Reviewerly/exHarmony huggingface dataset:
Lower Body Detection and Tracking with AlphaPose and Kalman Filters

The expansion of computer vision is currently experiencing significant growth, especially in healthcare, security, and other social life aspects. One of the concepts of computer vision advancement is using a regular camera to detect lower body movements, from the waist to toe, to see hyperactivity symptoms in children. This article presents a framework and analysis of lower body movement detection using the estimator alphaPose pre-trained using Halpe 26 keypoints and HOI-Det dataset. The next phase is object tracking using the optical flow of the Pyramidal Lucas-Kanade method. As the final step, we are using the Extended Kalman Filter to determine movement patterns. Finally, the framework will calculate the intensity of the object&#x0027;s movement. Testing this framework was conducted using several videos of human activities. It resulted in detection and movement intensity calculations with an accuracy of 95.17 percent for the lower leg detection, which is better than existing methods.
A joint structure of multi-distance based metric learning for person re-identification

Person re-identification problem is a valuable computer vision task, which aims at matching pedestrian images of different cameras in a non-overlapping surveillance network. Existing metric learning based methods address this problem by learning a robust distance function. These methods learn a mapping subspace by forcing the distance of the positive pair much smaller than the negative pair by a strict constraint. The metric model is over-fitting to the training dataset. Due to drastic appearance variations, the handcrafted features are weak of representation for person re-identification. To address these problems, we propose a joint distance measure based approach, which learns a Mahalanobis distance and a Euclidean distance with a novel feature jointly. The novel feature is represented with a dictionary representation based method which considers pedestrian images of different camera views with the same dictionary. The joint distance combine the Mahalanobis distance based on metric learning method with the Euclidean distance based on the novel feature to measure the similarity between matching pairs. Extensive experiments are conducted on the publicly available bench marking datasets VIPeR and CUHK01. The identification results show that the proposed method reaches a good performance than the comparison methods.
Reliance Model for Data Processing in Cloud for Consumers

In the recent years, Cloud has actually ended up being critical in the research, zone, and additionally corporation. On the alternative arms, Cloud computer is primarily based upon virtualization over IT assets wherein digital storage location and laptop services are provided. For that purpose, trusting of every cloud organizations and furthermore clients is considered a vital element to decorate integrity and moreover safety of the cloud surroundings. Although there are hundreds of studies worry about growing assume for cloud issuer. In this paper, a take delivery of as true with fund model has in reality been endorsed to decide out the bear in mind fund for cloud clients. The number one part of the proposed get preserve of as actual with fund version is the consider records level. The function of this diploma is to specify the remember fund percentage for every patron. Four boosted techniques have in reality been applied and additionally finished in this degree; Particle Swarm Optimization (PSO), Multiple Regressions (MR), Analytic Hierarchical Process (AHP), in addition to PSO-Multiple Regression (MR-PSO). According to execution consequences of these techniques, it is determined that the PSO is the right technique to be made use of to decide the take shipping of as right with percentage. The general performance of the proposed rely on format has truly been evaluated relative to Shield dataset. The speculative consequences display screen that the accept as true with fund percentage in step with our encouraged model is departed with the beneficial resource of 0.017 approximately Shield dataset.
SemML: Facilitating Development of ML Models for Condition Monitoring with Semantics

Monitoring of the state, performance, quality of operations and other parameters of equipment and productiosn processes, which is typically referred to as condition monitoring, is an important common practice in many industries including manufacturing, oil and gas, chemical and process industry. In the age of Industry 4.0, where the aim is a deep degree of production automation, unprecedented amounts of data are generated by equipment and processes, and this enables adoption of Machine Learning (ML) approaches for condition monitoring. Development of such ML models is challenging. On the one hand, it requires collaborative work of experts from different areas, including data scientists, engineers, process experts, and managers with asymmetric backgrounds. On the other hand, there is high variety and diversity of data relevant for condition monitoring. Both factors hampers ML modeling for condition monitoring. In this work, we address these challenges by empowering ML-based condition monitoring with semantic technologies. To this end we propose a software system SemML that allows to reuse and generalise ML pipelines for conditions monitoring by relying on semantics. In particular, SemML has several novel components and relies on ontologies and ontology templates for ML task negotiation and for data and ML feature annotation. SemML also allows to instantiate parametrised ML pipelines by semantic annotation of industrial data. With SemML, users do not need to dive into data and ML scripts when new datasets of a studied application scenario arrive. They only need to annotate data and then ML models will be constructed through the combination of semantic reasoning and ML modules. We demonstrate the benefits of SemML on a Bosch use-case of electric resistance welding with very promising results.
Now in the Network

