Reviewerly/exHarmony huggingface dataset:
Automation of Computer Vision Applications for Real-time Combat Sports Video Analysis

This study examines the potential applications of Human Action Recognition (HAR) in combat sports and aims to develop a prototype automation client that examines a video of a combat sports competition or training session and accurately classifies human movements. Computer Vision (CV) architectures that examine real-time video data streams are being investigated by integrating Deep Learning architectures into client-server systems for data storage and analysis using customised algorithms. The development of the automation client for training and deploying CV robots to watch and track specific chains of human actions is a central component of the project. Categorising specific chains of human actions allows for the comparison of multiple athletes' techniques as well as the identification of potential areas for improvement based on posture, accuracy, and other technical details, which can be used as an aid to improve athlete efficiency. The automation client will also be developed for the purpose of scoring, with a focus on the automation of the CV model to analyse and score a competition using a specific ruleset. The model will be validated by comparing performance and accuracy to that of combat sports experts. The primary research domains are CV, automation, robotics, combat sports, and decision science. Decision science is a set of quantitative techniques used to assist people to make decisions. The creation of a new automation client may contribute to the development of more efficient machine learning and CV applications in areas such as process efficiency, which improves user experience, workload management to reduce wait times, and run-time optimisation. This study found that real-time object detection and tracking can be combined with real-time pose estimation to generate performance statistics from a combat sports athlete's movements in a video.
Suggestions for a Revision of the European Smart Robot Liability Regime

In recent years, the need for regulation of robots and Artificial Intelligence, together with the urgency of reshaping the civil liability framework, has become apparent in Europe. Although the matter of civil liability has been the subject of many studies and resolutions, multiple attempts to harmonize EU tort law have been unsuccessful so far, and only the liability of producers for defective products has been harmonized so far. In 2021, by publishing the AI Act proposal, the European Commission reached the goal to regulate AI at the European level, classifying smart robots as ”high-risk systems”. This new piece of legislation, albeit tackling important issues, does not focus on liability rules. However, regulating the responsibility of developers and manufacturers of robots and AI systems, in order to avoid a fragmented legal framework across the EU and an uneven application of liability rules in each Member State, is still an important issue that raises many concerns in the industry sector. In particular, deep learning techniques need to be carefully regulated, as they challenge the traditional liability paradigm: it is often not possible to know the reason behind the output given by those models, and neither the programmer nor the manufacturer is able to predict the AI behavior. For this reason, some authors have argued that we need to take liability away from producers and programmers when robots are capable of acting autonomously from their original design, while others have proposed a strict liability regime. This article explores liability issues about AI and robots with regards to users, producers, and programmers, especially when the use of machine learning techniques is involved, and suggests some regulatory solutions for European lawmakers.
Probabilistic linguistic fuzzy cognitive maps: applications to the critical factors affecting the health of rural older adults

Achieving healthy ageing has become the only way for China to alleviate the pressure of ageing, especially in rural areas. However, the factors affecting the health of rural older adults are numerous and complex. It is important to identify the critical factors that affecting the health of older adults in rural areas and provide decision-making support for targeted health interventions.To overcome some limitations of existing works, an extended probabilistic linguistic fuzzy cognitive map model is proposed in this paper as a useful tool for modeling the cause-effect relationship between factors. The proposed model integrates the advantages of probabilistic linguistic term sets and fuzzy cognitive maps. In the end, to rank and identify the critical factors affecting the health, a novel similarity measure based on Euclidean distance and Z-mapping function is proposed.The proposed model can effectively deal with the uncertainty of experts and reflect different opinions of groups well. In terms of representing uncertainty and ambiguity, the proposed method outperforms other models in modeling complex systems. In the real-world case analysis, we find that education is the most important factor affecting the health of rural older adults, followed by previous occupational experiences, psychology, and physical exercise, among other things. Intergenerational relationship has become another important factor affecting the health of rural older adults in China as the development of Chinese society.From a macro perspective, social economic status, living environment, lifestyle, and health management, are the variables that have the greatest impact on the health of rural older adults. As a result, providing more precise health interventions with the characteristics of factors influencing health is a crucial guarantee for preserving and improving the health of rural older adults in China.
3D head-talk: speech synthesis 3D head movement face animation

