Miracle-dz/UniFAR-FTUs huggingface dataset:
Shared Representation Learning with Self-Attention for Cross-Domain Chinese Hedge Cue Recognition

Hedge is usually used to express uncertainty and possibility. Research on the Chinese hedge cue detection is of great significance to the Chinese factual information extraction. So far, existing approaches focus on learning a model for Chinese hedge detection based on domain-specific corpus. However, the difference between hedge cue distributions in various domains makes domain-specific models difficult to extend to other domains. To make full use of out-of-domain data and reduce the cost of manual data annotation, we propose a novel cross-domain hedge cue detection approach based on shared representations (domain-general representations). Our method uses stacked CNNs model with self-attention to learn the shared semantic representations across different domains through parameter sharing mechanism. Meanwhile, we introduce adversarial training to separate the private features of each domain from the shared representations by iteratively training the generator and the discriminator alternately. Experiments on Chinese Biomedical domain and Wikipedia domain show that our method gets a significant improvement on cross-domain Chinese hedge cue detection, compared with instance-based transfer learning and feature-based transfer learning methods.
Sparse Recovery With Block Multiple Measurement Vectors Algorithm

This paper investigates the performance of the block multiple measurement vectors (BMMV) algorithm in reconstructing block joint sparse matrices. We prove that if <inline-formula> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> obeys block restricted isometry property with <inline-formula> <tex-math notation="LaTeX">$\delta _{K+1} < \frac {1}{\sqrt {K+1} }$ </tex-math></inline-formula>, then BMMV perfectly reconstructs any block <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-joint sparse matrix <inline-formula> <tex-math notation="LaTeX">${\boldsymbol {X}}$ </tex-math></inline-formula> from observations <inline-formula> <tex-math notation="LaTeX">${\boldsymbol {Y}}=\Phi {\boldsymbol {X}} $ </tex-math></inline-formula> in <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> iterations. We also show that BMMV may not reconstruct block <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-joint sparse matrices in <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> iterations under the condition <inline-formula> <tex-math notation="LaTeX">$\delta _{K+1} \geq \frac {1}{\sqrt {K+1} }$ </tex-math></inline-formula>. That is to say, the condition <inline-formula> <tex-math notation="LaTeX">$\delta _{K+1} < \frac {1}{\sqrt {K+1} }$ </tex-math></inline-formula> is optimal for the BMMV algorithm.
Indonesian Corpus Constructing and Text Processing for Speech Synthesis

This paper focused on the development of Indonesian speech synthesis system, and it studied Indonesian text analysis and processing methods. It mainly studied Indonesian pronunciation corpus selection, text normalization and syllable division methods. Using the principle of combination of high frequency words and sentence length, we selected 5000 sentences as pronunciation corpus from a 566MB original text corpus. By using a combination of regular expressions and keywords, the numbers in the text are normalized. Furthermore, a combination of syllable lists and special rules are used to achieve syllable segmentation. The experimental results show that the above proposed methods laid a good foundation for the development of the Indonesian speech synthesis system.
Using Machine Learning Techniques to Earlier Predict Student's Performance

Education field is rich of data, and machine learning used in this field are increasing lately. Based on their first semester result, using machine learning techniques, the student's final year result (GPA) can be predicted. The data used in this experiment are from the computer science subjects, 6 subjects, 1 laboratories results and the GPA on their graduation year. The techniques used in this experiment are Generalized Linear Model, Deep Learning and Decision Tree. From this result, what are the important factors that impact on the result can be extracted to help the students prepared themselves earlier.
Parking Slot Identification using Local Binary Pattern and Support Vector Machine

Parking space availability has become common problem in many big cities. This problem occurs due to fast growing of vehicle ownership. Therefore, the demand of parking area in big cities is also increased. An information system on parking space availability may help the driver to find accurately the parking location. This real-time system can avoid the drivers waste their time in looking the available parking space. This paper aims to implement Local Binary Pattern (LBP) as a method for extracting distinguishable features of the vacant and occupied parking slot. Support Vector Machine (SVM) classifier is used to differentiate the status of parking slot, either vacant or occupied. Combination of LBP and SVM has been tested on a total of 7, 670 sample images. Validation result shows that the proposed algorithm can provide a high accuracy, 96.8%, in classification of parking slot availability.
Recognition of rice leaf diseases based on salient characteristics

