detecting cervical cancer in its early stages and upholding the significance of the lives of women.


Motion-aware object tracking for aerial images with deep features and discriminative correlation filter

Abstract: Abstract Object tracking is a challenging task which is required for different problems such as surveillance, traffic analysis and human-computer interaction. The problem of tracking an object can be considered in different categories such as single object tracking, multiple object tracking, short-term tracking, long-term tracking, tracking by detection and detection-free tracking. This study focuses on detection-free tracking for ground targets on aerial images. The studies in the literature show that correlation filter and deep learning based object trackers perform well recently. This paper proposes a new correlation filter-based tracker containing a strategy for re-detection issue. We improve the performance of correlation filter-based tracker by adding a lightweight re-detection ability to the correlation filter tracker in case of a long occlusion or complete loss of target. We use deep features to train Discriminative Correlation Filter(DCF) by integrating sub-networks from pre-trained ResNet and SAND models. The experimental results on the popular UAV123L dataset show that the proposed method(MADCF) improves the performance of DCF tracker and have a reasonable performance for long-term tracking problem. Moreover, we prepare a new tracking dataset (PESMOD tracking) consisting of UAV images, and we evaluate the proposed method and state-of-the-art method in this dataset. We observed that the proposed method performs much better in ground target tracking from VIVID and PESMOD aerial datasets. The proposed MADCF tracker performs better for small targets tracked by UAVs compared to the deep learning-based trackers. The source code and prepared dataset are available at http://github.com/mribrahim/MADCF


Estimation and interpretation problems and solutions when using proportion covariates in linear regression models

Abstract: Abstract Proportion variables, also known as compositional data, are very common in ecology. Unfortunately, few scientists are aware of how compositional data, when used as covariates, can adversely impact statistical analysis. We describe here how proportion covariates result in multicollinearity and parameter identifiability problems. Using simulated data on bird species richness as a function of land use, we show how these problems manifest when fitting a wide range of models in R, both in a frequentist and Bayesian framework. In particular, we show that similar models can often generate substantially different parameter estimates, leading to very different conclusions. Dropping a covariate or the intercept from the model can solve the multicollinearity and parameter identifiability problems. Unfortunately, these solutions do not fix the inherent challenges associated with interpreting parameter estimates. To this end, we propose focusing the interpretation on the difference of slope parameters to avoid the inherent unidentifiability of individual parameters. We also propose conditional plots with two x ‐axes and marginal plots as visualization techniques that can help users better interpret their modeling results. We illustrate these problems and proposed solutions using empirical data from the North American Breeding Bird Survey. The practical and straightforward approaches suggested in this article will help the fitting of linear models and interpretation of its results when some of the covariates are proportions.


Resonant entanglement of photon beams by a magnetic field

Abstract: Abstract In spite of the fact that photons do not interact with an external magnetic field unless nonlinearity of QED is taken into account, the latter field may indirectly affect photons in the presence of a charged environment. This opens up an interesting possibility to continuously control the entanglement of photon beams without using any crystalline devices. We study this possibility in the framework of an adequate QED model. In an approximation it was discovered that such entanglement has a resonant nature, namely, a peak behavior at certain magnetic field strengths, depending on characteristics of photon beams direction of the magnetic field and parameters of the charged medium. Numerical calculations illustrating the above-mentioned resonant behavior of the entanglement measure and some concluding remarks are presented.


On the Role of Informal vs. Formal Context of Language Experience in Italian–German Primary School Children

Abstract: This study focuses on the contexts of language experience in relation to language dominance in eighty-seven Italian–German primary school children in Germany using the MAIN narrative task. We compare current language experience in the heritage language (Italian) and the majority language (German) in both formal and informal settings, and we examine the respective impact on micro- and macrostructure measures, including different language domains. Some previous findings emphasized the importance of language experience in formal contexts. By contrast, our results suggest that, in particular, language experience in informal contexts determines vocabulary and fluency in the heritage and majority language, while there are no effects of exposure on syntactic complexity. Furthermore, while the younger children are relatively balanced, the older children are more dominant in the societal language. Our findings imply that the use of the minority language in informal contexts should be encouraged to promote its development and maintenance.


