Reviewerly/exHarmony huggingface dataset:
Augmented Deep Learning-Based Motion Estimation for Extreme Pose Tracking of Awake Rodents

Motion-compensated PET imaging of awake rodents is an exciting experimental regime in molecular imaging, but it relies heavily on the challenging task of sampling an animal’s motion accurately. This work investigates the feasibility of deep neural-network (DNN) based motion tracking for this application. In particular, we address the situation of estimating extreme poses (i.e. edge cases), which can occur frequently in awake animal studies, but where there are likely to be few, if any, ground-truth poses for proper training of a model. A conditional generative adversarial network (GAN) with pix2pix architecture was used to generate realistic synthetic stereo views for arbitrary poses of the animal. When combined with a DNN-based regression approach to estimate six rigid-body motion parameters, we show that synthetic augmentation provides significant performance gains in accuracy: RMSE 0.23° and 0.19 mm for rotation and translation estimation, respectively. Our advanced augmented DL-based rigid-body motion estimation from multi-view stereo appears promising as a solution for extreme pose tracking.
Assessment of Preparedness of Academic Libraries towards the Use and Adoption of Robotic Technologies in Public Universities in Bayelsa State

This study investigated assessment of preparedness of academic libraries towards the use and adoption of Robotic technologies in public university libraries in Bayelsa State. 3 research questions and 3 hypotheses guided the study. The population consists of 35 information professionals in the four university libraries in Bayelsa State. A sample size of 35 information professionals representing 100% of the population served as respondents. Census sampling technique was used for the study. A 15-item questionnaire was used for data collection. Cronbach alpha statistics was used to obtain 0.75 reliability. The mean scores and standard deviation were used to answer the research questions while the z-test statistics was used to test the hypotheses at 0.05 level of significance. The result amongst others revealed that, there are yet no provisions of uninterrupted power supply, no search record machines for easy access to library collections and no advanced digital technological infrastructure. Meanwhile, there are challenges like insufficient knowledge and awareness of robotic technologies and application by information professionals, inability of library administrators to bravely implement robotic technologies, lack of human resources with requisite skills to provide effective robotic library services. It was recommended that, Information professionals need to be trained and exposed to various trainings that will promote the adoption and use of the robotic technologies.
Postgraduate students' approaches to E-resources in the Academic Library with Reference to Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu

Electronic sources are faster and easier to use than print indexes when finding information. We can search multiple files simultaneously faster than flipping through book pages. Plus, we could save our searches and resources for future use. Electronic resources are updated more frequently than print materials, too. One of the biggest benefits of electronic resources is that they can be accessed from anywhere with dial-up access, which is great for people who can't attend the library in person. The researcher took the initiative to analyse the P.G. students' approaches to e resources available in the Manonmaniam Sundaranar University Library at Tirunelveli. The researcher constructed a strong questionnaire based on previous studies and consulted with field experts. The research supervisor made some corrections in the light of the field experts. The researcher randomly selected 150 P.G. students from the study area and distributed the questionnaire. The researcher received only 120 fully completed questionnaires from the respondents.
SCIENTIFIC AND PEDAGOGICAL CONSULTING AS A TOOL TO SUPPORT THE PROFESSIONAL DEVELOPMENT OF TEACHERS: EXPERIENCE, PROBLEMS AND DIRECTIONS

The article describes the results of a study of scientific and pedagogical consulting for the development of professionalism and rapid adaptation of young teachers to educational practice at school. The authors scrutinized the conceptual approaches to understanding professional and pedagogical support based on the integration of theory and practice, and the adaptation of a special program of scientific and pedagogical consulting to the demands and needs of young schoolteachers. The article describes the specifics of consulting in the school teaching environment and interaction in the context of consulting activities with leading teachers of a pedagogical institute. During the research work, the essence, basic concepts and directions of scientific and pedagogical consulting were determined. Pedagogical and methodological support is expressed in the form of consulting as an innovative tool. The article discusses the basic concepts of consulting as a systemic management of educational activities. The purpose of the study was to theoretically substantiate and experimentally test the effectiveness of organizational and pedagogical conditions for consulting support and support for young teachers. Choosing young teachers as the target audience is based on the fact that during the initial years of their professional journey, they face difficulties, make errors, and they may benefit from the guidance of seasoned mentors and specialized forms of guidance and support, such as educational and pedagogical consulting. The scientific novelty of the study lies in the fact that the identified organizational and pedagogical conditions will ensure motivation and stimulation of self-education, professional growth, innovative thinking and creativity in solving pedagogical problems. The results obtained during the study can be used in the practical activities of young teachers.
Increasing public knowledge and Skill in Emergency Response to enhance proficiency in handling emergencies at home

