Reviewerly/exHarmony huggingface dataset:
Corrigendum: How do circadian rhythms and neural synchrony shape networked cooperation?

In the published article, the reference for 12 was incorrectly written as “Müller V, Saarikivi K, Falcon M, Makkonen T, Martikainen S, Putkinen V, et al. Interbrain synchronization occurs without physical co-presence during cooperative online gaming. Neuropsychologia (2022) 174:108316. doi:10.1016/j.neuropsychologia.2022.108316.”. It should be “Wikström V, Saarikivi K, Falcon M, Makkonen T, Martikainen S, Putkinen V, et al. Inter-brain synchronization occurs without physical co-presence during cooperative online gaming. Neuropsychologia (2022) 174:108316. doi:10.1016/j.neuropsychologia.2022.108316”.The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Decision-Making of Phosphogypsum Treatment Based on Deepfm Recommendition System

The selection of a phosphogypsum yard treatment scheme has always been one of the key issues for phosphate fertilizer manufacturers in production and service work. The existing strategy is generally to organize expert scoring to complete the evaluation. However, the evaluation results are too subjective and often have significant uncertainty. Therefore, this paper takes intelligent recommendation systems as the core means to solve the uncertainty of the output results of existing methods. Thus, a treatment scheme recommendation system based on the DeepFM model is designed. For the scheme selection, we propose an expert scoring approach including scoring and confidence. Subsequently, a scheme recommendation strategy is proposed. In the application of real cases, it can be seen that the method proposed in this paper has obvious advantages in the recommendation of a phosphogypsum yard treatment scheme by comparing three common methods. That is, the scheme arrangement obtained through our method has a greater degree of differentiation, making it easier for decision-makers to select the best scheme.
Development of an Inertial Measurement Unit based Finger Flexion Measurement System for Functional Electrical Stimulation based Finger Assistive System

Purpose -This research study examines the use of Inertial Measurement Unit (IMU) sensors for measuring finger range of motion (ROM) as an initial phase towards the creation of an effective feedback system for FES applications.Method -In this paper, a mounting system for IMU sensors designed for stroke patients has been developed.A calibration technique and an orientation estimation algorithm for 2038 IMU sensors was developed and validated using Single IMU data validation.Finally, a joint angle measurement system is developed and validated using motion capture analysis.Results -For the Single IMU data validation, the resulting RMSE of the Euler angles pitch, roll, and yaw are 0.96°, 0.76°, and 2°, respectively.The RMSE of the developed IMU-based joint angle measurement system is at 5.3° with a Pearson Correlation Coefficient of 0.9979. Conclusion -The developed calibration procedure and orientation estimation algorithm for the IMU sensors have great performance, as indicated by the single IMU data validation results, while the Joint Angle Measurement Validation results shows that the method is already sufficient for FES applications.The proposed measurement system has the potential for effectively monitoring finger rehabilitation progress for FES applications.Recommendations -There are systematic offsets observed in the validation data results, this can be caused by the imperfect attachment of the markers and can be fixed by improving the motion capture analysis calibration.
Several Categories of Large Language Models (LLMs): A Short Survey

Large Language Models(LLMs)have become effective tools for natural language processing and have been used in many different fields. This essay offers a succinct summary of various LLM subcategories. The survey emphasizes recent developments and efforts made for various LLM kinds, including task-based financial LLMs, multilingual language LLMs, biomedical and clinical LLMs, vision language LLMs, and code language models. The survey gives a general summary of the methods, attributes, datasets, transformer models, and comparison metrics applied in each category of LLMs. Furthermore, it highlights unresolved problems in the field of developing chatbots and virtual assistants, such as boosting natural language processing, enhancing chatbot intelligence, and resolving moral and legal dilemmas. The purpose of this study is to provide readers, developers, academics, and users interested in LLM-based chatbots and virtual intelligent assistant technologies with useful information and future directions.
SEPULUH UNSUR PENGOPTIMAL METODE DRIL DAN PENGUASAAN HAFALAN BAHASA MANDARIN KOMPREHENSIF

This study discusses the advantages and optimization of the drill method in the memorization process for mastery of Mandarin. The drill method that does not require additional media, and only relies on the five human senses is an advantage that deserves to be maximized. This study aims to offer ten elements that can encourage better results in the process of memorizing Mandarin with the drill method. By using qualitative research methods, elements, such as, relying on continuity, familiarizing with Mandarin language culture, determining the appropriate mindset for improving mastery of Mandarin memorization with the dril method, are a summary of the results in the elements offered and in the discussion will be presented with in-depth elaboration.
An Effective Authentication Scheme for Videos using Invisible Watermark

