Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of telerehabilitation in the rehabilitation of children with cerebral palsy during COVID-19: A review

Individuals with cerebral palsy (CP) have limited mobility and are unable to actively participate in tasks that are part of their daily living. Thus, continuous therapeutic sessions are required to keep such individuals active and engaged in the environment. Due to the coronavirus disease of 2019 (COVID-19) lockdowns, rehabilitation for children with CP was inhibited which consequently put them at risk of losing their functional gains which were obtained through previous in-person therapies. In order to avoid this, an alternate to conventional therapy was required and this rendered it necessary to review the role of telerehabilitation (TR) and its various modes for the rehabilitation of children with CP. This study aimed to explore the effectiveness of TR for children with CP during COVID-19 through the present literature and to determine if TR is an alternate to conventional physical therapy in children with CP during the coronavirus outbreak. This scoping review was conducted by searching different databases such as PubMed, Cochrane Collaboration, Medline, and Google Scholar on the basis of inclusion criteria. Screening was performed from January 2019 to June 2022 and the initial screening attempt returned 469 studies. After applying the aforementioned criteria, all impertinent studies were excluded which resulted in 28 studies being included for this review as they contained information about the effectiveness of TR on children with CP during COVID-19. These 28 articles included randomised controlled trials, surveys, reviews, clinical trials, case reports, prospective studies, editorials, and longitudinal studies. Three out of the 7 randomised controlled trial studies revealed that action observation treatment can be a useful approach for TR in child with CP during similar pandemics. The other 3 studies supported the use of computer-based games, robots, nonimmersive virtual reality, and wearable haptic devices as a significant means of TR in child with CP as an alternate to routine therapy during COVID-19. TR is an affable mode of rehabilitation specifically for the pediatric population. In the future, it can be an alternate to routine therapy for those who are unlikely to get daily access to in-person therapeutic sessions due to various reasons or circumstances.
Analyses of plasma metabolites using a high performance four-channel CIL LC-MS method and identification of metabolites associated with enteric methane emissions in beef cattle

Reducing enteric methane (one greenhouse gas) emissions from beef cattle not only can be beneficial in reducing global warming, but also improve efficiency of nutrient utilization in the production system. However, direct measurement of enteric methane emissions on individual cattle is difficult and expensive. The objective of this study was to detect plasma metabolites that are associated with enteric methane emissions in beef cattle. Average enteric methane emissions (CH4) per day (AVG_DAILYCH4) for each individual cattle were measured using the GreenFeed emission monitoring (GEM) unit system, and beef cattle with divergent AVG_DAILYCH4 from Angus (n = 10 for the low CH4 group and 9 for the high CH4 group), Charolais (n = 10 for low and 10 for = high), and Kinsella Composite (n = 10 for low and 10 for high) populations were used for plasma metabolite quantification and metabolite-CH4 association analyses. Blood samples of these cattle were collected near the end of the GEM system tests and a high performance four-channel chemical isotope labeling (CIL) liquid chromatography (LC) mass spectrometer (MS) method was applied to identify and quantify concentrations of metabolites. The four-channel CIL LC-MS method detected 4235 metabolites, of which 1105 were found to be significantly associated with AVG_DAILYCH4 by a t-test, while 1305 were significantly associated with AVG_DAILYCH4 by a regression analysis at p<0.05. Both the results of the t-test and regression analysis revealed that metabolites that were associated with enteric methane emissions in beef cattle were largely breed-specific whereas 4.29% to 6.39% CH4 associated metabolites were common across the three breed populations and 11.07% to 19.08% were common between two breed populations. Pathway analyses of the CH4 associated metabolites identified top enriched molecular processes for each breed population, including arginine and proline metabolism, arginine biosynthesis, butanoate metabolism, and glutathione metabolism for Angus; beta-alanine metabolism, pyruvate metabolism, glycolysis / gluconeogenesis, and citrate cycle (TCA cycle) for Charolais; phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, and arginine and proline metabolism for Kinsella Composite. The detected CH4 associated metabolites and enriched molecular processes will help understand biological mechanisms of enteric methane emissions in beef cattle. The detected CH4 associated plasma metabolites will also provide valuable resources to further characterize the metabolites and verify their utility as biomarkers for selection of cattle with reduced methane emissions.
Lower Bound Founded Logic of Here-and-There : A Preliminary Report

