LLukas22/scidocs huggingface dataset:
Feature selection based on an improved cat swarm optimization algorithm for big data classification

Feature selection, which is a type of optimization problem, is generally achieved by combining an optimization algorithm with a classifier. Genetic algorithms and particle swarm optimization (PSO) are two commonly used optimal algorithms. Recently, cat swarm optimization (CSO) has been proposed and demonstrated to outperform PSO. However, CSO is limited by long computation times. In this paper, we modify CSO to present an improved algorithm, ICSO. We then apply the ICSO algorithm to select features in a text classification experiment for big data. Results show that the proposed ICSO outperforms traditional CSO. For big data classification, the results show that using term frequency-inverse document frequency (TF-IDF) with ICSO for feature selection is more accurate than using TF-IDF alone.
Which Program for Whom? Evidence on the Comparative Effectiveness of Public Sponsored Training Programs in Germany

We use a new and exceptionally rich administrative data set for Germany to evaluate the employment effects of a variety of public sponsored training programs in the early 2000s. Building on the work of Sianesi (2003, 2004), we employ propensity score matching methods in a dynamic, multiple treatment framework in order to address program heterogeneity and dynamic selection into programs. Our results suggest that in West Germany both short-term and medium-term programs show considerable employment effects for certain population subgroups but in some cases the effects are zero in the medium run. Short-term programs are surprisingly effective when compared to the traditional and more expensive longer-term programs. With a few exceptions, we find little evidence for significant positive treatment effects in East Germany. There is some evidence that the employment effects decline for older workers and for low–skilled workers.
Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models

2 The aim of statistical relational learning is to learn statistical models from relational or graph3 structured data. Three main statistical relational learning paradigms include weighted rule 4 learning, random walks on graphs, and tensor factorization. These paradigms have been mostly 5 developed and studied in isolation for many years, with few works attempting at understanding 6 the relationship among them or combining them. In this paper, we study the relationship between 7 the path ranking algorithm (PRA), one of the most well-known relational learning methods in 8 the graph random walk paradigm, and relational logistic regression (RLR), one of the recent 9 developments in weighted rule learning. We provide a simple way to normalize relations and 10 prove that relational logistic regression using normalized relations generalizes the path ranking 11 algorithm. This result provides a better understanding of relational learning, especially for the 12 weighted rule learning and graph random walk paradigms. It opens up the possibility of using the 13 more flexible RLR rules within PRA models and even generalizing both by including normalized 14 and unnormalized relations in the same model. 15
A Study on Knowledge, Attitude and Practice on Blood Donation among Health Professional Students in Chennai, Tamil Nadu, South India

Background: Role of youngsters in voluntary blood donation is crucial to meet the demand of safe blood. Therefore understanding the various factors contributing to knowledge, attitude and practice of Voluntary Blood Donation (VBD) among youngsters is important. Objectives: The present study aims to assess the level of knowledge, attitude and practice regarding blood donation among the health care medical students. Methods: A cross-sectional study was conducted among 400 health care medical students from Tagore Medical College and Hospital, Matha Medical College and Research Institute, and Tagore Dental College and Hospital Chennai, Tamil Nadu, South India using a structured survey questionnaire. Results: Overall knowledge on blood donation among respondents was 35.65%, majority of the participants (89.25%) never donated blood in which few of the non-donors had negative attitude (36.41%) like blood donation leads to weakness (12.61%), anemia (19.61%) and 4.2% of non-donors felt that blood donation leads to reduced immunity. Majority of nondonors showed positive attitude (63.59%) by expressing their willingness to donate blood if they were asked to donate blood (32.21%), about 7% of the non-donors don’t know the importance of blood donation and 24.37% of non-donors don’t know where to donate blood. In this study10.75% of students had donated blood only once, in which 34.88 % of blood donors said no privacy was provided while donation, few (11.63%) had experienced some discomfort after donation and 53.49% of respondent said that they have not tried again. Conclusion: The present study recommends that even student community needs to be educated about the importance and health benefits; awareness and motivation of blood donation on regular basis and addressing the problems faced during blood donation will strengthen the recruitment and retention of blood donors to donate blood on regular basis.
Diabetes quality improvement in Department of Veterans Affairs Ambulatory Care Clinics: a group-randomized clinical trial.

OBJECTIVE
To conduct a group-randomized clinical trial to determine whether regular feedback to primary care providers of synthesized information on patients' health, function, and satisfaction would demonstrate improved outcomes for their patients with diabetes.