In this paper, a novel reversible keyless invisible authentication method for video piracy protection which uses randomized pixel for embedding real identity information is proposed. Randomization at two different levels is not considered in any of the existing methods. Videos, with this proposed embedding of authentication information, ensure minimum distortions and maximum resistance to the removal of authentication information. Keyless invisible embedding process increases the security and reduces the cost. This proposed approach enhances the randomization of the specific pixels where authentication information will be stored in a frame and the location of such modified pixels is stored in an immediate next frame. Each pair is identified with an embedded random number. Modified Least Significant Bit (LSB) based invisible watermark mechanism is used to embed the bits which are cost effective due to simplicity and which can withstand statistical attacks. During extraction, frame with pixel locations is used. The extracted information will be compared to assure the authenticity of video. The Euclidean distance, PSNR, MSE, SSIM proved that the proposed method can withstand visual attacks. StirMark test proved that the proposed algorithm is highly robust.
Separable Reversible Data Hiding for Encrypted 3D Mesh Models Based on Octree Subdivision and Multi-MSB Prediction

Reversible data hiding in encrypted domain (RDH-ED) can perform data encryption to fulfill the privacy protection of original media and embed additional data for covert communication or access control. However, current researches are focusing on the encrypted images, and little attention is paid to encrypted three-dimensional (3D) models. In this paper, a high capacity separable RDH-ED method for encrypted 3D models is proposed based on octree spatial subdivision and multiple most significant bit (multi-MSB) prediction. Firstly, a 3D model is adaptively subdivided into non-overlapping subblocks by octree spatial subdivision, and the vertices in a subblock are classified into embedding set and reference set. To better utilize the spatial correlation of the two sets, the multi-MSB prediction error of the embedding set is used to embed the additional data, and the reference set is used to losslessly recover the embedded set. Then, the model is encrypted by a specified encrypted algorithm. At last, additional data is embedded into the reserved embedding room by multi-MSB substitution. Experimental results show that the proposed method can achieve a higher embedding capacity compared with the state-of-the-art methods, and guarantee the lossless recovery of the 3D model.
ActivBench: Leveraging Human Activity Inference from Smartphone Sensors for Human Computer Interactions

Human activity recognition (HAR) from smartphone sensors has gained significant attention due to its potential to enhance user experience (UX) and human computer interaction (HCI) in various domains, HAR can enable personalized, context-aware, and adaptive interfaces that improve accessibility and promote health and wellness in various applications such as healthcare, smart homes, fitness tracking, and context-aware systems. However, evaluating the performance of different machine learning (ML) algorithms on activity recognition tasks remains challenging, primarily due to the lack of standardized benchmark datasets and evaluation protocols. In this paper, we presented ActivBench, an end-to-end computational intelligence benchmark designed to facilitate the evaluation and comparison of ML algorithms for human activity inference from smartphone sensors. We addressed the challenges in benchmarking activity recognition systems by providing a unified evaluation protocol and standardized performance metrics. Through extensive experiments using various state-of-the-art algorithms, we demonstrated the effectiveness of ActivBench in assessing the strengths and limitations of different approaches. The benchmark results provide valuable insights into the strengths and limitations of different algorithms, facilitating the development of robust and accurate activity recognition systems that can enhance human computer interaction in various applications. ActivBench is serving as a valuable resource for researchers and practitioners in human activity recognition and human-computer interaction, enabling fair comparisons and fostering advancements in the field. It also serves as a catalyst for advancements in the field, enabling the exploration of novel algorithms, feature engineering techniques, and sensor modalities.
A Research Perspective on Data Management Techniques for Federated Cloud Environment

Cloud computing has given a large scope of improvement in processing, storage and retrieval of data that is generated in huge amount from devices and users. Heterogenous devices and users generates the multidisciplinary data that needs to take care for easy and efficient storage and fast retrieval by maintaining quality and service level agreements. By just storing the data in cloud will not full fill the user requirements, the data management techniques has to be applied so that data adaptiveness and proactiveness characteristics are upheld. To manage the effectiveness of entire eco system a middleware must be there in between users and cloud service providers. Middleware has set of events and trigger based policies that will act on generated data to intermediate users and cloud service providers. For cloud service providers to deliver an efficient utilization of resources is one of the major issues and has scope of improvement in the federation of cloud service providers to fulfill user’s dynamic demands. Along with providing adaptiveness of data management in the middleware layer is challenging. In this paper, the policies of middleware for adaptive data management have been reviewed extensively. The main objectives of middleware are also discussed to accomplish high throughput of cloud service providers by means of federation and qualitative data management by means of adaptiveness and proactiveness. The cloud federation techniques have been studied thoroughly along with the pros and cons of it. Also, the strategies to do management of data has been exponentially explored.
Assessment of open data kit mobile technology adoption to enhance reporting of supportive supervision conducted for oral poliovirus vaccine supplementary immunization activities in Nigeria, March 2017-February 2020

