Reviewerly/exHarmony huggingface dataset:
SPTS v2: Single-Point Scene Text Spotting

End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition. Previous methods commonly regard manual annotations such as horizontal rectangles, rotated rectangles, quadrangles, and polygons as a prerequisite, which are much more expensive than using single-point. Our new framework, SPTS v2, allows us to train high-performing text-spotting models using a single-point annotation. SPTS v2 reserves the advantage of the auto-regressive Transformer with an Instance Assignment Decoder (IAD) through sequentially predicting the center points of all text instances inside the same predicting sequence, while with a Parallel Recognition Decoder (PRD) for text recognition in parallel, which significantly reduces the requirement of the length of the sequence. These two decoders share the same parameters and are interactively connected with a simple but effective information transmission process to pass the gradient and information. Comprehensive experiments on various existing benchmark datasets demonstrate the SPTS v2 can outperform previous state-of-the-art single-point text spotters with fewer parameters while achieving 19× faster inference speed. Within the context of our SPTS v2 framework, our experiments suggest a potential preference for single-point representation in scene text spotting when compared to other representations. Such an attempt provides a significant opportunity for scene text spotting applications beyond the realms of existing paradigms.
Preparing the Vuk’uzenzele and ZA-gov-multilingual South African multilingual corpora

This paper introduces two multilingual government themed corpora in various South African languages. The corpora were collected by gathering South African government speeches (ZA-gov-multilingual), as well as the South African Government newspaper (Vuk’uzenzele), that are translated into all 11 South African official languages. The corpora can be used for a myriad of downstream NLP tasks. The corpora were created to allow researchers to study the language used in South African government publications, with a focus on understanding how South African government officials communicate with their constituents. In this paper we highlight the process of gathering, cleaning and making available the corpora. We create parallel sentence corpora for Neural Machine Translation tasks using Language-Agnostic Sentence Representations (LASER) embeddings. With these aligned sentences we then provide NMT benchmarks for 9 indigenous languages by fine-tuning massively multilingual pre-trained language model.
Multi-Scale Efficient Graph-Transformer for Whole Slide Image Classification

The multi-scale information among the whole slide images (WSIs) is essential for cancer diagnosis. Although the existing multi-scale vision Transformer has shown its effectiveness for learning multi-scale image representation, it still cannot work well on the gigapixel WSIs due to their extremely large image sizes. To this end, we propose a novel Multi-scale Efficient Graph-Transformer (MEGT) framework for WSI classification. The key idea of MEGT is to adopt two independent efficient Graph-based Transformer (EGT) branches to process the low-resolution and high-resolution patch embeddings (i.e., tokens in a Transformer) of WSIs, respectively, and then fuse these tokens via a multi-scale feature fusion module (MFFM). Specifically, we design an EGT to efficiently learn the local-global information of patch tokens, which integrates the graph representation into Transformer to capture spatial-related information of WSIs. Meanwhile, we propose a novel MFFM to alleviate the semantic gap among different resolution patches during feature fusion, which creates a non-patch token for each branch as an agent to exchange information with another branch by cross-attention mechanism. In addition, to expedite network training, a new token pruning module is developed in EGT to reduce the redundant tokens. Extensive experiments on both TCGA-RCC and CAMELYON16 datasets demonstrate the effectiveness of the proposed MEGT.
PEMBELAJARAN JOYFULL LEARNING DENGAN PUZZLE GAME BERBASIS EKSPERIMEN: MENINGKATKAN MINAT BELAJAR SISWA MATA PELAJARAN IPA