Abstract Network technologies have played an increasingly prominent role in sound art practices over the last forty years. The consequent creative possibilities associated with space and locality are clearly apparent: networks enable connections between geographically remote locations, and allow the creation of new virtual spaces for sound art to inhabit. Less immediately apparent—though equally significant—are the ways in which network technologies have enabled artists to explore temporal relationships in their work. In particular, network technologies inevitably involve latency—a short time delay between the moment a signal is created at one location and the moment it is perceived at another. Taking traditional notated music practice as a point of reference, this chapter considers a number of case studies in which sound artists have explored new types of temporal relationships in their work, as a consequence of their use of network technologies. This investigation is framed in the context of Bergson’s concept of durée and Clarke’s concept of subject position.
Holistic deep learning

Abstract This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep learning models, as demonstrated by extensive experiments on both tabular and image data sets. The results are further validated by ablation experiments and SHAP value analysis, which reveal the interactions and trade-offs between the different evaluation metrics. To support practitioners applying our framework, we provide a prescriptive approach that offers recommendations for selecting an appropriate training loss function based on their specific objectives. All the code to reproduce the results can be found at https://github.com/kimvc7/HDL .
Analytics-Based on Classification and Clustering Methods for Local Community Empowerment in Indonesia

West Papua is reportedly the second-most populous province in Indonesia. The United Nations International Children’s Emergency Fund (UNICEF) highlights Papua’s performance in selecting the Sustainable Development Goals (SDG) indicators compared to other provinces in the country. The data shows that food, nutrition, health, education, housing, water, sanitation, and protection are defined as multidimensional child poverty. Population statistics and poverty figures show that inter-provincial equity in Indonesia needs to be re-measured. In 2008, the Regional Governments of Papua and West Papua Provinces implemented a Community Empowerment Program called “PNPM RESPEK”, which provided direct community assistance for IDR 100 million per village. To determine the people’s level of understanding and perception towards this program, PNPM RESPEK, in collaboration with the Central Statistics Agency, conducted an integrated PNPM RESPEK Evaluation Survey in July 2009. Based on the survey results, this paper identifies a model (pattern) of understanding the people of Papua and West Papua towards the program and finds the best method to build this model through classification techniques. Then the data model was also tested using unsupervised learning, the clustering method. The experimental results show that the J48 decision tree produces the highest accuracy compared to the others. As for clustering, the clustering hierarchy provides the best accuracy. Decision Tree J48 has the best accuracy with an accuracy of 97.31%. In this case, 97.31% of the people of Papua and West Papua who receive direct community assistance meet the level of understanding and perception of the PNPM RESPEK Program.
Creating Cultural Experiences in a Cemetery: A Storyboard for a VR User Interaction

There is an increasing need to promote and disseminate cultural heritage. Tourism explores cultural heritage in many different contexts. However, in cemetery tourism, the approaches are scarce. Thus, a cemetery needs to be seen and also accepted as art, culture, and knowledge. This paper aims to present a pilot study to develop a VR-based experience, mainly considering the storytelling and storyboards as core factors for increasing user experience in cemetery tourism. Also, it aims to promote cemetery tourism as an important role in showing the history of a nation. For that, the Mausoleum of the Palmela’s Dukes, the biggest private mausoleum in Europe, in Prazeres Cemetery, Lisbon, will be used as an intervention place. Combining cultural heritage and the Palmela’s family history presented there with the creation of a virtual reality experience will give the users a unique cultural experience. Our research methodology comprises an analysis of guided visits to Prazeres Cemetery and the Mausoleum, a semi-structured interview, a qualitative analysis, and bibliographical research. All the information collected allowed us to develop a storytelling proposal that set the base for the storyboard, developed to be used for the VR experience.
Crowd Modeling using Temporal Association Rules

