Reviewerly/exHarmony huggingface dataset:
Role of indistinguishability and entanglement in Hong-Ou-Mandel interference and finite-bandwidth effects of frequency-entangled photons

We investigate the relation between indistinguishability and quantum entanglement in Hong-Ou-Mandel interference experiments theoretically and relate these quantum mechanical principles to the theorem of entanglement monogamy. Employing Glauber's theory of quantum coherence we compute the detection statistics in HOM interference of frequency-entangled photons and find an additional term in the coincidence detection probability, which is related to the spectral indistinguishability of the considered photons that arises from finite-bandwidth effects and therefore is relevant in the limit of low-frequency separations or large single-photon bandwidths. Compared to previous work in that context we treat all photonic degrees of freedom on equal footing.
A Machine Learning Approach for Monitoring and Classifying Healthcare Data-A Case of Emergency Department of KSA Hospitals

The Emergency Departments (EDs), in hospitals located in a few important areas in Saudi Arabia, experience a heavy inflow of patients due to viral illnesses, pandemics, and even on a few special occasions events such as Hajj or Umrah, when pilgrims travel from one region to another with severe disease conditions. Apart from the EDs, it is critical to monitor the movements of patients from EDs to other wards inside the hospital or in the region. This is to track the spread of viral illnesses that require more attention. In this scenario, Machine Learning (ML) algorithms can be used to classify the data into many classes and track the target audience. The current research article presents a Machine Learning-based Medical Data Monitoring and Classification Model for the EDs of the KSA hospitals and is named MLMDMC-ED technique. The most important aim of the proposed MLMDMC-ED technique is to monitor and track the patient's visits to the EDs, the treatment given to them based on the Canadian Emergency Department Triage and Acuity Scale (CTAS), and their Length Of Stay (LOS) in the hospital, based on their treatment requirements. A patient's clinical history is crucial in terms of making decisions during health emergencies or pandemics. So, the data should be processed so that it can be classified and visualized in different formats using the ML technique. The current research work aims at extracting the textual features from the patients' data using the metaheuristic Non-Defeatable Genetic Algorithm II (NSGA II). The data, collected from the hospitals, are classified using the Graph Convolutional Network (GCN) model. Grey Wolf Optimizer (GWO) is exploited for fine-tuning the parameters to optimize the performance of the GCN model. The proposed MLMDMC-ED technique was experimentally validated on the healthcare data and the outcomes indicated the improvements of the MLMDMC-ED technique over other models with a maximum accuracy of 91.87%.
Formation of Communicative Competence of Foreign Students in Post-Industrial Society: Electronic Tutoring and Mentoring

The theme of the article highlights the main aspects regarding the impact of the development trends in post-industrial society on the formation of communicative competencies. The aim of the article is to reveal the essence of electronic tutoring and mentoring as one of the important factors in the formation of communicative skills of foreign students. The article identifies the features of the organization of tutoring and mentoring using innovative technologies, the achievements of the information and post-industrial society. Theoretical bases for defining the concept of communicative competence, post-industrial society, electronic tutoring, and mentoring are substantiated. The urgency of the problem lies in the presentation of new psychological and pedagogical approaches, it also lies in the formation of competence in a post-industrial society such as the use of electronic learning tools to increase efficiency, the use of information and communication technologies to develop the competence of students and teachers, and organization of learning based on organizational approach. Methods of analysis and synthesis, research method, and method of content analysis were used to determine the peculiarities of the formation of communicative competence in foreign students in the conditions of electronic tutoring and mentoring. In the course of the study, it was proved that the implementation of theoretically sound conditions of electronic tutoring and mentoring will contribute to the formation of communicative competence among foreign students.
Research on Formation and Path Planning Method of Isomorphic Multi-unmanned Ground System Based on Humanoid Formation Configuration

Artificial intelligence has become the core driving force to promote a new round of military revolution. Based on the principle of“three-three-system”in military tactics, this paper studies the formation and path planning of multi-unmanned vehicles by artificial potential field method combined with A-star algorithm and reinforcement learning. Firstly, a three-vehicle collaborative structural framework based on leader and follower is constructed. Secondly, the autonomous cooperative path planning algorithm is designed, including the key technologies of global path planning and local path planning algorithm: state and action, reward function, sphere of influence, formation expansion and system simulation framework. Then the simulation experiment is carried out in the 2D MATLAB environment to verify the feasibility of the designed three-car formation. Finally, the feasibility and effectiveness of the designed basic three-vehicle formation and its extended formation are verified by simulation in the 3D Webots environment, which provides a research basis for the ground autonomous intelligent unmanned swarm system.
APPLICATION OF TWO-DIMENSIONAL PADÉ-TYPE APPROXIMATIONS FOR IMAGE PROCESSING