Abstract. Medical emergencies are not limited to hospitals, roads, or public facilities but frequently arise within households and families. Typical emergencies in residential and nearby settings include physical injuries, loss of consciousness, toxic substance exposure, and open wounds. According to the 2019 poisoning case report, incidents of poisoning that transpire within residential settings constitute Indonesia's second most prevalent form of poisoning cases across diverse categories of poisoning incidents. The prevalence of emergency incidents in such households underscores the significance of leveraging alertness and caution. Furthermore, fast and accurate handling is also requisite. Acquiring knowledge of first aid within the community is imperative for effectively managing emergencies that may arise in a domestic setting. Acquiring emergency first aid knowledge is crucial for the community as unanticipated emergencies can occur indiscriminately and anywhere.&#x0D; An intervention was implemented via community service at PCPM Ngaglik Sleman to address the issue mentioned earlier. The community service was being executed to impart knowledge on emergency management through a two-pronged strategy. The initial phase involved imparting knowledge on emergency medical care for conditions such as injuries, syncope, nosebleeds, toxic exposure, and open wounds. During the second phase, a Forum Group Discussion was conducted to simulate managing injuries, syncope, and open wounds. One or two instructors facilitated each group. The efficacy of education was evaluated by administering pre-test and post-test assessments utilizing the quizz.com platform. The simulation's efficacy was evaluated using a rubric to make observations.&#x0D; The outcome of this community service initiative led to a noticeable augmentation in the participants' comprehension of emergency response procedures. The study observed that the average pre-test and post-test scores of the 26 participants increased. Additionally, the proportion of participants who demonstrated good knowledge increased from 11.5% to 50%. Furthermore, there was an observed improvement in the participants' ability to handle emergencies. In assessing this approach, it was imperative to incorporate the time duration for stage 2 activities, specifically the emergency handling simulation. Furthermore, the intended collaborators aspire for this undertaking to be conducted regularly.
Dialogic Reading Strategy for Billingual Child with Language Difficulties

Development of language skills is an important factor in determining children's school readiness. However, problems in language development and communication in early childhood are common. Difficulties in children's language development can have impacts on social engagements, familial dynamics, and scholastic accomplishments. This study aims to assess the efficacy of dialogic reading intervention programs to enhance oral language skills in bilingual children with language difficulties. This study used a quasi-experimental research method, focused on a single participant, utilizing observation for data collection. Quantitative descriptive analysis was employed for data interpretation. The outcomes of this study found that the dialogic reading intervention program was effective in improving children's oral language skills as seen from the number of word responses on pretest and posttest. These findings can provide valuable insights for parents and educators in developing strategies to foster children's oral language skills
Legal Aspect of Copyright Transfer by Early Childhood

Valuable possessions or inheritances are objects that can be inherited, inheritance in this case is not only about tangible objects such as land, houses, jewelry, and so on, but works of art are also the inheritance rights of the creator that can be inherited. to his family. This article applies normative juridical research methods to analyze the copyright transfer process and identify its implications for Article 1320 of the Civil Code. Research highlights that while copyright is transferable, such actions can violate positive legal provisions. As a result, this study offers a new perspective on the conflict between copyright transfer and existing legal norms, providing a basis for a rethink of relevant regulations. The implications of these findings could impact on further understanding of the relationship between copyright, inheritance, and legal protection of minors.
Impact of Metaverse in Educational Environment

The purpose of this review is to define of metaverses and to explain their potential and limitations for pedagogical applications in educational environment. The Metaverse has infinite possibilities as a new social communication space. . In other words, the Metaverse is a post-reality universe, an eternal and permanent multi-user environment that combines physical reality and digital virtuality. Regarding the educational use of the metaverse, the following future tasks are proposed: First, teachers need to carefully analyze how students understand the metaverse. Next, teachers should design classes that encourage students to collaboratively and creatively solve problems and complete projects. Third, a educational metaverse platform should be developed to prevent the misuse of student data.
A Deep Dive into the Google Cluster Workload Traces: Analyzing the Application Failure Characteristics and User Behaviors