Understanding crowd behavior has attracted tremendous attention from researchers over the years. In this work, we propose an unsupervised approach for crowd scene modeling and anomaly detection using association rules mining. Using object tracklets, we identify events occurring in the scene, demonstrated by the paths or routes objects take while traversing the scene. Allen’s interval-based temporal logic is used to extract frequent temporal patterns from the scene. Temporal association rules are generated from these frequent temporal patterns. Our goal is to understand the scene grammar, which is encoded in both the spatial and spatio-temporal patterns. We perform anomaly detection and test the method on a well-known public data.
Methodology for assessing the competitive positions of a higher education institution on the national market of educational services

The fact that many Russian universities offer a wide, but often similar, range of educational services substantiates stiff competition between them, which aggravated in the conditions of protracted global COVID-19 pandemic due to the need for rapid implementation of the tools and methods of distance learning. The educational institutions compete for not only students, by their admission to each specific discipline. The goal is set to analyze the competitive positions and opportunities of the university on the market of educational services in the Russian Federation. The subject of this research is Kazan Innovative University named after V. G. Timiryasov. The research methodology leans on the general theoretical methods, as well as 7P and SWOT analysis. As a result, the author determines the merits and flaws of the university, market opportunities and threats; as well as offers the vectors for improving on the market of educational services. The acquired conclusions on the need to expand the material base, increase the academic degree holders rate, involvement in the grant mechanism, and integration into the real sector of economy are relevant for a great many universities in the Russian Federation.
A Structure Analysis Method of Radar Emitter Relation Network

The various detection scenes of radar emitter lead to the swallowing of radar features and low identification accuracy of radar individual targets, which makes it very difficult for the system to associate targets automatically. To solve the above problems, the adaptive ability of target identification and association can be improved by building radar emitter relation network. In this paper, based on the radar emitter relation network, the structure of the radar emitter relation network is analyzed by using the community detection method, which is one of many complex network structure analysis method, aiming at radar emitter relation network&#x0027;s characteristics of the complex network. The experiment results show that the method is effective in analyzing the structure of emitter relation network.
Making the Cloud Secure: Cirrus or Cumulonimbus?

We build on our experience of the commercial Information and Communications Technology (ICT) environment, market data from respected organisations and the technical literature; to examine how the sophistication offered by the cloud impacts approaches to defending information from attack by malignant actors. The cloud provides a networked and distributed approach to computing, using commercial models that are congruent with the outsourced principle of more efficient use of technology resources. The model however creates more complexity and more interfaces, which will in turn impact the approach needed to protect facilities against attack.
Handling Class Imbalance in Electroencephalography Data Using Synthetic Minority Oversampling Technique

The issue of class imbalance is very common in medical diagnosis applications. The problem of class imbalance arises when the distribution of samples across the known classes is skewed. It affects the performance of robust machine learning algorithms. The performance evaluation parameters used to measure the machine learning models are also very important in the case of class imbalanced datasets. This paper critically evaluates the performance of random forest classifier for the application of seizure state recognition using electroencephalography (EEG) data. Synthetic Minority Oversampling Technique (SMOTE) is used to balance the EEG dataset. Parameters such as accuracy, sensitivity, specificity, precision, false positive rate (FPR), f1-score, and area under the receiver operating characteristic curve (AUC) are considered for evaluation and analysis of the performance of actual class imbalanced dataset and class balanced dataset using SMOTE. The results indicate that the effect of balancing class does not affect the accuracy of the model much, however, it improves the sensitivity and AUC parameters significantly; which are the important parameters in case of measuring the predictive performance of class imbalanced dataset.
A Systematic Approach for English- Hindi Parallel Database Creation for Transliteration of General Domain English Words

Transliteration is the task of converting a text written in one language, called the source, to another language, called the target, by preserving the phonetic properties of the source language. The present work considers the task of transliteration with English and Hindi as the source and target language, respectively. The aim is to create a database by considering the diversity of letter and grapheme pronunciations in English and to develop a simple machine transliteration system from English to Hindi, using phrase-based statistical machine translation (PBSMT) model. The system was trained by using open-source toolkits Moses and GizaPP. This will help Hindi readers without the knowledge of English in learning the correct pronunciation of an English word, as the output will be written in Hindi script. To test the trained system, five short stories were transliterated and the results were compared with a transliteration tool available online ‘C-DAC Transliteration’.
Integration of Digital Art Works and Virtual Reality Technology