Context. The Gibbs phenomenon introduces significant distortions for most popular 2D graphics standards because they use a finite sum of harmonics when image processing by expansion of the signal into a two-dimensional Fourier series is used in order to reduce the size of the graphical file. Thus, the reduction of this phenomenon is a very important problem.&#x0D; Objective. The aim of the current work is the application of two-dimensional Padé-type approximations with the aim of elimination of the Gibbs phenomenon in image processing and reduction of the size of the resulting image file.&#x0D; Method. We use the two-dimensional Padé-type approximants method which we have developed earlier to reduce the Gibbs phenomenon for the harmonic two-dimensional Fourier series. A definition of a Padé-type functional is proposed. For this purpose, we use the generalized two-dimensional Padé approximation proposed by Chisholm when the range of the frequency values on the integer grid is selected according to the Vavilov method. The proposed scheme makes it possible to determine a set of series coefficients necessary and sufficient for construction of a Padé-type approximation with a given structure of the numerator and denominator. We consider some examples of Padé approximants application to simple discontinuous template functions for both formulaic and discrete representation.&#x0D; Results. The study gives us an opportunity to make some conclusions about practical usage of the Padé-type approximation and about its advantages. They demonstrate effective elimination of distortions inherent to Gibbs phenomena for the Padé-type approximant. It is well seen that Padé-type approximant is significantly more visually appropriate than Fourier one. Application of the Padétype approximation also leads to sufficient decrease of approximants’ parameter number without the loss of precision.&#x0D; Conclusions. The applicability of the technique and the possibility of its application to improve the accuracy of calculations are demonstrated. The study gives us an opportunity to make conclusions about the advantages of the Padé-type approximation practical usage.
Redesigning Knowledge Management Through Corporate Sustainability Strategy in the Post-Pandemic Era

This study investigates the role of Knowledge Management (KM) in integrating corporate sustainability practices in the post-pandemic context. It also examines the current literature on KM and sustainable development and develops a sustainable conceptual model. Based on a survey of contemporary literature and KM and corporate sustainability approach, this study proposes a conceptual framework with KM and corporate sustainability strategy as fundamental constructs to attain organisational excellence (OE) in the post-pandemic era. The research adds conceptual and situational elements such as the interaction between KM and sustainability strategy, creative approaches for developing a structural framework, and the right direction for boosting efficiency. The research is one of the first to present a comprehensive framework for achieving OE in the post-pandemic era. Furthermore, by focussing on COVID-19 and the post-pandemic environment, this research provides a new perspective on KM and corporate sustainability literature.
How Psycholinguistics and Social Factors Influence SLA on Elementary and Secondary Students in Indonesia

Psycholinguistic factors are part of internal factors which connect to the intellectual process. Social factors are part of external factors which connect to social interactions. This study aims to reveal the psychological and social factors in second language acquisition of elementary and secondary students in Indonesia. The research employs a qualitative methodology focused on library research. The data used documentation techniques to gather and analyse the data through the descriptive approach. Documentation data reveal that there are five psycholinguistic factors such as (1) attitude, (2) aptitude, (3) motivation, (4) personality qualities, and (5) biological processes. Also, this research found eight social factors such as (1) gender, (2) age, (3) social class, (4) motivation, (5) attitude, (6) cultural process, (7) students' characters and situation, and (8) economic status. No particular factor can be stated as dominant among psycholinguistic and social factors. Therefore, this research reveals the same element that occurred in psycholinguistic and social factors: motivation and attitude. This study suggests that teachers should pay extra attention to students' motivation and attitude since it relates to psychological and social factors, which could support their second language acquisition.
Securing Wireless Sensor Networks Against DoS attacks in Industrial 4.0

Wireless Sensor Networks (WSNs) play a vital role in Industrial 4.0 by facilitating significant data collection for monitoring and control purposes. However, their distributed and resource-constrained nature makes WSNs vulnerable to Denial-of-Service (DoS) attacks, which can impede their normal operation and jeopardize their functionality. To address this issue, we propose a new machine learning (ML) approach that enhances the security of WSNs against DoS attacks in Industrial 4.0. Our approach incorporates a spatial learning unit, which captures the positional information in WSN traffic flows, and a temporal learning unit which captures time interdependency features within periods of traffic flows. To evaluate the proposed approach, we tested it on a publicly available dataset. The results demonstrate that it achieves a high detection rate while maintaining a low false alarm rate. Moreover, our Intrusion Detection System (IDS) exhibits good scalability and robustness against various DoS attacks. Our approach provides a reliable and effective solution to secure WSNs in Industrial 4.0 against DoS attacks and can be further developed and tested in various real-world scenarios.
CableAuth: A Biometric Second Factor Authentication Scheme for Electric Vehicle Charging