Large-scale cloud data centers have gained popularity due to their high availability, rapid elasticity, scalability, and low cost. However, current data centers continue to have high failure rates due to the lack of proper resource utilization and early failure detection. To maximize resource efficiency and reduce failure rates in large-scale cloud data centers, it is crucial to understand the workload and failure characteristics. In this paper, we perform a deep analysis of the 2019 Google Cluster Trace Dataset, which contains 2.4TiB of workload traces from eight different clusters around the world. We explore the characteristics of failed and killed jobs in Google’s production cloud and attempt to correlate them with key attributes such as resource usage, job priority, scheduling class, job duration, and the number of task resubmissions. Our analysis reveals several important characteristics of failed jobs that contribute to job failure and hence, could be used for developing an early failure prediction system. Also, we present a novel usage analysis to identify heterogeneity in jobs and tasks submitted by users. We are able to identify specific users who control more than half of all collection events on a single cluster. We contend that these characteristics could be useful in developing an early job failure prediction system that could be utilized for dynamic rescheduling of the job scheduler and thus improving resource utilization in large-scale cloud data centers while reducing failure rates.
Research on Image Semantic Description Method Based on RVC Network

Image semantic description is a natural language description that the computer generates from an image that conforms to the logic of human language, which is an interdisciplinary discipline of computer vision and natural language processing, and has important application prospects in helping visually impaired patients, intelligent driving, and understanding traditional national costumes. Humans are the most basic ability to describe the content of an image in words, but it is difficult for computers to do so. Therefore, as a Xi problem of multimodal studies, image semantic description needs to accurately identify the attributes and relationships between objects in images, and also consider the accuracy and richness of the description sentence syntax. In order to solve the problems of inaccurate description sentences and a large amount of irrelevant information in the process of image semantic description, this study proposes an image semantic description method based on RVC network, which combines Vision Transformer and ResNeXt-101 network to extract image global and local area features, so that the model can extract image features more fully. In order to ensure the accuracy of the image description statement, the model combines the channel attention mechanism to enhance the salient region features of the image and weaken the irrelevant region features, and finally uses BLEU, METEOR, CIDEr, ROUGE and SPICE evaluation indicators to evaluate the results, and the simulation results show that the network can extract the image features well and make the description statements more accurate and rich.
Decoding Cyber Incident Reporting Requirements: A Cross-Regulatory Examination

With the rise of digital technologies, cyber incidents have become increasingly common and complex, presenting significant dangers to individuals and organizations. In response, various governments and regulatory bodies have established incident reporting regulations for organizations to abide by. However, the efficiency of these regulations in managing cyber incidents is still a matter of discussion. This paper assesses recent cyber incident reporting laws and evaluates their effectiveness in terms of factors such as timing rules and the level of detail in defining cyber incidents and reporting procedures. Finally, this paper suggests improvements to regulatory requirements to better address the reporting of cyber incidents in today’s rapidly changing regulatory landscape.
Method for Reducing MCI Misclassification Rate Based on Cross-modal Prototype Generation

As an early stage of Alzheimer's disease (AD), the accurate detection of mild cognitive impairment (MCI) is very important for its early intervention. Due to the small between-group differences, it is easy to cause misclassification of MCI. Intrigued by the idea of metric learning, a novel framework based on cross-modal prototype generation is proposed to reduce the misclassification rate of MCI. First, the single modal prototypes are generated from magnetic resonance images (MRI), and then the auxiliary modal is used to correct the original prototypes and generate new cross-modal prototypes. An adaptive mechanism that can dynamically adjust the proportion of the auxiliary modal data is added in the proposed framework to improve the practicability of the cross-modal prototype framework. Experiments show that the cross-modal prototype generation framework can significantly reduce the misclassification rate of MCI by 40.3%.
Improved image denoising method based on wavelet transformation

The 21st century is an era of information, where information takes various forms. According to statistics, 70% of the information people receive comes from images. Therefore, people expect images to have good visual effects and high accuracy. This requires images to undergo denoising before being presented to people. There are many methods for image denoising, and wavelet denoising is a good method because it can obtain frequency domain information while determining time domain information. This article proposes a method that combines wavelet-based hybrid median filtering with wavelet domain denoising, using PSNR to detect the denoising effect. Finally, using MATLAB, the algorithm is verified, and the results indicate that this method can achieve satisfactory denoising effect.
CervixMed: Detecting Cervical Cancer based on combinational data using Hybrid architecture

