Miracle-dz/UniFAR-FTUs huggingface dataset:
A Real-Time Lightweight Method to Detect the Sixteen Brushing Regions Based on a 9-axis Inertial Sensor and Random Forest Classifier

In this research, we propose a real-time lightweight method to detect the tooth-brushing regions. The system takes a sensor node as input device and a 2D dentition as output interface. Specifically, the sensor node can be attached onto the handle of a toothbrush to get the 3D Euler angles, which are used as the features for training Random Forester Classifier (RFC) Model. Moreover, the predicted brushing region will be displayed on the dentition interface in real-time. All teeth are divided into 16 regions to evaluate the detection accuracy. User-dependent offline experiment shows that the RFC-based automatic threshold definition method achieves 97.6% validation accuracy, which is about 35% higher than the manual threshold definition method. For the RFC-based method, the online real-time accuracy is 74.0%, which is about 23.6% less than the offline result.
Investigation of the relationship between serum sclerostin and dickkopf-1 protein levels with bone turnover in children and adolescents with type-1 diabetes mellitus

Abstract Objectives Diabetes mellitus (DM) is widely known to have a detrimental effect on bone health and is associated with increased fracture risk. Recently, the Wnt/beta-catenin signaling pathway and its inhibitors sclerostin and dickkopf-1 (Dkk-1) were found to be involved in the control of bone mass. The present study aimed to measure serum sclerostin and Dkk-1 protein levels in children and adolescents with type-1 DM and compare with other bone turnover markers and bone mineral density (BMD). Methods This study was performed on 40 children and adolescents with type-I DM and 40 healthy children and adolescents. Anthropometric measurements and pubertal examination were done. In addition to laboratory analysis, dickkopf-1, sclerostin, cross-linked N-telopeptides of type I collagen (NTx), bone alkaline phosphatase (bALP), and osteocalcin levels were studied. BMD of the participants was measured by calcaneus ultrasonography. Results Dickkopf-1 levels of the children and adolescents with type-1 DM were significantly higher, vitamin D, NTx, osteocalcin, and phosphorus levels were significantly lower than those of the controls (p<0.001). Fasting blood glucose, HbA1c, and insulin were significantly higher in the type 1 DM group (p<0.01). Conclusions Both bone remodeling and its compensatory mechanism bone loss are lower in children and adolescents with type-1 DM than in the controls. Also, higher levels of Dkk-1 play a role in decreased bone turnover in these patients. Since Dkk-1 and sclerostin seem to take a role in treating metabolic bone diseases in the future, we believe that our findings are significant in this respective.
Exploring and Analyzing Facebook as crowdsourcing platform for traffic updates using Selenium Support Vector Machine and Non-parametric LDA

Traffic is a major problem in the Philippines. Facebook is one of the social media platforms that is commonly used by Filipinos. Machine learning is a field of computer science that allows computers to perform tasks like human beings. In this study, the proponents explored Facebook as a source of traffic updates and as a source of traffic information. In this paper, as a partial result, a machine learning model was created to classify Facebook posts as related to traffic. To gather Facebook posts, a total of 1000 respondents were asked for consent to scrape their public post using the username link and selenium. The Support vector machine model was trained with 3000 Facebook posts. The SVM model was only trained to 3 classes {Road accident, Road activities and Other}. The SVM model was evaluated using 10-cross fold validation. The result shows that the accuracy is 76% and the recall is 69%. To analyze the narrative of the corpus, the Hierarchical Dirichlet Process model was created with the log-likelihood of -4.06 with 10 topic models. The following are the narratives of the corpus: {Traffic Management, Immediate Emergency Response, Seeking help, Busses causes majority of accidents. }
Mobile Crowdsensing with Imagery Tasks