When we combine virtual reality technology and digital media art, the virtual reality technology is a kind of expression, is a medium for the spread of art, and this technology is a kind of style with interactive media, digital media art by virtual reality technology to make the participants completed the art form of the interactive experience. This paper mainly studies the integration application of digital art works and virtual reality technology. Firstly, this paper introduces the background of digital art and virtual present technology, and Outlines the realization of virtual reality technology in detail. This paper analyzes the artistic elements of the application of virtual reality technology in digital art works. This paper also conducted a questionnaire survey on the teachers of a university, the survey results show that more than 70% of the teachers have accepted the integration of digital art works and virtual reality technology, and have a good prospect for it.
Learning Key Actors and Their Interactions for Group Activity Recognition

Group activity recognition is a challenging task. Group activities usually involves many actors, while only some key actors play a decisive role in group activity recognition. Therefore, extraction of key actors and their interactions is an important problem in group activity recognition. To tackle this problem, we propose a new method based on graph convolutional layers and self-attention mechanisms to extract the Subgraphs of the Actor Relation Graph (SARG). SARG is a scene representation that only contains key actors and their interactions, which is used to enhance the importance of key actors in each group activity. First, the actor relation graph was generated via the appearance and location information of the actors; it was further analyzed by using a graph convolutional layer. Second, we use the graph convolutional layer to generate the self-attention features for each participant and extract the actor relation subgraphs that can ascertain group activities. Finally, we fuse actor relation subgraphs, actor relation graphs and original features to recognize group activities. We evaluate our model over two datasets: the collective activity dataset and the volleyball dataset. SARG has an average improvement of \(4\%\), compared to 8 benchmarks.
A Novel Low Power Design of Brent Kung Adder Having Fault Tolerant Capability

In today’s technology, Parallel Prefix Adders is widely used in VLSI to get higher delay performance. The demand for adders that use reversible logic gates is growing every day as technology advances. However, since reversible logic gates have no information loss, reversible circuits consume less heat than conventional circuits. Reversible logic gates along with extra characteristics that input parity and output parity are equal is termed as PPRLG (parity preserving reversible logic gates). This characteristic allows any device to function normally even if any intermediary nodes fail. The Brent-Kung adder offers a faster response time as compared to many adders. As a consequence, designing an extremely fast, fault-tolerable, and low-power adder immediately results in increased device speed for quicker computing purposes, so Brent Kung adder with the help of PPRLG is presented in our work. The suggested Adder implementation is done using Verilog programming language in the Xilinx software with version 14.7. For the purpose of demonstration of its quality, presented Parity Preserving Reversible Adder, including many sub-parts, we used a number of parameters like Quantum-Cost, CI, GC, and Unused Output, including the schematic (Register Transfer Level) of the Brent-Kung Adder and its sub-parts. So yet, no Brent-Kung adder circuit employing Reversible Logic Gates with Fault-Tolerant capabilities has been suggested, to the best of our knowledge.
Learning Designed For Earning

The topic of study in this research paper is “learning designed for earning”, and an attitude towards the problem manifested by an inquiry of the existing element of truth in the above argument. Today’s society has reached a place where people study only for a living, either to meet ends or simply to earn money. Society has made the value of money more important by not giving meaning to the quality of education. The results of the study showed that giving importance to the quality of education will change the mentality of the people in society. Now, as the way of thinking of the people will change, so will our society.
Efficient Multi-Scale Feature Generation Adaptive Network

Recently, an early exit network, which dynamically adjusts the model complexity during inference time, has achieved remarkable performance and neural network efficiency to be used for various applications. So far, many researchers have been focusing on reducing the redundancy of input sample or model architecture. However, they were unsuccessful at resolving the performance drop of early classifiers that make predictions with insufficient high-level feature information. Consequently, the performance degradation of early classifiers had a devastating effect on the entire network performance sharing the backbone. Thus, in this paper, we propose an Efficient Multi-Scale Feature Generation Adaptive Network (EMGNet), which not only reduced the redundancy of the architecture but also generates multi-scale features to improve the performance of the early exit network. Our approach renders multi-scale feature generation highly efficient through sharing weights in the center of the convolution kernel. Also, our gating network effectively learns to automatically determine the proper multi-scale feature ratio required for each convolution layer in different locations of the network. We demonstrate that our proposed model outperforms the state-of-the-art adaptive networks on CIFAR10, CIFAR100, and ImageNet datasets. The implementation code is available at https://github.com/lee-gwang/EMGNet