The paper takes the advantage of colorimetric model, ellipse fitting and support vector machines to recognize the chronic type, acute type and white type of rice blast. First of all, with the help of the difference of lesion manifestation in HSV space, the images which are converted in HSV color space are respectively segmented by a green detection method and an OTSU algorithm, and the best segmentation result is selected for each sample by an evaluation mechanism. Then, five feature components based on morphology and color are proposed as the basis of classification model. To test the validity of these features, one of the three types of lesions is used as a positive example and the other is used as a counterexample for statistical validation. The results show that the differences between the three types of samples are significant. After that, a method of multi-classification SVM based on radial basis function is chosen to classify the three types of lesions. Experiments show that when compared with other methods, the proposed algorithm has a high recognition accuracy and can effectively identify three types of rice blast diseases.
Extra Domain Data Generation with Generative Adversarial Nets

This study focuses on supplementing data sets with data of absent classes by using other, similar data sets in which these classes are represented. The data is generated using Generative Adversarial Nets (GANs) trained on the CelebA and MNIST datasets. In particular we use and compare Coupled GANs (CoGANs), Auxiliary Classifier GANs (AC-GANs) and novel a combination of the two (CoAC-GANs) to generate image data of domain-class combinations that were removed from the training data. We also train classifiers on the generated data. The results show that AC-GANs and CoAC-GANs can be used successfully to generate labeled data from domain-class combinations that are absent from the training data. Furthermore, they suggest that the preference for one of the two types of generative models depends on training set characteristics. Classifiers trained on the generated data can accurately classify unseen data from the missing domain-class combinations.
Searching With Direction Awareness: Multi-Objective Genetic Algorithm Based on Angle Quantization and Crowding Distance MOGA-AQCD

Multi-objective optimization (MOO) is widely used for solving various engineering real-life problems. Meta-heuristic optimization has been regarded as an effective solution for such problems because it enables the successful examination of a broad range of candidate solutions and the selection of optimal ones. However, there is a high probability of the algorithms becoming ensnared in local minima due to the complex optimization surface and the unlimited number of viable solutions. Therefore, to provide the decision maker with the optimal non-dominated set of solutions, significant improvements must be made to the search process, where the efficient exploration of the population has a vital role in maintaining a good non-dominated solution in evolutionary algorithms. NSGA-II is regarded as the state of the art for the meta-heuristic MOO. NSGA-II and its variants have adopted the concept of crowding distance as a measure that can leverage the characteristics of the distribution of solutions in the search space and provide a high-level of exploration. However, this method is not sufficient to effectively explore the search space because it ignores the direction of the exploration. In this paper, the angle quantization of solutions is combined with the crowding distance to create the MOGA-AQCD algorithm, which preserves equal exploration in all directions and aims at finding equal distribution of solutions within the search space. This approach is applied to a set of standard benchmark MOO functions. The results show that MOGA-AQCD is superior to NSGA-II and NSGA-III on the most evaluation measures for MOO.
Direction of Arrival Estimation for Uniform Circular Array via “Virtual Two-Dimensional Source Impinging on Virtual Multiple UCAs Equivalently” in the Presence of Gain-Phase Errors

Direction-of-arrival (DOA) estimation in the presence of gain-phase errors for uniform-circular-array (UCA) is addressed in this paper. Thanks to the uniformity and cycle property of UCA, the long data vector constructed with the proper entries of the Hadamard product of autocorrelation matrix and its conjugate, can be regarded as the received data corresponding to virtual two-dimensional sources impinging on virtual multiple UCAs. This finding indicates that azimuths and elevations of virtual sources related to DOAs of original sources can be decoupled and estimated via multiple-UCAs-estimating signal parameter via rotational invariance techniques proposed in this paper. Furthermore, two novel methods named as spatial-filtering-method and parameter-estimation-method are proposed to cope with one-component, which affects the DOA estimation of adjacent sources. Compared with the one-component elimination method proposed by Cao and Ye, previously mentioned two methods have closed-form solutions and they do not require iteration. The proposed method requires neither the calibration sources nor multidimensional parameter search. Furthermore, it can cope with more sources and work for various apertures of UCA. Simulations verify the effectiveness of the proposed method.
Protective Effects of Aryl Hydrocarbon Receptor Signaling in Celiac Disease Mucosa and in Poly I:C-Induced Small Intestinal Atrophy Mouse Model

