Reviewerly/exHarmony huggingface dataset:
Improving Students Reading Comprehension Through Probing-Prompting Technique at the Eighth Grade Of SMP Negeri 4 Pancarijang

The objective of the research are find out whether or not the use of Probing-Pompting Technique improves te students reading comprehension and student interest at the eighth grade of SMP Negeri 4 Pancarijang. His research employed pre-experimental method with on group pre-test and post-test design. The population was Theeighth grade student of SMP Negeri 4 Pancarijang in academic 2014-2015. Total number of population was 66 student consist of 3 classes and one class (VIII.2) was taken as sample by using cluster sampling techniquetotal of sample 20 students’. The research instrument are reading test an questionnaire. The result this research showed that the pre-test got the mean score (56.00) while the post-test goat mean score (69.00). This showed that there was significant difference between pretest and posttest. The result of the t-test value (5.378) was higher than t-table (2.093). This mean that H1was accepted. The researcher concluded that teaching reading comprehension by using probing-prompting increased the students’ ability in reading comprehension of SMP Negeri 4 Pancarijangand the mean score of the questionnaire was 84.75 categorized as a strongly interested. The researcher concluded that teaching reading comprehension by using probing-prompting improve the students’ ability in reading comprehension at the eighth grade SMP negei 4 Pancarijang.
An adaptive GLNPSO method for inventory replenishment supply chain problem with multiple-warehouse policy and budget consideration

This paper presents an efficient computation tool for a multiple-warehouse inventory replenishment lot-sizing problem with supplier selection, quantity discounts, and budget constraint. The cost model of the considered inventory replenishment issue is formulated as a mixed-integer nonlinear programming (MINLP) problem. With the proposed MINLP problem being an NP-hard problem, the MINLP cost model was optimized by using a novel global–local neighbor particle swarm optimization algorithm (TPAGLNPSO), inspired by two-phase self-adaptive inertia weight and improved constraint-handling technique. Apart from introducing the velocity index to switch the search scheme from exploration to exploitation, the constraint-handling technique was utilized to guide and guarantee the particle search toward undiscovered regions in the solution space. The stability analysis for the proposed novel TPAGLNPSO was also conducted using the stochastic process theorem. The corresponding parameter selection ranges were obtained based on the convergent conditions. Finally, eight industry cases were provided as examples to evaluate the performance of the proposed TPAGLNPSO method, after which a sensitivity analysis for the MINLP cost model was performed. The results demonstrate the competitive performance of the proposed TPAGLNPSO optimizer in terms of solution quality (with near-optimal results) than the optimal solutions obtained from Lingo software and computational effort (CPU time). Such findings can provide practitioners marginal insights into the effective management of the supply chain inventory replenishment problem.
FBG strain monitoring data denoising in wind turbine blades based on parameter-optimized variational mode decomposition method

Herein, we aimed to solve the problem of difficulty in filtering the noise components in the monitoring of strain on wind turbine blades using fiber bragg grating, a denoising method based on parameter-optimized variational mode decomposition (VMD) is proposed. This method uses the minimum envelope entropy as the fitness function and the slime mould algorithm for self-adaptive optimization to find the optimal combination of modal decomposition components K and the quadratic penalty factor α of VMD. The optimized VMD was used to decompose the strain data of wind turbine blades over time into K intrinsic mode components, and the noise mode was removed using the sample entropy to obtain the effective signal. The proposed method was compared to ensemble empirical mode decomposition and complementary ensemble empirical mode decomposition using the simulated signals and engineering data. The experimental results show that the proposed method can effectively remove noise from the strain data of wind turbine blades and has better denoising performance than the other two methods, which provides a reliable basis for analyzing the strain data of wind turbine blades.
Search in Archival Facsimile Documents for Digital History

Recent advances in text digitization and processing have opened up many possibilities for historical archives to be processed and digitized in an efficient and automated manner. Processing steps, also involving language detection, optical character recognition (OCR), named entity recognition (NER), recognition error detection, and automated or manual correction can result in digitized archives providing both high-quality facsimile representations of original document scans and extracted text metadata close to the original text in a machine-friendly format. Exploration of digitally enhanced archives is an important step forward in the future workflow of archivists and historians alike. After analysing the requirements of these users, we propose a concept for dynamically generating retrieval-relevant facsimile image snippets. This work demonstrates a Human-in-the-Loop retrieval and research workflow based on these methods by providing a search user interface prototype geared towards intuitively exploring topics across a multilingual historical facsimile archive corpus.
Better Together: Combining Different Handwriting Input Sources Improves Dementia Screening