This study aims to describe the implementation of Joyful Leaning learning with Experiment-based Puzzle Games in increasing student learning interest in Class IV Science at MI Muhammadiyah 2 Kudus accompanied by various factors that support and hinder it. This research is included in the descriptive qualitative research where the type of research is field research. Data will be obtained through interviews, observation and documentation of research subjects, namely teachers and students at MI Muhammadiyah 2 Kudus. The validity of the data obtained will be tested by testing the validity of the data in the form of extending observations, increasing persistence and member checks. The data analysis that will be carried out uses Miles and Huberman's theory. The results of this study indicate that the application of Experiment-based Joyful Learning with Puzzle Games in class IV Science subjects at MI Muhammadiyah 2 Kudus begins with preliminary activities, core activities and closing activities. Joyful Learning Learning with Experiment-based Puzzle Game was able to increase students' interest in learning, namely at the 1st meeting with results (55%: Fairly Good), 2nd meeting with results (60%: Fairly Good), 3rd meeting with results ( 64% : Good), and the 4th meeting with results (66% : Good). The supporting factors for Joyful Learning with experimental-based Puzzle Games are teachers and students, while the inhibiting factors are facilities and infrastructure as well as observational tools and materials. Factors supporting interest in learning for grade IV students in Science Subjects, namely teachers, students, student conditions, class conditions and the inhibiting factors are the lack of parental assistance and appreciation given by outstanding students as well as item numbers 9 and 14 indicators of interest in learning.
Self-supervised depth super-resolution with contrastive multiview pre-training

Many low-level vision tasks, including guided depth super-resolution (GDSR), struggle with the issue of insufficient paired training data. Self-supervised learning is a promising solution, but it remains challenging to upsample depth maps without the explicit supervision of high-resolution target images. To alleviate this problem, we propose a self-supervised depth super-resolution method with contrastive multiview pre-training. Unlike existing contrastive learning methods for classification or segmentation tasks, our strategy can be applied to regression tasks even when trained on a small-scale dataset and can reduce information redundancy by extracting unique features from the guide. Furthermore, we propose a novel mutual modulation scheme that can effectively compute the local spatial correlation between cross-modal features. Exhaustive experiments demonstrate that our method attains superior performance with respect to state-of-the-art GDSR methods and exhibits good generalization to other modalities.
Optimizing the rodeo projection algorithm

The rodeo algorithm has been proposed recently as an efficient method in quantum computing for projection of a given initial state onto a state of fixed energy for systems with discrete spectra. In the initial formulation of the rodeo algorithm, evolution times were chosen randomly via a Gaussian distribution with a fixed standard deviation. In this paper, it is shown that such a random approach for choosing times suffers from exponentially large fluctuations in the suppression of unwanted components: as the number of iterations gets large, the distribution of suppression factors obtained from random selection approaches a log-normal distribution, leading to remarkably large fluctuations. We note that by choosing times intentionally rather than randomly, such fluctuations can be avoided and strict upper bounds on the suppression can be obtained. Moreover, the average suppression using fixed computational cost can be reduced by many orders of magnitude relative to the random algorithm. A key to doing this is to choose times that vary over exponentially many timescales, starting from a modest maximum scale and going down to timescales exponentially smaller.
Encoding Error Correction in an Integrated Photonic Chip

Integrated photonics provides a versatile platform for encoding and processing quantum information. However, the encoded quantum states are sensitive to noise, which limits their capability to perform complicated quantum computations. Here, we use a five-qubit linear cluster state on a silicon photonic chip to implement a quantum error-correction code and demonstrate its capability of identifying and correcting a single-qubit error. The encoded quantum information is reconstructed from a single-qubit error and an average state fidelity of 0.863±0.032 is achieved for different input states. We further extend the scheme to demonstrate a fault-tolerant measurement-based quantum computation (MBQC) on stabilizer formalism that allows us to redo the qubit operation against the failure of the teleportation process. Our work provides a proof-of-concept working prototype of error correction and MBQC in an integrated photonic chip.3 MoreReceived 30 April 2023Accepted 5 September 2023DOI:https://doi.org/10.1103/PRXQuantum.4.030340Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMeasurement-based quantum computingOptical quantum information processingQuantum error correctionQuantum Information, Science & Technology
FOND Planning for Pure-Past Linear Temporal Logic Goals