Electric vehicle charging sessions can be authorised in different ways, ranging from smartphone applications to smart cards with unique identifiers that link the electric vehicle to the charging station.However, these methods do not provide strong authentication guarantees.In this paper, we propose a novel second factor authentication scheme to tackle this problem.We show that by using inertial sensor data collected from IMU sensors either embedded in the handle of the charging cable or on a separate smartwatch, users can be authenticated implicitly by behavioural biometrics as they unhook the cable from the charging station and plug it into their car at the start of a charging session.To validate the system, we conducted a user study (n=20) to collect data and we developed a suite of authentication models for which we achieve EERs of 0.06.
Ethical Challenges in Blockchain Network Measurement Research

Public blockchains are the digital infrastructure that powers the multi-trillion-dollar economy in cryptocurrencies.Understanding the security and performance of deployed blockchain networks is critically important, especially when the open-membership nature of blockchain results in a large attack surface.However, measuring operational blockchain networks raises ethical concerns and could interfere with the businesses running atop the blockchains.This work presents a survey of the recent measurement studies on the Ethereum networks and discusses their ethical issues, practices, and solutions.The paper also identifies several open ethical challenges faced by blockchain researchers.
Impact of Organizational Support on Employee Creativity and Innovative Work Behavior: Mediation of Employee Creativity

Organizations are facing turbulent environment due to advancement in technology, globalization and intense competition. In order to survive in this competitive business world, organizations are forced to innovate and respond to the changing environment. To line with this, this study aims to find the impact of organizational support on employee creativity and innovative work behavior in the banking sector of Pakistan. For this purpose data was collected from 267 employees working in the banking sector of Pakistan. Data was collected by using self-administered structured questionnaire. Data was analyzed by using SMART PLS. The measurement model confirms the validity and reliability of the measures. The structural model provides that organizational support has positive and significant impact on the employee creativity and innovative work behavior. Moreover, employee creativity mediates the relationship between organizational support and innovative work behavior. Research implications and limitations have also been discussed.
THE IMPACT OF VIDEO GAME: “AGE OF EMPIRES II” TOWARD STUDENTS’ READING COMPREHENSION ON NARRATIVE TEXT

With the development of technology that never stops year by year, the development of learning media also follows particularly from digital media and all device. All media has been massively used by teachers and instructors to make learning activities interesting. Teachers always keep searching to find new learning media to keep students’ moods are still in line. One of media that able to use in education field is video game. A few studies have looked into the impact of video game on students learning English especially for reading comprehension. However video games is full with content that can increasing students’ English skill especially on their reading comprehension. One of the video game that full with reading text and also has historical thing is Age of Empires II. This study purposed to see how video game influeced students’ reading comprehension on narrative text games. The study consisted of 72 students from MAN 1 Bandar Lampung in tenth grade. X MIA 3 and X MIA 4 were the class samples of this study. The effect of video games can be reflected in the multiple-choice students’ score. The writer used a quantitative and quasi-experimental method as the methodology with a random sampling approach. Multiple-choice test was used as the tool of test. The research findings in this study explain that video games has significantly impact.
Let Me Unwind That For You: Exceptions to Backward-Edge Protection

Backward-edge control-flow hijacking via stack buffer overflow is the holy grail of software exploitation.The ability to directly control critical stack data and the hijacked target makes this exploitation strategy particularly appealing for attackers.As a result, the community has deployed strong backward-edge protections such as shadow stacks or stack canaries, forcing attackers to resort to less ideal e.g., heap-based exploitation strategies.However, such mitigations commonly rely on one key assumption, namely an attacker relying on return address corruption to directly hijack control flow upon function return.
Copy-on-Flip: Hardening ECC Memory Against Rowhammer Attacks