in Nigeria, supportive supervision of Supplementary Immunization Activities (SIA) is a quality improvement strategy for providing support to vaccination teams administering the poliovirus vaccines to children under 5 years of age. Supervision activities were initially reported in paper forms. This had significant limitations, which led to Open Data Kit (ODK) technology being adopted in March 2017. A review was conducted to assess the impact of ODK for supervision reporting in place of paper forms.issues with paper-based reporting and the benefits of ODK were recounted. We determined the average utilization of ODK per polio SIA rounds and assessed the supervision coverage over time based on the proportion of local government areas with ODK geolocation data per round.a total of 17 problematic issues were identified with paper-based reporting, and ODK addressed all the issues. Open Data Kit-based supervision reports increased from 3,125 in March 2017 to 51,060 in February 2020. Average ODK submissions for national rounds increased from 84 in March 2017 to 459 in February 2020 and for sub-national rounds increased from 533 in July 2017 to 1,596 in October 2019. Supportive supervision coverage improved from 42.5% in March 2017 to 97% in February 2020.the use of digital technologies in public health has comparative advantages over paper forms, and the adoption of ODK for supervision reporting during polio SIAs in Nigeria experienced the advantages. The visibility and coverage of supportive supervision improved, consequentially contributing to the improved quality of polio SIAs.
Three-photon time-bin entanglement generation using an optical switch

We realized the first three-photon time-bin GHZ states using a 2×2 optical switch as a time-dependent beam splitter to entangle a photon from a weak coherent light and a photon from a time-bin entangled photon pair.
The reform and innovation of vocal music teaching model for music performance majors in universities

Vocal music teaching for music performance majors has always been an important component of music education in universities.With the continuous development of China's economy and the increasing prosperity of culture, people's demand for music art education is also increasing.In this context, vocal music teaching for music performance majors in universities is facing new situations and challenges.The traditional teaching mode emphasizes skill and technical training, emphasizing the beauty and expressive power of sound, but often neglects the cultivation of comprehensive qualities and innovative abilities, and cannot meet the diverse needs of music performance and stage.Therefore, the vocal teaching of music performance majors in universities needs to be reformed and innovated to meet the needs of the times and society.
Application of Faceted Vocabularies to Cataloging of Textbooks

This article discusses the practical application of faceted vocabularies to the cataloging of textbooks. Consistent application of faceted vocabularies, specifically Library of Congress Genre/Form Terms for Library and Archival Materials (LCGFT) and Library of Congress Demographic Group Terms (LCDGT), would enhance the discovery of these resources. Alternatives to special cases in Subject Heading Manual H 2187 are proposed. A case study demonstrating the application of LCDGT is provided. Figures illustrate the results of the proposed best practices. The article includes four tables that are designed to streamline term assignments. Consistent cataloging of genre and audience prepares legacy records for future automated enhancement.
Teacher's Perception of Early Writing as a Readiness Skill in Kindergarten

&lt;em&gt;Early writing readiness is one of the basic abilities needed in preparation for elementary school. This study aims to obtain the perceptions of teachers regarding early writing readiness in kindergarten. This research was conducted through a survey to support the development of learning media products with a sample of 26 kindergarten teachers in Rengas Village. The results showed that most teachers used the assignment method of 38.4%, the lecture method of 34.6% the demonstration method of 23.1%. Then the greatest percentage of 42.3% is the perception of teachers using children's worksheet media, and also the largest percentage of teachers' perceptions using writing learning resources in the form of pencils or crayons by 50%, An analysis of learning activities at the beginning of class writing it is known that the largest percentage of 61.5% of activities carried out by playing. Based on the results of the survey above, the researcher saw that optimization of children's writing skills was still needed by the way the teacher made preparations including the selection of strategies, methods, and learning media to be used. Writing activities carried out in a fun way not only make children skilled in writing, but children will be interested and happy while writing.&lt;/em&gt;
Improving the Transferability of Adversarial Attacks on Face Recognition With Beneficial Perturbation Feature Augmentation

