Reviewerly/exHarmony huggingface dataset:
Curriculum Advisors’ and Teachers’ Perceptions of Teaching Reading in Foundation Phase Classrooms

This article aims to examine curriculum advisors’ and teachers’ perceptions of teaching early reading in Foundation Phase classrooms. It also aims to provide recommendations to alleviate overlaps based on the findings. A qualitative approach was used to gather data through semi-structured interviews. Purposeful convenience sampling was used to study two curriculum advisors and five Grade 1 teachers from five schools in the Mopani district of Limpopo Province. Data collected were analysed using thematic analysis. The findings showed that the curriculum advisors and teachers vary in their perceptions of early reading. Both curriculum advisors perceived that teachers change slowly, teach early reading unsystematically, and show superficial compliance, but do not really abide by guidelines, while teachers say the National Curriculum does not give them enough time to teach properly or attend to struggling learners. Teachers also expressed their frustration in managing large classes. This research article contributes to the body of knowledge in creating an awareness that new curriculum requirements are tools used for aligning teachers with change.
TikTok Use as Strategy to Improve Knowledge Acquisition and Build Engagement to Learn Literature in ESL Classrooms

The introduction of literature into English classes is an attempt to improve students' language skills. In Malaysian schools, students’ interest in the chalk-and-talk method of teaching and learning literature has waned, leading to a decline in students' knowledge acquisition and a reduction in their engagement in learning. One of the ways to solve this problem is to integrate technological platforms, such as social media platform TikTok, into literature assignments. However, there are not many studies that have investigated the use of this application in literature learning for Malaysian secondary school students. In this quasi-experimental quantitative study, the researchers used TikTok as a strategy in teaching literature to 150 Form Two students, selected through purposive sampling, to determine students' engagement in learning literature and improve their knowledge acquisition. The two instruments used were an adopted questionnaire, and pre-tests and post-tests administered to control and experimental groups. Descriptive statistics, such as number, percentage, independent and paired t-tests, were used to analyse the results. The results of the study show that the overall mean of students' engagement with TikTok in learning literature is very high and students' knowledge acquisition in learning literature is positive. The study elucidates how teachers can improve their students' knowledge and promote engagement in literature using TikTok. Both the teaching and learning literature become fun and foster creativity as an appropriate medium, like TikTok, enables student-centred learning.
Tinjauan Sistematis tentang Collaborative Learning pada Bidang Kimia

Collaborative Learning (CL) is a learning model that implies cooperation in a group consisting of two or more people to achieve a common goal while respecting each individuals contribution as a whole. CL is more synonymous with social interaction. In the learning process, the centre of attention is the interaction of educators with students, interactions between students, and interactions of students with their environment. The purpose of this review is to look at global trends related to CL research in the field of Chemistry Education. Obtained 62,555 articles discussing CL with various keywords, 88 of which are relevant to the purpose of this article review. After being mapped, there are five research themes related to CL, namely (1) CL based on student activities and the role of educators, (2) Collaboration Strategy for STEM, (3) CL based game, (4) Computer-supported collaborative learning (CSCL), and (5) CL in Laboratory Work. Based on the review findings, research related to CL has excellent potential to be continued and developed, such as collaboration with STEM, games, practicum in the laboratory, and CSCL.
On the third relative greedy weight of 4‐dimensional codes

Motivated by the concepts of the relative generalized Hamming weight and the greedy weight, the relative greedy weight is introduced, and then it is shown that the codes achieving the upper bounds on the relative greedy weight are optimal on the security of the transmitted data symbols in the wire-tap channel. Based on such applications, the finite geometry method is generalized, and by using the generalized finite geometry method, certain upper bounds on the third relative greedy weight of 4-dimensional codes with respect to 1-dimensional subcodes are first determined, and then optimal codes are constructed with respect to these obtained upper bounds.
Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions

We consider the problem of solving LP relaxations of MAP-MRF inference problems, and in particular the method proposed recently in (Swoboda, Kolmogorov 2019; Kolmogorov, Pock 2021). As a key computational subroutine, it uses a variant of the Frank-Wolfe (FW) method to minimize a smooth convex function over a combinatorial polytope. We propose an efficient implementation of this subproutine based on in-face Frank-Wolfe directions, introduced in (Freund et al. 2017) in a different context. More generally, we define an abstract data structure for a combinatorial subproblem that enables in-face FW directions, and describe its specialization for tree-structured MAP-MRF inference subproblems. Experimental results indicate that the resulting method is the current state-of-art LP solver for some classes of problems. Our code is available at https://pub.ist.ac.at/~vnk/papers/IN-FACE-FW.html.
A graph-based approach to client relationship management in fund administration