The amount of gadgets connected to the internet has grown rapidly in the recent years. These human owned devices can potentially be used to gather sensor data without active involvement of their owners. One of the types of platforms that contribute to the utilisation of these devices are mobile crowdsensing systems. These systems can be used for different tasks including different types of community support. While these systems are quite widely used, yet little research has been done for integration of imagery data into them which require also human involvement. This paper considers a mobile crowdsensing system where gathering data from sensors is supported by crowdsourcing human intelligence for providing both textual and visual information. We also explore the best settings for such a system. Imagery processing is integrated into an already existing mobile crowdsensing platform CrowdS. The solution was evaluated both by a limited number of real life users and by conducting simulations. The simulations represent complex scenarios with multi-level variables. The results of simulation allow suggest an efficient configuration for the parameters and characteristics of the environment used in imagery integration.
Playbour in the Digital Games:A Case Study of Fantasy Westward Journey

The problem of playbour in digital games has always been a key research field of the political economy of communication. The following issues are widely disputed: the productivity of playbour, the exploitation and monitoring of playbour, and the initiative of palybour. This article expolres the playbour problem in the game world by conducting a digital ethnographic investigation of the online game “ Fantasy Westward Journey”.
A study for the implementation of Banking 4.0 in Indonesia

Industry 4.0 is a smart factory where smart digital devices are connected and that communicate with raw materials, semi-finished products, products, machines, tools, robots and people. The pace of technological change in the banking sector and the current monetary environment in the industrial era 4.0 is very high. Realizing these opportunities, this paper aims to describe the possible relationships that can occur and be implemented between the two concepts. The framework designed in the paper focuses on self-service provided by banks by utilizing the development of one of the technologies from industry 4.0, namely the Internet of Things. The merging of Industry 4.0 and Banking 4.0 will later create an Integrated Customer Centric Ecosystem that implements several services such as Smart Home with Smart Procurement, Smart Wearables with smart Authentication for banking, Smart Banking Customer Services Using Virtual Assistant and Secure Banking with Smart Contracts.
The Implementation of Robotic Process Automation for Banking Sector Case Study of A Private Bank in Indonesia

The Covid-19 pandemic has brought changes to every organization to be able to make changes quickly and adaptively. Organizations need to accelerate the use of digital technology which has an impact on changes in business processes. The use of Robotic Process Automation (RPA) technology as software can support the automation of business processes in accordance with the rules of work performed by humans. RPA is a new technology that can be adopted by organizations to achieve various automation goals in the form of accuracy, efficiency, increase productivity and reduce operational costs. This research was conducted at one of the private bank in Indonesia. The purpose of this study is to provide an overview of the development of robotic process automation (RPA) in order to achieve various organizational automation goals. Data collection is carried out from primary data obtained by company employees who have used RPA and analyzed the condition of business process automation in various work units. In addition, interviews were conducted to identify the requirements for RPA development based on the needs and expectations of the automation objectives of the work unit. Secondary data were also obtained from a literature review on the implementation and benefits of RPA. The results show that the business process automation development company's has implemented 150 units of RPA robots in various work units ranging from operations, human capital, finance, compliance, and others. This research is expected to assist organizations in making decisions on the use of RPA and Automation Center of Excellence (CoE) support in order to achieve automation and digitization of business processes.
Filter bubbles created by collaborative filtering algorithms themselves, fact or fiction? An experimental comparison

By recommending only, or mostly, items close to our supposedly known tastes, a recommender system could lead to the narrowing of the diversity/novelty of the recommended list, leading to the creation of so-called filter bubbles. Authors however disagree on the real existence of filter bubbles and, if real, on the responsibility of algorithms in the process of their creation. This paper aims to experimentally analyze the potential creation of such filter bubbles by testing various collaborative filtering algorithms on their intrinsic propensity of changing the diversity/novelty of recommended items, in configurations where users behave, with various intensities, in a way that could influence the evolution in diversity/novelty. Results show that both human and algorithmic components could have an impact on diversity/novelty, depending on the type of algorithm.
Soft conductive polymer dry electrodes embedded in a comfortable headset for high-quality and user-friendly EEG measurements performed in a non-clinical setting