Structural and semantic features of adjectives across languages and registers

Abstract: Abstract This study focuses on the seemingly simple grammatical category of adjectives. However, the framework adopted for analysing adjectives from both a cross-linguistic and a cross-register perspective highlights features of grammatical complexity within phrases as well as contrastive complexity within and across languages and registers. Using English-Norwegian comparable data from the fictional sub-registers of narrative and dialogue, the analysis uncovers both similarities and differences in the structural and semantic features of adjectives. Most notable is perhaps the impact of register at all levels of analysis, including adjectival function (attributive vs. predicative), the degree of phrase complexity and semantic class. The contrastive analysis proper, i.e. comparing adjective use across languages, reveals that English and Norwegian have more in common within each sub-register than across them. For example, the lexico-grammatical behaviour of adjectives in English narrative has more in common with Norwegian narrative than it has with English dialogue. To the extent that there are clear general differences between English and Norwegian, these are mainly found in the proportions and preferences of a few specific features.


Reflections about Blockchain in Health Data Sharing: Navigating a Disruptive Technology

Abstract: A comprehensive analysis was performed, considering blockchain technology (BT) properties in digital health, addressing medicolegal, privacy, and regulatory considerations. Adherence to personal data protection and healthcare regulatory guidelines were analyzed and compared for GDPR (Europe), HIPAA (United States), CCPA (California), PIPEDA (Canada), the Privacy Act of 1988 (Australia), APPI (Japan), and LGPD (Brazil). Issues such as health systems, strengthening and aligning policy orientations and initiatives, and emphasizing the role of data analysis in shaping health policies were explored. The study addressed conflicts between the legal frameworks and blockchain, comparing and suggesting solutions like the revision of laws and the integration of compliance mechanisms. Additionally, it sought to enhance IT-health literacy by integrating the healthcare and legal domains. Ongoing collaboration between legal, health, and IT experts is essential for designing systems that effectively balance privacy rights and data protection while maximizing the benefits of disruptive technologies like blockchain.


Unlocking Language Competence: Challenges and Strategies of English Language Educators in the Post-Pandemic Landscape

Abstract: This descriptive-qualitative study explores the challenges and strategies of English language educators, specifically Senior High School (SHS) teachers, in Iligan City in adapting to the post-pandemic educational landscape. This research employs a phenomenological approach, informed by the works of Merleau-Ponty and Van Manen, to delve into the lived experiences of educators and their efforts to enhance language competence in the context of English language instruction. Data were gathered through in-depth literature reviews, informal interviews, observations, and thematic analysis offering valuable insights into the challenges faced and methods employed to overcome them. The study reveals that educators in the Schools Division of Iligan City have shown resilience and adaptability by integrating technology, adopting flexible instructional approaches, and embracing professional development. These strategies have not only enhanced their language competence but also improved their interactions with students. Moreover, the research underscores the pivotal role of language competence in English language instruction and emphasizes the need for educators to embrace new pedagogical approaches. The findings contribute to the field of English language education, providing a nuanced understanding of post-pandemic teaching methods and highlighting the significance of language competence in education. The study calls for a continued focus on professional language development and adaptability to promote linguistic proficiency, cultural awareness, and effective communication in the post-pandemic era, underlining the dedication and resilience of educators in the field of English language instruction.


Determining dense velocity fields for fluid images based on affine motion

Abstract: In this article, we address the problem of estimating fluid flows between two adjacent images containing fluid and non-fluid objects. Typically, traditional optical flow estimation methods lack accuracy, because of the highly deformable nature of fluid, the lack of definitive features, and the motion differences between fluid and non-fluid objects. Our approach captures fluid motions using an affine motion model for each small patch of an image. To obtain robust patch matches, we propose a best-buddies similarity-based method to address the lack of definitive features but many similar features in fluid phenomena. A dense set of affine motion models was then obtained by performing nearest-neighbor interpolation. Finally, dense fluid flow was recovered by applying the affine transformation to each patch and was improved by minimizing a variational energy function. Our method was validated using different types of fluid images. Experimental results show that the proposed method achieves the best performance.


Integrated geochemical and magnetic potentially toxic elements assessment: a statistical solution discriminating anthropogenic and lithogenic magnetic signals in a complex area of the southeast Nile Delta

Abstract: Magnetic proxy approaches proved to be efficient for potentially toxic elements (PTEs) pollution assessment when targeting forests or areas with a homogenous background where anthropogenic magnetic signals could be easily distinguished. Here, we present a multidisciplinary approach for magnetic susceptibility ([Formula: see text]) and HM assessment in a complex area in the Nile Delta, where geogenic input, land use, and various industries with different fly ash and surface water emissions interfere. Statistical analysis discriminates between the effects of lithologic elements and the concentrations of toxic anthropogenic elements. The studied elements are classified into lithogenic and anthropogenic-related (HMs, Au industry, and fertilizers industry) groups with maximum contamination levels of eight anthropogenic-related and highly toxic PTEs (Cu, Zn, Mo, Cd, Sb, Pb, Hg, and As) in the Akrasha industrial area (pollution load index = 15.84). Considering the whole data set, the numerical correlation of [Formula: see text] with most PTE concentrations and the pollution load index (PLI) is weak, while it is moderate to strong with lithogenic elements. However, a comparison of lithogenic elements and PTE concentrations along with x-values in two separate clusters supports the correspondence of lithology with elevated x-values in silt and clay-rich soil samples as well as HM concentration in industrial sandy soils. Correspondence between magnetic maps and chemistry data with land use reflects the potential of magnetic proxy methods for qualitative PTE pollution pre-delineation of the polluted spots, provided that lithological conditions are carefully considered.


