Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor scene understanding

Intelligent robots that interact and blend in with humans have been one of the core goals of AI. One of the important sensors on such a robot is its vision system. Humans are known to derive maximal sensory feedback and experience from what they see. To enable a capability to imitate a fraction of the interaction and derived knowledge is a challenging task. It has become clear that the task involves devising algorithms that allow for adaptive and incremental instance based learning. We study some of the basic problems in designing such vision systems, specifically for indoor environments. Specifically, this thesis seeks to address (1) object discovery through unsupervised scene segmentation, (2) organization of object library for efficient recognition and retrieval, (3) purpose based object classification and part labeling, and (4) scene classification and place recognition. We present novel approaches and algorithms towards solutions for the above problems. We also apply some of the techniques to solve problems in facial analysis and outdoor scenes, thus demonstrating the generality of the proposed schemes. The key motivation in our work has been the use of geometry at various levels and the close relation to findings from various cognitive science studies. We have shown that knowledge of scene and object geometry can significantly improve performance when compared to previous state-of-the-art methods. Our experiments show that use of depth, 3D structure and shape can prove robust and effective for practical solutions. We have used geometry and properties derived thereof at various degrees of specificity and abstraction. Further we generalize the use of geometry to include notion of arrangement in scenes. With advent of better computing and imaging resources, using cameras on robotic systems is becoming increasingly popular. We have designed a cost effective lens adapter which can be used to convert any single camera to a stereo camera, to handle cases where multiple camera cannot be mounted. Thus geometry can be effectively acquired to allow the use of our approaches. In addition to prior work in scene understanding area of computer vision, many of our algorithms have also been inspired from theories and findings from human vision and perception studies. We demonstrate that practical and computationally efficient machine vision systems can be based on concepts from cognitive science. We have taken inspiration from seminal works of Palmer, Rosch, Minsky, Tversky and many others. We observed that many results from our and related works correlate with hypothesis and findings from human vision. Though the underlying representations and mechanisms are different, at a conceptual level we may replicate certain processes of human vision into machines, thus contributing to the success of many vision algorithms including ours.
Joint Estimation of TOA and DOA in IR‐UWB System Using Sparse Representation Framework

This paper addresses the problem of joint time of arrival (TOA) and direction of arrival (DOA) estimation in impulse radio ultra‐wideband systems with a two‐antenna receiver and links the joint estimation of TOA and DOA to the sparse representation framework. Exploiting this link, an orthogonal matching pursuit algorithm is used for TOA estimation in the two antennas, and then the DOA parameters are estimated via the difference in the TOAs between the two antennas. The proposed algorithm can work well with a single measurement vector and can pair TOA and DOA parameters. Furthermore, it has better parameter‐estimation performance than traditional propagator methods, such as, estimation of signal parameters via rotational invariance techniques algorithms matrix pencil algorithms, and other new joint‐estimation schemes, with one single snapshot. The simulation results verify the usefulness of the proposed algorithm.
Harness Human Sensor Networks for Situational Awareness in Disaster Reliefs: A Survey

Abstract Complete, accurate, and up-to-the-minute situational awareness can help disaster relief organizations stabilize the dangers and prevent further losses. Social media (SM) users can be regarded as human sensors since they are analogous to the response of physical sensors to stimuli. Human sensor network (HSN) provides a way to capture situational awareness information coming directly from the grassroots observers in the disasters. The disaster relief organizations can utilize the HSN data to gain the real-time live situations. It saves time and money. This paper surveys on the works of viewing SM as HSNs and extract the situational awareness information from HSN in disaster reliefs. It is found that HSN data, used together with other scientific data, can contribute to better situational awareness in the disaster reliefs.
The Cost of Mental Health Care for Victims of Crime

This article reports on a nationally representative survey of 168 mental health care professionals about their treatment of crime victims. It is estimated that in 1991, between 3.1 and 4.7 million individuals in the United States received some form of mental health counseling or therapy in which they were being treated primarily as a result of victimization. More than half of these victims are estimated to be adults being seen for child sexual or physical abuse that occurred years prior to their victimization. Crime victims are estimated to represent about 20% to 25% of the total client population of mental health care professionals. Actual expenditures for mental health care services to victims of crime are estimated to be between $5.8 and $6.8 billion. This survey estimated the actual use of mental health care services as opposed to the mental health status of victims. Because many crime victims do not receive mental health care services that would be beneficial, the authors' estimates understate the true costs of mental illness caused by criminal victimization.
Does Training in Syllable Recognition Improve Reading Speed? A Computer-Based Trial With Poor Readers From Second and Third Grade

