Miracle-dz/UniFAR-FTUs huggingface dataset:
TSGOE: A web tool for tissue-specific gene ontology enrichment

Gene Ontology (GO) is one of the most popular Bioinformatics resources for studying the functional relationships between genes. GO enrichment analysis has become a routine method for gene function analysis, and it is of great significance for revealing the molecular mechanism of biology. However, existing GO enrichment analysis tools do not take into account tissue specificity and cannot distinguish gene sets that users interested. In this paper, we propose an easy-to-use web application, termed as TSGOE, which allows users to perform tissue specific-based GO enrichment analysis conveniently. Besides, TSGOE also provides rigorous statistical testing to improve the reliability of analytical result. To evaluate its performance, we apply TSGOE on several cancer-related gene lists. The experimental results show that our tool perform better than traditional GO enrichment analysis method. TSGOE is available at http://120.77. 47.2:5678.
Making Sense: An Innovative Data Visualization Application Utilized Via Mobile Platform

Data visualization is an innovative method of displaying and analysising data. Currently, as more and more users view, analyze, and visualize big data through mobile devices, developers face the challenges that how to address the visualization of large datasets on mobile devices. This paper aims to develop an innovative prototype of data visualization application, using open source resources for the visualization presentation, which will allow data from the crime and health of Yorkopendata and Obesity, Physical Activity and Diet of NHS be presented, evaluated and analyzed at anytime and anywhere. D3. js, one of the most popular visualization library, was used to beautify the data, and then put D3.js integrated into a cross-platform development framework PhonrGap to display the data on the device.
A Low Power 12-bit 1-kS/s SAR ADC for Biomedical Signal Processing

In this paper, a 12-bit 1-kS/s successive approximation register analog-to-digital converter (ADC) is presented for biomedical signal processing system. A multi-segmentation digital-to-analog converter architecture and a hybrid switching scheme are proposed to reduce the total unit capacitor count. A new ultra low-leakage switch is proposed for sample-and-hold and capacitor control switch blocks in order to improve conversion accuracy and reduce leakage current. The proposed ADC is fabricated in 130-nm CMOS process with a core area of 0.16 mm2. The measurement results show that the ADC achieves 10.47 ENOB and consumes 110-nW power at a sampling rate of 1 kS/s with 1.0-V supply voltage. A Walden figure of merit of 76 fJ/conversion-step is obtained.
A Salient Region Detection Method Based on Frequency-Tuning and One-Cut Algorithm

Domestic robots have shown up in more and more families in recent years. When they follow the order from the host and serve a family, it is involved in many techniques such as object recognition, object grasping and so on. So target segmentation is a particularly important technology, which is also a very challenging problem in the field of image analysis and computer vision. Based on the previous work, we can see that the Frequency- Tuned salient region detection (FT) is superior in performance and computational efficiency and can meet the realtime requirements of the robot system. However, the output result is a saliency map in pixels at full resolution, which contains no “object“ concept. In order to extract the complete salient region, we fuse FT algorithm and One-Cut segmentation method to achieve automatic segmentation of salient goals. In addition, the accuracy and robustness of saliency region segmentation are improved by using connected component attributes and depth gradient features.
Near-Optimal Detection of TAST Codes Over Multimode Optical Fiber Channels

In this work we propose a new low-complexity, essentially optimal detection algorithm for n × n TAST codes over n-mode optical fiber channels with Mode-Dependent Loss (MDL). Our algorithm is derived from the analysis of the Orthogonality Defect Factor (ODF) of the equivalent channel matrix where we derive its closed-form expression for general n as well as characterize its statistical properties. Using the ODF properties allows us to modify the detection algorithm such that it still achieves essentially optimal performance but at a much smaller cost than exhaustive or sphere decoding algorithms. The theoretical results are validated by simulations.
Voltage Closed-Loop Feedback Control of Hybrid Excited Switching Flux Machine

In the hybrid excited switching flux permanent magnet (HESFPM) machine, the d-axis flux linkage can be adjusted by both the armature current and the dc field current. In order to produce high torque within a widen speed range, a voltage closed-loop feedback control strategy is proposed in this paper. The error between the feedback voltage and the maximum phase voltage is used to determine the operation region of flux weakening control and adjust the dc field current and d-axis current in the flux weakening region. The proposed method is verified by simulation results.
A KM-Net Model Based on k-Means Weight Initialization for Images Classification

