Miracle-dz/UniFAR-FTUs huggingface dataset:
Document Clustering With Genetic Based k-Means Algorithm

There are two important problems worth conducting research in the fields of personalized information services based on user model. One is how to get and describe user personal information, i.e. building user model, the other is how to organize the information resources, i.e. document clustering. It is difficult to find out the desired information without a proper clustering algorithm. Several new ideas have been proposed in recent years. But most of them only took into account the text information, but some other useful information may have more contributions for documents clustering, such as the text size, font and other appearance characteristics, so called visual features.In this paper we introduce a new technique called Closed Document Clustering Method (CDCM) by using advanced clustering metrics. This method enhances the previous method of cluster the scientific documents based on visual features, so called VF-Clustering algorithm. Five kinds of visual features of documents are defined, including body, abstract, subtitle, keyword and title. The thought of crossover and mutation in genetic algorithm is used to adjust the value of k and cluster center in the k-means algorithm dynamically. Experimental result supports our approach as better concept.The main aim ofthis paper is to eliminate the redundant documents and set priority to each document in the cluster. In the five visual features, the clustering accuracy and steadiness of subtitle are only less than that of body, but the efficiency is much better than body because the subtitle size is much less than body size. The accuracy of clustering by combining subtitle and keyword is better than each of them individually, but is a little less than that by combining subtitle, keyword and body. If the efficiency is an essential factor, clustering by combining subtitle and keyword can be an optimal choice. The proposed system outperforms than the previous system.
REDUCING ATTRIBUTES of FACEBOOK USERS USING ROUGH SET THEORY

: Using social networks have become one of the daily activities that billions of peoples around the world do. So, great research efforts had been done to analyze and understand these virtual communities. Among other things, link prediction is a paramount task to analyze and understand these social networks. In this paper, we investigate link prediction problem using rough set theory to discard the irrelevant attributes that could be found in the profiles of Facebook users and the proposed work induces accuracy 97.79%.
Stigma among doctors towards people with mental illness

Background: Stigma towards adults with mental illness is a longstanding and widespread phenomenon. Stigmatizing attitudes are prevalent not only among the general population but also among doctors. Negative stereotyping of people with mental illness (PMI) leads to prejudice and discrimination, affecting all aspects of their medical care and well-being. The present study attempted to explore stigmatizing attitudes among doctors towards PMI.Methods: The research was observational and cross-sectional in design carried out on doctors in a medical college. Socio-demographic data including field of specialization, experience, and academic post were recorded. The community attitudes towards mental illness (CAMI) and social distance scale were administered. Social desirability bias was corrected for by using the Marlowe-Crowne social desirability scale.Results: Around 54 doctors from the specializations of medicine (n=24), surgery (n=19), and non-clinical fields (n=11) participated. We found no significant differences in attitudes towards mentally ill and social distance between medical specializations (p-values >0.05) even after adjusting for the effects of social desirability bias. Years of specialization experience (p=0.037) and having a family member or close friend with mental illness (p=0.012) were significantly associated with higher scores in the community mental health ideology sub-scale of CAMI. Higher social restrictiveness (p=0.014) and lower community mental health ideology (p=0.008) were associated with greater social distance from PMI.Conclusions: Doctors are not immune to biases and stigmatizing attitudes towards PMI. These attitudes are present across all fields of medical specialization and must be addressed by mental health professionals to ensure optimal care of this vulnerable population.
Let's get connected: A new graph theory‐based approach and toolbox for understanding braided river morphodynamics

Our understanding of braided river morphodynamics has improved significantly in recent years, however, there are still large knowledge gaps relating to both long‐term and event‐based change in braided river morphologies. Furthermore, we still lack methods that can take full advantage of the increasing availability of remotely sensed datasets that are well suited to braided river research. Network analysis based on graph theory, the mathematics of networks, offers a largely unexplored toolbox that can be applied to remotely sensed data to quantify the structure and function of braided rivers across nearly the full range of spatiotemporal scales relevant to braided river evolution. In this article, important commonalities between braided rivers and other types of complex network are described, providing a compelling argument for the wider uptake of complex network analysis methods in the study of braided rivers. We provide an overview of the extraction of graph representations of braided river networks from remotely sensed data and detail a suite of metrics for quantitative analysis of these networks. Application of these metrics as new tools for multiscale characterization of braided river planforms that improve upon traditional, spatially averaged approaches is discussed and potential approaches to network‐based analysis of braided river dynamics are proposed, drawing on a range of different concepts from braided river research and other network sciences. Finally, the potential for using graph theory metrics to validate numerical models of braided rivers is discussed.
Implementasi Metode Hough Dan Jarak Mahalanobis Pada SistemBiometrik Pengenalan TelingaDengan Menggunakan Library Open Cv