RESEARCH DESIGN AND METHODS
Patients in General Internal Medicine Clinics Department of Veterans Affairs (VA) Medical Centers were randomized into seven intervention or control firms. Patient self-reported information was collected by mail on general health, diabetes, and up to five other chronic conditions. Patients with diabetes received the Seattle Diabetes Questionnaire, the 36-item Medical Outcomes Study short form (SF-36), and a validated patient satisfaction questionnaire at regular intervals. Data from self-report, clinical, pharmacy, and laboratory sources were synthesized into patient-specific feedback reports that intervention providers received before patients' visits.


RESULTS
The timely delivery to primary care providers of state-of-the-art patient-feedback reports that identified patient issues and areas for improvement did not result in significant improvements in patient outcomes between the intervention and control firms.


CONCLUSIONS
Outcomes in diabetic patients whose providers received synthesized patient data before visits were no better than in those receiving care from control firms. Future studies may benefit from substantial involvement in patients discussing, problem solving, and goal setting in addition to use of timely synthesized patient data.
Can perceived risks affect the relationship of switching costs and customer loyalty in e-commerce?

Purpose – The purpose of this paper is to verify the relationship between switching costs and customer loyalty in e-commerce. Design/methodology/approach – The study conducted an empirical research. A total of 425 online shopping customers were invited from northeastern USA as samples. Findings – The findings show that switching costs positively influence customer loyalty. In addition, perceived risks will affect the relationship of switching costs and customer loyalty. For customers with low perceived risks, switching costs are also positively associated with customer loyalty. However, for customers with high perceived risks, the relationship of switching costs and customer loyalty is weak or negative. Research limitations/implications – One limitation is that mostly students were selected in the sample. The insights of this study can further validate the previous studies about the relationship of switching costs and customer loyalty, and suggest that perceived risks can be a moderating factor affecting this relationship. Practical implications – The study suggests that the practitioners should further understand the relationship among switching costs, perceived risks and customer loyalty for their customers. Originality/value – The paper contributes to the knowledge of perceived risks and how switching costs affect customer loyalty, particularly in e-commerce.
Internal inflammatory root resorption: the unknown resorption of the tooth

Internal inflammatory root resorption is a relatively rare resorption that begins in the root canal and destroys surrounding dental hard tissues. Odontoclastic multinuclear cells are responsible for the resorption, which can grow to perforate the root if untreated. The initiating factor in internal root resorption is thought to be trauma or chronic pulpal inflammation, but other etiological factors have also been suggested. Active, expanding resorption requires vital pulp tissue and continuous microbiological irritation, likely from the necrotic coronal part of the root canal. In its classical form, internal root resorption is easy to diagnose. However, in many instances advanced diagnostic methods may be required for a definitive diagnosis. Internal root resorption is usually symptom free, but in cases of perforation, a sinus tract usually forms. The prognosis for treatment of small lesions of internal root resorption is very good. If, however, the tooth structure is greatly weakened and perforation has occurred, the prognosis is poor and tooth extraction must be considered. Sodium hypochlorite, ultrasonic instrumentation and calcium hydroxide are the cornerstones of treatment of internal inflammatory root resorption. Mineral trioxide aggregate is being increasingly used as a root canal filling material, particularly in cases of perforation.
RFID in the Retail Supply Chain

The use of RFID (radio-frequency identification) in the retail supply chain and at the point of sale (POS) holds much promise to revolutionize the process by which products pass from manufacturer to retailer to consumer. The basic idea of RFID is a tiny computer chip placed on pallets, cases, or items. The data on the chip can be read using a radio beam. RFID is a newer technology than bar codes, which are read using a laser beam. RFID is also more effective than bar codes at tracking moving objects in environments where bar code labels would be suboptimal or could not be used as no direct line of sight is available, or where information needs to be automatically updated. RFID is based on wireless (radio) systems, which allows for noncontact reading of data about products, places, times, or transactions, thereby giving retailers and manufacturers alike timely and accurate data about the flow of products through their factories, warehouses, and stores. Background
SmartBuckle: human activity recognition using a 3-axis accelerometer and a wearable camera

Recognizing human activity is one of the most important concerns in many ubiquitous computing systems. In this paper, we present a wearable intelligence device for medical monitoring applications. We called the SmartBuckle that is designed to recognize human activity and to monitor vitality. We developed human activity recognition algorithms and evaluated them by using data acquired from a 3-axis accelerometer with embedded one image sensor in a belt. In order to evaluate, acceleration data was collected from 9 activity labels. In the image sensor, we extracted activity features based on grid-based optical flow method. In the 3-axis accelerometer sensor, we used the correlation between axes and the magnitude of the FFT for feature extraction. In the experiments, our classifiers showed the excellent performance in recognizing activities with an overall accuracy rate of 93%.
Adult polyglucosan body disease associated with lewy bodies and tremor.