Ensuring effective and timely communication in the fund administration industry has become an essential element of client relationship management (CRM). A CRM team periodically evaluates asset managers perception of service quality using surveys, where it can be difficult to evaluate service quality objectively. Within the asset management industry, despite ongoing technological advances, the main channel of communication remains email. The sheer volume of email, and the industry’s reliance on it, can lead to practical problems that impact CRM. In this work we draw insights from the email communications between an operations team and two-sample clients to understand client relationships in a way not previously possible. The results are presented and we discuss how these can quantitatively support and improve service quality evaluations in CRM. For this application, we exploit the social relations in emails via a graph-based approach. A deep learning framework is described that allows a graph-based inspection of the email communications between asset managers and their fund administrators operations teams. The presented framework integrates a natural language processing model to transform email subject lines to embedding representations, a knowledge graph to transform the email communication links into a graph representation, and a graph neural network to process the embedding representations and classify the email communications. The classification of critical conversations via email is a demonstrative example of a scalable graph-based approach that allows the use of machine learning to process, learn, and explore the relations existing between the emails.
High-performance 1560 nm Entangled Photon Source for high secure key rates QKD satellite-based communications

In the frame of the Secure And cryptoGrAphic (SAGA) project under ESA ARTES 4.0 program, we report the design and the test of a High-Performance Entangled Photon Source (HP-EPS) dedicated to QKD satellite-based communication, by using the Conventional-band optical fiber telecom components.We developed an asynchronous time binning higher than 10 bits/sec and 5 bits/sec for respectively a 60 dB (LEO) and a 65 dB (GEO) transmission loss budgets (both downlinks combined).The compactness and simplicity of the optical design, the low electrical consumption and the low mass combined with the robustness of the all-fibered design to the space environment (mechanical vibrations, shock, and radiations) make the HP-EPS a valuable and serious candidate for the satellite-based QKD quest.
Recent Advances in Fully Dynamic Graph Algorithms

In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms.
A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition

Next-generation communication systems will face new challenges related to efficiently managing the available resources, such as the radio spectrum. DL is one of the optimization approaches to address and solve these challenges. However, there is a gap between research and industry. Most AI models that solve communication problems cannot be implemented in current communication devices due to their high computational capacity requirements. New approaches seek to reduce the size of DL models through quantization techniques, changing the traditional method of operations from a 32 (or 64) floating-point representation to a fixed point (usually small) one. However, there is no analytical method to determine the level of quantification that can be used to obtain the best trade-off between the reduction of computational costs and an acceptable accuracy in a specific problem. In this work, we propose an analysis methodology to determine the degree of quantization in a DNN model to solve the problem of AMR in a radio system. We use the Brevitas framework to build and analyze different quantized variants of the DL architecture VGG10 adapted to the AMR problem. The evaluation of the computational cost is performed with the FINN framework of Xilinx Research Labs to obtain the computational inference cost. The proposed design methodology allows us to obtain the combination of quantization bits per layer that provides an optimal trade-off between the model performance (i.e., accuracy) and the model complexity (i.e., size) according to a set of weights associated with each optimization objective. For example, using the proposed methodology, we found a model architecture that reduced 75.8% of the model size compared to the non-quantized baseline model, with a performance degradation of only 0.06%.
WPL-Based Constraint for 3D Human Pose Estimation from a Single Depth Image

Three-dimensional human pose estimation from depth maps is a fast-growing research area in computer vision. The distal joints of the human body are more flexible than the proximal joints, making it more difficult to estimate the distal joints. However, most existing methods ignore the difference between the distal joints and proximal joints. Moreover, the distal joint can be constrained by the proximal joint on the same kinematic chain. In our work, we model the human skeleton as the tree structure called the human-tree. Then, motivated by the WPL (weighted path length) in the data structure, we propose a WPL-based loss function to constrain the distal joints with the proximal joints in a global-to-local manner. Extensive experiments on benchmarks demonstrate that our method can effectively improve the performance of the distal joints.
Automated Quantum Memory Compilation with Improved Dynamic Range