Face recognition (FR) models can be easily fooled by adversarial examples, which are crafted by adding imperceptible perturbations on benign face images. The existence of adversarial face examples poses a great threat to the security of society. To build a more sustainable digital nation, in this article, we improve the transferability of adversarial face examples to expose more blind spots of the existing FR models. Though generating hard samples has shown its effectiveness in improving the generalization of models in training tasks, the effectiveness of using this idea to improve the transferability of adversarial face examples remains unexplored. To this end, based on the property of hard samples and the symmetry between training tasks and adversarial attack tasks, we propose the concept of hard models, which have similar effects as hard samples for adversarial attack tasks. Using the concept of hard models, we propose a novel attack method called beneficial perturbation feature augmentation attack (BPFA), which reduces the overfitting of adversarial examples to surrogate FR models by constantly generating new hard models to craft the adversarial examples. Specifically, in the backpropagation, BPFA records the gradients on preselected feature maps and uses the gradient on the input image to craft the adversarial example. In the next forward propagation, BPFA leverages the recorded gradients to add beneficial perturbations on their corresponding feature maps to increase the loss. Extensive experiments demonstrate that BPFA can significantly boost the transferability of adversarial attacks on FR.
Utilizing Greedy Nature for Multimodal Conditional Image Synthesis in Transformers

Multimodal Conditional Image Synthesis(MCIS) aims to generate images according to different modalities input and their combination, which allows users to describe their requirements in complementary ways, e.g. segmentation for shapes and text for attributes. Despite satisfying results in MCIS, a non-trivial issue is neglected. Some modalities are fully optimized and dominate the generation, while other modalities are sub-optimized and fail to contribute their complementary information. We coin this phenomenon as Modality Bias. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Our analysis reveals that generative models own greedy nature</i> . Specifically, the modality that shares less semantic gap with the synthesized modality will be greedily incorporated and thus takes a larger proportion in synthesis. The main idea of previous works in Modality Bias is to punish the greedy nature, which hurts the performance of dominant modalities and impedes their contribution to multimodal synthesis. Instead, we propose to utilize the greedy nature by setting dominant modalities as guidance for sub-optimized modalities through coordinated feature space, named Coordinated Knowledge Mining. Afterwards, improved uni-modalities are aggregated by fusing coordinated features to further boost the performance of multimodal image synthesis, called Coordinated Knowledge Fusion. Extensive experiments prove that our method not only increases uni-modal performance by a large margin, but also promotes multimodal image synthesis by fully utilizing complementary information from different modalities.
Symmetric Generation of <i>J</i> <sub>2</sub> on 32 Letters

We give a computer-free proof that J 2 is isomorphic to the progenitor 2 *32 : (2 1+4 : A 5 ) factored by two relations, one of length 3 and and one of length 6, in the symmetric generators.
Prediction of La Niña Events Based on BP Neural Network Model

As an extreme weather phenomenon, La Niña event will cause low water temperature in the equatorial eastern Pacific Ocean and abnormal longitudinal circulation in East Asia, resulting in widespread and continuous abnormal cooling of sea surface temperature in the eastern and central equatorial Pacific Ocean, which will cause frequent precipitation and flooding in the north and high temperature and low rainfall and frequent droughts in the south[1]. Therefore, it is particularly important to assess the damage and propose response strategies[2]. For the main countries involved in the global triple La Niña event, this paper selects four representative countries located in different regions to analyze, through descriptive analysis and graphical visualization, and selects the relevant index data of the last three years: monthly average maximum and minimum temperatures, monthly average precipitation rate, monthly average precipitation days, monthly average wind speed and monthly average water temperature, and finds that New York City and Brazil were affected by the triple La Niña event at the same time and were affected to a higher degree, and the temperature changes in Malasila and Jakara were stable, and the temperature effects brought by the triple La Niña event were smaller[3]. Then, a BP neural network model was established to predict the probability of future La Niña events in each region by modeling pressure, temperature, and salinity data, and it was found that a total of 110 regions would experience La Niña events in the future, with a general probability of 55%-60%..
Adaptively weighted difference model of anisotropic and isotropic total variation for image denoising

This paper proposes a novel nonconvex regularization functional by using an adaptively weighted difference model of anisotropic and isotropic total variation.By choosing the weights adaptively at each pixel, our model can enhance the anisotropic diffusion so as to achieve robust image recovery.Regarding to numerical implementations, we express the proposed model into a saddle point problem with the help of a dual formulation of the total variation, followed by a primal dual method to find a model solution.Numerical experiments demonstrate that the proposed approach is superior over several gradient-based methods for image denoising in terms of both visual appearance and quantitative metrics of signal noise ratio (SNR) and structural similarity index measure (SSIM).