Miracle-dz/UniFAR-FTUs huggingface dataset:
What's love got to do with it? Place brand love and viral videos

Purpose The purpose of this paper is to explore the role of brand love in place brand communication by incorporating potential antecedents and behavioral outcomes of place brand love in a social media setting. Design/methodology/approach Data were gathered from 281 residents and visitors of a place through an online survey focusing on a place brand video. Structural equation modeling was employed to examine the research model. Findings Results show that place brand love has a strong direct relationship with positive word of mouth (WOM), and an indirect effect on intention to share the place brand message. Self-expressiveness of the place brand message also seems to influence place brand love as well as intention to share the message. Research limitations/implications The role of self-related concepts and brand love to a place has theoretical implications for research in place branding and electronic word of mouth. The study has limitations to its generalizability in terms of cultural aspects and sample representativeness. Practical implications Place marketers need to successfully reflect the self-concept of key stakeholders in communication messages in order to increase the probability that recipients will engage in positive WOM and share the message. Originality/value Research on place brand love is scarce and previous studies have focused solely on brand love in connection to tourists. The main contribution of the current study is the exploration of the role of brand love in connection to residents, who are vital co-creators of the place brand.
A Virtual Assistant on Campus for Blind and Low Vision Students

"I want to make the most of my college experience…pass the courses I need to get into law school, make new friends, and travel. Maybe study abroad. " These are the sentiments of one undergraduate student. These goals are not so different from those of hundreds of students attending college, yet, the mere fact she is blind creates a completely different perspective. This exploratory study seeks to learn how undergraduate students who are blind or have low vision (BLV), experience Aira, an augmented reality application, as a visual interpreter, in post-secondary settings. Semi-structured interviews illuminated three major themes: (a) accessibility impacts productivity, (b) incidental learning affects social interactions, and (c) sense of self. Theoretical conclusions are made on how Aira positively contributes to a complete college experience for students who are BLV, as outlined in the Seven Vectors of Identification Development (Chickering & Reisser, 1993).
The Learning Trajectory of Social Arithmetic using an Indonesian Traditional Game

The aim of this study is to design mathematics learning trajectory in social arithmetic using an Indonesian traditional game called the Kubuk Manuk. This game was used as the starting point in the learning process by applying the Indonesian Realistic Mathematics Education approach. A design research method was applied in the three stages, namely the preliminary design, the design experiment, and the retrospective analysis. The results of this study indicates that in the learning of social arithmetic, the learning trajectory can be practiced using local contexts such as culture or other things easily found in the daily activities of the students. With the use of the Kubuk Manuk Indonesian traditional game, it helped to stimulate the students to understand their knowledge of social arithmetic concept. Additionally, the stages in the learning trajectory have important roles in understanding the mathematics concepts of expenditure, income, profits and loss within the trading activities.
CausalBG: Causal Recurrent Neural Network for the Blood Glucose Inference With IoT Platform

Predicting blood glucose concentration facilitates timely preventive measures against health risks induced by abnormal glucose events. Advances in IoT devices, such as continuous blood glucose monitors (CGMs) have made it convenient for measurements of blood glucose in real time. However, accurate and personalized blood glucose concentration prediction is still challenging. Previous inference models yield low-inference accuracy due to the ineffective feature extraction and the limited, imbalanced personal training data. The underlying causal correlations among the blood glucose series are scarcely captured by these models. In this article, we propose CausalBG, a causal recurrent neural network (CausalRNN) deployed on an IoT platform with smartphones and CGM for the accurate and efficient individual blood glucose concentration prediction. CausalBG automatically captures the underlying causal relationships embedded in the blood glucose features through CausalRNN, and efficiently shares the limited personal data among users for the sufficient training via the multitask framework. Evaluations and case studies on 112 users demonstrate that CausalBG significantly outperforms the conventional predictive models on the blood glucose dynamics inference.
Investigation of Additively Manufactured Wind Tunnel Models with Integrated Pressure Taps for Vortex Flow Analysis

Wind tunnel models are traditionally machined from high-quality metal material; this condition reduces the possibility to test different geometric variations or models as it corresponds to incremental cost. In the last decade, the quality of additive manufacturing techniques has been progressively increasing, while the cost has been decreasing. The utilization of 3D-printing techniques suggests the possibility to improve the cost, time, and flexibility of a wind tunnel model production. Possible disadvantages in terms of quality of the model finishing, stiffness, and geometric accuracy are investigated, to understand if the production technique is capable of providing a suitable test device. Additionally, pressure taps for steady surface pressure measurements are integrated during the printing procedure and the production of complex three-dimensional highly swept wings have been selected as targets. Computational fluid dynamics tools are exploited to confirm the experimental results in accordance with the best practice approaches characterizing flow patterns dominated by leading-edge vortices. The fidelity level of the experimental data for scientific research of the described flow fields is investigated. An insight of the most important guidelines and the possible improvements is provided as well as the main features of the approach.
Sentiment Discovery and Analysis as a Mean of Student Experience Improvement