Repeated reading of infrequent syllables has been shown to increase reading speed at the word level in a transparent orthography. This study confirms these results with a computer-based training method and extends them by comparing the training effects of short syllables and long frequent and infrequent syllables, controlling for rapid automatized naming. Our results, based on a sample of 150 poor readers of Finnish, showed clear gains in reading speed regarding all trained syllables, but a transfer effect to the word level was evident only in the case of long infrequent syllables. Rapid automatized naming was associated with initial reading speed, but not with the training effect.
Determinants of gestural imitation in young children

Generalised imitation has often been cited in the behaviour analytic literature as a paradigm case of a higher-order response class. However, its determinants have not been established, When, as is the case in published experimental studies of children's imitative performances to date, the to-be-matched behaviours are actions on objects, many nonimitative processes can result in apparently emergent matching. Such confounding sources of control are minimised when the target behaviours are arbitrary gestures. The present experiments explored the matching of (i) arbitrary actions on novel objects with minimal affordances in 3 infants (9 - 15 months), and (ii) gestures alone in 13 infants (15 - 25 months), and in 20 young children (24 - 42 months). In Experiment 1, the infants' performance of the target actions was measured firstly in response to each of four novel objects (Baseline) and next to the target action (Modelling) on each of these objects. In Experiments 2 and 3, participants' unreinforced responses to target behaviours, and their intermittently-reinforced responses to four behaviours that featured in their trained baseline matching relations, were measured. No evidence of higber-order matching was found; rather, the performances of the infants in Experiments 1 and 2, and of the young children in Experiment 3, could be explained in terms of generalisation of extra-experimentally trained matching repertoires. Infants' bigher-order matching abilities were directly tested in Experiments 4 and 5- Following training of four baseline matching relations, and identification of four target behaviours that the infants failed to match, they were trained to produce the target behaviours in the absence of the corresponding modelled behaviour. Infants' unreinforced responses to the modelled target behaviours, interspersed with modelling of the intermittently reinforced baseline behaviours, were then re-tested. The data showed no evidence of higher-order matching and suggest that infants' higher-order matching abilities, not previously directly tested, have been overestimated in the behaviour analytic literature.
Managing hepatitis C in liver transplant patients with recurrent infection

Hepatitis C virus (HCV) reinfection after liver transplantation (LT) and recurrent hepatitis C often lead to recurrent cirrhosis (RC). RC is one of the most frequent complications resulting in organ failure and early death after LT in HCV-positive patients with reported 5-year rates from 20% to 40%. As HCV-cirrhosis is one of the leading indications for LT, the therapeutic management is a central issue. To date, the best available therapy is a combination of pegylated interferon + ribavirin in patients with established recurrent hepatitis C proven by liver biopsy. Although increasing experience in using interferon therapy after LT has suggested better response rates, treatment is limited by a poor tolerability and high rates of severe side effects, necessitating lower doses or withdrawal of therapy. The extent to which dose reductions and the concomitant administration of growth factors affect virological response or prevent complications is still to be determined. Prospective clinical trials are mandatory to identify the best time point and schedule of antiviral treatment in transplant patients. Currently, therapeutic options need to be discussed for each individual patient. Therefore therapy should be carried out only in transplant centers with experience in managing hepatitis C after LT.
Big Doctoring in America: Profiles in Primary Care

Introduction 1. Primary Care Roots 2. The New GPs: The Family Physician Comes of Age 3. Roots Rediscovered: The Internist and the Pediatrician as Generalists 4. The New Clinicians: Nurse Practitioners and Physician Assistants 5. The System Doctors: Managed Care and Primary Care 6. The Quixote Factor: Generalists Doing Special Battle 7. Building a Better Future: The Case for Primary Care
Flood evacuation simulations using cellular automata and multiagent systems -a human-environment relationship perspective

ABSTRACT A flood evacuation represents a complex geographic phenomenon that includes comprehensive interactions among humans, the flood and urban environments; thus, the simulation of flood evacuations requires crowd simulation models to be coupled with flood models. This paper studies the human-environment relationship during flooding and promotes a simulation model that combines cellular automata and a multiagent system to simulate crowd evacuations in flood disasters. A case study of Niaodao Island was used to evaluate the performance of flood evacuation experiments, and real-participant experiments based on the virtual reality (VR) environment were employed for bench-mark comparisons. The integrated model can provide a comprehensive solution to assist flood risk analysis.
Commentary on Special Issue- Minimally Invasive Spine Surgery