Aryl hydrocarbon receptor (AhR), a transcription factor activated by a large number of natural and synthetic agents, modulates the activity of immune cells in the gut and represents an important link between the environment and immune-mediated pathologies. In this study, we investigated the role of AhR in celiac disease (CD), a gluten-driven enteropathy. AhR expression was evaluated in intestinal biopsies taken from patients with CD and controls by real-time polymerase chain reaction (PCR), immunohistochemistry and flow cytometry. AhR was also analyzed in ex vivo organ cultures of duodenal biopsies taken from inactive CD patients incubated in presence or absence of peptic-tryptic digest of gliadin. IFN-γ, TNF-α, granzyme B, and perforin expression was evaluated in anti-CD3/CD28-activated intestinal lamina propria mononuclear cells (LPMC) and intestinal intra-epithelial cells (IEL) of active CD patients cultured in the presence or absence of the AhR agonist 6-formylindolo(3, 2-b)carbazole (Ficz). Finally, the protective role of AhR was evaluated in a mouse model of poly I:C-driven small intestine damage. AhR RNA transcripts were reduced in active CD samples as compared to inactive CD and normal controls. Flow cytometry confirmed such results and showed a reduction of AhR in both IEL and LPMC of active CD patients. The addition of a peptic-tryptic digest of gliadin to ex vivo organ cultures of duodenal biopsies taken from inactive CD patients reduced AhR expression. Treatment of CD IEL and LPMC with Ficz reduced the levels of inflammatory cytokines, granzyme B and perforin. Mice injected with Ficz were protected against poly I:C-induced intestinal lesions. Our findings suggest that defective AhR-driven signals could contribute to amplify pathogenic responses in the gut of CD patients.
Investigating Thermal Cooling Mechanisms of Human Body Under Exposure to Electromagnetic Radiation

Thermal cooling mechanisms of human exposed to electromagnetic (EM) radiation are studied in detail. The EM and thermal co-simulation method is utilized to calculate the and temperature distributions. Moreover, Pennes’ bioheat equation is solved to understand different thermal cooling mechanisms, including blood flow, convective cooling, and radiative cooling separately or jointly. The numerical results demonstrate the characteristics and functions for each cooling mechanism. Different from the traditional view that the cooling effect of blood is usually reflected by its influence on sweat secretion and evaporation, this paper indicates that the blood flow itself is an important factor of thermal cooling, especially for high-intensity EM radiation. This paper contributes to the fundamental understanding of thermal cooling mechanisms of human.
An Overview of Named Entity Recognition

Named Entity Recognition (NER) is essential for some Natural Language Processing (NLP) tasks. Previous researchers gave a survey of NER in statistical machine learning era, however, research on NER has already changed a lot in recent decade. On the one hand, more and more NER systems adopt deep learning, transfer learning, knowledge base and other methods. On the other hand, multilingual and low resource languages NER researches increase rapidly. To reflect these changes, we here give an overview of NER based on 162 papers of NLP related conferences from 1996 to 2017. In this survey, we discuss two main aspects of NER research - target languages and technical approaches with statistical analysis. Finally, we summarize some conclusions and explore potential future issues in NER research.
The Study of Synthetic Minority Over-sampling Technique (SMOTE) and Weighted Extreme Learning Machine for Handling Imbalance Problem on Multiclass Microarray classification

Microarray data classification has a great challenge due to number of samples which is much smaller compared to the number of genes. The problem is getting harder when the dataset has multiclass target and the number of samples in each class is not well distributed (which is called imbalance data distribution). In this research, two different approaches to handle imbalance data distribution are studied, they are SMOTE (based on data approach) and weighted ELM (based on algorithmic approach). To evaluate the performance of the proposed method, two public imbalanced multiclass microarray dataset are used, GCM (Global Cancer Map) and Subtypes-Leukemia dataset. The results of experiment show that the implementation of SMOTE and weighted ELM on GCM dataset have no significant effect in the classification performance. Different with the Subtypes-Leukemia dataset, the implementation of SMOTE and weighted ELM has improved the classification performance compared to the previous research. Generally, the results show that weighted ELM perform slightly better compared to SMOTE to increase the accuracy of the minority class.
Mass Spectral Substance Detections Using Long Short-Term Memory Networks

In this paper, mass spectral substance detection methods are proposed, which employ long short-term memory (LSTM) recurrent neural networks to classify the mass spectrometry data and can accurately detect chemical substances. As the LSTM has the excellent understanding ability for the historical information and classification capability for the time series data, a high detection rate is obtained for the dataset which was collected by a time-of-flight proton-transfer mass spectrometer. In addition, the differential operation is used as the pre-processing method to determine the start time points of the detections which significantly improve the accuracy performance by 123%. The feature selection algorithm of Relief is also used in this paper to select the most significant channels for the mass spectrometer. It can reduce the computing resource cost, and the results show that the network size is reduced by 28% and the training speed is improved by 35%. By using these two pre-processing methods, the LSTM-based substance detection system can achieve the tradeoff between high detection rate and low computing resource consumption, which is beneficial to the devices with constraint computing resources such as low-cost embedded hardware systems.
Insensitive Zone based Pinball Loss Twin Support Vector Machine for Pattern Classification