Sentiment analysis has found broad usage helping institutions to better understand the choices, intentions, and behaviors of an individual acting as a buyer, consumer or service user. However its utilization in the domain of higher education is scarce. Therefore, the paper provides an insight into most relevant research and diversified applications of sentiment analysis in higher education, describing its unexploited potentials and benefits, such as leveraging students’ attraction/retention, evaluating the institution’s competitiveness or tracking performance indicators over time. JEL CLASSIFICATIONS: I, I2, I23, C880
Occupational Stress and Quality of Life in Nursing

Reducir el estres ocupacional y riesgos psicosociales no es solo una cuestion imperativa, sino tambien moral. El objetivo deste estudio epidemiologico es verificar la presencia de estres laboral y la calidad de vida relacionada con la salud en profesionales de enfermeria. Se utilizaron tres cuestionarios: (a) Cuestionario sociodemografico, (b) Job Strain Scale y (c) Item Short Form Health Survey. Analisis estadistico se realizo mediante asociaciones entre los cuestionarios, analisis de varianza (ANOVA) y de regresion lineal simple y multiple. Se encontro que 60,8% de los participantes experimentan una alta demanda en el trabajo, 71,8% un alto control sobre la actividad realizada, y 85,5% bajo apoyo social. De los ocho dominios de la calidad de vida relacionada con la salud, los mas afectados fueron: dolor (μ = 61,87) y la vitalidad (μ = 62,25). Se concluyo que, ademas que la presente muestra tenga riesgo intermedio de estres, la calidad ser comprometida.
Forecasting Energy Consumption using Sequence to Sequence Attention models

To combat negative environmental conditions, reduce operating costs, and identify energy savings opportunities, it is essential to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely-used smart meters, have created possibilities for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting, such as FeedForward Neural Networks, are not well-suited for interpreting the time dimensionality of a signal. Consequently, this thesis applies Sequence-to-Sequence (S2S) Recurrent Neural Networks (RNNs) with attention for electrical load forecasting. The S2S and S2Sattention architectures commonly used for neural machine translation are adapted for energy forecasting. An RNN enables capturing time dependencies present in the load data, while the S2S RNN model strengthens consecutive sequence prediction by combining two RNNs: encoder and decoder. Adding the attention mechanism to these S2S RNNs alleviates the burden of connecting the encoder and decoder. Presented experiments compare a regular S2S model and four S2S attention models with two baseline models, the conventional Non-S2S RNN and a Deep Neural Network (DNN). Furthermore, each RNN model was evaluated with three different RNN-cells: Vanilla RNN, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) cell. All models were trained and tested on one building-level electrical load dataset, with five-minute incremental data. Results showed that the S2S Bahdanau et al. attention model was the dominant model as it outperformed all other models for nearly all forecasting lengths.
Providing Culturally Appropriate Physical Therapy to Somali Refugees in Minnesota

Minnesota is home to the largest community of Somali refugees in the United States (Pavlish, Noor, and Brandt, 2010, p. 353). The Somali community has unique healthcare needs that current Western medicine is not meeting adequately. The Somali living in Minnesota are at risk for increased rates of obesity, diabetes, and other chronic conditions due to the change in lifestyle, eating habits, and stress caused by moving to the United States. The purpose of this research is to identify the healthcare needs, preferences, and practices of the Somali refugee community in Minnesota to better inform and guide physical therapy clinicians in providing effective, culturally appropriate healthcare. Data was collected from focus groups of Somali refugees currently living in Minnesota and interviews of select healthcare practitioners who have had significant experience working with patients who are Somali. Results highlight the difference in accessing healthcare in Somalia versus the United States, the lack of cultural reciprocity between healthcare providers and the Somali refugee community, and additional structural and internal barriers that exist for Somali refugees in receiving adequate healthcare. Recommendations include suggestions for physical therapy education, clinical practice, and the professional association which guides the practice of physical therapy.
Efficacy of itraconazole in 18 cases of Malassezia dermatitis in dogs