Abstract Speech-driven 3D human face animation has made admirable progress. However, synthesizing 3D facial speakers with head motion is still an unsolved problem. This is because head motion, as a speech-independent appearance representation, is difficult to model by a speech-driven approach. To solve this problem, we propose 3DHead-Talk, which generates 3D face animations combined with extreme head motion. In this work, we face a key challenge to generate natural head movements that match the speech rhythm. We first form an end-to-end autoregressive model by combining a dual-tower and single-tower Transformer, with a speech encoder encoding the long-term audio environment, a facial grid encoder encoding subtle changes in the vertices of the 3D facial grid, and a single-tower decoder automatically regressing to predict a series of 3D facial animation grids. Next the predicted 3D facial animation sequence is edited by a motion field generator containing head motion to obtain an output sequence containing extreme head motion. Finally, the natural 3D face animation under extreme head motion is presented in combination with the input audio. The quantitative and qualitative results show that our method outperforms current state-of-the-art methods. And stabilizes the non-area region while maintaining the appearance of extreme head motion.
Blackbox Test on Web Based Employed Attendance Information System Design

The Hapesong New Village Office is a village asset building specifically designated for the operational activities of the Hapesong Baru Village government. The absence of employees at the Hapesong Baru Village office is an important and mandatory role for all employees to be used for official purposes in administration and administration. So far, the government of Batang Toru District, South Tapanuli Regency is still using the attendance system in a manual way that is still not computerized where employees must fill in attendance by writing down attendance signs for example the signature of the employee concerned, this way of attendance has the risk of committing fraud. quite large, because there is still an opportunity to fake employee attendance signs. Data accumulation and errors often become obstacles in processing employee attendance data. Therefore, the authors chose Batang Toru District, South Tapanuli Regency as the object of research. With the development of digital technology, one of which is in the field of computerization, it can help the system in employee attendance. The result of this research is the Design of an Employee Attendance Information System at the Web-Based Hapesong Baru Village Office. In making attendance attendance applications, which are built using a programming language, on the server side is PHP while on the client server side it is HTML. In the database using SQL language with MYSQL DBMS. The modeling of this expert system uses the Unified Modeling Language model.
CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Dataset

Abstract We present a CNN-BiLSTM system for the problem of offline English handwriting recognition, with extensive evaluations on the public IAM dataset, including the effects of model size, data augmentation and the lexicon. Our best model achieves 3.59% CER and 9.44% WER using CNN-BiLSTM network with CTC layer.Test time augmentation with rotation and shear transformations applied to the input image, is proposed to increase recognition of difficult cases and found to reduce the word error rate by 2.5% points. We also conduct an error analysis of our proposed method on IAM dataset, show hard cases of handwriting images and explore samples with erroneous labels. We provide our source code as public-domain, to foster further research to encourage scientific reproducibility.
Remote Sensing Image Super-Resolution via Residual-Dense Hybrid Attention Network

Nowadays, remote sensing datasets with long temporal coverage generally have a limited spatial resolution, most of the existing research uses the single image super-resolution (SISR) method to reconstruct high-resolution (HR) images. However, due to the lack of information in low-resolution (LR) images and the ill-posed nature of SISR, it is difficult to reconstruct the fine texture of HR images under large-scale magnification factors (e.g., four times). To address this problem, we propose a new reference-based super-resolution method called a Residual-Dense Hybrid Attention Network (R-DHAN), which uses the rich texture information in the reference image to make up for the deficiency of the original LR image. The proposed SR model employs Super-Resolution by Neural Texture Transfer (SRNTT) as a backbone. Based on this structure, we propose a dense hybrid attention block (DHAB) as a building block of R-DHAN. The DHAB fuses the input and its internal features of current block. While making full use of the feature information, it uses the interdependence between different channels and different spatial dimensions to model and obtains a strong representation ability. In addition, a hybrid channel-spatial attention mechanism is introduced to focus on important and useful regions to better reconstruct the final image. Experiments show that compared with SRNTT and some classical SR techniques, the proposed R-DHAN method performs well in quantitative evaluation and visual quality.
Robust and High-Fidelity 3D Face Reconstruction Using Multiple RGB-D Cameras

In this paper, we propose a robust and high-fidelity 3D face reconstruction method that uses multiple depth cameras. This method automatically reconstructs high-quality 3D face models from aligned RGB-D image pairs using multi-view consumer-grade depth cameras. To this end, we mainly analyze the problems in existing traditional and classical multi-view 3D face reconstruction systems and propose targeted improvement strategies for the issues related. In particular, we propose a fast two-stage point cloud filtering method that combines coarse filtering and fine filtering to rapidly extract the reconstructed subject point cloud with high purity. Meanwhile, in order to improve the integrity and accuracy of the point cloud for reconstruction, we propose a depth data restoration and optimization method based on the joint space–time domain. In addition, we also propose a method of multi-view texture alignment for the final texture fusion session that is more conducive for fusing face textures with better uniformity and visual performance. The above-proposed methods are reproducible and can be extended to the 3D reconstruction of any subject. The final experimental results show that the method is able to robustly generate 3D face models having high geometric and visual quality.
ANEMIA ESTIMATION FOR PATIENTS USING A MACHINE LEARNING MODEL