A distinguishing feature of Answer Set Programming is that all atoms belonging to a stable model must be founded. That is, an atom must not only be true but provably true. This can be made precise by means of the constructive logic of Here-and-There, whose equilibrium models correspond to stable models. One way to look at foundedness is to regard Boolean truth values as ordered by letting true be greater than false. Then, each Boolean variable takes the smallest truth values that can be proven for it. This idea was generalized by Aziz to ordered domains and applied to constraint satisfaction problems. As before, the idea is that a, say integer, variable gets only assigned to the smallest integer that can be justified. In this paper, we present a logical reconstruction of Aziz’ idea in the setting of the logic of Here-and-There. More precisely, we start by defining the logic of Here-and-There with lower bound founded variables along with its equilibrium models and elaborate upon their formal properties. We then define a logic program fragment dealing with linear constraints over integers and analyze it in terms of concepts from logic programming. Finally, we compare our approach with related ones and sketch
Wasserstein diffusion on graphs with missing attributes

Many real-world graphs are attributed graphs where nodes are associated with non-topological features. While attributes can be missing anywhere in an attributed graph, most of existing node representation learning approaches do not consider such incomplete information. In this paper, we propose a general non-parametric framework to mitigate this problem. Starting from a decomposition of the attribute matrix, we transform node features into discrete distributions in a lower-dimensional space equipped with the Wasserstein metric. On this Wasserstein space, we propose Wasserstein graph diffusion to smooth the distribution representations of nodes with information from their local neighborhoods. This allows us to reduce the distortion caused by missing attributes and obtain integrated representations expressing information of both topology structure and attributes. We then pull the nodes back to the original space and produce corresponding point representations to facilitate various downstream tasks. To show the power of our representation method, we designed two algorithms based on it for node classification (with missing attributes) and matrix completion respectively, and demonstrate their effectiveness in experiments.
A Real-Time Tracking System Developed for an Interactive Stage Performance

A real-time tracking system was built to track performers on an interactive stage. Using an ordinary, up to date, desktop workstation, the performers’ silhouette was segmented from the background and parameterized by calculating the normalized central image moments. In the stage system, the silhouette moments were then sent to a parallel workstation, which used them to generate corresponding 3D virtual geometry and projected the generated graphic back onto the stage. Keywords—image moment, interactive stage, real-time, silhouette.
Artificial Neural Network‐Based Framework for Developing Ground‐Motion Models for Natural and Induced Earthquakes in Oklahoma, Kansas, and Texas

This article puts forward an artificial neural network (ANN) framework to develop ground-motion models (GMMs) for natural and induced earthquakes in Oklahoma, Kansas, and Texas. The developed GMMs are mathematical equations that predict peak ground acceleration, peak ground velocity, and spectral accelerations at different frequencies given earthquake magnitude, hypocentral distance, and site condition. The motivation of this research stems from the recent increase in the seismicity rate of this particular region, which is mainly believed to be the result of the human activities related to petroleum production and wastewater disposal. Literature has shown that such events generally have shallow depths, leading to large-amplitude shaking, especially at short hypocentral distances. Thus, there is a pressing need to develop site-specific GMMs for this region. This study proposes an ANN-based framework to develop GMMs using a selected database of 4528 ground motions, including 376 seismic events with magnitudes of 3 to 5.8, recorded over the 4to 500-km hypocentral distance range in these three states since 2005. The results show that the proposed GMMs lead to accurate estimations and have generalization capability for ground motions with a range of seismic characteristics similar to those considered in the database. The sensitivity of the equations to predictive parameters is also presented. Finally, the attenuation of ground motions in this particular region is compared with those in other areas of North America. Electronic Supplement:Text and figures describing the selection of the hidden layer size of the artificial neural network (ANN) models, as well as sensitivity of ANN models to modeling assumptions
Sustaining Ecosystem Functions Under Environmental Change: The Combined Impacts of Temperature, Species Diversity and Limiting Resources on Phytoplankton Communities