Copyright: © 2014 Kuroki H. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Recently, Minimally Invasive Surgery (MIS) has been broadly employed in the spine field to decrease approach-related morbidity associated with conventional open surgery. This might minimize damage of paraspinal muscles, blood loss, risk of infection, postoperative pain, functional disturbance, and prolonged hospitalization. Hence, application of a MIS technique can be beneficial for the various spinal pathologies, especially in elderly or debilitated patients desired long spinal constructs.
The H.E.R.B.A.L. Guide Dietary Supplement Resources for the Clinician

INTRODUCTION The H.E. R.B.A.L. Mnemonic: Introduction & Overview Hear the Patient Out Educating the Patient: What's Your Source of Information & Supplement? Record Be Aware of Reactions/Interactions Agree to Discuss Learn Frequently Asked Questions: A Roadmap to Supplement Answers A Pathway to Supplement Management UNDERSTANDING DIETARY SUPPLEMENTS Dietary Supplements in the Context of Complementary & Alternative Medicine (CAM) What Is A Dietary Supplement? Understanding Dietary Supplement Labels & Dosing The Dietary Supplement User: Predictors & Implications for the Clinician Dietary Supplements & Prescribing: A Legal Perspective REGULATION Evaluating Dietary Supplements: Regulation DSHEA & Beyond: An Overview of Dietary Supplement Regulation NLEA & DSHEA: Health Claims & Structure/Function Claims International Perspectives on the Regulation of Herbs & Phytomedicines The Role of Industry The Role of Trade Associations USP Verified Dietary Supplements 3rd Party Dietary Supplement Testing: Consumerlab.com NPA: Industry Self Regulation REACTIONS & INTERACTIONS Herbal & Dietary Supplements: Important Reactions & Interactions Herbal Adverse Reactions Herb-Drug Interactions EFFICACY Evaluating Dietary Supplements: Efficacy The Importance of Botanical Gardens Dose-Reliable Botanical Extracts for Clinical Studies Evaluating the Botanical Dietary Supplement Literature Supplements Evaluated in Clinical Trials: Why Specific Formulations Matter The Role & Programs of the NIH Office of Dietary Supplements CLINICAL MANAGEMENT Dietary Supplement Management: A Clinical Team Approach Herbalism & the Role of the Herbalist: Past, Present & Future Role of the Physician Role of the Pharmacist Role of the Dietitian Role of the Nurse Role of the Naturopathic Physician Role of the Traditional Chinese Medicine (TCM) Practitioner RESOURCES & EDUCATION Dietary Supplement Learning: Resources & Education Resources on Dietary Supplements Dietary Supplement Education in Health Care Training CASE STUDIES Introduction to HERBAL Mnemonic in Practice: Patient Case Studies Acne Benign Prostatic Hyperplasia Diabetes Migraine Headache Borderline Hypercholesterolemia & Hypertension Chronic Low Back Pain Use of Botanicals During Pregnancy & Lactation Urinary Tract Infection QUICK REFERENCE GUIDE Natural Medicines in Clinical Management: A Quick Reference Guide to Common Conditions Attention Deficit-Hyperactivity Disorder (ADHD) Allergic Rhinitis Alzheimer's Anxiety Athletic Performance Benign Prostatic Hyperplasia (BPH) Breast Cancer (Prevention) Cataracts Cold & Flu Congestive Heart Failure Depression Diabetes Fatigue Fibromyalgia Glaucoma Migraine Headache Hyperlipidemia Hypertension Insomnia Irritable Bowel Syndrome (IBS) Low Back Pain Macular Degeneration Menopause Obesity Osteoarthritis Osteoporosis Premenstrual Syndrome Urinary Tract Infection
Analyse automatique de documents anciens : tirer parti d’un corpus incomplet, hétérogène et bruité

