Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing multiple evaluation frameworks in an older adults care information system project: A case study from Taiwan

Many hospital managers agree that they can copy their successful experience in the medical center through implementing the information system (IS) successfully in the older adults care institution. However, these facilities are not the same care model as general hospitals. In addition, the scales of operation in older adults care institution are often rather small and they can not undertake IS project with a high failure risk. Thus, the same standards for evaluating a successful implementation cannot be guaranteed for both types of institutions. A suitable system evaluation framework before the whole system migrates is very important, especially in the older adults care institution with low informatization levels. The goal of this study is emphasized by developing an innovative system project and its’ multiple evaluation framework in our case which turned to be a research-action, study participant comprising whole staffs and its residents of the older adults care village of biggest health care group in Taiwan. The results describe our resulting functional design of the system, multiple evaluation framework phases, and several challenges, difficulties or attentions. The contribution of this study is our project design and practical feasibility and execution of its evaluation methodology to an older adults care institution.
GENETIC POPULATION STRUCTURE AND CONSERVATION OF BULL TROUT IN THE EAST FORK BITTERROOT RIVER DRAINAGE, MONTANA

Investigating the genetic population structure of bull trout Salvelinus confluentus can be useful for developing biologically sound conservation and management strategies. We focused on the East Fork Bitterroot River (East Fork) drainage because it is a connected, core conservation area for bull trout that contains a migratory life history component. Non-lethal fin samples were collected from 17 sites: nine East Fork tributaries, the main stem East Fork, and seven other tributaries across the Bitterroot drainage. Considering all the samples, principal component analysis of allele frequencies at 15 microsatellite loci indicated the East Fork samples formed a distinct cluster compared to other tributaries sampled. Within the East Fork drainage, there was significant divergence among samples with pairwise FST ranging from 0.016 to 0.188. Based on multiple locus genotypes, most individuals assigned to their tributary of capture with over 90% probability, suggesting the tributaries contain genetically divergent populations. The main stem East Fork sample tended to form its own group, but some fish collected from it also assigned to tributaries. Four tributaries had individuals that assigned to the East Fork indicating migration from the East Fork to tributaries. Likewise, four tributaries also had individuals that assigned to tributaries different than where they were collected indicating migration from tributary to tributary. These data suggest the East Fork may contain a mixture of individuals produced from spawning in the upper main stem and migrants from different tributaries. The main stem East Fork appears to be an integral component for maintaining the migratory form of bull trout in the drainage and serves as a vehicle for potential genetic exchange among tributary populations. Thus, conservation and management efforts in the drainage need to simultaneously focus on the tributaries and the main stem East Fork.
Intestinal histomorphology in Pseudoplatystoma fasciatum fed bovine colostrum as source of protein and bioactive peptides

Histological responses of the intestine are key for evaluating nutritional value of feed ingredients, since the organ is not only the chief site of feed digestion and nutrient absorption but also plays an important immunological function. Histomorphological alterations were evaluated in the intestine of juvenile striped catfish, Pseudoplatystoma fasciatum, fed diets containing 0 (control), 10 or 20% inclusion of lyophilized bovine colostrum (LBC), as source of protein or bioactive peptides, for either 30 or 60 days. Fish fed 20LBC presented at 60d a distinct pattern of macrophages and, some of them, higher number of vacuoles in rectum mucosa. The thickness of the muscle layer (TML) in fish fed diets with LBC was higher in the first portion of medium intestine than fish fed 0LBC. All fish presented significant increase of TML in the second portion of medium intestine along feeding period, but fish fed 20LBC had smaller values of TML than those of fish fed 0 and 10LBC which might be related to the higher intestinal coefficient found for this group. The TML of rectum was higher just for fish fed 10LBC. Dietary LBC altered morphometrical features of juvenile striped catfish intestine and possibly induced inflammatory reaction in the rectal mucosa, as a function of level of inclusion, feeding period and segment of intestine analyzed.
A Survey on Multiview Video Synthesis and Editing

Multiview video can provide more immersive perception than traditional single 2-D video. It enables both interactive free navigation applications as well as high-end autostereoscopic displays on which multiple users can perceive genuine 3-D content without glasses. The multiview format also comprises much more visual information than classical 2-D or stereo 3-D content, which makes it possible to perform various interesting editing operations both on pixel-level and object-level. This survey provides a comprehensive review of existing multiview video synthesis and editing algorithms and applications. For each topic, the related technologies in classical 2-D image and video processing are reviewed. We then continue to the discussion of recent advanced techniques for multiview video virtual view synthesis and various interactive editing applications. Due to the ongoing progress on multiview video synthesis and editing, we can foresee more and more immersive 3-D video applications will appear in the future.
SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach

We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst’s trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing high-dimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.
An Efficient Method on Identification of Product Aspect and Ranking System

Identification of important product aspects become necessary as both consumers and firms are benefited by this. Consumers can easily make purchasing decision by paying attention to the important aspects as well as firms can focus on improving the quality of these aspects and thus enhance product reputation efficiently. The consumer reviews contain rich and valuable knowledge about the product. And this knowledge is very useful for both consumer and firm. Consumer can make wise purchasing decision by paying more attention towards important aspect or feature. And firm will concentrate on important features or aspect while improving the quality of the aspect. So in this proposed framework, this will identify the important aspect of product from online consumer reviews. The important aspects are commented again and again in consumer review and the consumers opinions on the important aspects are greatly influence their overall opinions on the product. From the consumer reviews the important aspect are identified by using NPL tool, and will classify the sentiment on that aspect, and finally we are going apply the ranking algorithm to determine the particular product
Multi-Channel Distributed Representation for Classifying Tweets by using Convolutional Neural Networks

With the increasing interest in social media, many text messages are being posted on micro-blogging sites. Tweets on Twitter often reflect the user’s reaction for topics and events around people. Therefore, microblog mining is one of the most attractive research topics for real-world monitoring. However, since a message on a microblogging site, such as Twitter, is short, classifying such text messages is a challenging task. In our previous work, we proposed a method based on Naive Bayes classifier for classifying tweets under two classes: “relevant” and “irrelevant” to a monitoring topic. The classification performance has limitations because Naive Bayes is based on words’ probabilities. To solve this problem, we propose a deeplearning-based method with multi-channel distributed representation. Distributed representation indicates word vectors representing latent features of words. Multi-channel distributed representation enhances the representing ability of distributed representation. In experiments, some models are used for comparing each other, convolutional neural networks (CNN), long short-term memory are used in performance evaluation. The results showed that the classification performance of deep learning outperformed that of the Naive Bayes. Moreover, CNN with multi-channel distributed representation can classify tweets better than CNN without multi-channel distributed representation.
Adolescent HIV Prevention: An Application of the Elaboration Likelihood Model.

Ninth grade students (n=298) participated in a study to examine the influence source credibility, message, quality, and personal relevance on HIV prevention message efficacy. A pilot study with adolescent focus groups created the high and low quality messages, as well as the high (HIV+) and low (worried parent) credibility sources. Participants appeared to respond low in involvement and as expected, credibility proved important to those exposed to the low quality message. More favorable responses were recorded when participants were exposed to a high quality message. Participants exposed to a low quality message condition produced less counter-attitudinal responses when receiving the low quality message from a highly credible source (i.e., the HIV+ adolescent). The potential influence of message source credibility as a crucial peripheral cue is discussed in terms of HIV/AIDS prevention. (Contains 30 references.) (JDM) Reproductions supplied by EDRS are the best that can be made from the original document. Adolescent HIV Prevention 1 Running head: ADOLESCENT HIV PREVENTION Adolescent HIV Prevention: An Application of the Elaboration Likelihood Model April E. Metzler, David Weiskotten, and Keith J. Morgen Lehigh University Please address all correspondence to: April E. Metzler, Ph.D. Associate Professor Dept. of Education & Human Services College of Education Lehigh University Iacocca Hall, 111 Research Dr. Bethlehem, PA 18015 610-758-6093 aem3@lehigh.edu U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION 1.0 CENTER (ERIC) O This document has been reproduced as received from the person or organization originating it. CD 0 Minor changes have been made to improve reproduction quality. 0CD ° Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. 2 BEST COPY AVAILABLE PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) Adolescent HIV Prevention 2 Adolescent HIV Prevention: An Application of the Elaboration Likelihood Model
Supermarkets, wholesalers, and tomato growers in Guatemala

The article shows the asset-related determinants and the impacts of the participation of small farmers in supermarkets versus traditional market channels in Guatemala. Compared to farmers selling only to the traditional market channels, farmers selling to supermarket channels are larger (but are in the upper tier of the "small farmer" category), have more capital, and are much more specialized in commercial horticulture in general and in tomatoes in particular. While they have higher yields, they also have higher input use, including use of chemicals. In fact, they severely overuse pesticides and fungicides. Moreover, these greater input expenditures mean that their profit rates are roughly similar to those of farmers in the traditional market channel. Supermarket-channel farmers prefer the more demanding wholesale-supermarket channel because it offers lower risks and lower transaction costs to market, a variety of quality grades of tomatoes, all year long. In turn, the supermarkets, who do not buy direct but rather source from a few specialized-dedicated wholesalers, rely on this year-round supply, lower transaction costs, and consistency of quality. Copyright 2007 International Association of Agricultural Economists.
Weighted Multi-band Summary Correlogram (MBSC)-based Pitch Estimation and Voice Activity Detection for Noisy Speech

