Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Movie Playback Based on Ordinal Support Vector Machine Classification

This paper aims at predicting the volume of online movie play on an Video On Demand(VOD) platform using movie characteristics together with related social media data collected from the Internet. An ordinal support vector machine classification approach is employed to distinguish movies playback volume levels. This study collected 1,266 online movie data from year 2013 to 2015, and divided it into three types: high, medium, and low, according to view counts. For each movie, the first two months of video broadcasts are collected from the VOD website. Country, movie type, director, actor, box office are collected from professional movie database. Total number of news and tweets of the movies from Baidu and Sina Weibo are crawled to reflect the publicity level of the movie and the attention of the audience. After feature selection and feature creation, the ordinal support vector machine model is used to predict movie view counts with focus on identifying movies with low play counts and high play counts. The empirical results show that our ordinal support vector machine approach has better out-of-sample prediction accuracy, comparing with standard support vector machine models and other classification models.
Mashup-Oriented API Recommendation via Random Walk on Knowledge Graph

With the growing prosperity of the Web API economy, mashup-oriented API recommendation has become an important requirement. Various methods based on different principles of technology have been used to deal with this issue. In recent years, the Web API ecosystem has accumulated a wealth of knowledge that can be used to enhance the recommendation models, and however, current concerns in this regard still remain. To cope with this issue, we present a graph-based algorithmic framework for the task of mashup-oriented API recommendation. Especially, we design a concise schema of the knowledge graph to encode the mashup-specific contexts and model the mashup requirement with graphic entities. We then exploit random walks with restart to assess the potential relevance between the mashup requirement and the Web APIs according to the knowledge graph. In addition, we propose the query-specific weighting strategies to enhance the knowledge graph construction. The experimental results demonstrate that our proposed method is much superior to some state-of-the-art methods, also achieves robust effects on reducing computational overhead, and suppresses the negative Matthew effect in APIs’ recommendation.
Full-attention Based Drug Drug Interaction Extraction Exploiting User-generated Content

When a patient takes multiple medications simultaneously under treatment, it is vital for the doctor to comprehend all interactions between drugs in the prescription entirely. Drug drug interaction (DDI) extraction aims to obtain interactions between drugs from biomedical literature automatically. Nowadays, researchers apply artificial intelligence and natural language processing techniques to perform DDI extraction task. Existing DDI extraction methods have utilized some kinds of external resources such as biomedical databases or ontologies to offer more knowledge and improve the performance. However, these kinds of external resources are delayed because of the hardship of updating. User-generated content (UGC) is another sort of external biomedical resource which is up-to-date and can be updated rapidly. We attempt to utilize UGC resource in our deep learning DDI extraction method to provide more fresh information. We propose a DDI extraction method that merges UGC information and contextual information together by a new attention mechanism called full-attention. We conduct a series of experiments on the DDI 2013 Evaluation dataset to evaluate our method. UGC-DDI outperforms the other state-of-the-art methods and achieves a competitive F-score of 0.712.
A method for improving the reliability of causal inference from large-scale data in biomedicine

Causal inference is an essential problem in the field of drug discovery. Most existing methods depend on hypothesis testing with the analysis of drug-disease or drug-target-disease relations. However, a remaining challenge is how to choose a significance level for conditional independence tests to achieve the most reliable causal relation result. In this paper, we propose a constraint-based causal discovery method to achieve more reliable causal relations. An independence test reliability (ITR) is adopted to measure the reliability of each independence test result, and an aggregated causal relation reliability (ACRR) is introduced to evaluate the global reliability of a causal discovery. A drug-target-disease dataset is established by collecting information from the literature in PubMed using data mining techniques. The result of our experiments on different datasets shows that our proposed method obtains more stable causal relations than the existing approaches.
Prediction of Long Non-coding RNA-protein Interaction through Kernel Soft-neighborhood Similarity

Long non-coding RNA (IncRNA) has a close relationship with multiple biological processes and complex diseases. Generally speaking, it functions through the interaction with corresponding RNA-binding proteins. However, it is costly and time-consuming to use experimental methods to detect IncRNA-protein interactions. Network-based prediction methods have been developed recently, but very few methods consider the integration of multiple features and the non-linear relationship of IncRNAs (proteins). In this paper, we propose a kernel-based soft-neighborhood propagation algorithm (LKSNS) to predict the potential IncRNA-protein interactions. The method not only makes use of the non-neighborhood information, but also excavates the potential non-linear relationship. We compare LKSNS with other state-of-the-art methods based on multiple datasets and the results show that LKSNS has significantly better prediction performance. The case study further demonstrates that the LKSNS has the good practicality for IncRNA-protein interaction prediction.
Evolution of GWAS results through ADNI cohorts