Emerging quantum algorithms that process data require that classical input data be represented as a quantum state. These data-processing algorithms often follow the gate model of quantum computing--which requires qubits to be initialized to a basis state, typically $\lvert 0 \rangle$--and thus often employ state generation circuits to transform the initialized basis state to a data-representation state. There are many ways to encode classical data in a qubit, and the oft-applied approach of basis encoding does not allow optimization to the extent that other variants do. In this work, we thus consider automatic synthesis of addressable, quantum read-only memory (QROM) circuits, which act as data-encoding state-generation circuits. We investigate three data encoding approaches, one of which we introduce to provide improved dynamic range and precision. We present experimental results that compare these encoding methods for QROM synthesis to better understand the implications of and applications for each.
CHARTER: Identifying the Most-Critical Gate Operations in Quantum Circuits via Amplified Gate Reversibility

When quantum programs are executed on noisy intermediate-scale quantum (NISQ) computers, they experience hardware noise; consequently, the program outputs are often erroneous. To mitigate the adverse effects of hardware noise, it is necessary to understand the effect of hardware noise on the program output and more fundamentally, understand the impact of hardware noise on specific regions within a quantum program. Identifying and optimizing regions that are more noise-sensitive is the key to expanding the capabilities of NISQ computers. Toward achieving that goal, we propose CHARTER, a novel technique to pinpoint specific gates and regions within a quantum program that are the most affected by the hardware noise and that have the highest impact on the program output. Using CHARTER's methodology, programmers can obtain a precise understanding of how different components of their code affect the output and optimize those components without the need for non-scalable quantum simulation on classical computers.
Malaria Transmission and Asymptomatic Malaria during Rainy Season among Nomads, North-eastern Nigeria

Aim: To determine the point prevalence of malaria infection and asymptomatic malaria during rainy season among some nomads of North Eastern Nigeria.&#x0D; Study Design: A cross sectional observational study.&#x0D; Place and Duration of Study: The study was conducted across 11 randomly selected nomads’ camps around the Rivers Gongola and Benue basins spread over 3 Local Government Areas of Southern Adamawa State of North Eastern Nigeria. Data was collected during rainy season between July and September, 2016.&#x0D; Methodology: Fifty-five randomly selected households (5 from each camp) were covered in the survey. One hundred and ninety two (192) consenting participants aged between 1 and 79 years (inclusive) were involved in the survey. Structured questionnaires were administered (care givers consented and responded on behalf of children) and blood samples collected. Blood samples were examined for malaria parasite using a microscopes and results of both survey and microscopy analysed.&#x0D; Results: Overall malaria parasite prevalence was 87.5% and mean parasite density was 36,168 parasites per μl of blood. Thirty five (18.2%) of participant were of low parasite density, 35.5% were of moderate parasite density while 32.8% were of high parasite density. More than half (53%) of the malaria positive participants did not experience febrile symptoms within one month prior to the survey and were therefore asymptomatic. Tendency of manifestation of symptoms significantly increased with parasite density and decreased with age. The use of preventive measures against mosquito bite was 7.7% and only 16.1% of participants used antimalarial medicines or sought medical attention during their most recent fever episode.&#x0D; Conclusion: The high prevalence of asymptomatic carriers with high parasite densities and abysmally low usage of preventive and curative measures among the study population represents an ideal condition for effective malaria transmission which is unlikely to abate unless control measures are intensified.
Tangible Digital Tourism Heritage: National Ecotourism Plan in Malaysia

Lately, many kinds of research demonstrate the digital technologies Industrial Revolution 4.0 (IR 4.0) like social media, virtual reality, and augmented realities are vital for future digital tourism.Digital tourism can work across borders, and collaborative work among nations is already growing in recent years.Tourism sector performance contributes to a large part of the Malaysian service economy (The World Factbook, 2019).In 2021, Malaysia has faced an economic crisis and it affects tourism sectors.Moreover, data from the Ministry of Tourism have shown that the number of tourists is decreasing by 1.5% in March 2020.The model specification will assess the most important factors of tourism performance such as the Tangible Heritage and Intangible Heritage that was used by many previous researchers.Using the technique of hybridization this study will improvise the model by adding digital perspectives.The result of this dtudy has indicated that almost all of respondents have a high perception toward tangible digital heritage in Melaka or Penang.This scenario has become a serious issue for the government because Tourism Sector is one of the government's important agendas in the NKEAs through the National Ecotourism Plan.According to the Ministry of Tourism (MOTAC, 2018), the nation's tourism sector is now the sixth-largest contributor to the national economy in 2017.Unfortunately, among all agendas in 21 Action Plans, none of it mentions digital heritage tourism.Hence, the main objective of this study is to adopt a new Heritage Digital Industrial Revolution 4.0 (IR 4.0) Model for National Ecotourism Plan.The findings will help the government in updating the National Ecotourism Plan under NKEAs.Lastly, it will guide the government by understanding the important need of visitors and it can apply to other heritages sites in Malaysia.
Potential Auto-driving Threat: Universal Rain-removal Attack