BACKGROUND
Adult polyglucosan body disease (PGBD) is rare and typically presents with upper and lower motor neuron involvement and neurogenic bladder. Extrapyramidal features are unusual in PGBD and are presumed secondary to widespread pathology that includes the basal ganglia. There are no prior reports of Lewy bodies in PGBD.


OBJECTIVE
To report a unique finding of Lewy bodies in a patient with PGBD. REPORT OF A CASE A 46-year-old woman initially presented with a 4-year history of resting tremor. The tremor responded to levodopa therapy. Several months later, she developed upper and lower motor neuron involvement and other clinical features of PGBD. A sural nerve biopsy specimen revealed intra-axonal polyglucosan bodies that confirmed the clinical diagnosis. Bulbar and limb weakness progressed, and she developed dementia. She died 6 years after onset. At autopsy, extensive polyglucosan body formation was found in many regions of the central nervous system. In addition, numerous alpha-synuclein staining Lewy bodies were observed in the substantia nigra, accompanied by marked neuron depopulation.


CONCLUSIONS
To our knowledge, this is the first report of adult PGBD associated with Lewy bodies and levodopa-responsive tremor. Although polyglucosan bodies were seen in substantia nigra, it is most likely that our patient had coexisting Parkinson disease.
Together we stand: Siamese Networks for Similar Question Retrieval

Community Question Answering (cQA) services like Yahoo! Answers1, Baidu Zhidao2, Quora3, StackOverflow4 etc. provide a platform for interaction with experts and help users to obtain precise and accurate answers to their questions. The time lag between the user posting a question and receiving its answer could be reduced by retrieving similar historic questions from the cQA archives. The main challenge in this task is the “lexicosyntactic” gap between the current and the previous questions. In this paper, we propose a novel approach called “Siamese Convolutional Neural Network for cQA (SCQA)” to find the semantic similarity between the current and the archived questions. SCQA consist of twin convolutional neural networks with shared parameters and a contrastive loss function joining them. SCQA learns the similarity metric for question-question pairs by leveraging the question-answer pairs available in cQA forum archives. The model projects semantically similar question pairs nearer to each other and dissimilar question pairs farther away from each other in the semantic space. Experiments on large scale reallife “Yahoo! Answers” dataset reveals that SCQA outperforms current state-of-theart approaches based on translation models, topic models and deep neural network https://answers.yahoo.com/ http://zhidao.baidu.com/ http://www.quora.com/ http://stackoverflow.com/ based models which use non-shared parameters.
Sleep problems are associated with poor outcomes in remedial teaching programmes: a preliminary study.

AIM
Problematic behaviour and deficient academic performance have been reported in children with sleep problems, but whether sleep problems are common among children presenting with primary behavioural and performance concerns in remedial programmes is not well studied. We studied this possibility in 80 Australian school children aged 6-15 years and then compared 15 of these children from mainstream schools to 15 demographically matched children in specialist behavioural programmes for problematic behaviour and academic difficulties.


METHODS
Parents completed the Child Behaviour Checklist and the Sleep Disorders Scale for Children. Questionnaires assessed behaviour, academic performance and symptoms of diverse sleep disorders, expressed as T-scores (mean (SD) = 50 (10)). Teachers rated students' academic performance (A, B, C, D, E).


RESULTS
When compared with the 15 controls, the 15 index children had significantly more sleep problems, in addition to parental concerns about school performance. In the total sample (n = 80), poor sleep including symptoms of daytime sleepiness, parasomnias, behavioural sleep problems and combined sleep problems was associated with poor academic performance and daytime behavioural issues.


CONCLUSIONS
This preliminary study suggests that children in remedial school programmes may have poor sleep compared with those in mainstream schools. Sleep problems were associated with problematic behaviour and poor academic performance. If sleep disturbances worsen daytime behaviour, then diagnosis and treatment of underlying sleep disorders could offer a novel therapeutic opportunity.
Impact of pharmaceutical care on knowledge, quality of life and satisfaction of postmenopausal women with osteoporosis