Detailed testing has shown that active dry electrodes made from conductive polymers can be applied for user-friendly, high quality EEG recordings. Using a dedicated elastic interconnect technique, the dry electrodes can be integrated in a headset ensuring good skin contact. Combined with an elegant headset design, a promising solution is offered for user-friendly EEG monitoring outside the hospital.
Manifold Learning Projection Quality Quantitative Evaluation

A large number dimensions may cause a variety of problems in real-world applications: some dimensions might be redundant and can worsen the quality of the workflow output, and, in the vast majority of exercises with datasets, data are distributed along a highly nonlinear manifold whose structure is unknown. This paper focuses on analyzing the outputs of nonlinear dimensionality reduction, or Manifold Learning, techniques. We introduce three meaningful measures that are capable of providing context behind projections onto lower-dimensional spaces. The measures will enable us to compare techniques with each other and assist in choosing suitable hyperparameters. Moreover, we propose to view projections from the standpoint of simplicial complex distortion. In connection to that, we establish the process of a dimension-agnostic graph-based data tessellation technique that builds a simplicial skeleton of high-dimensional data. Alongside our new tessellation technique, we evaluate the proposed quality measures on the Delaunay-tessellation-based simplicial approximations of manifolds.
Fuzzy Community Detection with Multi-View Correlated Topics

In this paper, we present a novel fuzzy framework, dubbed as Fuzzy Multi-View Featured Network Clustering (FMVFNC), for effectively uncovering overlapping communities in social network data. Unlike most previous efforts which utilize only edge structure and single view of vertex features to perform the community discovery task, the proposed FMVFNC is able to take advantage of both edge structure and correlated vertex features which may be collected from multiple views. As the uncovered social communities are described by both network structure and semantically correlated features from diverse modalities, their practical significance can be well revealed. We innovatively design a unified fuzzy objective for FMVFNC to perform the task. We then derive an iterative algorithm for the proposed framework to optimize the formulated objective function. FMVFNC has been tested with a number of well-established datasets and has been compared with a number of state-of-the-art baselines for community detection. The notable results obtained may validate the effectiveness of FMVFNC.
Proposed Face Recognition System Based on Immune Inspired Anomaly Detection Using Symbiotic Agents

This paper proposes the design of a face recognition system based on immune inspired anomaly detection using intelligent agents with symbiotic relationships. Based on the reviewed literature, the advantages of the aforementioned technologies will synergise to create an effective system in scenarios where a person's details are not as important as maintaining a current representation of their face and detecting anomalous individuals. The paper discusses the working of the individual agents and their symbiotic relationships in the proposed model. Two example scenarios illustrate how the proposed system may be implemented to take advantage of the characteristics of immune inspired anomaly detection.
An analysis of parameters affecting volumetric airflow and efficiency of an ionic air moving device

Ionic airflow is a phenomenon that is observed when a high electric potential is supplied to at least two electrodes classified as an emitter and a collector. The emitter has a sharp geometric profile such as a needle or thin wire. The collector has a smooth, blunt geometric profile such as a flat plate or ring with sufficient thickness and rounded edges. Air molecules are ionized once high electric potential is supplied. As ionized molecules collide with uncharged molecules in close proximity to the sharp electrode, steady air flow is generated due to transfer of momentum. Ionic air moving devices have the potential to replace fans and blowers, however, they are rarely utilized due to their low efficiency. These devices are of great interest to industry for electronic cooling applications due to their lack of moving parts and potentially silent operation. Previous research has demonstrated that device efficiency and airflow volume can be increased by varying parameters such as: the number of ionization sites, the distance between the ionization sites and the collector, the height of the collector, and the amount of supplied electric potential. In this study, an analysis of parameters including collector material, collector shape, and number of collectors has been conducted. The ionic air moving device parameters were evaluated through the use of a DOE (Design of Experiments) to determine their statistical significance related to volumetric airflow output. Additional investigation was performed to determine the effect of the device parameters on the overall efficiency. Results indicate that collector material, and collector shape have a statistically significant effect on volumetric airflow output. It was also observed that collector material, collector shape, and number of collectors have some effect on overall device efficiency. Further research focused on ionic air moving device parameters will need to be conducted in order to justify device implementation as a practical standalone electronics-cooling device. By optimizing device parameters, ionic air moving device efficiency could be increased to a level that would support their utilization for practical applications.
Structure of phage λ Redβ177 annealase shows how it anneals DNA strands during single-strand annealing homologous DNA recombination