Cet article concerne un ensemble de textes anciens (datant du milieu du 17e siecle), que les specialistes d'histoire et de litterature ont l'habitude de nommer "corpus des mazarinades". Ces quelque 5500 textes offrent une variete de problematiques qui s'inscrivent pleinement dans le domaine des humanites numeriques. Nous montrons en premier lieu qu'il ne s'agit pas a proprement parler d'un corpus puisqu'on ne dispose pas, malgre un important travail bibliographique sur le sujet, d'une definition ni d'un recensement rigoureux de cet ensemble. Il s'agit ensuite de voir l'impact de cette definition instable sur le travail des chercheurs qui s'interessent a ce "corpus", tout en proposant de corriger ces biais grâce a un outillage automatique. Nous montrons que, si le but est d'exploiter le materiau textuel et non de l'interpreter, il est interessant de s'autoriser a traiter des donnees brutes (avec un minimum de traitements preparatoires). Enfin, nous exposons un premier cadre d'application sur la sous-partie de cet ensemble actuellement disponible sous forme numerique : la datation de documents. La methode utilisee se fonde sur une analyse en chaines de caracteres qui permet a la fois de fonctionner sur un corpus partiellement bruite (etats de langue divers, scories de l'ocerisation. . .) et sur un corpus heterogene, comprenant des documents de tailles et surtout de genres tres varies. Nous montrons que, dans certains cas, le bruitage du corpus peut etre un avantage pour certaines tâches de classification, notamment grâce a l'utilisation de methodes exploitant des chaines de caracteres. Les approches en caracteres permettent en effet de surmonter un certain nombre de difficultes liees a la variete des donnees disponibles. Aussi ce travail donne-t-il des outils pour extraire des sous-corpus coherents, pour exploiter des jeux de donnees issus de la numerisation en economisant le post-traitement, et pour identifier des metadonnees manquantes : trois enjeux essentiels pour ce "corpus" qui reste encore pour une bonne part a divulguer a la communaute dans un format numerique raisonne.
An Efficient Privacy-Preserving Ranked Keyword Search Method

Cloud computing economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service a very challenging task. Although traditional searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending undifferentiated results, and further ensures the file retrieval accuracy.
Healthcare IoT-Based Affective State Mining Using a Deep Convolutional Neural Network

Human effects are complex phenomena, which are studied for pervasive healthcare and well-being. The legacy pen and paper-based affective state determination methods are limited in their scientific explanation of causes and effects. Therefore, due to advances in intelligence technology, researchers are trying to apply some advanced artificial intelligence (AI) methods to realize individuals’ affective states. To recognize, realize, and predict a human’s affective state, domain experts have studied facial expressions, speeches, social posts, neuroimages, and physiological signals. However, with the advancement of the Internet of Medical Things (IoMT) and wearable computing technology, on-body non-invasive medical sensor observations are an effective source for studying users’ effects or emotions. Therefore, this paper proposes an IoMT-based emotion recognition system for affective state mining. Human psychophysiological observations are collected through electromyography (EMG), electro-dermal activity (EDA), and electrocardiogram (ECG) medical sensors and analyzed through a deep convolutional neural network (CNN) to determine the covert affective state. According to Russell’s circumplex model of effects, the five basic emotional states, i.e., happy, relaxed, disgust, sad, and neutral, are considered for affective state mining. An experimental study is performed, and a benchmark dataset is used to analyze the performance of the proposed method. The higher classification accuracy of the primary affective states has justified the performance of the proposed method.
Frappe Technique for Detecting Fb Malicious Application in OSN

With 20 million installs a day, third-party apps are a main reason for the reputation and addictiveness of Facebook. Unluckily, hackers have realized the potential of using apps for scattering malware and spam. The problem is already major, as we find that at least 13% of apps in our dataset are malicious to date, the research community has focused on detecting malicious posts and campaigns. In this paper, we ask the question: given a Facebook application, can we decide if it is malicious? Our key contribution is in developing FRAppE Facebook’s Rigorous Application Evaluator arguably the first tool focused on finding malicious apps on Facebook. To develop FRAppE, we use information gathered by observing the posting behavior of 111K Facebook apps seen across 2.2 million users on Facebook. First, we identify a set of features that aids us distinguish malicious apps from benign ones. For example, we discover that malicious apps often share names with other apps, and they typically request fewer permissions than benign apps. Second, leveraging these distinguishing features, we demonstrate that FRAppE can identify malicious apps with 99.5% accuracy, with no false positives and a low false negative rate (4.1%). With 20 million installs a day, third-party apps are a main reason for the reputation and addictiveness of Facebook. Unluckily, hackers have realized the potential of using apps for scattering malware and spam. The problem is already major, as we find that at least 13% of apps in our dataset are malicious to date, the research community has focused on detecting malicious posts and campaigns. In this paper, we ask the question: given a Facebook application, can we decide if it is malicious? Our key contribution is in developing FRAppE Facebook’s Rigorous Application Evaluator arguably the first tool focused on finding malicious apps on Facebook. To develop FRAppE, we use information gathered by observing the posting behavior of 111K Facebook apps seen across 2.2 million users on Facebook. First, we identify a set of features that aids us distinguish malicious apps from benign ones. For example, we discover that malicious apps often share names with other apps, and they typically request fewer permissions than benign apps. Second, leveraging these distinguishing features, we demonstrate that FRAppE can identify malicious apps with 99.5% accuracy, with no false positives and a low false negative rate (4.1%).
Critical: What We Can Do About the Health-Care Crisis

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading critical what we can do about the health care crisis is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.