Genome Wide Association Studies (GWAS) of Alzheimer's disease with quantitative phenotype is an active field of research that has pointed out a number of SNPs potentially related to the disease. Some initiatives such as Alzheimer’s Disease Neuroimaging Initiative (ADNI) have been established in order to proof whether anatomical or biological markers (from magnetic resonance imaging (MRI) or positron emission tomography (PET)), genetic information, and clinical and neuropsychological assessments can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). In the last decade, ADNI database has been progressively augmented, including a considerable number of patients yielding to the subsequent projects and cohorts ADNI1, ADNIGO, and ADNI2. Although some GWAS have been carried out with subpopulations of ADNI1, to the best of our knowledge, these works have not been replicated with the whole ADNI1 or the subsequent cohorts. In this work, we intend to study which SNPs are consistently preserved through ADNI cohorts in GWAS.
Multi-Attribute Topic Feature Construction for Social Media-based Prediction

The effectiveness of social media-based prediction highly depends on whether we can construct effective content-based features based on social media text data. Features constructed based on topics learned using a topic model are very attractive due to their expressiveness in semantic representation and accommodation of inexact matching of semantically related words. We develop a novel general framework for constructing multi-attribute topic features using multi-views of the text data defined according to metadata attributes and study their effectiveness for a text-based prediction task. Furthermore we propose and study multiple weighting strategies to align text-based features and prediction outcomes. We evaluate the proposed method on a Twitter corpus of over 100 million tweets collected over a seven year period in 2009-2015 to predict human immunodeficiency virus (HIV) new diagnosis and other sexually transmitted infections (STIs) new diagnosis in the United States at the zipcode-level and county-level resolutions. The results show that feature representations based on attributes such as authors, locations, and hashtags are generally more effective than the conventional topic feature representation.
Transient Response Improvement of DC-DC Converter by Current Mode Variable on Time Control

Constant on Time (COT) control limits equivalent duty cycle due to the minimum off time and fixed on time. Thus the transient response and voltage scaling speed can be improved by adaptively changing on time. With Current Mode Variable on Time (CM-VOT) control, the on time is adaptively controlled according to output current of error amplifier. The proposed DC-DC converter is designed with 12V input and 1.2V output voltage. The recovery time is improved from 6$\mu$s to 3$\mu$s and undershoot voltage is reduced from 40mV to 26mV. Owing to Predictive Error Amplifier (PEA) and Floating Ground Compensation (FGC), the overshoot voltage is then reduced from 50mV to 20mV and the reference tracking time is reduced from 13$\mu$s to 9$\mu$s.
Density-aware Local Siamese Autoencoder Network Embedding with Autoencoder Graph Clustering

Network embedding aims to learn latent low dimensional representation of vertices in graphs while preserving the intrinsic characteristics of graph data. In this paper, we propose a density-aware local autoencoder embedding architecture, DAL-SAE, with three features. First, we develop a flexible density-aware local deep autoencoder embedding method to perform local embedding on each of K clustering-based subgraphs with the optimization at both vertex and subgraph levels, in response to imbalanced density-based local characteristics of vertices and subgraphs. We design K local autoencoder embedding models, each with individual parameters and structure, to jointly train K subgraphs and optimize the loss functions within and across clusters. Second, we design an autoencoder graph clustering method to optimize local embedding and graph clustering simultaneously and capture local, clustering, and global network structure in the learning process. Third but last, a density-aware local Siamese autoencoder embedding approach can be utilized to train multiple clustering-based subgraphs with similar local characteristics on the common Siamese networks, to save the memory consumption of multiple local embedding models as well as maintain the similar embedding features.
Bone Suppression of Chest Radiographs With Cascaded Convolutional Networks in Wavelet Domain

Bone suppression of chest radiographs (CXRs) is potentially useful for diagnosing lung diseases for radiologists and computer–aided diagnosis. This paper presents a cascaded convolutional network model in wavelet domain (Wavelet-CCN) for bone suppression in single conventional CXR. Wavelet coefficients are sparse and suitable as the output of convolutional network. The convolutional networks are trained to predict the wavelet coefficients of bone images from the wavelet coefficients of CXRs, using real two-exposure dual energy subtraction (DES) CXRs as training data. By combining the multi-level wavelet decomposition and a cascaded refinement framework, the Wavelet-CCN model can work automatically with a multi-scale approach and progressively refine the prediction in terms of accuracy and spatial resolution. Compared with previous work of CamsNet model which preforms bone prediction in gradient domain, the Wavelet-CCN model predicts the wavelet coefficients to reconstruct bone images and can avoid the inconsistent background intensity caused by 2D integration of gradients. The predicted bone image is subtracted from the original CXR to produce a soft-tissue image. The Wavelet-CCN model and its variants with different wavelet basis are evaluated on a dataset that consists of 504 cases of real two-exposure DES CXRs (404 cases for training and 100 cases for test). Experimental results show that among all the variants and different wavelet bases, the Wavelet-CCN model with Haar wavelet performs best. The average peak signal-to-noise ratio and structural similarity index of the soft-tissue images produced by the proposed Wavelet-CCN model are both higher than those of the previous CamsNet model in gradient domain, reaching values of 39.4 (±0.94) dB and 0.977 (±0.004), respectively. The results also demonstrate that the Wavelet-CCN model can process the CXRs acquired by four types of X-ray machines.
On-Chip Toggle Generators to Provide Realistic Conditions during Test of Digital 2D-SoCs and 3D-SICs