Plankton play a key role in regulating nutrient and carbon cycles in freshwater ecosystems. The uptake and processing of nutrients in planktonic biomass are highly sensitive to changes in the environment, such as alterations in the availability of limiting nutrients, increasing temperature due to climate change, and changes to the composition of interacting species. The focus of this thesis is to use a variety of experimental and theoretical methods to assess and predict the impact of multiple perturbations on community structure, dynamics and ecosystem function, with a particular focus on interactions between phytoplankton and their consumers (zooplankton). Increases in both temperature and phytoplankton species diversity independently decreased CO2 concentrations when the number of non-resource species (those inedible to the zooplankton) were high. Using structural equation modeling I show that the effect is indirect, resulting largely from the positive impacts on total biomass of phytoplankton. Phytoplankton are limited by a range of resources, and differences in the functional traits used to utilize light and nutrients can explain the distributions of species under different temperature regimes. I found that under light and nitrogen limitation, resource requirements are generally lowest at intermediate temperatures, and that changes in temperature may therefore alter the competitive hierarchy amongst species. Using the model freshwater phytoplankton Chlamydomonas reinhardtii, I also find that previous selection environments govern future competitive abilities in phytoplankton. Adaptation to a high salt and low nutrient stress increases competitive ability under light limited conditions, indicating a strong dependency of selection environment for overall competitiveness. This thesis provides a mechanistic insight into the role of diverse plankton communities for community dynamics and ecosystem functioning.
Parameterized Complexity News

Congratulations to all for awards, graduates, new jobs, and research. Read research articles: Parameterized Algorithmics for Temporal Graph Problems by Hendrik Molter (TU Berlin) and Phylogenetics and parameterized complexity by Steven Kelk (Maastricht Univ). Update Wikipedia with your results. Add your presentations to the FPT Complexity Youtube. Contact Jungho Ahn (Kaist) junghoahn@kaist.ac. kr for assistance. Send us news via Google form (https://forms.gle/sfZNzKFsbfqSywgy7) or directly to our emails. We love hearing from you. Editors: Frances.Rosamond@uib.no, Valia Mitsou vmitsou@irif.fr, Benjamin.Bergougnoux@uib.no and the News Team.
Neuroinformatics : the integration of shared databases and tools towards integrative neuroscience . Abbreviated Title : Sharing neuroscience databases and tools

Authors S. -I. Amari, F. Beltrame, J.G. Bjaalie, T. Dalkara, E. De Schutter, G.F. Egan*, N.H. Goddard, C. Gonzalez, S. Grillner, A. Herz, K. -P. Hoffmann, I. Jaaskelainen, S.H. Koslow, S. -Y. Lee, L. Matthiessen, P.L. Miller, F.M. da Silva, M. Novak, V. Ravindranath, R. Ritz, U. Ruotsalainen, V. Sebestra, S. Subramaniam, A.W. Toga, S. Usui, J. van Pelt, P. Verschure, D. Willshaw, A. Wrobel, T. Yiyuan
Sustainable Computing: Informatics and Systems Hybrid Resource Provisioning for Minimizing Data Center Sla Violations and Power Consumption