Convolutional neural networks (CNNs) have been successfully applied in the fields of image classification. The training cost of various large CNN models increases rapidly, which has become a challenging task to balance the recognition accuracy and the convergence speed in the training procedure of CNN models for a particular dataset. In this paper, a KM-Net model with convolutional kernels in the first layer initialized by k-Means clustering algorithm is proposed. Experimental results show that when compared with some other methods on the most widely-used SVHN dataset and ASL dataset, the proposed KM-Net model can not only improve the recognition accuracy, but also accelerate the convergence speed of the training procedure.
Composite control of speed sensorless for bearingless permanent magnet synchronous motor

It is difficult to achieve precise estimation of rotor position for a bearingless permanent magnet synchronous motor(BPMSM) with a single position sensorless control method in different speed range. In order to solve this problem, a composite control strategy combining an improved pulsating high frequency signal injection and second order generalized integrator frequency-locked phase-locked loop(SOGI-FLL-PLL) method is proposed. Firstly, the structure and working principle of the BPMSM are introduced. Secondly, the principle of position and speed estimation by high frequency pulsating signal injection method based on SOGI is studied. Thirdly, the method of SOGI-FLL-PLL used in medium and high speed operation is analyzed. In the state of low and medium switching interval, a linear weighted control algorithm is adopted to make the two control strategies transit smoothly. Finally, the simulations and experiments are carried out. The results show that the proposed composite control method can estimate rotor position and speed accurately and the rotor can return to the balance position quickly under the conditions of load disturbance, which validate good effectiveness of composite strategy and good robustness of the whole sensorless control system.
Source-to-Source Instrumentation for Profiling Runtime Behavior of C++ Containers

With the growing popularity of C++ in the HPC community and increasingly heterogeneous memory layouts it is ever more important to understand the runtime behavior of applications and their data structures. Data locality and access patterns play a central role when identifying potential for optimization. However, C++'s combination of efficient low level features and high level abstractions like STL containers make it difficult to reason about data accesses and the lifetime of objects. We present data-instrumenter, a source-to-source transformation tool that instruments containers as well as application code to efficiently gather runtime data while still preserving static information from the AST. We evaluate the tool with the C++ LULESH implementation and other applications of varying complexity. The gathered metrics are used to identify costly operations while density-based clustering detects outliers in the program execution. We find that data-instrumenter can fully observe the interactions between containers, elements and the application while still being portable and platform-independent.
Degradation characteristics of epoxy resin of GFRP rod in the decay-like fracture of composite insulator

The decay-like fracture is an emerging mode of abnormal mechanical failure of composite insulators the study of which is still in its infancy. The degradation of epoxy resin of glass fiber reinforced polymer (GFRP) rod caused by discharge and current distinguish the decay-like fracture from both normal mechanical fracture and brittle fracture. In the present work, the detailed degradation of epoxy resin of GFRP rod in the decay-like fracture of composite insulator were examined using scanning electron microscopy (SEM), thermogravimetric analysis (TGA) and X-ray photoelectron spectroscopy (XPS). The results show that the generation, expansion and release of pyrolysis gas is responsible for the formation of the bubbles and holes in the epoxy resin and the initial formation of holes in the silicone rubber housing during the degradation process. The erosion and pyrolyzation of epoxy resin results in mass loss of epoxy resin leading to degradation of the GFRP rod. High resolution XPS spectra of C1s and N 1s in GFRP rod reflects that oxidization process, exposure of glass fiber, and the introduction of nitrogen-containing compounds occurs during the degradation process. Microscopic photographs to explain the degradation of the epoxy resin and the initial formation of puncture holes in the silicone rubber housing are proposed based on the experimental results.
Technology Assisted Review of Images using Machine Vision

Technology Assisted Review, the term used in the legal industry for a family of functions including document clustering and classification using machine learning, has traditionally focused on textual raw data and excluded image files, mainly due to the lack of image analytics technology with the ability to produce satisfactory results. The emergence of deep learning and convolutional neural networks and consequent advances in the field of computer vision have generated the potential to incorporate image processing to legal industry workflows. We exploit the well-known VGG16 model, pretrained with ImageNet pictures, to encode images, which we then cluster using standard methods. Finally, we apply transfer learning to -and touch on fine tuning of - the pretrained network to perform binary classification of a test dataset. We test our methodology on scenarios of similar looking image classes as well as normal and very low responsiveness (positive class) rate. In parallel, we examine the process from a review cost savings perspective.
Optimizing U-Net to Segment Left Ventricle from Magnetic Resonance Imaging