Recently, Pure-Past Temporal Logic (PPLTL) has proven highly effective in specifying temporally extended goals in deterministic planning domains. In this paper, we show its effectiveness also for fully observable nondeterministic (FOND) planning, both for strong and strong-cyclic plans. We present a notably simple encoding of FOND planning for PPLTL goals into standard FOND planning for final-state goals. The encoding only introduces few fluents (at most linear in the PPLTL goal) without adding any spurious action and allows planners to lazily build the relevant part of the deterministic automaton for the goal formula on-the-fly during the search. We formally prove its correctness, implement it in a tool called Plan4Past, and experimentally show its practical effectiveness.
EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes

The adoption of electronic health records (EHRs) has created opportunities to analyze historical data for predicting clinical outcomes and improving patient care. However, non-standardized data representations and anomalies pose major challenges to the use of EHRs in digital health research. To address these challenges, we have developed EHR-QC, a tool comprising two modules: the data standardization module and the preprocessing module. The data standardization module migrates source EHR data to a standard format using advanced concept mapping techniques, surpassing expert curation in benchmarking analysis. The preprocessing module includes several functions designed specifically to handle healthcare data subtleties. We provide automated detection of data anomalies and solutions to handle those anomalies. We believe that the development and adoption of tools like EHR-QC is critical for advancing digital health. Our ultimate goal is to accelerate clinical research by enabling rapid experimentation with data-driven observational research to generate robust, generalisable biomedical knowledge.
Single-photon scattering from a chain of giant atoms coupled to a one-dimensional waveguide

We investigate coherent single-photon transport in a waveguide quantum electrodynamics structure containing multiple giant atoms. The single-photon scattering amplitudes are solved using a real-space method. The results give rise to a clear picture of the multichannel scattering process. In the case of identical and equally spaced giant atoms in a separate configuration, we also use the transfer-matrix method to express the scattering amplitudes in terms of compact analytical expressions, which allow us to conveniently analyze the properties of the scattering spectra. Based on these theoretical results, we find that the nondipole effects of giant atoms, which are relevant to the design of the setup, can strongly manipulate several types of collective properties of the output fields, including the superradiant phenomenon, the multiple Fano interference, and the photonic band gap. This makes it possible to manipulate the photon transport in a more versatile way than with small atoms. We also make a proposal to probe the topological states of a chain of braided giant atoms by using photon scattering spectra, showing that waveguide quantum electrodynamics systems with giant atoms are ideal platforms to merge topological physics and on-chip quantum optics.
Experimental Work on the Formation of Professionally Significant Values and Incentives of Primary General Education Teachers

Professional and pedagogical preparedness of a teacher of primary general education is theoretical, practical and individual-personal preparedness to solve problems related to the implementation of the State educational standard of school general education of the Kyrgyz Republic of the new generation, covering relevant professional and pedagogical activities (individual and personal qualities, properties, abilities, psycho-emotional stability, goal orientation, etc.). The article is devoted to the problem of the formation of professionally necessary values and motivations of teachers of primary general education in professional and pedagogical activities. The conscious and meaningful attitude of teachers to the needs of transforming their professional and pedagogical activities, their assimilation of the necessary knowledge, abilities, skills and experience in the use of innovative pedagogical and information and digitalization technologies, reflection of their pedagogical activities, individual-personal orientation and motivations are formed through special an organized course-seminar to improve the professional and pedagogical preparedness of teachers. The experimental work carried out within the framework of the personality-oriented component was assessed according to professionally significant values and motivation; the formation in the experimental group has a positive change in this component, which was assessed according to the incentive-value criterion.
Object Detection in Adverse Weather for Autonomous Driving through Data Merging and YOLOv8

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is challenging, wherein it suffers from dynamic objects and continuous environmental changes. The issue grows worse due to interrupting the quality of perception via adverse weather such as snow, rain, fog, night light, sand storms, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous-driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights was collected from training on the individual datasets, their merged versions, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the datasets mentioned earlier and their subsets. The evaluation revealed that using custom datasets for training significantly improved the detection performance compared to the YOLOv8 base weights. Furthermore, using more images through the feature-related data merging technique steadily increased the object detection performance.
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current MMEA algorithms rely on KG-level modality fusion strategies for multi-modal entity representation, which ignores the variations of modality preferences of different entities, thus compromising robustness against noise in modalities such as blurry images and relations. This paper introduces MEAformer, a mlti-modal entity alignment transformer approach for meta modality hybrid, which dynamically predicts the mutual correlation coefficients among modalities for more fine-grained entity-level modality fusion and alignment. Experimental results demonstrate that our model not only achieves SOTA performance in multiple training scenarios, including supervised, unsupervised, iterative, and low-resource settings, but also has a limited number of parameters, efficient runtime, and interpretability. Our code is available at https://github.com/zjukg/MEAformer.
An efficient watermarking algorithm for digital audio data in security applications

