Reviewerly/exHarmony huggingface dataset:
Development Human Activity Recognition for the Elderly Using Inertial Sensor and Statistical Feature

The Elderly is a human being over 60 years old. Humans over the age of 60 years have the potential to fall due to balance and gait disturbances. The disturbance is caused by a decrease in the sensory system that focuses the view. This impact will result in the risk of developing fractures and even death if it cannot be treated quickly. Previous research has conducted tests on Human Activity Recognition. In this study, we will test the selection of feature extraction and machine learning methods regarding Human Activity Recognition. The extraction features tested in this study are the mean, median, maximum, minimum, skewness, kurtosis, and variance with Fast Fourier Transform from each value obtained by the IMU sensor. The machine learning methods studied were Stochastic Gradient Descent, Random Forest, K-Nearest Neighbor, Decision Tree, and Gaussian Naive Bayes. The test results demonstrate that Y-Axis Gyroscope means, X-Axis Acceleration skewness, X-Axis Gyroscope variance, and X-Axis Gyroscope max are the optimum extraction features for machine learning. The most accurate machine learning algorithm is Random Forest, which has a 99.59% accuracy rate. Therefore, the author made this Human Activity Recognition system to monitor the activities of the elderly. With this research, elderly monitors can keep track of the elderly’s activities and concentrate more attention on all of the elderly’s activities, especially fall activities. Thus, monitors can provide early assistance to the elderly.
Sonnet full of knowing

Sonnet full of knowing William Joel (bio) "Many men go fishing all of their liveswithout knowing it is not fish they are after." Henry David Thoreau The act of holding thoughts inside one's head;the effort given to the task; the fearthat some may leak away; the utter dreadthat they may not return; the single tearthat falls when seeing that a thought has goneforever, holding dearly to the onesit left behind, and making sure that nonemight follow soon; the golden web that's spunfrom what's inside, to make up for the thingsyou've lost or things you've not yet learned; the joyof finding something shiny, new to bringinside and snuggle tightly, like the boyyou used to be, so full of hope for wherea life might lead, without a knowing care. [End Page 155] William Joel All things are connected. That's the premise of what william j. joel does. Each of Mr. Joel's interests informs each other. Mr. Joel has been teaching computer science since 1983 and has been a writer even longer. His works have recently appeared in Common Ground Review, DASH Literary Journal, The Blend International, Liminality, and North Dakota Quarterly. Copyright © 2023 University of North Dakota
preface of a riddle, and: Training Day, and: REM: A Case Study

preface of a riddle, and: Training Day, and: REM: A Case Study Dorsía Smith Silva (bio) preface of a riddle gunshots // or fireworks // this is what i ponder onNew Year's Eve in Puerto Rico // you tell me asmatter-of-factly as running for a bus // that i have tocount the rat-a-tat-tat-tats // if it is five or moresprinting bursts // then gunshots // if it is rat pause thentat pause // hand clap hand clap // then fireworks // howto decipher // when all of the rat-a-tat-tat-tats chimealike // everything booms in neon thunder // queues ofswarming smoky sky // barking dilates throughout theroom // goblets grind-grenade with clink tanks // my earsare littered with unshuttering taps // as the clappers belchtheir innards // pinioning like rolled tugs on both sides ofmy best dress // will i be antipática if i write myself out //the last time i checked whatever was a complete sentence [End Page 146] Training Day Teach me how to bea good she-wolfthe kind that knows when to blade-biteclamp hot down break the skin inside out& bone-lick the wounduntil blood buries in the bellythe kind that unanswerswhen name is long-toothed howledlet it come & go as it pleasesto get he-hunters off scentlet paw pads dipped in moon juicesteer prints between your legsyou want me to ask againI won't [End Page 147] REM: A Case Study The drowning happens now likewind strong-arming trees. Birdsare witnesses. They screech don'tleave a red mess. Mercy made acameo once. She said time is moneyand clock punched out. The skywidens enough to scaffold myholler. Rain is snuffing out mystray breath. I see the uncombedgardens last where only hurricanes stay. [End Page 148] Dorsía Smith Silva dorsía smith silva is a four-time Pushcart Prize nominee, a three-time Best of the Net nominee, and full professor at the University of Puerto Rico. Her poetry is forthcoming in Shenandoah, The Minnesota Review, The Offing, and elsewhere. She has a Ph.D. in Caribbean Literature and posts at @DSmithSilva. Copyright © 2023 University of North Dakota
Learning Management Systems in Flexible Learning Environments - A Study of Teachers’ Experiences