Left ventricle segmentation is an important medical imaging task to measure several diagnostic parameters related to the heart such as ejection fraction and stroke volume. Recently, convolutional neural networks (CNN) have shown great potential in achieving state-of-the-art segmentation results for such applications. However, most of the existing research is focusing on building complicated variations of the neural networks with modest changes to their performance. In this study, the popular U-Net architecture is optimized by analyzing its behaviour once fully trained from which one can simplify its architecture by fixing layers weights or eliminating some of them completely. For instance, by performing a Fourier analysis of the convolution at each layer, we were able to discover that some early layers can be approximated by simple uniform filters. Furthermore, in a separate experiment by removing the middle layers of the U-Net one can reduce the number of U-Net parameters from 31 million to 0.5 million weights without compromising its performance. The experimental evaluations show that the new optimized U-Net achieves 0.93 for the Dice score in comparison to manual ground truth.
Testing Resistive Memories: Where are We and What is Missing?

Resistive RAM (RRAM) is one of the emerging non-volatile memories that may not only replace DRAM and/or Flash in the future, but also enable new computing paradigms such as computation-in-memory. Providing high quality and efficient test solutions are of great importance in order to enable the commercialization of such products. This paper discusses all aspects of RRAM testing including defects, fault models, test algorithms, Design-for-Testability (DFT) schemes, and future challenges. The paper highlights also the limitations and the inaccuracies of existing approaches and shows that using a linear resistor to model a defect in RRAM (as it is done today) is too pessimistic, and unable to represent the non-linear behavior of the defective RRAM devices. This may result in incorrect fault models, which in turn leads to low quality test solutions. The paper therefore also presents a novel defect modeling methodology that appropriately captures the non-linear RRAM behavior. To show its superiority, the methodology is applied to a forming defect and the results are compared with those of traditional approach.
Secure RFID Authentication Schemes Based on Security Analysis and Improvements of the USI Protocol

Wireless radio frequency identification (RFID) has been widely used as the core technology of the Internet of Things, but it also brings many potential security risks. In this paper, two typical RFID security authentication protocols are analyzed in depth. The results show that the two protocols have security risks, and two targeted attack methods are proposed. Next, two RFID security authentication protocols which can resist the DoS, replay, and tracing attacks are designed. The first protocol uses index grouping and dynamic renewal mechanism. Since the Rabin public key encryption algorithm verification only requires square and modulo operations, the second protocol introduces the public key encryption algorithm into the cost-sensitive RFID tag, and formally verifies the functionality of the proposed scheme by SPIN. Furthermore, the security of the protocol is proven by the BAN logic. The grouping index method can effectively improve the ability to locate tags in a large database and enhance the practicability of the protocol. The introduction of the Rabin public key algorithm improves the security of the protocol under the condition of cost control.
Collaborative Recommender Systems Based on User-Generated Reviews: A Concise Survey

Recommender systems are powerful tools that help users to deal with information overload problem. Collaborative Filtering (CF) approach has been widely used to build recommender systems over the past decades. However, the performance of CF is limited by sparsity and cold start problems, which are very common in real world situations. In recent years, many review-based approaches have been developed to integrate textual reviews into recommendation process, since they provide much more information about item/user profiles than ratings. The use of text analysis and opinion mining methods helps extracting such information. In this paper, we first introduce standard CF techniques and their main challenges. Then, we present different kind of information that can be extracted from user reviews. After that, we describe recent works that exploit review elements to improve the CF-based recommendations. Finally, we discuss their practical implications.
A study on modeling using big data and deep learning method for failure diagnosis of system

Power data of the customers are generated in real time and in large quantities according to the activation of the low voltage meter reading service and accumulated and stored in the central FEP, NMS and SMS server. The power data has characteristics of big data and unlike big data of other types, it has the advantage of applying efficient and useful services by applying various analysis and prediction techniques without any additional data processing. Despite these advantages, the field of automated meter reading still focuses on the construction of the system, so data analysis and application aren’t actively implemented. However, the auto metering infrastructure is being installed continuously and the amount of data is continuously increasing. Therefore, the importance of utilization of power data based on the auto metering infrastructure becomes an issue, and research on power big data analysis and data mining is actively being carried out. In this paper, we have performed a probabilistic analysis, diagnosis, and prediction of the fault condition of the system through application of the artificial intelligent deep learning algorithm using the system state data stored in real time in the low voltage meter reading system. In this paper, we propose an optimal method for designing and operating a reasonable automated meter reading system with stability and reliability.
Long Short-Term Memory Neural Networks for Identifying Type 1 Diabetes Patients with Functional Magnetic Resonance Imaging