Despite nearly decade-long mitigation efforts in academia and industry, the community is yet to find a practical solution to the Rowhammer vulnerability.Comprehensive software mitigations require complex changes to commodity systems, yielding significant run-time overhead and deterring practical adoption.Hardware mitigations, on the other hand, have generally grown more robust and efficient, but are difficult to deploy on commodity systems.Until recently, ECC memory implemented by the memory controller on server platforms seemed to provide the best of both worlds: use hardware features already on commodity systems to efficiently turn Rowhammer into a denial-of-service attack vector.Unfortunately, researchers have recently shown that attackers can perform one-bit-at-a-time memory templating and mount ECC-aware Rowhammer attacks.In this paper, we reconsider ECC memory as an avenue for Rowhammer mitigations and show that not all hope is lost.In particular, we show that it is feasible to devise a softwarebased design to both efficiently and effectively harden commodity ECC memory against ECC-aware Rowhammer attacks.To support this claim, we present Copy-on-Flip (CoF), an ECCbased software mitigation which uses a combination of memory migration and offlining to stop Rowhammer attacks on commodity server systems in a practical way.The key idea is to let the operating system interpose on all the error correction events and offline the vulnerable victim page as soon as the attacker has successfully templated a sufficient number of bit flipswhile transparently migrating the victim data to a new page.We present a CoF prototype on Linux, where we also show it is feasible to operate simple memory management changes to support migration for the relevant user and kernel memory pages.Our evaluation shows CoF incurs low performance and memory overhead, while significantly reducing the Rowhammer attack surface.On typical benchmarks such as SPEC CPU2017 and Google Chrome, CoF reports a <1.5% overhead, and, on extreme I/O-intensive scenarios (saturated nginx), up to ∼11%.1 We use the term memory (or page) offlining as used in the Linux kernel source and by hardware vendors to refer to those pages which are poisoned and rendered unavailable to the system [19], [26].
QUICforge: Client-side Request Forgery in QUIC

The QUIC protocol is gaining more and more traction through its recent standardization and the rising interest by various big tech companies, developing new implementations.QUIC promises to make security and privacy a first-class citizen; yet, challenging these claims is of utmost importance.To this end, this paper provides an initial analysis of client-side request forgery attacks that directly emerge from the QUIC protocol design and not from common vulnerabilities.In particular, we investigate three request forgery attack modalities with respect to their capabilities to be used for protocol impersonation and traffic amplification.We analyze the controllable attack space of the respective protocol messages and demonstrate that one of the attack modalities can indeed be utilized to impersonate other UDP-based protocols, e.g., DNS requests.Furthermore, we identify traffic amplification vectors.Although the QUIC protocol specification states anti-amplification limits, our evaluation of 13 QUIC server implementations shows that in some cases these mitigations are missing or insufficiently implemented.Lastly, we propose mitigation approaches for protocol impersonation and discuss ambiguities in the specification.
Test-time bi-directional adaptation between image and model for robust segmentation

Deep learning models often suffer from performance degradations when deployed in real clinical environments due to appearance shifts between training and testing images. Most extant methods use training-time adaptation, which almost require target domain samples in the training phase. However, these solutions are limited by the training process and cannot guarantee the accurate prediction of test samples with unforeseen appearance shifts. Further, it is impractical to collect target samples in advance. In this paper, we provide a general method of making existing segmentation models robust to samples with unknown appearance shifts when deployed in daily clinical practice.Our proposed test-time bi-directional adaptation framework combines two complementary strategies. First, our image-to-model (I2M) adaptation strategy adapts appearance-agnostic test images to the learned segmentation model using a novel plug-and-play statistical alignment style transfer module during testing. Second, our model-to-image (M2I) adaptation strategy adapts the learned segmentation model to test images with unknown appearance shifts. This strategy applies an augmented self-supervised learning module to fine-tune the learned model with proxy labels that it generates. This innovative procedure can be adaptively constrained using our novel proxy consistency criterion. This complementary I2M and M2I framework demonstrably achieves robust segmentation against unknown appearance shifts using existing deep-learning models.Extensive experiments on 10 datasets containing fetal ultrasound, chest X-ray, and retinal fundus images demonstrate that our proposed method achieves promising robustness and efficiency in segmenting images with unknown appearance shifts.To address the appearance shift problem in clinically acquired medical images, we provide robust segmentation by using two complementary strategies. Our solution is general and amenable for deployment in clinical settings.
Human Pose Estimation Based on Lightweight Convolutional Neural Networks

Aiming at the problems of large network model parameters and high computational complexity in human pose estimation network, an improved lightweight human pose estimation network (MobileNetV3 And Convolutional Block Attention Module Pose, MACPose) based on Lightweight OpenPose is proposed. Firstly, an improved MobileNetV3 network is introduced for feature extraction. Secondly, CBAM (Convolutional Block Attention Module) attention mechanism is integrated in the refinement stage. Therefore, human pose estimation can be carried out under the condition of reducing the model parameters and computational complexity. Under the same experimental parameters and environment configuration, the experimental results of COCO validation set show that compared with Lightweight OpenPose, MACPose reduces 16.9% in model parameters and 22.2% in computational complexity. The experimental results show that compared with Lightweight OpenPose, MACPose proposed in this paper can still achieve good results in human pose estimation with fewer model parameters and computational complexity.