The pitch estimation and Voice activity detection (VAD) is the task of classifying an acoustic signal stream into voiced and unvoiced segments that plays as a crucial preprocessing tool to a wide range of speech applications. In this paper, a weighted multi-band summary correlogram (MBSC)-based pitch estimation algorithm (PEA) as well as voice activity detection (VAD) is proposed. The PEA performs pitch estimation and voiced/unvoiced (V/UV) detection via novel signal processing schemes that are designed to enhance the MBSC’s peaks at the most likely pitch period. This technique computes an independent normalized auto-correlation function (NACF) for each channel or frame which is relatively insensitive to phase changes across channels firstly and then filtered these NACFs to remove a significant portion beyond the pitch range 50-500 Hz and then finding an adaptive threshold from filtered NACFs. This threshold acts as a pitch position indicator and a voiced/unvoiced region detector. The accurate pitch period is obtained from the weighted MBSC. The proposed algorithm has the lowest gross pitch error (%GPE) for noisy speech in the evaluation set among the algorithms evaluated. The proposed PDA also achieves the lowest average voicing detection errors
Unchain Your Blockchain

Blockchain is emerging as a preeminent decentralized ledger and receiving increasing attention from researchers, practitioners, organizations and the public. Initially, blockchain was developed to address the “double spending” problem in cryptocurrencies, but recently, many new applications of blockchain have been proposed or are being developed. Blockchain allows sharing data in a decentralized, transparent and immutable way, using a peer-to-peer network, without the need to trust any particular entity. To achieve that in public blockchain, where the peers are a priori unknown, efficiency and scalability are often sacrificed. In this paper we present a novel partition of the blockchain into smaller chains, to allow association of sub-chains, wallets and transactions with real-world concepts, such as geographical areas, and by this, improve scalability and security. Our contribution is threefold. First, we discuss the utilization of a real-world hierarchical structure, such as a geospatial subdivision, to partition the ledger into a tree of connected blockchains, in order to increase scalability and provide a tradeoff between privacy and transaction latency. Second, we illustrate the use of a geospatial partitioning to support geofencing, in order to add security to cryptocurrencies and other blockchain applications. Third, we present proof-of-location as an alternative to proof-of-work, to cope with the large waste of energy caused by proof-ofwork, which may be inflated by the partitioning.
Symptoms of Generalised Anxiety disorder but not Panic Disorder at age 15 increase the risk of depression at 18 in the ALSPAC cohort study

Affiliations: (1) Centre for Academic Mental Health, School of Social and Community Medicine, University of Bristol, U.K. (2) Geriatric Psychiatry Division, CAMH, University of Toronto, Canada (3) NHMRC Centre for Research Excellence in Mental Health and Substance Use, National Drug and Alcohol Research Centre, University of New South Wales, Australia. (4) Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, U.K. (5) Division of Psychiatry, University College London, U.K.
EMPIRICAL STUDY ON ENSEMBLE METHOD OF DEEP NEURAL NETWORKS FOR ACOUSTIC SCENE CLASSIFICATION

The deep neural network has shown superior classification or regression performances in wide range of applications. In particular, the ensemble of deep machines was reported to effectively decrease test errors in many studies. In this work, we extend the scale of deep machines to include hundreds of networks, and apply it to acoustic scene classification. In so doing, several recent learning techniques are employed to accelerate the training process, and a novel stochastic feature diversification method is proposed to allow different contributions from each constituent network. Experimental results with the DCASE2016 dataset indicate that an ensemble of deep machines leads to better performances on the acoustic scene classification. �ndex Terms— neural network, ensemble model, ZCA whitening, bagging,
Another Important Parameter’s Research on Estimating Self-Similarity

It is convincingly demonstrated by numerous studies that the self-similarity of modern multimedia network traffic is presented by Hurst parameter (H). The specific performance is that the similar degree is higher along with the increase of H when H is between 0.5 and 1. However, it is doubtable that whether the complicated process of self-similarity can be described comprehensively by the parameter H only. Therefore, another important parameter cf has been proposed based on the discrete wavelet decomposition in this paper. The significance of the parameters is provided and the performance of the self-similarity process is described better.
Guess What I Attend: Interface-Free Object Selection Using Brain Signals