The neuronal activation in the human brain is one of the most complex systems known nowadays that can be measured through functional magnetic resonance imaging (fMRI). Modeling this phenomenon could help in better understanding diseases with an impact on brain. Type 1 diabetes is a disease associated with the metabolism of energy, that has been associated to cognitive disorders. Here, we propose to classify Type 1 diabetic fMRIs during a working memory test using Long Short-Term Memory (LSTM) recurrent artificial neural networks due to its ability to model complex time series. We compared 20 different LSTM architectures on our database using mean and standard deviations of accuracy, specificity and F1 score. Our best result was obtained with a bidirectional LSTM obtaining a mean accuracy of 0.87, mean specificity of 0.89 and mean F1 score of 0.86. Our results have paved the way for doing similar models for other diseases and larger databases.
Accelerating CNN Algorithm with Fine-Grained Dataflow Architectures

Convolutional Neural Network(CNN) is a hot and state-of-the-art algorithm which is widely used in applications such as face recognition, intelligent monitoring, image recognition and text recognition. Because of its high computational complexity, many efficient hardware accelerators have been proposed to exploit high degree of parallel processing for CNN. However, accelerators which are implemented on FPGAs and ASICs usually sacrifice generality for higher performance and lower power consumption. Other accelerators, such as GPUs, are general enough, but they lead to higher power consumption. Fine-grained dataflow architectures, which break conventional Von Neumann architectures, show natural advantages in processing CNN-like algorithms with high computational efficiency and low power consumption. At the same time, it remains broadly applicable and adaptable. In this paper, we propose a scheme for implementing and optimizing CNN on fine-grained dataflow architecture based accelerators. The experiment results reveal that by using our scheme, the performance of AlexNet running on the dataflow accelerator is 3.11× higher than that on NVIDIA Tesla K80, and the power consumption of our hardware is 8.52× lower than that of K80.
Numerical simulation of temperature field of converter transformer based on electromagnetic-flow-heat coupling field

The converter transformer plays a key role in the safe operation of power system. Due its high voltage level and complex heat transfer characteristics, the heating problem becomes the important problem affecting the safe and stable operation of converter transformer. With a 500Kv oil-immersed converter transformer as research object in this study, the distribution of magnetic flux leakage, eddy current loss and temperature field were calculated by three - dimensional electromagnetic-flow-thermal coupling field analysis method, considering the effects of conduction, convection and radiation on the heat transfer of structures. The results showed that the magnetic flux leakage distribution was symmetrical. The transverse distribution was characterized by small leakage magnetic flux in the middle, while large leakage magnetic flux at both ends. The longitudinal distribution was characterized by small leakage magnetic flux at both ends, while large leakage magnetic flux in the middle. The eddy current loss had a great influence on the middle of the winding while little influence on the both ends. For the same position, the temperature of the valve winding was lower than that of the net winding. The position of the highest temperature point inside of iron core was within the range of 70%~90% of the total height. This study provides certain theoretical basis for the study of long-term stable operation of transformer.
Toward Human-in-the-Loop Collaboration Between Software Engineers and Machine Learning Algorithms

Several papers have recently contained reports on applying machine learning (ML) to the automation of software engineering (SE) tasks, such as project management, modeling and development. However, there appear to be no approaches comparing how software engineers fare against machine-learning algorithms as applied to specific software development tasks. Such a comparison is essential to gain insight into which tasks are better performed by humans and which by machine learning and how cooperative work or human-in-the-loop processes can be implemented more effectively. In this paper, we present an empirical study that compares how software engineers and machine-learning algorithms perform and reuse tasks. The empirical study involves the synthesis of the control structure of an autonomous streetlight application.
An Evaluation of Character Level N-gram Termsets in Text Categorization

Text categorization is a text mining process and it aims to discover relevant information and relationship in a huge amounts of text data. Feature extraction is an important preprocessing step of text categorization, as extracted features are used to represent texts. Several methods, feature models, and algorithms are needed to extract useful features from textual contents. One of the these methods is frequent itemset mining which is a basic data mining technique that employed to find interesting patterns in data. As the frequent itemsets (termsets) reflect strong associations between items they provide more underlying contextual semantic than an individual word. Therefore, it is used in text mining domain for different purposes (e.g., frequent itemset based text clustering). In this paper, we employ termsets for text representation, and use both binary and cardinality based approaches for termset weighting. Unlike existing studies, we use character level n-grams to represent items in a transaction in addition to the traditional bag of words model. Through our experimental results on Mod-Apte split of the Reuters-21578 dataset, we determined that performing document-transaction conversion at level of character n-grams improves the performance of the Support Vector Machine classifier.