The bacteriophage λ red recombination system catalyzes the single-strand annealing homologous DNA recombination reaction, in which Redβ annealase protein plays a critical role. Using cryogenic electron microscopy, we were able to determine a structure of a C-terminally truncated Redβ with the residues 1-177 bound to two complementary 27mer oligonucleotides forming an annealing intermediate, to a final resolution of 3.3 Å. This structure validates and rationalizes decades of experimental observations on the biochemistry of Redβ. Definition of the interaction surfaces between subunits explains not only the DNA binding properties of Redβ, but also its propensity to oligomerize into long helical filaments, which are also formed by almost all annealases and are known to be functionally important. In addition, this annealing intermediate structure provides a detailed picture of the hydrogen bonding network that positions the DNA strands in a planar orientation to facilitate base pairing. Residues 133-138, which are missing from our structure, form a flexible loop. Molecular dynamics simulations were used to model the range of motion of the flexible loop, which suggested that it has a crucial role in keeping the DNA strands in the DNA-binding groove long enough to allow homology searching. The combination of structural and computational observations has allowed us to propose a detailed mechanism for the action of Redβ. More than half a century after its discovery, our work shines a light not only on the structure and mechanisms of Redβ, but also of other proteins within the annealase superfamilies. Significance Statement Single-strand annealing homologous DNA recombination is a process that is conserved throughout evolution from bacteriophages to humans, highlighting its importance and indispensability. It is a process that repairs double-stranded DNA breaks and is particularly vital in dsDNA viruses. The Redβ protein from the bacteriophage lambda is the archetypal annealase protein, forming the basis of our knowledge about this class of proteins. Along with the exonuclease λExo, these two proteins not only form the model system for single-strand annealing homologous recombination, but are also used in thousands of laboratories worldwide for performing genetic manipulations. After its discovery in 1966, we report the first structure of the DNA-binding and oligomerization domain of Redβ, providing details about the mechanism of homologous DNA annealing.
Neuronal HLH-30/TFEB modulates muscle mitochondrial fragmentation to improve thermoresistance in C. elegans

Transcription factor EB (TFEB) is a conserved master transcriptional activator of autophagy and lysosomal genes that modulates organismal lifespan regulation and stress resistance. As neurons can coordinate organism-wide mechanisms, we investigated the role of neuronal TFEB in stress resistance and longevity. To this end, the C. elegans TFEB orthologue, hlh-30, was rescued panneuronally in hlh-30 loss of function mutants. While important in the long lifespan of daf-2 animals, neuronal hlh-30 was not sufficient to restore normal lifespan in short-lived hlh-30 mutants. However, neuronal HLH-30/TFEB rescue mediated robust improvements in the heat stress resistance of wild-type but not daf-2 animals. Notably, these mechanisms can be uncoupled, as neuronal HLH-30/TFEB regulates longevity and thermoresistance dependently and independently of DAF-16/FOXO respectively. Through transcriptomics profiling and functional analysis, we identified the uncharacterized gene W06A11.1 as a bona fide mediator of heat stress resistance via the induction of mitochondrial fragmentation in distal muscles. Neuron-to-muscle communication occurred through a modulation of neurotransmission. Taken together, this study uncovers a novel mechanism of heat stress protection mediated by neuronal HLH-30/TFEB.
Cbln1 Directs Axon Targeting by Corticospinal Neurons Specifically toward Thoraco-Lumbar Spinal Cord