Teknologi biometrik menjadi dasar dari sistem identifikasi untuk mengontrol individu-individu dalam kelompok yang berada di bawah pengawasan. Sistem biometrik memberikan identifikasi secara otomatis dari individu berdasarkan ciri atau karakteristik unik yang dimiliki setiap individu. Pada sistem biometrik berbasis telinga, tahapan umum yang dilakukan adalah akuisisi citra, segmentasi citra, ekstraksi citra dan pengenalan citra. Metode Hough digunakan pada tahap segmentasi dan Metode jarak Mahalanobis digunakan pada tahap pengenalan citra. Implementasi sistem ini menggunakan bahasa pemrograman OpenCV dan sebagai objek penelitian menggunakan citra telinga yang diperoleh sebagian dari data pada internet, dan sebagian diperoleh dengan menggunakan kamera handphone dan kamera digital beresolusi tinggi.
Facial Expression Recognition Based on Structural Changes in Facial Skin

Facial expressions are the most powerful and direct means of presenting human emotions and feelings and offer a window into a persons’ state of mind. In recent years, the study of facial expression and recognition has gained prominence; as industry and services are keen on expanding on the potential advantages of facial recognition technology. As machine vision and artificial intelligence advances, facial recognition has become more accessible and is now a key technique to be employed and used in creating more natural man-machine interactions, Computer vision, and health care. In this paper, we empirically evaluate facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition. Different machine learning methods are systematically examined on several databases. Extensive experiments illustrate that LBP features are effective and efficient for facial expression recognition. In this paper, we proposed a face expression detection method based on the difference of a face expression and the allocated special pattern to each expression. The analysis of the image detection system locally and through a sliding window (sliding) at multiple scales, are estimated. Multiple scales are extracted aslocally binary features. Through using the change point between windows, points of face are getting a directional movement. Through using points movement of whole facial expressions and rating system that is created the superfluous points are eliminated. The classifications are taken based on the nearest neighbor. To sum up this paper, the proposed algorithms are tested on Cohn-Kanade data set and the results showed the best performance and reliability into other algorithms. We investigated LBP features for the facial skin structural changes, which is seldom addressed in the existing literature.
Comparison of ANN and RKS approaches to model SCC strength

Self compacting concrete (SCC) is a high performance concrete that has high flowability and can be used in heavily reinforced concrete members with minimal compaction segregation and bleeding. The mix proportioning of SCC is highly complex and large number of trials are required to get the mix with the desired properties resulting in the wastage of materials and time. The research on SCC has been highly empirical and no theoretical relationships have been developed between the mixture proportioning and engineering properties of SCC. In this work effectiveness of artificial neural network (ANN) and random kitchen sink algorithm(RKS) with regularized least square algorithm(RLS) in predicting the split tensile strength of the SCC is analysed. Random kitchen sink algorithm is used for mapping data to higher dimension and classification of this data is done using Regularized least square algorithm. The training and testing data for the algorithm was obtained experimentally using standard test procedures and materials available. Total of 40 trials were done which were used as the training and testing data. Trials were performed by varying the amount of fine aggregate, coarse aggregate, dosage and type of super plasticizer and water. Prediction accuracy of the ANN and RKS model is checked by comparing the RMSE value of both ANN and RKS. Analysis shows that eventhough the RKS model is good for large data set, its prediction accuracy is as good as conventional prediction method like ANN so the split tensile strength model developed by RKS can be used in industries for the proportioning of SCC with tailor made property.
Acupuncture Enhances Communication between Cortices with Damaged White Matters in Poststroke Motor Impairment

