Miracle-dz/UniFAR-FTUs huggingface dataset:
Distinct prognostic value of dynactin subunit 4 (DCTN4) and diagnostic value of DCTN1, DCTN2, and DCTN4 in colon adenocarcinoma

Background Colon adenocarcinoma (COAD) is ranked as the third most commonly diagnosed cancer in both women and men, and it is the most frequently occurring malignant tumor. Dynactin is a protein compound based on multiple subunits, including dynactin 1–6 (DCTN1–6), in most categories of cytoplasmic dynein performance in eukaryotes. Nevertheless, correlations between the DCTN family and the prognosis and diagnosis of COAD remain unidentified. Methods Statistics for DCTN mRNA expression in patients with COAD were acquired from The Cancer Genome Atlas. Kaplan–Meier analyses and a Cox regression model were applied to determine overall survival, with computation of HRs and 95% CIs. Several online data portals were used to assess the biological process, and pathway examination was performed using the Kyoto Encyclopedia of Genes and Genomes to predict the biological functionality of DCTN genes. Results We found that high expression of DCTN4 was linked with satisfactory results for overall survival (P=0.042, HR=0.650, 95% CI 0.429–0.985). The expression of DCTN1, DATN2, and DCTN4 was closely correlated with the frequency of colon tumors (P<0.001, area under the curve [AUC]=0.8811, 95% CI 0.8311–0.9312; P<0.001, AUC=0.870, 96% CI 0.833–0.9071; and P=0.0051, AUC=0.6317, 95% CI 0.5725–0.6908, respectively). In the enrichment examination, the level of gene expression was related to the cell cycle, cell apoptosis, and the cell metastasis pathway. Conclusion The expression levels of DCTN1, DCTN2, and DCTN4 could allow differentiation between cancer-bearing tissues and paracancerous tissue. These genes can be applied as biomarkers to predict the prognosis and diagnosis of COAD.
Sparse Autoencoders Using Non-smooth Regularization

Autoencoder, at the heart of a deep learning structure, plays an important role in extracting abstract representation of a set of input training patterns. Abstract representation contains informative features to demonstrate a large set of data patterns in an optimal way in certain applications. It is shown that through sparse regularization of outputs of the hidden units (codes) in an autoencoder, the quality of codes can be enhanced that leads to a higher learning performance in applications like classification. Almost all methods trying to achieve code sparsity in an autoencoder use a smooth approximation of l1 norm, as the best convex approximation of pseudo l0 norm. In this paper, we incorporate sparsity to autoencoder training optimization process using non-smooth convex l1 norm and propose an efficient algorithm to train the structure. The non-smooth l1 regularization have shown its efficiency in imposing sparsity in various applications including feature selection via lasso and sparse representation using basis pursuit. Our experimental results on three benchmark datasets show superiority of this term in training a sparse autoencoder over previously proposed ones. As a byproduct of the proposed method, it can also be used to apply different types of non-smooth regularizers to autoencoder training problem.
Wavelet Total Variation Method Applied to Partial Discharge Signal Denoising

Signal processing techniques are used with the purpose of assisting in the process of equipment maintenance, avoiding failures and unwanted stops. One such fault is known as Partial Discharge (PD). The objective of this work is to evaluate the denoising performance of the Wavelet Total Variation method when applied to synthetic and laboratorial PD signals corrupted by Gaussian, harmonic and impulsive noises, as a way to improve their measurement conditions. The results obtained are promising and show that the method reconstructs the signal, maintaining just the discharges.
Analysis and Control of Deep-Level Defects in Dilute Nitride Semiconductor GaInNAsSb

Deep-level defects in as-grown and annealed dilute nitride semiconductor GaInNAsSb solar cells grown by molecular beam epitaxy were investigated by admittance spectroscopy measurement and compared to determine the mechanism of post-annealing effects on improving the collection of photo carriers. The annealing was confirmed to be effective to decrease nitrogen-related defects in the as-grown GaInNAsSb sample as well as the background carrier concentration. This effect is considered to be mainly responsible for the improved collection of photo carriers. In addition, we found an optimal annealing temperature that suppresses the formation of new defects, leading to a remarkable improvement of the photo carrier collection.
Time Efficient Energy-Aware Routing in Software Defined Networks