Corticospinal neurons (CSN) are centrally required for skilled voluntary movement, which necessitates that they establish precise subcerebral connectivity with the brainstem and spinal cord. However, molecular controls regulating specificity of this projection targeting remain largely unknown. We previously identified that developing CSN subpopulations exhibit striking axon targeting specificity in the spinal white matter. These CSN subpopulations with segmentally distinct spinal projections are also molecularly distinct; a subset of differentially expressed genes between these distinct CSN subpopulations regulate differential axon projection targeting. Rostrolateral CSN extend axons exclusively to bulbar-cervical segments (CSNBC-lat), while caudomedial CSN (CSNmedial) are more heterogeneous, with distinct, intermingled subpopulations extending axons to either bulbar-cervical or thoraco-lumbar segments. Here, we report, in male and female mice, that Cerebellin 1 (Cbln1) is expressed specifically by CSN in medial, but not lateral, sensorimotor cortex. Cbln1 shows highly dynamic temporal expression, with Cbln1 levels in CSN highest during the period of peak axon extension toward thoraco-lumbar segments. Using gain-of-function experiments, we identify that Cbln1 is sufficient to direct thoraco-lumbar axon extension by CSN. Misexpression of Cbln1 in CSNBC-lat either by in utero electroporation, or by postmitotic AAV-mediated gene delivery, redirects these axons past their normal bulbar-cervical targets toward thoracic segments. Further, Cbln1 overexpression in postmitotic CSNBC-lat increases the number of CSNmedial axons that extend past cervical segments into the thoracic cord. Collectively, these results identify that Cbln1 functions as a potent molecular control over thoraco-lumbar CSN axon extension, part of an integrated network of controls over segmentally-specific CSN axon projection targeting. SIGNIFICANCE STATEMENT Corticospinal neurons (CSN) exhibit remarkable diversity and precision of axonal projections to targets in the brainstem and distinct spinal segments; the molecular basis for this targeting diversity is largely unknown. CSN subpopulations projecting to distinct targets are also molecularly distinguishable. Distinct subpopulations degenerate in specific motor neuron diseases, further suggesting that intrinsic molecular differences might underlie differential vulnerability to disease. Here, we identify a novel molecular control, Cbln1, expressed by CSN extending axons to thoraco-lumbar spinal segments. Cbln1 is sufficient, but not required, for CSN axon extension toward distal spinal segments, and Cbln1 expression is controlled by recently identified, CSN-intrinsic regulators of axon extension. Our results identify that Cbln1, together with other regulators, coordinates segmentally precise CSN axon targeting.
Organization of gene programs revealed by unsupervised analysis of diverse gene–trait associations

Abstract Genome wide association studies provide statistical measures of gene–trait associations that reveal how genetic variation influences phenotypes. This study develops an unsupervised dimensionality reduction method called UnTANGLeD (Unsupervised Trait Analysis of Networks from Gene Level Data) which organizes 16,849 genes into discrete gene programs by measuring the statistical association between genetic variants and 1,393 diverse complex traits. UnTANGLeD reveals 173 gene clusters enriched for protein–protein interactions and highly distinct biological processes governing development, signalling, disease, and homeostasis. We identify diverse gene networks with robust interactions but not associated with known biological processes. Analysis of independent disease traits shows that UnTANGLeD gene clusters are conserved across all complex traits, providing a simple and powerful framework to predict novel gene candidates and programs influencing orthogonal disease phenotypes. Collectively, this study demonstrates that gene programs co-ordinately orchestrating cell functions can be identified without reliance on prior knowledge, providing a method for use in functional annotation, hypothesis generation, machine learning and prediction algorithms, and the interpretation of diverse genomic data.