In digital logic circuits, unconstrained scan tests are known to evoke much higher switching activity than functional modes. State-of-the-art ATPG tools have knobs to constrain the switching activity of the generated test to a user-defined functional level. Two-dimensional System-on-Chips (2D-SoCs) and three-dimensional stacked ICs (3D-SICs) are typically tested in a modular fashion, i.e., per embedded core or stacked die. At any moment during the test, one or more modules are tested ('module-under-test', MUT). In this work, we present the impact of the switching activity in the currently not-tested modules (which we refer to as 'neighbors' of the MUT) on overall IR-drop and propose a method to provide realistic conditions during modular test of digital 2D-SoCs and 3D-SICs, using on-chip programmable toggle generators.
Adaptive Multimodal Hypergraph Learning for Image Classification

Image classification is one of the most important fundamental research topics in machine learning and image processing. Recently, hypergraph learning, which can model the high-order relationship of samples and fusion multimodal features, has received the attention of many researchers. However, existing multimodal hypergraph learning methods face two problems, i.e., how to construct hyperedges and how to determine the weights of hyperedges. This paper proposes an adaptive multimodal hypergraph learning method (AMH) to address these two challenges. AMH uses multiple neighborhoods method to avoid generating a k-uniform hyperedge, and optimizes the weights with the penalty function method to take the initial labels into consideration. The experimental results demonstrate the effectiveness of AMH compared with the state-of-the-art methods.
Tree-Based Sentiment Dictionary for Affective Computing: A New Approach

Affective computing is an emerging academic study area in computing technologies. Its purpose is to allow computers to understand emotions as humans do. At present, for affective computing with textual signals, the most common approach is sentiment lexicon approach. The majority of sentiment lexicons are stored in list of words with positivity and negativity. However, the one word might have various sentiment tendencies in various context. This problem can't be solved by these traditional sentiment lexicons which refers to list-based sentiment dictionary in domain of affective computing. In order to solve this problem, this paper presents a new approach that is a tree-based sentiment dictionary. In the tree structure, the similarities between sibling nodes under the same parent node is great but sibling nodes under different parent nodes is small. This paper uses these features to store the item classification in a tree structure and adds the features and sentiment words of the item which are extracted by using syntactic analysis and association rules to the tree structure to form a tree-based sentiment dictionary. In this way, we solve the problem that the one word may have various sentiment tendencies in various context and the problem of finding no sentiment words under the item classification in the dictionary. Comparing with sentiment lexicons, the tree-based sentiment dictionary outperforms for affective computing in the criteria, such as precision, recall, F-measure, etc.
A Deep Learning Approach to Sentiment Analysis in Turkish

This study proposes using deep learning for sentiment analysis in Turkish. Traditional machine learning methods such as logistic regression or Naive Bayes are often applied to this problem however their applicability is limited since they use bag-of-words model which does not take into account the order of the words in a sentence. In this study we compare these approaches with a modern technique called recurrent neural networks using LSTM units on a dataset crawled from Turkish shopping and movie websites. Our results show that RNN based approaches improve the classification accuracies.
Analyzing and Predicting the US Midterm Elections on Twitter with Recurrent Neural Networks

We propose a method and a system that aim to gauge local support for the two major US political parties in the 68 most competitive House of Representative districts during the mid-term elections. We analyze tweets explicitly posted from locations within each district. To distinguish between Republican and Democratic tweets, we adopt a RNN-LSTM binary classifier which reached validation accuracy of 85% over individual tweets, despite the highly implicit and short content shared on the social network. The method was able to predict the correct winner on 60% of the highly competitive (and thus extremely hard to predict also with traditional methods) districts. The lower result at district level is also an indicator of the population bias of the Twitter platform with respect to the actual voters. The classifier architecture, along with the other methods and tools we propose, is domain- and language- independent and may be applied to any highly polarizing topic with enough social media activity.
A Deep Learning-based Visual Perception Approach for Mobile Robots