Computer-aided illness diagnosis is less expensive, saves time, is more accurate, and removes the need for additional personnel in medical decision making.Many nutrition surveys indicate that about a quarter of the world's population is anaemic.As a result, there is a pressing need to create an effective machine learning regressor capable of properly detecting anaemia.The goal is to find out which individual classifier or group of classifier combinations obtain the highest accuracy in Red blood cell categorization for anaemia detection.We used Lasso and Ridge regressions to detect and estimate the anaemia.However, the classifier Ridge performs better achieves an accuracy higher than the Lasso regression.Hence to achieve maximum accuracy in medical decision making, a better and powerful algorithm should be used.The outcomes of this algorithms decides whether the patient is infected with anaemia or not.The proposed version generates a better response to the inputs to confirm the disease.
Intelligent UAV path planning framework using artificial neural network and artificial potential field

Unmanned aerial vehicles (UAVs) are utilized extensively in various fields of daily activities in the day to day life and industrial applications. The raises of utilization of UAVs guide the researchers to concentrate on various problems like handling rich and large-scale information and uninterrupted communication. Further, to achieve the above the obstacle free zone is mandatory and the present autonomous drones may fail to handle such situations. To address the mentioned issues, an effective path planning algorithm is needed, to find the optimal path and obstacle free mobility. Hence, UAV path planning needs intelligent and autonomous navigation system by providing high level of optimization in order to attain optimal path with the obstacles avoidance. In this paper, AI employed framework for UAV path planning is proposed by utilizing the salient features of both artificial neural network (ANN) and artificial potential field (APF). ANN is implemented for obtaining optimal path and APF is utilized for evading the obstacles throughout the path. Further, the implementation results show the better performance than the existing works in terms of the collision free optimal path for UAVs.
AdaptKeyBERT: An Attention-Based approach towards Few-Shot &amp; Zero-Shot Domain Adaptation of KeyBERT

Keyword extraction has been an important topic for modern natural language processing. With its applications ranging from ontology generation, fact verification in summarized text, and recommendation systems. While it has had significant data-intensive applications, it is often hampered when the data set is small. Downstream training for keyword extractors is a lengthy process and requires a significant amount of data. Recently, Few-shot Learning (FSL) and Zero-Shot Learning (ZSL) have been proposed to tackle this problem. Therefore, we propose AdaptKeyBERT, a pipeline for training keyword extractors with LLM bases by incorporating the concept of regularized attention into a pre-training phase for downstream domain adaptation. As we believe our work has implications to be utilized in the pipeline of FSL/ZSL and keyword extraction, we open-source our code as well as provide the fine-tuning library of the same name AdaptKeyBERT at https://github.com/AmanPriyanshu/AdaptKeyBERT.
Research on the Reform of Online Teaching Mode in Colleges and Universities

With the opening of a worldwide epidemic - COVID-19, a new teaching model in the history of education - "online teaching" model has been promoted and popularized, which will be a good opportunity for the reform and innovation of traditional teaching models. This paper probes into the problems and reform of online teaching mode in colleges and universities.
Implementation of Features Selection Based on Dragonfly Optimization Algorithm

Nowadays increasing dimensionality of data produces several issues in machine learning. Therefore, it is needed to decrease the number of features by choosing just the most important ones and eliminating duplicate features, also reducing the number of features that are important to the model. For this purpose, many methodologies known as Feature Selection are applied. In this study, a feature selection approach is proposed based on Swarm Intelligence methods, which search for the best points in the search area to achieve optimization. In this paper, a wrapper feature selection technique based on the Dragonfly algorithm is proposed. The dragonfly optimization technique is used to find the optimal subset of features that could accurately classify breast cancer as benign or malignant. Many times, the fitness function is defined as classification accuracy. In this study, hard vote classes are employed as a model developed to evaluate feature subsets that have been chosen. It is used as an evaluation function (fitness function) to evaluate each dragonfly in the population. The proposed ensemble hard voting classifier utilizes a combination of five machine-learning algorithms to produce a binary classification for feature selection: Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF). According to the results of the experiments, the voting ensemble classifier has the greatest accuracy value among the single classifiers. The proposed method showed that when training the subset features, the accuracy generated by the voting classifier is high at 98.24%, whereas the training of all features achieved an accuracy of 96.49%. The proposed approach makes use of the UCI repository's Wisconsin Diagnostic Breast Cancer (WDBC) Dataset. Which consists of 569 instances and 30 features.&#x0D;
Mineralogy of the ores on the southern flank of the Mutnovskoe gold-silver-polymetallic deposit (Southern Kamchatka)

