310 research outputs found
Revisiting the problem of audio-based hit song prediction using convolutional neural networks
Being able to predict whether a song can be a hit has impor- tant
applications in the music industry. Although it is true that the popularity of
a song can be greatly affected by exter- nal factors such as social and
commercial influences, to which degree audio features computed from musical
signals (whom we regard as internal factors) can predict song popularity is an
interesting research question on its own. Motivated by the recent success of
deep learning techniques, we attempt to ex- tend previous work on hit song
prediction by jointly learning the audio features and prediction models using
deep learning. Specifically, we experiment with a convolutional neural net-
work model that takes the primitive mel-spectrogram as the input for feature
learning, a more advanced JYnet model that uses an external song dataset for
supervised pre-training and auto-tagging, and the combination of these two
models. We also consider the inception model to characterize audio infor-
mation in different scales. Our experiments suggest that deep structures are
indeed more accurate than shallow structures in predicting the popularity of
either Chinese or Western Pop songs in Taiwan. We also use the tags predicted
by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP
Reliable Data Transmission through Private CBRS Networks
We consider the use of a domain proxy assisted private citizen broadband
radio service (CBRS) network and propose a Maximum Transmission Continuity
(MTC) scheme to transmit Internet of Things (IoT) data reliably. MTC
dynamically allocates available CBRS channels to sustain the continuity of data
transmission without violating the channel access requirements. MTC allocates
the granted CBRS channels according to the priority of each user, the instant
channel access status, interference among users, and the fairness. The
simulation results demonstrate the improvement in managing reliable IoT data
transmission in the private CBRS network.Comment: 5 pages, 5 figure
General anesthesia with local infiltration reduces urine retention rate and prolongs analgesic effect than spinal anesthesia for hemorrhoidectomy
IntroductionPostoperative pain and complications pose significant challenges following a hemorrhoidectomy. Attaining effective anesthesia with minimal complications is crucial. The ideal anesthesia method for ambulatory hemorrhoidectomy remains uncertain. This study aimed to investigate whether the combination of general anesthesia plus local infiltration (GAL) is associated with lower complications and reduced pain compared to spinal anesthesia (SA) in the context of hemorrhoidectomy.MethodsThis retrospective single-center cohort study, conducted in a tertiary medical center in East Asia, evaluated excisional hemorrhoidectomies performed between January 1, 2017, and March 31, 2023, utilizing GAL or SA. Data on the six most common complications-pain, constipation, acute urine retention (AUR), bleeding, nausea, and headache-were extracted from medical records. A total of 550 hemorrhoidectomies were included: 220 in the GAL group and 330 in the SA group. Patient characteristics were comparable between the two groups.ResultsThe AUR rate was significantly lower in the GAL group compared to the SA group (15.5% vs. 32.1%, P < 0.001). Although the proportion of pain scores ≥4 did not differ significantly between the GAL and SA groups (36.2% vs. 39.8%, P = 0.429), the pain score curve indicated a stable trend. Overall, the GAL group exhibited a lower rate of adverse effects (56.9% vs. 67.4%, P = 0.023). There were no significant differences in the rates of other complications and emergency department readmission between the GAL and SA groups.DiscussionGAL emerges as a favorable choice for anesthesia in hemorrhoidectomy, demonstrating a lower incidence of urine retention and a prolonged analgesic effect in multiple hemorrhoidectomies. These findings support the conclusion that GAL represents an optimal anesthetic method for enhancing the postoperative experience in patients undergoing hemorrhoidectomy
The Effect of Prenatal Exposure to Radiation on Birth Outcomes: Exploiting a Natural Experiment in Taiwan
We estimate the effect of prenatal exposure to radiation on infant health. By exploiting the 1983 Taiwanese radiation-contaminated buildings (RCBs) accident as a natural experiment, we compare birth outcomes between siblings and cousins exposed to different radiation levels. Given the 1983 accident was unanticipated and exposed cohorts were unaware of the risk until 1992, our design isolates the effect of radiation exposure during pregnancy from other effects. We provide the first evidence that prenatal exposure to a continuous low-level dose of radiation significantly reduces gestational length and increases the probabilities of prematurity and low birth weight
A Blueprint for Applications in Enterprise Information Portals
Electronic Commerce (E-Commerce, EC) is thoroughly changing business models of organizations (governments, corporations, and communities) and individuals the way of living and working. However, the major success will accrue to those companies that are willing to transform their organizations and business processes, which is the scope of e-Business. An Enterprise Information Portal (EIP) provides real time information and integrated applications to knowledge workers, employees, customers, business partners and the general public as well. Effective applications of EIP facilitate high quality strategic decisions. That is, an EIP can enhance an organization’s productivity, improve the collaboration to facilitate E-Commerce and gain competitive advantages. However, the EIP solutions are usually too expensive to small businesses. With Enterprise Application Integration (EAI) approach, this paper presents an economic way to design a low-cost EIP that leverages existing systems. Moreover, a prototype is implemented to show the feasibility.