In this work, a deep learning-based approach was developed for the visual path perception of mobile robots, combined with computer vision technology. An experimental platform of differential wheeled mobile robots and a LabViewupper computing platform to realize the basic motion control and test the actual effect of the pre-trained DNN(Deep Neural Network) model was built. The training method of DNN, including the acquisition of data set, Deep Neural Network structure and training program was given out. Experimental results demonstrate the feasibility of applying deep learning to mobile robot's visual perception of path. It has a reference significance for improving the intelligent level of mobile robots.
Joint Template Matching by Candidate Set Voting

To jointly match multiply images for one specific object has more and more applications in practice. In this paper, given a small annotated subset as templates, we propose to jointly match a series target images by a novel idea: selecting the initial matching results by the bidirectional voting between template set and candidate set. In our method, firstly, the forward pairwise matching from template set to target set is performed. These matching results are denoted as candidates. When multiply results appear for one target, that means the matching results are inconsistent. Then, a novel screening method is proposed by the backward matching and voting, until all results reach consistent. Experiments conducted on various moving objects in the wild demonstrate the proposed method can overcome the matching difficulty by a single template and improve the matching accuracy under complex conditions.
Web Service Solution for Adverse Drug Events and Medication Errors

Investigating the incidence, type, and preventability of adverse drug events (ADE) and medication errors is crucial to improving the quality of health care service. ADEs, medications errors can be extracted from practice data, patients feedback and especially from medication order. In this study, we examine the dataset filled with medication orders and with a web service based approach, each medication order is analyzed to avoid from drug-drug interaction. Firstly, the study is performed on a training set to explore the drug-drug interactions based on the clinical drug component of each medication. Then the proposed web service approach is integrated into the actual centralized Medication Order Management System (MOMS) of the hospital. With this real time checking ADE mechanism, doctors are warned in case there is a possible ADE or a medication error between the medications.
Power Efficient and Reliable Nonvolatile TCAM With Hi-PFO and Semi-Complementary Driver

In this paper, we propose a high priority first out (HiPFO) architecture of ternary content addressable memory (TCAM) to extend functions, not only “data comparison” but also “priority encoding. ” With that architecture, we can select the longest matching prefix efficiently in terms of area and power consumption. We also introduce a semi-complementary data line driver (SCD<sup>2</sup>) and self-tuning V<sub>ON</sub> (STV). With those techniques, we can eliminate the dc current in the data line and guarantee sensing margins even under small R-ratio nonvolatile memory in a 3T-2R-based HiPFO cell. As in experimental results, the PRAM-based HiPFO structure reduces the existing power consumption of priority encoder, thereby reducing the total power consumption by 27%. Also, thanks to the SCD<sup>2</sup>, the power loss from the dc current is reduced by more than 26%. In addition, the STV is helpful to overcome match line sensing margin at low R-ratio, which resulted in 1.5 times or larger sensing margin window improvement in the voltage swing of 1.2 V and R-ratio of 3. All those techniques are built in nvTCAM with 64-bit <inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> 32 rows array, and showed 46% improvement of total power consumption with simulation. And the nvTCAM fabricated at 180-nm CMOS technology demonstrates HiPFO operation.
A Soft Computing Framework for Image Classification Based on Recurrence Plots

Suitable time series representations play an important role in classification tasks. In this letter, we investigate the use of recurrence-plot-(RP)-based representations in the classification of eucalyptus regions in remote sensing images. The proposed framework is composed of three steps. First, time series associated with image pixels are represented by RP images; next, RP images are characterized by means of visual description approaches; finally, we use a soft computing framework based on genetic programing to discover an effective combination of time series dissimilarity functions to combine extracted features. Performed experiments in a eucalyptus classification problem demonstrated that the proposed framework is effective when compared to approaches based on the use of time series itself.
Whole-Body Motion Blending Under Physical Constraints Using Functional PCA

This paper presents a method for motion synthesis using Functional Principal Component Analysis (Functional PCA) to generate complex humanoid robot motions in a low-dimensional space while considering physical consistency. Since each motion can be expressed by a point in a space called FPC space, this method allows blending different motions. For more complex motion synthesis, we introduce a novel framework to synthesize blended motions by configuring a local FPC space and a global FPC space. This method enables to merge data while considering data features. However, physical consistency was not ensured in our previous work, we here apply optimization under constraints after synthesis. We show the dynamic feasibility and the feature of the synthesized blended motions and also an interesting observation opening to the possibility to generate a variety of motions from a few motion data in a local space at low cost and time.