Alzheimer's disease (AD) is a cognitive disorder, marked by memory loss and impaired reasoning, that requires early detection methods to better manage and potentially slow down the disease's progression. Recent advances in machine learning have offered new possibilities for AD detection using handwriting analysis, however previous work has considered only one type of input source, e.g. clock or pentagon drawings. Here we propose to develop an efficient method for detecting AD's early symptoms using Deep Feature Concatenation (DFC) models considering multiple handwriting sources: pentagon drawings, self-reported sentences, and signatures. Substantial performance improvements were observed when considering all input sources together with data augmentation techniques. For example, classification accuracy increased from 60% (best model, without data augmentation) to 80% (DFC and data augmentation). Our findings show that the use of diverse input sources can lead to an efficient and cost-effective method for early AD detection. Looking forward into the future, our study highlights the potential of DFC in supporting home-based healthcare diagnoses which is a crucial step in integrating artificial intelligence into healthcare practices.
Quantum Computer Simulations at Warp Speed: Assessing the Impact of GPU Acceleration: A Case Study with IBM Qiskit Aer, Nvidia Thrust &amp; cuQuantum

Quantum computer simulators are crucial for the development of quantum computing. This work investigates GPU and multi-GPU systems' suitability and performance impact on a widely used simulation tool – the state vector simulator Qiskit Aer. In particular, we evaluate the performance of both Qiskit's default Nvidia Thrust backend and the recent Nvidia cuQuantum backend on Nvidia A100 GPUs. We provide a benchmark suite of representative quantum applications for characterization. For simulations with a large number of qubits, the two GPU backends can provide up to 14× speedup over the CPU backend, with Nvidia cuQuantum providing a further 1.5–3× speedup over the default Thrust backend. Our evaluation on a single GPU identifies the most important functions in Nvidia Thrust and cuQuantum for different quantum applications and their compute and memory bottlenecks. We also evaluate the gate fusion and cache-blocking optimizations on different quantum applications. Finally, we evaluate large-number qubit quantum applications on multi-GPU and identify data movement between host and GPU as the limiting factor for the performance.
Why We Need a Reference Architecture for Research Data

There is little doubt that we have entered an era where data underpins modern science and research in general. In support of this, numerous infrastructures have been designed and built, ranging from proprietary on-prem systems through to distributed commercial clouds. Such implementations provide a range of functions during the research lifecycle from provisioning and cataloguing data assets through to storing and presenting data to computing platforms. In this paper we analyse the underlying principles of such systems and develop a high-level Research Data Reference Architecture (RDRA). Specifically, we identify eight key features of a RDRA that can guide the design, construction, and procurement of implementations without mandating any domain, approach, technical solution, or product choice. As a result, it allows implementers to make local and commercial decisions while still meeting the core requirements of a research data management platform. The intended audience is teams charged with implementing infrastructure in research organizations.
Synopsis of Cybersecurity and Risks Associated with Cybercrime to Susceptible and Blameless Global Citizenries

Cybercrime, a collapse of the world economy, social and economic insecurity are all continual threats to the global society. Our interconnected, global civilization will soon be affected negatively and economically by the rapidly evolving cybersecurity challenge. Leaders of both developed and developing countries must be ready to launch a defence strategy against those who carry out cybersecurity attacks. In the past, local, national, and worldwide communications relied on radio networks, conventional mail systems, and fax machines to send data and documents. This paper critically performed a synopsis of cybersecurity and the risks associated with cybercrime to susceptible and blameless global citizenries. The world's transmission and communication processes are now controlled by the culture of high definition television, broadband direct connection, electronic mail, Internet access, and cyber-technology. All segments of the global innocent population are constantly at risk as a result of the excessive rate of advancement in cyber-technology. Attacks on blameless people in Paris and San Bernardino serve as a reminder to leaders of developed and developing countries to develop cyber security-savvy workforces in order to avert future catastrophes. Cybercrime poses a grave threat to the physical wellbeing of innocent members of the society worldwide. Individuals, governmental bodies, businesses, financial institutions, and higher education systems are all at risk from cybercriminals operating in isolation. The establishment of a unified front to cooperate via confidence, dedication, and commitment must be among the most credible and reliable approaches to stop cybercriminals today and in the future. All facets of international organizations and educational settings are being affected by cybersecurity concerns. The exchange of ideas, values, and beliefs as well as the teaching and learning process are all parts of the culture of the educational enterprise. Presidents, vice chancellors, professors, instructors, and other educators are urged to develop a plan of action to safeguard the infrastructure, critical data, and other assets of the entire educational system against cyber-attacks. The unanticipated events in Paris, France, and San Bernardino, California, in the United States, are a persistent trend and a decisive step that empowers national and higher education leaders, professors, instructors, and allied educators to put in place a well-organized action plan, efficient security measures, and workforces to neutralize perpetrators' malign intent to destroy all the institutions of human civilization. According to the research findings, a Global Collaborative Partnership (GCP) should be established with policies and directives to offer steady security to vulnerable global citizens. Additionally, as part of the GCP ground plan, software designers must have preventative built-in capabilities to deter cybercriminals.
Semantic‐aware two‐phase test case prioritization for continuous integration