Cooperative task allocation method for multi‐unmanned aerial vehicles based on the modified genetic algorithm

Abstract: Abstract Unmanned aerial vehicles (UAVs) play a crucial role in various domains such as military, civil, industrial, and so on. However, the coordination and task allocation of multiple UAVs in engineering practices face numerous challenges. As the scale of battlefields expands, and the diversity of UAV missions and constraints increases, the existing task allocation methods suffer from issues such as a mismatch between theoretical models and real‐world applications, low task execution efficiency, and poor responsiveness in dynamic environments. To address these challenges, this paper proposes an improved genetic algorithm (GA)‐based approach for multi‐UAV cooperative task allocation. By collecting battlefield information, decomposing tasks, and considering UAV resource types, an optimization model for multi‐UAV cooperative task allocation is constructed. The proposed method, using an improved GA, generates a set of Pareto‐optimal task solutions for decision‐makers. Case studies demonstrate that this approach effectively enhances task execution efficiency and reduces the total flight distance cost of UAVs.


Cross-linguistic Dependency Length Minimization in scientific language

Abstract: Abstract We use Universal Dependencies (UD) for the study of cross-linguistic diachronic syntactic complexity reduction. Specifically, we look at whether and how scientific English and German minimize the length of syntactic dependency relations in the Late Modern period (ca. 1650–1900). Our linguistic analysis follows the assumption that over time, scientific discourse cross-linguistically develops towards an increasingly efficient syntactic code by minimizing Dependency Length (DL) as a factor of syntactic complexity. For each language, we analyse a large UD-annotated scientific and general language corpus for comparison. While on a macro level, our analysis suggests that there is an overall diachronic cross-linguistic and cross-register reduction in Average Dependency Length (ADL), on the micro level we find that only scientific language shows a sentence length independent reduction of ADL, while general language shows an overall decrease of ADL due to sentence length reduction. We further analyse the syntactic constructions responsible for this reduction in both languages, showing that both scientific English and German increasingly make use of short, intra-phrasal dependency relations while long dependency relations such as clausal embeddings become rather disfavoured over time.


Relational sensitivity in participatory design

Abstract: Relational sensitivity involves attuning to subtle and complex collaborative dynamics and plays an important role in participatory design (PD) processes. PD practitioners, including researchers, designers, and facilitators, need abilities to navigate group dynamics while responsibly considering their own impact on the process. This article focuses on the term ‘relational sensitivity’ to develop perspectives and a vocabulary for discussing the competence and readiness needed in PD. This knowledge can contribute to design research by addressing the lack of understanding regarding how practitioners are trained in design facilitation and sensitivity to relationships. The article articulates relational sensitivity through the perspectives: sensitivity to self, to intersubjectivity, and to materiality and process. It makes a valuable contribution to the PD field by bringing together and exemplifying concepts and aspects connected to relational sensitivity, as well as applying Wilber’s four-quadrant model and Ofman’s adapted model in a new research context.


Consumer attitude toward using artificial intelligence (AI) devices in hospitality services

Abstract: Purpose The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions. Design/methodology/approach This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses. Findings The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices. Practical implications This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article. Originality/value This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.


Generating Independent RDCs Using Paramagnetic Tags

Abstract: A metal ion with magnetic anisotropy in a biomolecule exerts strong paramagnetic effects on the nucleus spins of biomolecules in the NMR spectrum. The paramagnetic effects including paramagnetic relaxation enhancement (PRE), pseudocontact shift (PCS) and residual dipolar coupling (RDC) are valuable structural and dynamic restraints of biomolecules, and generation of these paramagnetic effects is a prerequisite. The susceptibility of magnetic anisotropy plays a similar role in the alignment of biomolecules to the commonly used alignment media in an external magnetic field. We summarize the strategies available for generating RDCs in proteins by paramagnetic labeling, selection of paramagnetic ions, and the factors influencing the magnitude of RDCs.