The Mutnovskoe deposit is one of the largest and most prospective ore deposits in South Kamchatka. The northern and southern flanks within the main veining zone Opredelyayushchaya, composed of low-sulfi (goldsilver) and sulfide-polymetallic (gold-silver-polymetallic) types of ores, respectively, are distinguished. The paper presents the results of the complex mineralogical and geochemical studies of the gold-silver-polymetallic ores of the southern flank of the deposit. Features of textures and structures, mineral, chemical compositions and genesis of the ores, as well forms of precious and base metals occurrences are shown. Typomorphic features of pyrite, sphalerite, galena, chalcopyrite, tennantite-tetrahedrite, Au, Ag, Pb and Bi tellurides, native gold, Bi, Se and Ag sulfosalts are characterized. The pyrite-sphaleritequartz, sphalerite-galena-quartz, and chalcopyrite-tennantite-tetrahedrite mineral associations are distinguished. The temperatures and composition of ore-forming solutions are shown.
An exploration of challenges affecting the Adoption of Information and communication Technologies (ICTs) by Government libraries in Kenya.

In Kenya, many libraries have embraced the use of ICTs to leverage their services. While the extent of the use of ICTs in these libraries vary vastly, it is reported in literature that the use of these technologies are also faced by different challenges. Accordingly, this study sought to the key challenges affecting the adoption of ICTs by government libraries in Kenya under two objectives: to describe the challenges affecting the adoption of ICTs by government libraries in Kenya and recommend measures that should be taken to arrest the challenges. The study was informed by e-readiness model and guided by qualitative research methods. Twelve (12) libraries were selected purposively. Data was collected from the heads of selected libraries using interview method. Website and documentary analysis was also used to enhance data quality. Data presentation and analysis was done qualitatively, however this was simplified using figures and tables based on excel software. According to the findings, the adoption of ICTs by government libraries is affected by different managerial, financial, technological and staff related challenges. Based on these findings, the study recommended the formation of ICT committee at the department of government Library Services (GLS), enhancing internet access in libraries, adapting a standard system for government libraries, development of ICT policy for libraries and exploring alternative sources of funding by libraries
Nutritional Information and Characteristics Required by Adolescents and Mothers in Developing Health Applications "MoGiz"

Mobile applications about health have been widely developed but not yet effective as well as suit to user needs. It is because the design process does not involve potential users or user-centered design (UCD). This study aims to obtain information about the needs of groups of adolescents and mothers of children under five on the health application namely MoGiz (Monitoring Gizi). The method uses a qualitative approach through Focus Group Discussion with 6 and 9 informants from group of youths and mothers, respectively. Data analysis was carried out by interpreting the results of a series of transcripts, coding, and arranging the topics and themes found. The results show that teenagers want applications about adolescent health, while mothers of toddlers want applications about toddler health. Both groups expect applications to be small, attractive, containing articles and videos. In conclusion, it is recommended that the application contains health information according to the characteristics of the needs of potential users.
Video Action Recognition Using Motion and Multi-View Excitation with Temporal Aggregation

Spatiotemporal and motion feature representations are the key to video action recognition. Typical previous approaches are to utilize 3D CNNs to cope with both spatial and temporal features, but they suffer from huge computations. Other approaches are to utilize (1+2)D CNNs to learn spatial and temporal features in an efficient way, but they neglect the importance of motion representations. To overcome problems with previous approaches, we propose a novel block which makes it possible to alleviate the aforementioned problems, since our block can capture spatial and temporal features more faithfully and efficiently learn motion features. This proposed block includes Motion Excitation (ME), Multi-view Excitation (MvE), and Densely Connected Temporal Aggregation (DCTA). The purpose of ME is to encode feature-level frame differences; MvE is designed to enrich spatiotemporal features with multiple view representations adaptively; and DCTA is to model long-range temporal dependencies. We inject the proposed building block, which we refer to as the META block (or simply "META"), into 2D ResNet-50. Through extensive experiments, we demonstrate that our proposed method architecture outperforms previous CNN-based methods in terms of "Val Top-1 %" measure with Something-Something v1 and Jester datasets, while the META yielded competitive results with the Moment-in-Time Mini dataset.