The problem of robustness in adverse weather conditions is considered a significant challenge for computer vision algorithms in the applicants of autonomous driving. Image rain removal algorithms are a general solution to this problem. They find a deep connection between raindrops/rain-streaks and images by mining the hidden features and restoring information about the rain-free environment based on the powerful representation capabilities of neural networks. However, previous research has focused on architecture innovations and has yet to consider the vulnerability issues that already exist in neural networks. This research gap hints at a potential security threat geared toward the intelligent perception of autonomous driving in the rain. In this paper, we propose a universal rain-removal attack (URA) on the vulnerability of image rain-removal algorithms by generating a non-additive spatial perturbation that significantly reduces the similarity and image quality of scene restoration. Notably, this perturbation is difficult to recognise by humans and is also the same for different target images. Thus, URA could be considered a critical tool for the vulnerability detection of image rain-removal algorithms. It also could be developed as a real-world artificial intelligence attack method. Experimental results show that URA can reduce the scene repair capability by 39.5% and the image generation quality by 26.4%, targeting the state-of-the-art (SOTA) single-image rain-removal algorithms currently available.
Secure Quantum Computing for Healthcare Sector: A Short Analysis

Quantum computing research might lead to "quantum leaps," and it could have unanticipated repercussions in the medical field. This technique has the potential to be used in a broad range of contexts, some of which include the development of novel drugs, the individualization of medical treatments, and the speeding of DNA sequencing. This work has assembled a list of the numerous methodologies presently employed in quantum medicine and other disciplines pertaining to healthcare. This work has created a list of the most critical concerns that need to be addressed before the broad use of quantum computing can be realized. In addition, this work investigates in detail the ways in which potential future applications of quantum computing might compromise the safety of healthcare delivery systems from the perspective of the medical industry and the patient-centric healthcare system. The primary objective of this investigation into quantum cryptography is to locate any potential flaws in the cryptographic protocols and strategies that have only very recently been the focus of scrutiny from academic research community members.
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning

Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. In this paper, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-domain detection accuracy given very limited data samples in new domains. DASECount leverages the wisdom of few-shot learning (FSL) paradigm consisting of two major stages: source domain meta training and target domain meta testing. Specifically, in the meta-training stage, we design and train two separate convolutional neural network (CNN) modules on the source domain dataset to fully capture the implicit amplitude and phase features of CSI measurements related to human activities. A subsequent knowledge distillation procedure is designed to iteratively update the CNN parameters for better generalization performance. In the meta-testing stage, we use the partial CNN modules to extract low-dimension features out of the high-dimension input target domain CSI data. With the obtained low-dimension CSI features, we can even use very few shots of target domain data samples (e.g., 5-shot samples) to train a lightweight logistic regression (LR) classifier, and attain very high cross-domain ICC accuracy. Experiment results show that the proposed DASECount method achieves over 92.68\%, and on average 96.37\% detection accuracy in a 0-8 people counting task under various domain setups, which significantly outperforms the other representative benchmark methods considered.
The communication cost of security and privacy in federated frequency estimation

We consider the federated frequency estimation problem, where each user holds a private item $X_i$ from a size-$d$ domain and a server aims to estimate the empirical frequency (i.e., histogram) of $n$ items with $n \ll d$. Without any security and privacy considerations, each user can communicate its item to the server by using $\log d$ bits. A naive application of secure aggregation protocols would, however, require $d\log n$ bits per user. Can we reduce the communication needed for secure aggregation, and does security come with a fundamental cost in communication? In this paper, we develop an information-theoretic model for secure aggregation that allows us to characterize the fundamental cost of security and privacy in terms of communication. We show that with security (and without privacy) $\Omega\left( n \log d \right)$ bits per user are necessary and sufficient to allow the server to compute the frequency distribution. This is significantly smaller than the $d\log n$ bits per user needed by the naive scheme, but significantly higher than the $\log d$ bits per user needed without security. To achieve differential privacy, we construct a linear scheme based on a noisy sketch which locally perturbs the data and does not require a trusted server (a.k.a. distributed differential privacy). We analyze this scheme under $\ell_2$ and $\ell_\infty$ loss. By using our information-theoretic framework, we show that the scheme achieves the optimal accuracy-privacy trade-off with optimal communication cost, while matching the performance in the centralized case where data is stored in the central server.