Digital transformation in education is expected to progress teaching and learning. To meet this expectation, new types of classrooms called flexible learning environments are designed where digital resources such as learning management systems (LMS) are integrated. This raises the question of how LMS are experienced by teachers in flexible learning environments and how their teaching practice and competence development is supported by the LMS. In this study, ten teachers working in flexible working environments have been interviewed about their experiences with LMS. The study resulted in four themes of experiences (1) Lack of adoption, (2) Control within the system, (3) Collaboration and competence development, (4) Direct feedback and interactions. The insights of the study contributes with implications for choosing and integrating LMS in flexible learning environments.
The effectiveness of developing schoolchildren's science literacy in biology classes with a focus on the study of Genetics within a flexible blended learning model

Introduction. The article is devoted to the problem of schoolchildren’s functional literacy formation within the framework of blended learning. The purpose of the study is to substantiate the effectiveness of the use of an electronic educational resource to achieve a stable increase in indicators of science literacy at the stage of compulsory education. Materials and Methods. The research methodology is based on the ideas of humanization, innovativeness in the formation of schoolchildren’s functional literacy, learner-centered and systematic approach to the organization of learning biology and genetics, and the educational process as a whole. Experimental work was conducted in 7 types of educational organizations of the Republic of Bashkortostan: schools with in-depth study of a number of subjects, individual education schools, private schools, comprehensive secondary schools, basic general education schools, lyceums and gymnasiums. 191 schoolchildren, 9 teachers, 112 parents participated in the study. The following methods were used: theoretical analysis, meta-analysis, methods of analogy, comparison, modification, questionnaires, interviews; tests, and statistical data processing. Results. In the process of the research, the necessity of developing educational technologies and tools for implementing an integrated approach to the formation and development of science literacy at the stage of general education are substantiated. The role of studying Genetics in the formation of schoolchildren’s science literacy is clarified; trends and needs of school practice in teaching Genetics are revealed. The authors identify and describe the following prospects of using the technology of mixed learning and the electronic educational course for schoolchildren and parents called ‘Genetics for everyone: about the difficult’: updating knowledge about the influence of studying genetics on the formation and development of interest in biological sciences, increasing motivation to master knowledge about complex processes occurring in the microcosm, as well as expanding the prospects for the development of a new educational paradigm of personalization of students when using electronic educational materials in mixed learning courses. Conclusions. The results obtained prove the effectiveness of schoolchildren’s science literacy formation within the implementation of a flexible model; the proposed electronic educational resource ‘Genetics for everyone: about the difficult’ for schoolchildren and parents contributes to the development of the subjective position of schoolchildren, provides a steady trend towards improving the quality of knowledge, and interest in Genetics as a science.
Generalized Stereo Matching Method Based on Iterative Optimization of Hierarchical Graph Structure Consistency Cost for Urban 3D Reconstruction

Generalized stereo matching faces the radiation difference and small ground feature difference brought by different satellites and different time phases, while the texture-less and disparity discontinuity phenomenon seriously affects the correspondence between matching points. To address the above problems, a novel generalized stereo matching method based on the iterative optimization of hierarchical graph structure consistency cost is proposed for urban 3D scene reconstruction. First, the self-similarity of images is used to construct k-nearest neighbor graphs. The left-view and right-view graph structures are mapped to the same neighborhood, and the graph structure consistency (GSC) cost is proposed to evaluate the similarity of the graph structures. Then, cross-scale cost aggregation is used to adaptively weight and combine multi-scale GSC costs. Next, object-based iterative optimization is proposed to optimize outliers in pixel-wise matching and mismatches in disparity discontinuity regions. The visibility term and the disparity discontinuity term are iterated to continuously detect occlusions and optimize the boundary disparity. Finally, fractal net evolution is used to optimize the disparity map. This paper verifies the effectiveness of the proposed method on a public US3D dataset and a self-made dataset, and compares it with state-of-the-art stereo matching methods.
Smooth Hand Preshaping for Visually Realistic Grasping in Virtual Reality

In virtual reality, grasping interaction techniques that use controllers often lack a smooth transition between the neutral hand pose and the one that fits the topology of the grabbed object. This transition is referred to as preshaping. We explore the impact on embodiment and task speed of progressively and automatically adapting the fingers' closure of the hand when it gets close to an object by anticipating the user's intention. A comparison with existing methods suggests that this approach negatively impacts the sense of agency but does not impact task speed, body ownership, and interaction and finger movement realism.
Research on Transmission Line Stereo Matching Based on Twin Residual Network