Summary Continuous integration (CI) is a widely applied development practice to allow frequent integration of software changes, detecting early faults. However, extremely frequent builds consume amounts of time and resources in such a scenario. It is quite challenging for existing test case prioritization (TCP) to address this issue due to the time‐consuming information collection (e.g. test coverage) or inaccurately modelling code semantics to result in the unsatisfied prioritization. In this paper, we propose a semantic‐aware two‐phase TCP framework, named SatTCP, which combines the coarse‐grained filtering and fine‐grained prioritization to perform the precise TCP with low time costs for CI. It consists of three parts: (1) code representation, parsing the programme changes and test cases to obtain the code change and test case representations; (2) coarse‐grained filtering, conducting the preliminary ranking and filtering of test cases based on information retrieval; and (3) fine‐grained prioritization, training a pretrained Siamese language model based on the filtered test set to further sort the test cases via semantic similarity. We evaluate SatTCP on a large‐scale, real‐world dataset with cross‐project validation from fault detection efficiency and time costs and compare it with five baselines. The results show that SatTCP outperforms all baselines by 6.3%–45.6% for mean average percentage of fault detected per cost (APFDc), representing an obvious upward trend as the project scale increases. Meanwhile, SatTCP can reduce the real CI testing by 71.4%, outperforming the best baseline by 17.2% for time costs on average. Furthermore, we discuss the impact of different configurations, flaky tests and hybrid techniques on the performance of SatTCP, respectively.
Mixture extreme learning machine algorithm for robust regression

The extreme learning machine (ELM) is a well-known approach for training single hidden layer feedforward neural networks (SLFNs) in machine learning. However, ELM is most effective when used for regression on datasets with simple Gaussian distributed error because it often employs a squared loss in its objective function. In contrast, real-world data is often collected from unpredictable and diverse contexts, which may contain complex noise that cannot be characterized by a single distribution. To address this challenge, we propose a robust mixture ELM algorithm, called Mixture-ELM, that enhances modeling capability and resilience to both Gaussian and non-Gaussian noise. The Mixture-ELM algorithm uses an adjusted objective function that blends Gaussian and Laplacian distributions to approximate any continuous distribution and match the noise. The Gaussian mixture accurately models the residual distribution, while the inclusion of the Laplacian distribution addresses the limitations of the Gaussian distribution in identifying outliers. We derive a solution to the novel objective function using the expectation maximization (EM) and iteratively reweighted least squares (IRLS) algorithms. We evaluate the effectiveness of the algorithm through numerical simulation and experiments on benchmark datasets, thereby demonstrating its superiority over other state-of-the-art machine learning methods in terms of robustness and generalization.
Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Vision gets obscured in adverse weather conditions, such as heavy downpours, dense fog, haze, snowfall, etc., which increase the number of road accidents yearly. Modern methodologies are being developed at various academics and laboratories to enhance visibility in such adverse weather with the help of technologies. We review different dehazing techniques in many applications, such as outdoor surveillance, underwater navigation, intelligent transportation systems, object detection, etc. Dehazing is achieved in four primary steps: the capture of hazy images, estimation of atmospheric light with transmission map, image enhancement, and restoration. These four dehazing procedures allow for a step-by-step method for resolving the complicated haze removal issue. Furthermore, it also explores the limitations of existing deep learning-based methods with the available datasets and the challenges of the algorithms for enhancing visibility in adverse weather. Reviewed techniques reveal gaps in the case of remote sensing, satellite, and telescopic imaging. In the experimental analysis of various image dehazing approaches, one can learn the effectiveness of each phase in the image dehazing process and create more effective dehazing techniques.
Deep neural network based downlink power domain multi-user NOMA-OFDM signal detection

Abstract Non-orthogonal multiple access-based orthogonal frequency division multiplexing (NOMA-OFDM) is a promising waveform-based multiple access technology for future wireless networks for multiple-user symbol transmission (MUST) in the same time-frequency resource block. However, it differs in the power domain, enhancing its spectrum efficiency. This is essential to meet the high data rate required for ever-increasing connected devices and the Internet of Things (IoT). However, NOMA-OFDM systems suffer from impairments such as imperfect successive interference cancellation (SIC) caused by channel impairments like channel fading, carrier frequency offset, and non-linearity caused by non-linear power amplifiers. This paper identifies and addresses the key impairments mentioned in the NOMA-OFDM system and proposes DNN-based estimation in offline training and detection in online testing for downlink power domain multi-user NOMA-OFDM symbols. The reported 2 dB SNR gain compared to least square-SIC/minimum mean square error SIC-based methods is a significant finding and demonstrates the robustness of the proposed DNN-aided approach against various channel impairments.
Joint enhancement and denoising method using non-subsampled shearlet transform for low-light images