Abstract Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor. In this paper, we propose a fast watermarking system for audio signals operating in the hybrid transform domain formed by the fractional Charlier transform (FrCT) and the dual-tree complex wavelet transform (DTCWT). The central idea of the proposed algorithm is to parallelize the intensive and repetitive steps in the audio watermarking system and then implement them simultaneously on the available physical cores on an embedded systems cluster. In order to have a low power consumption and a low-cost cluster with a large number of physical cores, four Raspberry Pis 4B are used where the communication between them is ensured using the Message Passing Interface (MPI). The adopted Raspberry Pi cluster is also characterized by its portability and mobility, which are required in watermarking-based smart city applications. In addition to its resistance to any possible manipulation (intentional or unintentional), high payload capacity, and high imperceptibility, the proposed parallel system presents a temporal improvement of about 70%, 80%, and 90% using 4, 8, and 16 physical cores of the adopted cluster, respectively.
pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python’s iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH & ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).
The Internal Efficiency of the College of Engineering and Information Technology

The study examined the College of Engineering and Information Technology's efficiency. Delivering better educational services within the limits of available resources is considered internal efficiency. It tried to assess the college's social, cultural, and economic subsystems. A survey was employed to gather data. The 150 respondents were college seniors. It received 35 responses from the ag and c &amp; e departments. Forty-five replies came from civil engineering. Seventy-five (75) were from IT. These were chosen at random. Internally, the college's social, cultural, and economic subsystems were ranked and counted. The college's social subsystem is efficient. The teachers are far more capable. The communication flow is much better in college. The company is in a good mood. Each of the four functions is well stated. However, the ruling system is reasonable. CEIT's cultural component also works well. More detailed, achievable, and relevant to the school, community, and country's growth. They are found globally. The courses have been considerably enlarged, upgraded, and streamlined. Diverse instructional methodologies are employed. Co-curricular activities should be adequate, useful, and motivating. The same goes for teaching aids, reading materials, audio-visual equipment, and recorded video lectures. The college's economic structure is moderately efficient. The available resources are heavily exploited to create labs, libraries, and other facilities and train teachers.
Enhancing UAV Visual Landing Recognition with YOLO’s Object Detection by Onboard Edge Computing

A visual camera system combined with the unmanned aerial vehicle (UAV) onboard edge computer should deploy an efficient object detection ability, increase the frame per second rate of the object of interest, and the wide searching ability of the gimbal camera for finding the emergent landing platform and for future reconnaissance area missions. This paper proposes an approach to enhance the visual capabilities of this system by using the You Only Look Once (YOLO)-based object detection (OD) with Tensor RTTM acceleration technique, an automated visual tracking gimbal camera control system, and multithread programing for image transmission to the ground station. With lightweight edge computing (EC), the mean average precision (mAP) was satisfied and we achieved a higher frame per second (FPS) rate via YOLO accelerated with TensorRT for an onboard UAV. The OD compares four YOLO models to recognize objects of interest for landing spots at the home university first. Then, the trained dataset with YOLOv4-tiny was successfully applied to another field with a distance of more than 100 km. The system’s capability to accurately recognize a different landing point in new and unknown environments is demonstrated successfully. The proposed approach substantially reduces the data transmission and processing time to ground stations with automated visual tracking gimbal control, and results in rapid OD and the feasibility of using NVIDIA JetsonTM Xavier NX by deploying YOLOs with more than 35 FPS for the UAV. The enhanced visual landing and future reconnaissance mission capabilities of real-time UAVs were demonstrated.