Stroke is a leading cause of motor disability. Acupuncture is an effective therapeutic strategy for poststroke motor impairment. However, its mechanism is still elusive. Twenty-two stroke patients having a right-hemispheric subcortical infarct and 22 matched healthy controls were recruited to undergo diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) scanning. The resting-state fMRI was implemented before and after needling at GB34 (Yanglingquan). The stroke patients presented a substantially reduced fractional anisotropy value in the right superior longitudinal fasciculus (SLF), corticospinal tract, and corpus callosum. The structural integrity of the frontoparietal part of the SLF (SLF-FP) correlated with the motor scores of lower limbs in stroke patients. This corticocortical association bundle originated from the premotor cortex (PM) and the adjacent supplementary motor area (SMA), known as secondary motor areas, and terminated in the supramarginal gyrus (SMG). After acupuncture intervention, the corresponding functional connectivity between the PM/SMA and SMG was enhanced in stroke patients compared with healthy controls. These findings suggested that the integrity of the SLF is a potential neuroimaging biomarker for motor disability of lower limbs following a stroke. Acupuncture could increase the communication between the cortices connected by the impaired white matter tracts, implying the neural mechanism underlying the acupuncture intervention.
Color Atlas of Medical Bacteriology

The first of the authors is affiliated with the Department of Medical Genetics and Microbiology, University of Toronto, the second author is member of the Department of Veterinary Medicine Microbiology, Iowa State University. The list of contributors comprises 60 specialists in microbiology, genetics, molecular biology and related sciences – about one half of the authors comes from USA, the second half from Canada, Mexico, Japan and European countries. As indicated in the preface, plasmids are extra-chromosomal, autonomously replicating genetic elements found in cells of all kingdoms of life. Their presence influences, often dramatically, the biology of the organisms that they inhabit. Plasmids have played, and continue to play, indispensable roles in the development of molecular biology and in our understanding of basic biological processes that are fundamental to all organisms. There is great variety of plasmid systems in nature and the authors have devoted about half this volume to describing many in detail. The volume is arranged into six sections encompassing 29 chapters. Each chapter is concluded with a list of 100–200 or more references. Many of the references provided are review articles or a review of work of particular laboratory. Funnel, Barbara E. , Phillips, G. J., editors
Unique Views on Obesity-Related Behaviors and Environments: Research Using Still and Video Images.

Objectives To document challenges to and benefits from research involving the use of images by capturing examples of such research to assess physical activity- or nutrition-related behaviors and/or environments. Methods Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study. Results Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (n = 13; e.g., participants using smartphones for dietary assessment), researcher generated (n = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (n = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%). Conclusions Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes is critical.
A Study on the Acceleration of Arrival Curve Construction and Regular Specification Mining using GPUs

Data analytics is a process of examining datasets using various analytical and statistical techniques. Several tools have been proposed in the literature to extract hidden patterns, gather insights and build mathematical models from large datasets. However, these tools have been known to be computationally demanding as the datasets become larger over time. Two such recently proposed tools are the construction of arrival curves from execution traces and mining specifications in the form of regular expressions from execution traces. Though the architectures in CPUs have extensively improved over the years to execute such computationally intensive tasks, further enhancements have been impeded due to increased heat dissipation. This has resulted in enabling parallel computing through GPUs as a vastly favorable alternative to overcome the computational challenges. In this thesis, we present an exploratory work on applying GPU computing to the construction of arrival curves and mining specifications in the form of regular expressions as case studies. The novel approaches taken for each of the case studies are first presented followed by the algorithmic breakdown to expose the parallelism involved. Lastly, experiments using commodity GPUs are presented to showcase the significant speedups obtained in comparison to the equivalent non-parallel implementations.
3D recognition based on ordered images reconstruction

Nowadays, more and more applications require precise and quickly 3D recognition, such as augmented reality and robot navigation. In recent years, model-based methods can get accurate object or scene recognition, but it takes a lot of time to reconstruct the model. Therefore, we propose a fast 3D reconstruction method based on ordered images for robust and accurate 3D recognition. The proposed algorithm consists of two parts, the offline processing stage, and the online processing stage. First, in the offline processing stage, the sparse point cloud model of the scene or object is reconstructed based on the sequential images, optimized using the BA algorithm based on the local correlation frame, and then the local descriptor of the resulting model points is stored. Secondly, in the online processing stage, for each image frame of the camera video, a matching relationship between the stored point cloud and the 2D feature points on the image frame is established, based on which the pose of the camera can be solved accurately.
An Inertial Device-based User Interaction with Occlusion-free Object Handling in a Handheld Augmented Reality