Innovating the humanities, reimagining the future: empirical evidence from virtual reality practice in China

Abstract: The influence of digital technologies on culture and the liberal arts is a global phenomenon. To contribute a non-Western perspective to such debates, this article considers the uptake of virtual reality technology in Chinese humanities education via the policy lens of a higher education reform called New Humanities Education, which was issued by the Ministry of Education of the People’s Republic of China in 2018. The paper draws on the concept of a ‘techno-cultural imaginary’ to show how the humanities and STEM fields have merged and have contributed to the task of national rejuvenation in the past decade. Drawing on interviews with virtual reality practitioners in universities and industry, the results indicate two ideological shifts in humanities education in China: The first is a shift from praising scholar-officials (shi) to praising academic entrepreneurs; and the second is from valuing knowledge to valuing power. The article argues that those being cultivated and nurtured in the contemporary Chinese humanities education system are expected to become pragmatic problem-solvers rather than critical thinkers.


Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants

Abstract: The paper presents a technique of motion planning for autonomous vehicles (AV) based on simultaneous trajectory and speed optimization. The method includes representing the trajectory by a finite element (FE), determining trajectory parameters in Frenet coordinates, composing a model of vehicle kinematics, defining optimization criteria and a cost function, forming a set of constraints, and adapting the Gaussian N-point scheme for quadrature numerical integration. The study also defines a set of minimum optimization parameters sufficient for making motion predictions with smooth functions of the trajectory and speed. For this, piecewise functions with three degrees of freedom (DOF) in FE’s nodes are implemented. Therefore, the high differentiability of the trajectory and speed functions is ensured to obtain motion criteria such as linear and angular speeds, acceleration, and jerks used in the cost function and constraints. To form the AV roadway position, the Frenet coordinate system and two variable parameters are used: the reference path length and the lateral displacement perpendicular to reference line’s tangent. The trajectory shape, then, depends only on the final position of the AV’s mass center and the final reference’s curvature. The method uses geometric, kinematic, dynamic, and physical constraints, some of which are related to hard restrictions and some to soft restrictions. The planning technique involves parallel forecasting for several variants of the AV maneuver followed by selecting the one corresponding to a specified criterion. The sequential quadratic programming (SQP) technique is used to find the optimal solution. Graphs of trajectories, speeds, accelerations, jerks, and other parameters are presented based on the simulation results. Finally, the efficiency, rapidity, and prognosis quality are evaluated.


A Comparison of Head Movement Classification Methods

Abstract: To understand human behavior, it is essential to study it in the context of natural movement in immersive, three-dimensional environments. Virtual reality (VR), with head-mounted displays, offers an unprecedented compromise between ecological validity and experimental control. However, such technological advancements mean that new data streams will become more widely available, and therefore, a need arises to standardize methodologies by which these streams are analyzed. One such data stream is that of head position and rotation tracking, now made easily available from head-mounted systems. The current study presents five candidate algorithms of varying complexity for classifying head movements. Each algorithm is compared against human rater classifications and graded based on the overall agreement as well as biases in metrics such as movement onset/offset time and movement amplitude. Finally, we conclude this article by offering recommendations for the best practices and considerations for VR researchers looking to incorporate head movement analysis in their future studies.


Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

Abstract: Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions.


Real-Time Moving Object Tracking on Smartphone Using Cradle Head Servo Motor

Abstract: The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.


Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics

Abstract: The exploration of premium and new locations is regarded as a fundamental function of every evolutionary algorithm. This is achieved using the crossover and mutation stages of the differential evolution (DE) method. A best-and-worst position-guided novel exploration approach for the DE algorithm is provided in this study. The proposed version, known as "Improved DE with Best and Worst positions (IDEBW)", offers a more advantageous alternative for exploring new locations, either proceeding directly towards the best location or evacuating the worst location. The performance of the proposed IDEBW is investigated and compared with other DE variants and meta-heuristics algorithms based on 42 benchmark functions, including 13 classical and 29 non-traditional IEEE CEC-2017 test functions and 3 real-life applications of the IEEE CEC-2011 test suite. The results prove that the proposed approach successfully completes its task and makes the DE algorithm more efficient.


Hybrid Quantum-Classical Search Algorithms

Abstract: Search is one of the most commonly used primitives in quantum algorithm design. It is known that quadratic speedups provided by Grover’s algorithm are optimal, and no faster quantum algorithms for Search exist. While it is known that at least some quantum computation is required to achieve these speedups, the existing bounds do not rule out the possibility of an equally fast hybrid quantum-classical algorithm where most of the computation is classical. In this work, we study such hybrid algorithms and we show that classical computation, unless it by itself can solve the Search problem, cannot assist quantum computation. In addition, we generalize this result to algorithms with subconstant success probabilities.