Keywords: Resource provisioning Power management Sustainable computing Workload analysis a b s t r a c t This paper presents a practical and systematic approach to correctly provision server resources in data centers, such that SLA violations and energy consumption are minimized. In particular, we describe a hybrid method for server provisioning. Our method first applies a novel discretization technique on historical workload traces to identify long-term workload demand patterns that establish a " base " load. It then employs two techniques to dynamically allocate capacity: predictive provisioning handles the predicted base load at coarse time scales (e.g., hours) and reactive provisioning handles any excess workload at finer time scales (e.g., minutes). The combination of predictive and reactive provisioning achieves a significant improvement in meeting SLAs, conserving energy and reducing provisioning cost. We implement and evaluate our approach using traces from four production systems. The results show that our approach can provide up to 35% savings in power consumption and reduce SLA violations by as much as 21% compared to existing techniques, while avoiding frequent power cycling of servers.
Lattice Index Codes From Algebraic Number Fields

Broadcasting $K$ independent messages to multiple users where each user demands all the messages and already has a subset of the messages as side information is studied. Recently, Natarajan et al. proposed a novel broadcasting strategy called lattice index coding, which adopts lattices constructed over some principal ideal domains (PID) for transmission. Using the structure of lattices over PID, they showed that this scheme provides uniform side information gain for any side information configuration, a desired property which essentially guarantees a fair signal-to-noise ratio gain when normalized by the amount of information contained in side information. In this paper, we generalize this strategy to a general ring of algebraic integers, which may not be a PID. Upper and lower bounds on the side information gains for the proposed scheme constructed over some interesting classes of number fields are provided and are shown to coincide asymptotically in message rate. This generalization substantially enlarges the design space and partially includes the scheme by Natarajan et al. as a special case. Perhaps more importantly, in addition to side information gains, the proposed lattice index codes benefit from diversity gains inherent in constellations carved from number fields when used over Rayleigh fading channel. Some interesting examples are provided for which the proposed scheme allows the messages to be from the same field.
Machine Learning with Annotator Rationales to Reduce Annotation Cost

We review two novel methods for text categorization, based on a new framework that utilizes richer annotations that we call annotator rationales. A human annotator provides hints to a machine learner by highlighting contextual “rationales” in support of each of his or her annotations. We have collected such rationales, in the form of substrings, for an existing document sentiment classification dataset [1]. We have developed two methods, one discriminative [2] and one generative [3], that use these rationales during training to obtain significant accuracy improvements over two strong baselines. Our generative model in particular could be adapted to help learn other kinds of probabilistic classifiers for quite different tasks. Based on a small study of annotation speed, we posit that for some tasks, providing rationales can be a more fruitful use of an annotator’s time than annotating more examples.
A Professional Development Process Model for Online and Blended Learning: Introducing Digital Capital

Since information and communication technologies were introduced into education, the number of courses delivered in an online or blended learning (OBL) format has increased significantly. However, not all teachers are experienced in teaching in this new digital environment. While various teacher professional development (TPD) models exist, few target OBL and teachers’ change processes during professional development. Therefore, this article presents a five-phase TPD process model for OBL. The five phases of the model are (a) a need for TPD for OBL, (b) the professional development strategy, (c) the teacher change associated with OBL, (d) the recognition and appreciation of these changes, and (e) the anchoring of the changes made in the teachers’ everyday practice. The model presented can offer a valuable and new approach toward TPD for OBL and introduces the notion of digital capital into TPD for OBL.
Introduction to Data Mining