Augmented Reality (AR) is a technology used to merge virtual objects with real environments in real-time. In AR, the interaction which occurs between the end-user and the AR system has always been the frequently discussed topic. In addition, handheld AR is a new approach in which it delivers enriched 3D virtual objects when a user looks through the device’s video camera. One of the most accepted handheld devices nowadays is the smartphones which are equipped with powerful processors and cameras for capturing still images and video with a range of sensors capable of tracking location, orientation and motion of the user. These modern smartphones offer a sophisticated platform for implementing handheld AR applications. However, handheld display provides interface with the interaction metaphors which are developed with head-mounted display attached along and it might restrict with hardware which is inappropriate for handheld. Therefore, this paper will discuss a proposed real-time inertial device-based interaction technique for 3D object manipulation. It also explains the methods used such for selection, holding, translation and rotation. It aims to improve the limitation in 3D object manipulation when a user can hold the device with both hands without requiring the need to stretch out one hand to manipulate the 3D object. This paper will also recap of previous works in the field of AR and handheld AR. Finally, the paper provides the experimental results to offer new metaphors to manipulate the 3D objects using handheld devices.
Monitoring Suhu Pemanas Portable Berbasis Arduino Yang Terintegrasi Dengan Android

The heater can not be separated from regular monitoring of temperature and stabilizing the temperature so as not to damage the unit (heating). If the heater is enabled for production will affect production. Thus the need to be made a tool monitors the temperature. Already many tools monitoring temperature, but integrated with wireless is still not so much Application temperature monitor that can run on this smartphone is integrated wirelessly making it easier for the operator to control the tool monitor the temperature even from a distance. The components used are DHT 22 sensors, relay, arduino uno, bluetooth module, and app inventor to create application. In the end, will form a circuit that can be integrated with a wireless (bluetooth) and android. In the circuit there are 3 temperature sensors will measure the temperature and the results will be transferred to the android smartphone in real time, except that the circuit can be controlled also from android smartphone. This tool can also be controlled from a maximum distance of 10 meters and the sensitivity of the sensor is fast enough that 100 seconds to reach a temperature of 1000C at a distance of 15 cm from the object. Keywords: android, arduino, bluetooth, DHT 22, monitoring, heater.
The Minories Eagle: A New Sculpture from London's Eastern Roman Cemetery

The limestone sculpture of an eagle firmly clasping a serpent in its beak was recovered from within the eastern Roman cemetery of London on the last day of excavations at 24–26 Minories, EC3 in September 2013. The sculpture, which is dated stylistically to the late first or early second century a.d . , had been carefully buried within the backfill of a roadside ditch no later than the mid-second century. The Minories eagle is one of the finest and earliest examples of freestone sculpture from the London cemeteries and presumably adorned the tomb of a rich and important individual or family located nearby. Petrological analysis of the sculpture has revealed it is carved from oolitic limestone quarried from the south Cotswolds. The article presents the context of the findspot and a detailed description of the eagle sculpture with an in-depth discussion of the iconography of the image and the results of the petrological examination. The Supplementary Material available online ( http://journals.cambridge.org/bri ) presents an account of the site stratigraphy, integrated with the specialist finds and the environmental reports.
Researching Theatre History and Historiography

How have theatre and performance research methods and methodologies engaged the expanding diversity of performing arts practices? How can students best combine performance/theatre research approaches in their projects? This book's 29 contributors provide hands-on answers to such questions. Challenging and debating received research wisdom and exploring innovative procedures for rigorous inquiry via archives, technology, practice-as-research, scenography, performer training, applied theatre/performance, body in performance and more, they create a focused compendium of future research options. Key Features Created in association with TaPRA, the leading UK Theatre and Performance Research organisation, with chapters produced by specialist groupings. Provides many detailed project case studies and examples - including successful practice-based PhDs - plus analysis of dynamic couplings between methods, methodologies and skill-sets. Introduction interrogates crucial qualities of performing arts research that constitute theatre and performance as, variously, single-, multi-, inter-, and trans-disciplinary. Contributors include: Maggie B. Gale (Chair of Drama, University of Manchester); Steve Dixon (Professor of Digital Performance, Brunel University); Joanne 'Bob' Whalley and Lee Miller (University Lecturers and founders Fictional Dogshelf Theatre Company); Simon Ellis and Rosemary Lee (independent performance/dance makers); Roberta Mock (Professor of Performance, University of Plymouth).