Itraconazole was found to be an effective antifungal for the treatment of canine Malassezia dermatitis (MD). MD was diagnosed in 18 dogs, which were treated with itraconazole administered orally at 5 mg/kg of body weight, q12hrs, for 21 to 30 days. High prevalence breeds of MD were Maltese (22%), Cocker Spaniel (17%), Pekingese (11%), and Vizsla (11%). The dermatological signs of Malassezia dermatitis were crust (31%), alopecia (25%), hyperpigmentation (25%), scales (19%), erythema (13%), lichenification (11%), pustule (11%), ear swelling (11%), papules (5%), and offensive odor (5%). Commonly affected areas were ear canal (41%), axillae (18%), groin (15%), perianal (12%), ventral aspect of the neck (9%), interdigital spaces (1%), and muzzle (1%). Sixty seven percent of dogs with MD had cocci. Clinical responses of itraconazole were seen good, moderate, no responses of itraconazole, in 89%, 0%, and 11%, respectively, according to the owner’s satisfaction to follow up call. Recurrence was detected on five good responsive dogs and adverse effects of the treatment were detected in only one dog. On the basis of this clinical study, itraconazole is a good choice in the treatment of canine Malassezia dermatitis. Efficacy, frequency of administration and veterinary approval are the major advantages.
The Development and Challenges of Face Alignment Algorithms

A comprehensive survey of face alignment using different methods is presented in this paper. Face alignment is the fundamental task of facial applications, e.g., face recognition, 3D face modelling and face expression analysis, etc. State-of-the-art methods can be mainly categorized into the three groups: gradient descent-based, deep learning-based, and 3D model-based. In gradient descent-based methods, landmarks are localized and adjusted by solving a nonlinear regression function. Deep learning-based methods construct one or several cascaded neural networks to improve landmark localization accuracy. Beside the above two categories of methods solving problems on a 2D plane, there is also other category of methods like 3D model-based methods. Despite significant progress that has been made, face alignment faces challenges from real-world conditions: variation across poses, genders and ages, facial expressions, and facial attributes. This paper offers a brief illustration and analysis of several typical methods of face alignment, provides an overall understanding and insight into the field, which will motivate us to explore promising future directions.
Maintaining Scalability of Test Generation Using Multicore Shared Memory Systems

Taking advantage of multicore architectures can provide significant improvement for many design automation problems. However, the parallelization procedure introduces challenges, such as workload duplication, limited search space exploration, and race contention among different threads. In this article, we propose a parallel framework for automatic test pattern generation using shared memory multicore systems that support test generation (TG) for both single-detect and multiple-detect fault models. The framework follows a two-epoch approach, each focusing on a different category of faults, during which a test seed generation is followed by compatibility merging. Various optimization techniques are incorporated in each epoch, designed to achieve higher speedup for the overall TG procedure without impacting much the test set size. A cluster-based approach is also presented, extending the proposed framework to consider multiple-detect fault models without affecting its efficiency. The obtained experimental results demonstrate increased speedup rates compared with the state-of-the-art multicore-based tools while, at the same time, the test inflation problem is restrained. For the multiple-detect extension, these properties are maintained despite the increased workload and the additional constraint of retaining the number of detections for each fault while merging.
A study of training needs of rural women towards improved dairy farming practices

Training of rural women is one of the important components in the process of technology transfer. To develop competence in dairying, women must have access to technical information. In this context, the present study was undertaken in Rudrapur block of district Udham Singh Nagar, Uttaranchal to identify the training needs of the rural women in specific areas of dairy husbandry practices. A total of 120 female respondents were selected randomly from three villages of Rudrapur block. The results of the study revealed that the majority of the respondents preferred female extension workers as compared to scientists for imparting training and preferred their own village as the venue of training. First preference was given to combination of lecture, group discussion and method demonstration. The respondents were most interested to receive training in the field of management of sick animals, preparation of balanced ration for animals, management of repeat breeding and production of clean milk.
Phase I trial of bevacizumab plus sunitinib in patients with metastatic renal cell carcinoma

5100 Background: Bevacizumab and sunitinib both have activity in metastatic renal cell carcinoma (mRCC). Combining bevacizumab and sunitinib may increase antitumor efficacy by maximizing inhibition of the VEGF pathway. The safety and maximum tolerated dose (MTD) of sunitinib in combination with bevacizumab was assessed in this Phase I trial. Methods: Cohorts of 3–6 patients (pts) with mRCC received escalating doses of sunitinib (dose levels: 25, 37.5, and 50 mg po) daily for 4 weeks (wks) followed by 2 wks off with fixed-dose bevacizumab (10 mg/kg iv) every 2 wks continuously. Pre- determined dose-limiting toxicities (DLTs) were assessed in the first 6-wk cycle. Results: Of 26 pts (median age 57) enrolled, 25 were treated including 7 (1 replaced) in cohort 1 (sunitinib 25 mg, bevacizumab 10 mg/kg), 6 in cohort 2 (sunitinib 37.5 mg, bevacizumab 10 mg/kg) and 12 (6 for MTD, additional 6 for safety) in cohort 3 (sunitinib 50 mg, bevacizumab 10 mg/kg). Two DLTs of G4 hemorrhage occurred (1 cohort 2, 1 cohort .
Efficient and Scalable Calibration-Free Indoor Positioning Using Crowdsourced Data