1 Introduction 1.1 What is Data Mining? 1.2 Motivating Challenges 1.3 The Origins of Data Mining 1.4 Data Mining Tasks 1.5 Scope and Organization of the Book 1.6 Bibliographic Notes 1.7 Exercises 2 Data 2.1 Types of Data 2.2 Data Quality 2.3 Data Preprocessing 2.4 Measures of Similarity and Dissimilarity 2.5 Bibliographic Notes 2.6 Exercises 3 Exploring Data 3.1 The Iris Data Set 3.2 Summary Statistics 3.3 Visualization 3.4 OLAP and Multidimensional Data Analysis 3.5 Bibliographic Notes 3.6 Exercises 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 4.1 Preliminaries 4.2 General Approach to Solving a Classification Problem 4.3 Decision Tree Induction 4.4 Model Overfitting 4.5 Evaluating the Performance of a Classifier 4.6 Methods for Comparing Classifiers 4.7 Bibliographic Notes 4.8 Exercises 5 Classification: Alternative Techniques 5.1 Rule-Based Classifier 5.2 Nearest-Neighbor Classifiers 5.3 Bayesian Classifiers 5.4 Artificial Neural Network (ANN) 5.5 Support Vector Machine (SVM) 5.6 Ensemble Methods 5.7 Class Imbalance Problem 5.8 Multiclass Problem 5.9 Bibliographic Notes 5.10 Exercises 6 Association Analysis: Basic Concepts and Algorithms 6.1 Problem Definition 6.2 Frequent Itemset Generation 6.3 Rule Generation 6.4 Compact Representation of Frequent Itemsets 6.5 Alternative Methods for Generating Frequent Itemsets 6.6 FP-Growth Algorithm 6.7 Evaluation of Association Patterns 6.8 Effect of Skewed Support Distribution 6.9 Bibliographic Notes 6.10 Exercises 7 Association Analysis: Advanced Concepts 7.1 Handling Categorical Attributes 7.2 Handling Continuous Attributes 7.3 Handling a Concept Hierarchy 7.4 Sequential Patterns 7.5 Subgraph Patterns 7.6 Infrequent Patterns 7.7 Bibliographic Notes 7.8 Exercises 8 Cluster Analysis: Basic Concepts and Algorithms 8.1 Overview 8.2 K-means 8.3 Agglomerative Hierarchical Clustering 8.4 DBSCAN 8.5 Cluster Evaluation 8.6 Bibliographic Notes 8.7 Exercises 9 Cluster Analysis: Additional Issues and Algorithms 9.1 Characteristics of Data, Clusters, and Clustering Algorithms 9.2 Prototype-Based Clustering 9.3 Density-Based Clustering 9.4 Graph-Based Clustering 9.5 Scalable Clustering Algorithms 9.6 Which Clustering Algorithm? 9.7 Bibliographic Notes 9.8 Exercises 10 Anomaly Detection 10.1 Preliminaries 10.2 Statistical Approaches 10.3 Proximity-Based Outlier Detection 10.4 Density-Based Outlier Detection 10.5 Clustering-Based Techniques 10.6 Bibliographic Notes 10.7 Exercises Appendix A Linear Algebra Appendix B Dimensionality Reduction Appendix C Probability and Statistics Appendix D Regression Appendix E Optimization Author Index Subject Index
International Journal of Advance Research in Computer Science and Management Studies

Location-Based Service (LBS) becomes increasingly popular with the dramatic growth of smartphones and social network services (SNS), and its context-rich functionalities attract considerable users. Most of LBS providers make use of users’ location information to provide them convenience and useful functions. Location based applications exploits the focusing capabilities of mobile device to determine the current position of user, and specifies the query results to capture the neighboring points of interest. In this paper the propose system is an novel approach that simultaneously ensures both the privacy and the integrity. This is achieved by using space encryption as the basis of this approach and then conceives techniques that enable the data users to audit the integrity of the query result for the range queries and k -nearest-neighbor queries (k NN). And it can be accomplish by using the MR-tree, an index based on the R*-tree, capable of authenticating arbitrary spatial queries. Here it can be shown, analytically and experimentally, that the MR-tree is considerably faster to build and consumes less space.
Efficient Run-Length Encoding of Binary Sources with Unknown Statistics

We present a new binary entropy coder of the Golomb family, with an adaptation strategy that is nearly optimum in a maximum-likelihood sense. This new encoder can be implemented efficiently in practice, since uses only integer arithmetic and no divisions. That way, the proposed encoder has a complexity nearly identical to that of popular adaptive Rice coders. However, whereas Golomb-Rice coders have an excess rate with respect to the source entropy of up to 4.2% for binary sources with unknown statistics, the proposed encoder has an excess rate of less than 2%.