For electric power visual technology in the construction process to ensure safe distance in the research, because the transmission line structure, special "sick area", using local information for three-dimensional matching speed is faster, but can not guarantee accuracy, especially the edge, occlusion, weak texture "sick area" has serious deficiencies, makes the transmission line identification and binocular vision three-dimensional matching task difficult. In order to ensure the accuracy of stereo matching and make more effective use of global information, this paper proposes a stereo matching algorithm of twin network combining segmentation results and fusion of geometric information, which can better stereo match such targets of transmission lines, and thus output depth images. In addition, there is a wide problem of empty pixels in the depth map. Introducing the depth smoothing function can better handle the position of the depth change in the depth map, and further improve the accuracy of three-dimensional matching. At the same time, the experimental results show that the depth map output of the proposed method is better accurate.
PV Prediction Using Hierarchical BiLSTM-RFR Model Considering Meteorological Factors

Prediction to the photovoltaic (PV) power is critical to maintain power balance and secure operation of power system. To improve the prediction accuracy, this paper newly proposes a hierarchical prediction model with the complementary empirical mode decomposition (CEEMD) by combining bidirectional long short-term memory neural network (BiLSTM) and the random forest regression (RFR). At first, the PV power is decomposed to several intrinsic modes using the CEEMD. Then by analyzing the correlation between intrinsic modes and meteorological factors with the Spearman correlation coefficient, the prediction model based on the BiLSTM and RFR is proposed for the strongly and weakly correlated components. Finally, the resultant components are combined to obtain the predicted PV power. The simulation results validate the prediction accuracy with the proposed model.
Cognitive performance of 4-6-year-old children: a longitudinal study

Background Many children in low and middle-income countries (LMIC) have reduced cognitive abilities, but few studies have explored the influencing factors.&#x0D; Objective To determine the determinants of children's low cognitive development at 4 to 6-year-old.&#x0D; Methods This is a retrospective cohort study in year 2021. The data was obtained from Bogor Longitudinal Study of Child Growth and Development (BLSCGD) that started from year 2012. For this analysis, we considered 165 of children aged 4-6 years. The dependent variable in this analysis was cognitive development as measured by the Indonesian Wechsler Preschool and Primary Scale of Intelligence (WPPSI) with the indicators of full-scale IQ (FSIQ), verbal IQ (VIQ) and performance IQ (PIQ). The independent variables were determinants of children’s cognitive development at 4-6 years, consisted of socio-demographic factors, determinants from early life (0-23 months), as well as determinants from current conditions. Multivariate analysis done with the logistic regression test.&#x0D; Results Bivariate analysis revealed that paternal education level was significantly associated with FSIQ and inadequate calorie intake in infancy and poor child stimulation were significantly associated with VIQ. In the final model of multivariate analysis, low paternal education level retained a significant association with FSIQ. Psychosocial stimulation was significantly associated with VIQ and inadequate calorie intake during infancy was significantly associated with PIQ.&#x0D; Conclusion Low paternal education level and inadequate psychosocial stimulation are risk factors for reduced child cognitive development in 4-6-year-olds. Inadequate calorie intake in infancy even though not statistically significant it has high OR values for low VIQ and PIQ, thus the results showed the importance of calory intake in infancy.
Rank-based Ensemble of CNN Models for Breast Cancer Detection in Histopathology Images

People today suffer from a variety of diseases as a Breast cancer is one of the most common types of cancer in women worldwide. Breast cancer can be invasive or non-invasive. Invasive breast cancer can spread into surrounding tissues also in distant organs whereas non-invasive breast cancer spread surrounding the milk ducts or lobules of the mammary glands in the breast. However early detection followed by the diagnosis can not only reduce the treatment cost but also increase the chance of survival. One of the most effective methods for the detection of tumor malignancies is breast histopathology image analysis. However, manual image analysis for breast histopathology is subjective, time-consuming, and prone to human error. Convolution neural network (CNN) based models have produced encouraging results for the classification of breast histopathology imaging. In this work, we will combine the results of three transfer learning experiments to create a model utilizing a novel rank-based ensemble method rather than depending on a single CNN model, namely GoogleNet, VGG16 and MobileNetV3 Small. Here, it is suggested that the 2-class classification problem for breast histopathology pictures can be handled using the Gompertz function. This strategy yields superior classification results in contrast to publicly available standard datasets such as BreakHis and ICIAR-2018, state-of-the-art methodologies.
Research on Spam Detection with a Hybrid Machine Learning Model

Since the beginning of the twentieth century, with the rapid development and popularization of computer technology, e-mail has become an indispensable communication tool in people's social life, and greatly simplified people's daily life such as learning and working methods. However, following the rapid development of computer information technology, e-mail brings convenience to people, also generates some spam messages, which seriously threaten and discount the safety of e-mail users. Although the spam detection technology has been deeply studied and widely used, the traditional spam detection methods mostly rely on the static features extracted from the mails while these methods have great limitations and cannot effectively deal with new malicious mail attacks that are complex, aggressive, destructive, and targeted. Thus, with the rapid development of machine learning and artificial intelligence technology, this paper proposed a spam detection model with a hybrid machine learning method: first presented text pre-processing process including word tokenization, removing stop word, and extracting feature vector, then a spam detection classifier based on linear-regression hybrid model with three commonly-used machine learning methods, namely SVM, ANN, and RF, was discussed. The results show that the three models are able to produce a good result, but the hybrid model would present a better performance and demonstrate the effectiveness of the hybrid method.
Quantifying the Dissimilarity of Texts

Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, we compared the properties and performance of different dissimilarity measures $D$ using three different representations of texts -- vocabularies, word frequency distributions, and vector embeddings -- and three simple tasks -- clustering texts by author, subject, and time period. Using the Project Gutenberg database, we found that the generalised Jensen--Shannon divergence applied to word frequencies performed strongly across all tasks, that $D$'s based on vector embedding representations led to stronger performance for smaller texts, and that the optimal choice of approach was ultimately task-dependent. We also investigated, both analytically and numerically, the behaviour of the different $D$'s when the two texts varied in length by a factor $h$. We demonstrated that the (natural) estimator of the Jaccard distance between vocabularies was inconsistent and computed explicitly the $h$-dependency of the bias of the estimator of the generalised Jensen--Shannon divergence applied to word frequencies. We also found numerically that the Jensen--Shannon divergence and embedding-based approaches were robust to changes in $h$, while the Jaccard distance was not.
Preliminary Study: Development of STEM-Based E-Students Worksheet to Improve Learners' Science Literacy Skills

The purpose of this study was to describe the results of the needs analysis for the development of e-Students Worksheet based on the STEM approach to improve the science literacy skills of students in several high schools in Padang City. This research design uses the R&amp;D method by using the Plomp development model which consists of three steps, namely: 1) preliminary research; 2) prototyping phase, and 3) assessment phase. The instrument used in this research is a questionnaire validation sheet filled out by educators and students. Based on the analysis of the results of preliminary data observations conducted in several high schools in Padang City, the results obtained are in the analysis of graduate competency standards, assessment, learning activities have an average percentage with a very good category. However, in the analysis of students, the STEM approach, the use of electronic teaching materials, and science literacy generally have an average percentage that is in the sufficient category. To overcome the existing problems educators can develop STEM-based e-Students Worksheet to improve students' scientific literacy
VacancySBERT: the approach for representation of titles and skillsfor semantic similarity search in the recruitment domain

The paper focuses on deep learning semantic search algorithms applied in the HR domain. The aim of the article is developing a novel approach to training a Siamese network to link the skills mentioned in the job ad with the title. It has been shown that the title normalization process can be based either on classification or similarity comparison approaches. While classification algorithms strive to classify a sample into predefined set of categories, similarity search algorithms take a more flexible approach, since they are designed to find samples that are similar to a given query sample, without requiring pre-defined classes and labels. In this article semantic similarity search to find candidates for title normalization has been used. A pre-trained language model has been adapted while teaching it to match titles and skills based on co-occurrence information. For the purpose of this research fifty billion title-descriptions pairs had been collected for training the model and thirty three thousand title-description-normalized title triplets, where normalized job title was picked up manually by job ad creator for testing purposes. As baselines FastText, BERT, SentenceBert and JobBert have been used. As a metric of the accuracy of the designed algorithm is Recall in top one, five and ten model's suggestions. It has been shown that the novel training objective lets it achieve significant improvement in comparison to other generic and specific text encoders. Two settings with treating titles as standalone strings, and with included skills as additional features during inference have been used and the results have been compared in this article. Improvements by 10% and 21.5% have been achieved using VacancySBERT and VacancySBERT (with skills) respectively. The benchmark has been developed as open-source to foster further research in the area.
Semi-Federated Learning for Edge Intelligence with Imperfect SIC

In this paper, we propose a semi-federated learning (SemiFL) framework that allows computing-limited clients to collaboratively train a shared model with resource-abundant clients. Specifically, by supporting the coexistence of model-updating and data-offloading, the SemiFL framework enables both centralized and federated learning in a hybrid fashion. Due to the decoding error, we consider the practical case with residual interference. To improve uplink throughput for centralized learning while reducing aggregation distortion for federated learning, we formulate a non-convex optimization problem to jointly optimize the transmit power and receive strategy. Then, we propose an efficient algorithm to solve the challenging problem by using successive convex approximation. Simulation results demonstrate the effectiveness of our SemiFL framework for heterogeneous networks, and reveal the impact of imperfect signal decoding on communication rates.
Collages of String Diagrams

We introduce collages of string diagrams as a diagrammatic syntax for glueing multiple monoidal categories. Collages of string diagrams are interpreted as pointed bimodular profunctors. As the main examples of this technique, we introduce string diagrams for bimodular categories, string diagrams for functor boxes, and string diagrams for internal diagrams.