Many algorithms to enhance low-light images have been proposed. They often assume that low-light images are clean. However, images acquired in low-light environments often contain noise. Consequently, those enhancement algorithms often simultaneously amplify the noise. For the purpose of overcoming the above-mentioned shortcoming, we propose a framework of joint enhancement and denoising. It is accomplished using the non-subsampled shearlet transform with adaptive soft-threshold shrinkage. The multi-scale feature of the proposed model can better represent edge textures in an image and decomposes an image into two parts of low frequency and high frequency for separate processing. Experimental results show that, compared with state-of-the-art methods, the proposed algorithm can achieve better quality in terms of objective assessment and subjective assessment.
Learning-based Homography Matrix Optimization for Dual-fisheye Video Stitching

In this paper, we propose a novel feature-based video stitching algorithm for stitching back-to-back fisheye camera videos into one omnidirectional video in a video live streaming scenario. Our main contribution lies in a learning-based approach that refines the homography matrix in an online manner via gradient descent. The homography matrix is updated by training on a rolling dataset of feature points that are extracted and matched as new video frames are captured. Experimental results show that our method can create stitched images that better align matching features with lower mean squared error (MSE) than traditional feature-based stitching method. Furthermore, compared to vendor-supplied software (VUZE VR Studio) that uses calibration-based stitching, our method also produces visibly better results.
RTCSR: Zero-latency Aware Super-resolution for WebRTC Mobile Video Streaming

The real-time video streaming market is currently undergoing rapid development. Various existing methods aim to optimize network bandwidth utilization by adjusting the sending rate and implementing forward error correction techniques. Meanwhile, other methods enhance the quality of video streams using frame-level super-resolution (SR), leveraging the computational power on the receiving end, which often consumes a lot of time and increases the end-to-end delay of a real-time video stream. To this end, we proposed a real-time video stream enhancement scheme based on patch-level super-resolution. The approach adaptively selects the locations of super-resolution patches based on the time interval between video frame receiving and rendering. Additionally, the quality benefit of SR patches continuously would be transferred to subsequent video frames during the decoding and upscaling process. We evaluate the performance of our scheme on several commercial mobile devices with real network traces and construct an open-source real-time video streaming system. Overall, our scheme can improve the PSNR of real-time video streaming by 2.3dB.
Resource-Efficient and Privacy-Preserving Edge for Augmented Reality

This position paper describes three directions to support network-enabled, interactive, DL-powered augmented reality experience. The discussion is based on a generic sensing-understanding-rendering pipeline. This paper envisions a platform called SpeAR, encompassing all the proposed techniques, that can support multi-user AR experience in a resource-efficient and privacy-preserving manner.
Mobile Volumetric Video Streaming System through Implicit Neural Representation

Volumetric video (VV) emerges as a new video paradigm with six degree-of-freedom (DoF) immersive viewing experience. Most existing VV systems focus on the point cloud (PtCl)-based architecture, which is however far from effective due to the huge video size, unrealistic color variations, and specialized player platform requirement. The recent advance of implicit neural representations (INR) such as NeRF brings great opportunities to VV given its potential in creating photorealistic 3D appearances and lighting consistency. However, there still exist arduous challenges in many aspects such as model training, display rendering, streaming optimization, and system implementation. To address the above challenges, we develop NeRVo, an INR-based VV representation for mobile VV. NeRVo improves the training and rendering speed over 300x and 1000x with photorealism, mobile compatibility, and desirable datarates compared to NeRF. We adopt NeRVo as a building block, design and implement a holistic INR-enhanced VV streaming system VoINR.
Understanding the Impact of Wi-Fi Configuration on Volumetric Video Streaming Applications

Emerging multimedia applications often use a wireless LAN (Wi-Fi) infrastructure to stream content. These Wi-Fi deployments vary vastly in terms of their system configurations. In this paper, we take a step toward characterizing the Quality of Experience (QoE) of volumetric video streaming over an enterprise-grade Wi-Fi network to: (i) understand the impact of Wi-Fi control parameters on user QoE, (ii) analyze the relation between Quality of Service (QoS) metrics of Wi-Fi networks and application QoE, and (iii) compare the QoE of volumetric video streaming to traditional 2D video applications. We find that Wi-Fi configuration parameters such as channel width, radio interface, access category, and priority queues are important for optimizing Wi-Fi networks for streaming immersive videos.