The Pinball error loss function has attracted wide research attention recently due to its excellent capability in handling feature noise present in data which eventually leads to robust classification models. Recently proposed Pinball loss based Support Vector Machine (Pin-SVM) [1] and Twin Support Vector Machine (Pin-TWSVM) [2] have shown effective experimental performances to ensure this claim. However, the main focus is to ensure robustness to noise and outliers, that has led to limited sparsity in the classifier(s) which result in the increased testing time and unstability in re-sampling. In this paper, we develop two robust Pin-TWSVM based models termed as $\epsilon -$ TWSVM and Flex-Pin-TWSVM that not only handles the noise present in data but also control model sparsity using user-regulated and self-optimized insensitive zone respectively. The insensitive zone obtained in the model correspond to zero value of the dual variable and thus lead to simpler model. Extensive experiments performed on standard machine learning datasets confirm the efficacy of the proposed noise-insensitive models which also presents better sparseness than related traditional classifiers.
An Observer-Based Fixed-Time Consensus Control for Second-Order Multi-Agent Systems With Disturbances

This brief considers the fixed-time consensus problem of second-order multi-agent systems with disturbances. With the proposed observer, the state information can be exactly estimated through output information in fixed time with the effects of bounded disturbances, and the second-order multi-agent system can also reach consensus in fixed time without using any velocity information. Finally, a numerical example is given to illustrate the effectiveness of the theoretical result.
An Objective Reduction Algorithm based on Non-dominated Solution Pairs

Evolutionary Multi-objective optimization (EMO) Algorithms have been successfully applied in many practical problems. However, their performance deteriorated seriously for the problems with many objectives, i.e. many-objective optimization problems (MaOPs). In some practical applications, there exist some redundant objectives in an MaOP. Reducing the number of objectives of the optimization problem is one of the effective ways to solve the MaOPs with redundant objectives. It can improve the search efficiency of EMO algorithms, and reduce the computational cost. In this paper, we propose a new objective reduction algorithm. A criterion based on the number of non-dominated solution paired is presented to measure the conflict degree between objectives. Furthermore, we develop a effective objective reduction algorithm using feature selection technique. We compared the proposed algorithm with the LPCA, NLMVUP-CA and $\delta$-MOSS algorithm in some benchmark problems, and the results show the effectiveness of the proposed algorithm.
A CMOS-MEMS Electromagnetic-Type Tactile Sensor with Polymer-Filler and Chrome-Steel Ball Sensing Interface

This study presents the approach to implement an electromagnetic type active wireless CMOS-MEMS tactile sensor. The tactile force applied on sensor will deform the flexible polymer and further cause the displacement of rigid chrome-steel ball. Thus, the magnetic flux and the voltage of sensing coil induced by the driving coil are changed, which are employed to measure the tactile load. The proposed tactile sensor has four merits: (1) flexible polymer filler as spring, (2) rigid chrome-steel ball as movable sensing interface (for loading and introducing magnetic flux change), (3) CMOS chip as sensing unit, and (4) sensitivity is enhanced by increasing the driving voltage. Note: no suspended thin film structures are required. The presented design is implemented by the standard TSM C O. 18μm CMOS process. Measurements indicate the sensitivity of tactile sensor (with 5Vpp driving voltage) is O.53mV/N with loading range of 0-2.4N.
Comparative Study on Language Rule Based Methods for Aspect Extraction in Sentiment Analysis

Sentiment analysis is a research topic that attracts the attention of the world today coincide the growth of internet technology. One of the tasks in aspect based sentiment analysis that plays a very important role is aspect extraction. In this paper, we conducted a comparative study of several existing language rule based aspect extraction methods. We re-implement the part of the system as written in the original paper and compare the performance of these methods. Our observations show that the success of a language rule-based aspect extraction method is largely determined by the completeness and accuracy of the rules. More specifically, rules enhancement needed to improve the performance of an aspect based sentiment analysis system are part-of-speech rules, dependency relations, complex sentences and implicit expressions handling.
Scalable Bio-inspired Fault Detection, Isolation and Recovery in NoCs

High density silicon architectures, including Networkson-Chip (NoC), require an increasing level of fault tolerance with adaptivity to faulty conditions post deployment. An unobtrusive bio-inspired approach is proposed to facilitate both online and real-time detection and isolation of faulty NoC channels. The proposed approach is based on a novel Fault Detection Unit (FDU) which uses the combination of synapse inspired excitatory and inhibitory responses as a method of fault detection. The FDU integrates with the NoC router to inform the routing algorithms when faults are detected, enabling re-routing as a recovery method. The key contribution is results for online detection, isolation and recovery in a NoC architecture for a range of test conditions. The low area/power overheads and scalability advantages over similar online techniques are also presented.