With the development of Internet of Things (IoT), a variety of crowdsourced data becomes available. In this article, we propose to utilize the crowdsourced WiFi received signal strength (RSS) data to perform indoor positioning. In many instances, the size of crowdsourced WiFi RSS data is potentially large, and the ground truths of the corresponding RSS fingerprints are unavailable. Therefore, it is challenging to construct the associated radio map. In the proposed method, a heuristic geometrical algorithm is developed to convert the RSS data into pairwise distances among the fingerprints. Based on these pairwise distances, multidimensional scaling (MDS) is then applied to compute the positions of all the fingerprints, thereby building a radio map. We show that the accuracy of the proposed method is only reasonably lower than a state-of-the-art calibration-based method. Further, we present a ${k}$ -means clustering-based region partitioning method that partitions the large crowdsourced data set effectively. The parallel processing on the partitioned data and computational simplicity of MDS result in significantly shorter runtime for the proposed method than the previous optimization-based methods.
User Experience Design for E-Voting: How mental models align with security mechanisms

This paper presents a mobile application for vote-casting and vote-verification based on the Selene e-voting protocol and explains how it was developed and implemented using the User Experience Design process. The resulting interface was tested with 38 participants, and user experience data was collected via questionnaires and semi-structured interviews on user experience and perceived security. Results concerning the impact of displaying security mechanisms on UX were presented in a complementary paper. Here we expand on this analysis by studying the mental models revealed during the interviews and compare them with theoretical security notions. Finally, we propose a list of improvements for designs of future voting protocols.
Aging Memories Generate More Fluent Dialogue Responses with Memory Networks

The integration of a Knowledge Base (KB) into a neural dialogue agent is one of the key challenges in Conversational AI. Memory networks has proven to be effective to encode KB information into an external memory to thus generate more fluent and informed responses. Unfortunately, such memory becomes full of latent representations during training, so the most common strategy is to overwrite old memory entries randomly. In this paper, we question this approach and provide experimental evidence showing that conventional memory networks generate many redundant latent vectors resulting in overfitting and the need for larger memories. We introduce memory dropout as an automatic technique that encourages diversity in the latent space by 1) Aging redundant memories to increase their probability of being overwritten during training 2) Sampling new memories that summarize the knowledge acquired by redundant memories. This technique allows us to incorporate Knowledge Bases to achieve state-of-the-art dialogue generation in the Stanford Multi-Turn Dialogue dataset. Considering the same architecture, its use provides an improvement of +2.2 BLEU points for the automatic generation of responses and an increase of +8.1% in the recognition of named entities.
Dermoscopy in hair disorders

Hair loss can have significant effects on patients' quality of life, and a prompt diagnosis of the different types of alopecias and early intervention are needed. This review highlights the main dermoscopic findings in the different types of alopecia, such as androgenetic alopecia, alopecia areata, trichotillomania, lichen planopilaris, and discoid lupus erythematosus of the scalp. We believe that this important tool has been demonstrated to help dermatologists in finding the right site for the biopsy or, furthermore, avoiding unnecessary biopsies. Data sources were medical text books, medical journals, and medical websites that have updated research with the key word Dermoscopy in the title of the paper. Systematic reviews that addressed dermoscopy, its impact on dermatological lesions, and the role of physicians in prevention and management were selected. A special search was conducted at midline with the key word Dermoscopy in the title of the papers; extraction was made, including assessment of the quality and the validity of papers that met the prior criteria that describe Dermoscopy and its use in the diagnosis of hair disorders. Each study was reviewed independently, and the data obtained were rebuilt in new language according to the need of the researcher and arranged into topics through the article. Hair and scalp dermoscopy (trichoscopy) is a fast and noninvasive technique that allows the identification of hair and scalp diseases on the basis of analysis of trichoscopy structures and patterns without the need for removing hair for diagnostic purposes or unnecessary biopsies. Dermoscopy may be useful in the differential diagnosis of various hair and scalp disorders such as alopecia areata, androgenetic alopecia, and tenia capitis and also in the differential diagnosis between discoid lupus erythematosus and lichen planopilaris.