Interpreting the brain activity to identify user goals or to ground a robot's hypotheses about them is a promising direction for non-intrusive and intuitive communication. Such a capability can be of particular relevance in the context of human-robot cooperation scenarios. This paper proposes a novel approach to utilize the natural brain responses to highlighted objects in the scene for object selection. By this, it circumvents the need for additional interfaces or user training. Our approach uses methods from information geometry to classify the target/non-target response of these event-related potentials. Online experiments carried out with a real robot demonstrate an accurate detection of target objects solely based on the user's attention.
Transportation of Pacific salmon carcasses from streams to riparian forests by bears

Predation on Pacific salmon by bears (genus Ursus L., 1758) can be an important ecosystem process because the spatial distribution of carcasses largely determines whether marine-derived nutrients cycle through aquatic or terrestrial pathways. Direct observations on three streams in southeastern Alaska indicated that 49% of the pink (Oncorhynchus gorbuscha (Walbaum, 1792)) and chum (Oncorhynchus keta (Walbaum in Artedi, 1792)) salmon killed by bears were carried into the forest. The tendency of bears to transport carcasses was independent of the sex and species of salmon, but unspawned fish were more often transported than fish that had completed spawning. Data on tagged sockeye salmon (Oncorhynchus nerka (Walbaum in Artedi, 1792)) in one southwestern Alaska stream indicated that 42.6% of the killed salmon were transported, and that higher percentages were transported in years when salmon densities were greater. At six other streams, on average, 68.1% of the sockeye salmon killed were apparently transporte.
Built in Self Test for Programmable System on Chip’s Analog to Digital Converter

In this paper the built in self test for analog to digital converter of programmable system on chip is implemented. The new approach for finding out static errors in ADC is based on time. This approach uses ramp signal and determines time difference between two successive samples, which in turn leads to finding out the static error like DNL, INL of the ADC. The proposed work uses PSoC devices which enable us to design a complete mixed signal system with few off chip components To test static parameter of ADC on chip components are used. Ramp signal is generated by using on chip IDAC of PSoC. The BIST circuit implemented here calculate DNL, INL based on timing i.e. a new approach to calculate DNL, INL is implemented and verified.
Solving the Perspective-2-Point Problem for Flying-Camera Photo Composition

Drone-mounted flying cameras will revolutionize photo-taking. The user, instead of holding a camera in hand and manually searching for a viewpoint, will interact directly with image contents in the viewfinder through simple gestures, and the flying camera will achieve the desired viewpoint through the autonomous flying capability of the drone. This work studies a common situation in photo-taking, i.e., the underlying viewpoint search problem for composing a photo with two objects of interest. We model it as a Perspective-2-Point (P2P) problem, which is under-constrained to determine the six degrees-of-freedom camera pose uniquely. By incorporating the user's composition requirements and minimizing the camera's flying distance, we form a constrained nonlinear optimization problem and solve it in closed form. Experiments on synthetic data sets and on a flying camera system indicate promising results.
SOUND SCENE IDENTIFICATION BASED ON MONAURAL AND BINAURAL FEATURES

This submission to the sub-task acoustic scene classification of the IEEE DCASE 2016 Challenge: Acoustic scene classification is based on a feature extraction module based on the concatenation of monaural and binaural features. Monaural features are based on Mel Frequency Cepstrums summarized using recurrence quantification analysis. Binaural features are based on the extraction of inter-aural differences (level and time) and the coherence between the two channel stereo recordings. These features are used in conjunction with a support vector-machine for the classification of the acoustic sound scenes. In this short paper the impact of different features is analyzed.
Automated non-contact heart rate measurement using conventional video cameras

In this work we propose an algorithm for noncontact heart rate estimation using conventional cameras. The algorithm is robust to movements and works under non-controlled illumination. We divide the frames in micro-regions that are tracked using an optical flow algorithm to compensate for movements and we apply a clustering algorithm to automatically select the best micro-regions to use for heart rate estimation. We also propose a temporal and spatial filtering scheme to reduce noise and an adaptive filter to improve the signal to noise ratio of the signal employed for heart rate estimation. We evaluated our algorithm for real and synthetic data, comparing its results to a fingertip pulse oximeter and a state-of-the-art algorithm for heart rate estimation through video. Results show that the algorithm can work well under challenging situations. Keywords— Photoplestimography, Video processing, Pulse measurements, Heart rate.