Cervical cancer is women’s second most frequent malignancy globally, with a 60% mortality rate. Early identification through routine checkups is crucial because cervical cancer has a long latent period and begins with no overt signs. One of the most evident barriers to not having an early detection is the time required for the diagnostic process, which also accounts for indifference regarding its automation. These uncertainties occur for several reasons, while one of the most prominent causes is providing the report by the computer-aided diagnostic process only based on the patient’s information in tabular form or only predicting the patient’s cervix image. In this study, we proposed three concepts 1. Hybrid Architecture, 2. CervixMed, a novel CNN-based architecture, and 3. GUI (Graphical User Interface). In Hybrid Architecture, we propose a methodology that takes both types of data, i.e., tabular and image, and then merges the prediction provided by the models and shows the final output. For tabular data classification, we use traditional-machine learning models like KNN, SVM, LR, and RF, where LR and SVM provided the best accuracy of 99%. On the other hand, the image data is classified into one of the five categories using our novel lightweight CNN-based architecture, CervixMed, that outperforms other pre-trained models like DenseNet121, DenseNet169, DenseNet201, ResNetl52v2, and VGG19 by 94% in accuracy and 98% in sensitivity. The hybrid architecture combines the abovementioned models and chooses the best pair regarding accuracy. In this study, we demonstrated that combining LR and SVM with CervixMed outperforms the other combinations by 96.5% accuracy. Lastly, we demonstrated the methodology of the proposed GUI for interacting with the end-users with this hybrid model.
Research on Photovoltaic Power Prediction Method Based on TCN-BiLSTM Neural Network

The factors affecting photovoltaic power generation show a non-linear relationship with each other, and it is difficult to align traditional linear models for accurate prediction. Predicting photovoltaic power generation is important for achieving low carbon. Focusing on the problem of the poor accuracy of traditional photovoltaic power prediction, a novel short-term and ultra-short-term photovoltaic power prediction method based on TCN-BiLSTM was proposed. Combining the characteristics of the TCN-BiLSTM integrated model that could handle time-series data and extract feature vectors well, we first subjected the collected data to operations such as outlier processing by the KNN algorithm, and then the extracted features and normalized data were used as inputs to the model for training. The experimental results have shown that the combined TCN-BiLSTM model has been able to achieve better prediction accuracy compared to the traditional prediction model, with a maximum reduction of 36.5% in RMSE, which has provided a constructive basis for improving the power prediction accuracy of photovoltaic power generation.
Real time confidence estimation and multi-scale camouflage object detection

Due to the fact that objects with camouflage blend seamlessly into their surroundings, exhibiting a exceptional resemblance in terms of texture, color, shape, etc., and possessing boundaries that are indistinguishable from their environment, the detection of such camouflaged objects becomes significantly more challenging. In this research, we introduce a novel network called CRNet, which incorporates a detection network for identifying camouflage objects with a real-time network for estimating confidence. Unlike other networks, in the camouflage object detection network, we design MSF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M by capturing maps at different scales and using the relationship between their maps. In the real-time confidence estimation network, we use spatial adaptive normalization for the edge region to combine the semantic information from high latitude images embedded in downstream low dimensional features. The network’s outstanding performance, as described in the paper, is demonstrated through tests conducted on three standard datasets.
Using Improved DeepLabV3+ for Complex Scene Segmentation

This paper presents a lightweight convolutional neural network model based on DeepLabV3+. By collecting and annotating an image dataset, sufficient sample support is provided for the model’s training. In order to enhance the model’s training speed and applicability, this study proposes replacing the backbone network Xception with MobileNetv2. This successfully reduces the number of model parameters, making the training process more rapid and efficient. Additionally, by refining the model structure and incorporating Coordinate Attention (CA) and Cascaded Feature Fusion (CFF) for feature integration, the feature map processing is further optimized. This allows the model to more accurately identify in complex background environments. Verification shows that the improved model performs excellently in metrics such as mIoU, mPA, mPrecision, and mRecall. During testing, mIoU reached 82.66%, mPA reached 88.20%, mPrecision reached 92.26%, and mRecall reached 87.67%. These results fully demonstrate the feasibility, accuracy, and robustness of the proposed method, providing strong support in practical applications.
Detection Method of Insulator String Pin Based on YOLOv5-nS Algorithm

Aiming at the difficulty and slow speed of multi-pose insulator pin detection in single-chip insulator replacement robot in substation insulator replacement robot, a fast and reliable insulator pin detection method YOLOv5-nS based on YOLOv5 is proposed. First of all, a new backbone network structure is proposed for the Backbone part of the original network, which is based on the improved nSNet of ShuffleNetV2; Secondly, the Neck of the network is lightweight while adding a feature fusion layer; Thirdly, the boundary box regression loss function is modified to improve the accuracy of the boundary box prediction; Finally, the Anchor scale is re-selected. The experimental results show that under the condition that the accuracy of the improved YOLOv5-nS algorithm model is basically unchanged, the parameters are reduced by 89.7%, the model size is reduced by 87.5%, and the frame rate is increased by 7%.