For the external data access, the web mining technology is utilized to mine some relevant and valuable web contents from the Internet and put these contents into the document warehouse. By combining the textual information inside the document warehouse and the numeric data from the data warehouse, competitive advantages can be provided over those who work with just the numbers
Sex difference in the associations among obesity-related indices with incidence of diabetes mellitus in a large Taiwanese population follow-up study
BackgroundObesity is a major risk factor for diabetes mellitus (DM), which is in turn a major risk factor for cardiovascular diseases such as coronary artery disease and stroke. As few studies have investigated sex differences in the association between obesity and incidence of DM, the aim of this longitudinal study was to explore this issue in a large group of Taiwanese participants.MethodsA total of 24,346 participants were enrolled in this study, of whom 8,334 (mean age, 50.6 ± 11.0 years) were male and 16,012 (mean age, 50.5 ± 10.1 years) were female. The following obesity-related indices were studied: body mass index, waist-to-height ratio, waist-to-hip ratio (WHR), body roundness index, conicity index (CI), body adiposity index, abdominal volume index, lipid accumulation product (LAP), and visceral adiposity index (VAI).ResultsThe analysis showed significant associations between all of these indices with incidence of DM (all p < 0.001). In the male participants, the strongest predictors for incidence of DM were LAP (AUC = 0.692), WHtR (AUC = 0.684), and WHR (AUC = 0.683). In the female participants, the strongest predictors were LAP (AUC = 0.744), WHtR (AUC = 0.710) and VAI (AUC = 0.710), followed by BRI (AUC = 0.708).ConclusionStrong associations were found between the studied obesity-related indices and incidence of DM, and sex differences were found. Hence, to better control DM, reducing body weight may be beneficial in addition to lifestyle modifications, diet control, and pharmacological interventions
Ginsenoside-Rg1 Protects the Liver against Exhaustive Exercise-Induced Oxidative Stress in Rats
Despite regular exercise benefits, acute exhaustive exercise elicits oxidative damage in liver. The present study determined the hepatoprotective properties of ginsenoside-Rg1 against exhaustive exercise-induced oxidative stress in rats. Forty rats were assigned into vehicle and ginsenoside-Rg1 groups (0.1 mg/kg bodyweight). After 10-week treatment, ten rats from each group performed exhaustive swimming. Estimated oxidative damage markers, including thiobarbituric acid reactive substance (TBARS) (67%) and protein carbonyls (56%), were significantly (P < 0.01) elevated after exhaustive exercise but alleviated in ginsenoside-Rg1 pretreated rats. Furthermore, exhaustive exercise drastically decreased glutathione (GSH) content (∼79%) with concurrent decreased superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) activities. However, these changes were attenuated in Rg1 group. Additionally, increased xanthine oxidase (XO) activity and nitric oxide (NO) levels after exercise were also inhibited by Rg1 pretreatment. For the first time, our findings provide strong evidence that ginsenoside-Rg1 can protect the liver against exhaustive exercise-induced oxidative damage
Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs
Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology; this research solves the problem that the original cutting technology cannot extract certain single teeth; and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN); which can identify caries and restorations from the bitewing images. Moreover; it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image; which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization; (2) a dental image cropping procedure to obtain individually separated tooth samples; and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks; namely; AlexNet; GoogleNet; Vgg19; and ResNet50; experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%; respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film
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