Background This study describes the analysis of secondary outcomes from a previously published randomised controlled trial, which assessed the effects of pharmaceutical care on medication adherence, persistence and bone turnover markers. The main focus of this manuscript is the effect of the provision of pharmaceutical care on these secondary outcomes, and details on the design of the intervention provided, the osteoporosis care plan and materials used to deliver the intervention. Objectives To evaluate the effects of pharmaceutical care on knowledge, quality of life (QOL) and satisfaction of postmenopausal osteoporotic women prescribed bisphosphonates, and their associating factors. Setting Randomised controlled trial, performed at an osteoporosis clinic of a tertiary hospital in Malaysia. Methods Postmenopausal women diagnosed with osteoporosis (T-score ≤−2.5/lowtrauma fracture), just been prescribed weekly alendronate/risedronate were randomly allocated to receive intervention or standard care (controls). Intervention participants received a medication review, education on osteoporosis, risk factors, lifestyle modifications, goals of therapy, side effects and the importance of medication adherence at months 0, 3, 6 and 12. Main outcomes measure Knowledge, QOL and satisfaction. Results A total of 198 postmenopausal osteoporotic women were recruited: intervention = 100 and control = 98. Intervention participants reported significantly higher knowledge scores at months 3 (72.50 vs. 62.50 %), 6 (75.00 vs. 65.00 %) and 12 (78.75 vs. 68.75 %) compared to control participants. QOL scores were also lower (which indicates better QOL) at months 3 (29.33 vs. 38.41), 6 (27.50 vs. 36.56) and 12 (27.53 vs. 37.56) compared to control participants. Similarly, satisfaction score was higher in intervention participants (93.67 vs. 84.83 %). More educated women, with back pain, who were provided pharmaceutical care had better knowledge levels. Similarly, older, more educated women, with previous falls and back pain tend to have poorer QOL, whilst women who exercised more frequently and were provided pharmaceutical care had better QOL. Satisfaction also increased as QOL increases and when provided pharmaceutical care. Conclusion The provision of pharmaceutical care improved knowledge, QOL and satisfaction in Malaysian postmenopausal osteoporotic women, showing that pharmacists have the potential to improve patients’ overall bone health. Policymakers should consider placing a clinical pharmacist in the osteoporosis clinic to provide counselling to improve these outcomes.
Multiple-Loop Digital Control Method for a 400-Hz Inverter System Based on Phase Feedback

To keep phase balance and improve output voltage quality, a novel multiple-loop control system in 400-Hz inverter power supply based on unipolar sinusoidal pulse width modulation (SPWM) is proposed. The causes of phase imbalance in a three-phase combined inverter power system are analyzed, and a new multiple-loop control strategy based on phase control is presented to eliminate it. For reducing the total harmonic distortion (THD) of high-power inverter output, unipolar SPWM is added with this new control strategy. The designs of drive circuit and snubber circuit are discussed to eliminate dead time effects and voltage oscillation. The stability of the control system is analyzed and the power system and the control strategies are realized with digital signal processing. Contrastive simulation results indicate the advantages and the correctness of this novel controller for inverter. A three-phase combined inverter system based on a multiple-loop control strategy is constructed, and experimental results show that the inverter system can make a phase difference between each phase less than 2°, and the THD of output voltage can be controlled within 2%.
A Developed Collaborative Filtering Similarity Method to Improve the Accuracy of Recommendations under Data Sparsity

This paper presented a new similarity method to improve the accuracy of traditional Collaborative Filtering (CF) method under sparse data issue. CF provides the user with items, that what they need, based on analyses the preferences of users who have a strong correlation to him/her preference. However, the accuracy is influencing by the method that use to find neighbors. Pearson correlation coefficient and Cosine measures, as the most widely used methods, depending on the rating of only co-rated items to find the correlations between users. Consequently, these methods have lack of ability in addressing the sparsity. This paper presented a new proposed similarity method based on the global user preference to address the sparsity issue and improve the accuracy of recommendation. Thus, the novelty of this method is the ability to solve the similarity issue with a capability of finding the relationship among non-correlated users. Furthermore, to determine the right neighbors during the process of computing the similarity between a pair of users, the developed method considered two main factors (fairness and proportion of co-rated). The MovieLens 100K benchmark dataset is used to evaluate the developed method accuracy. The experiments’ result showed that the accuracy of the developed method is improved compared to the traditional CF similarity methods using a specific common CF evaluation metrics. Keywords—Recommendation system; collaborative filtering; similarity method; data sparsity
Progressive collapse analysis of high-rise building with 3-D finite element modeling method

Using the general purpose finite element package ABAQUS, 3-D finite element a model representing 20 storey buildings were first built by the authors to perform the progressive collapse analysis. Shell elements and beam elements were used to simulate the whole building incorporating nonlinear material characteristics and non-linear geometric behavior. The modeling techniques were described in detail. Numerical results are compared with the experimental data and good agreement is obtained. Using this model, the structural behavior of the building under the sudden loss of columns for different structural systems and different scenarios of column removal were assessed in detail. The models accurately displayed the overall behavior of the 20 storey buildings under the sudden loss of columns, which provided important information for the additional design guidance on progressive collapse.