As devices and traffic in the Internet increase, power consumption becomes a critical issue for Information and Communication Technology (ICT) industry. The emerging technology, Software Defined Networking (SDN), can adaptively manage the network resources and control the power consumption. In this paper, we first investigate the time efficient energy aware routing in SDN enabled networks, and then propose a Time Efficient Energy Aware Routing (TEAR) algorithm. The goal of this work is to effectively reduce the number of used links for packet delivery so that the energy consumption can be decreased. Different from most existing schemes which only focus on static demands, the proposed TEAR algorithm considers both the temporal variation in demand and computational efficiency when finding routing paths. Specifically, the energy consumption problem for routing traffic in a network is formulated as an integer linear programming problem. Hence, the proposed TEAR algorithm can dynamically route flows in a time efficient manner. The evaluation results show that the proposed scheme outperforms other schemes in terms of computation time and energy consumption of the network.
Semantic Textual Similarity in Bengali Text

Measuring the textual similarity is indispensable in many information retrieval applications. Researchers proposed numerous similarity measures to compute the semantic similarity between texts for monolingual and multilingual texts. But methods for measuring similarity for Bengali text segments are not so commonly available. In this paper, we propose an approach to estimate the semantic similarity between Bengali text segments. The similarity score is computed with the help of word-level semantics from a pre-trained word-embedding model trained on Bengali Wikipedia texts. In this regard, we employ an algorithm to measure the semantic similarity of Bengali texts. To test the performance of our method, we conducted experiments on a dataset for semantic textural similarity for Bengali texts. We prepare the dataset using the same approach as SemEval applied in the STS 2017. The experimental results in terms of Pearson correlation coefficient conclude that our method achieves a state-of-the-art performance for semantic textual similarity in Bengali texts.
Stability of nonlinear impulsive differential equations with non-fixed moments of jumps

This paper studies stability properties of the trivial solution to a system of nonlinear differential equations that undergo impulsive perturbations at non-fixed moments of time. We are motivated by modeling of networked control systems in which the communication between subsystems can be statedependent. This leads to the impulsive system with multiple impulsive time sequences and a distinct jump map for each sequence. Lyapunov-like theorems equipped with novel dwelltime conditions for global asymptotic stability of the origin have been proven. We treat the cases of a stable continuous dynamics that is being destabilized by impulsive perturbations, and vice versa, the case of unstable continuous dynamics that is being stabilized by impulses. Our results are less conservative comparing to the existing ones since we propose the concept of a candidate Lyapunov function with multiple nonlinear rate functions to characterize its behaviour during flows and jumps and account the influence of impulses for each impulsive time sequence separately. Also, we demonstrate the application of the results to stability analysis of impulsive differential equations with fixed moments of jumps and compare them with the existing ones.
Bone Age Assessment with X-Ray Images Based on Contourlet Motivated Deep Convolutional Networks

Bone age assessment (BAA) is a widely performed procedure for skeletal maturity evaluation in pediatric radiology. It has various clinical applications such as diagnosis of endocrine disorders, monitoring of growth hormone therapy and prediction of final adult height for adolescents. Recent studies indicate that deep learning techniques have great potential in developing automated BAA methods with significant improvements in terms of conventional computer-assisted approaches. In this paper, we propose a multi-scale feature fusion framework for bone age assessment based on deep convolutional neural networks. In our method, the non-subsampled contourlet transform (NSCT) is firstly performed on an input left-hand radiograph to obtain its multi-scale and multi-direction representations. Then, the decomposed bands at each scale are fed to a convolutional network that contains a series of convolutional and pooling layers for feature extraction, respectively. Finally, the feature maps from different branches are concatenated and put into a regression network consisting of several fully connected layers to obtain the bone age estimation. Experimental results on a public BAA dataset demonstrate that the proposed method can achieve state-of-the-art performance.
Counter-Propagating Core Assignment in Multi-Core Fiber Optical Networks to Reduce Inter-Core Crosstalk and Capacity Wastage

Inter-core crosstalk is one of the most serious impairments for signal transmission in a multi-core fiber (MCF) optical network. On the other hand, because of wide deployment of data centers (DCs), we are seeing an increasing bidirectional traffic demand asymmetry, which leads to significant capacity wastage in designing and operating an optical transport network. To alleviate these effects, for an MCF optical network, we propose to assign fiber cores in an MCF in an asymmetric and counter-propagating manner. This can not only significantly reduce inter-core crosstalk between counter-propagating fiber cores but also flexibly assign different numbers of fiber cores in the opposite directions of a fiber link, thereby overcoming network capacity wastage due to the bidirectional traffic demand asymmetry. To evaluate the benefits of the proposed strategy, we consider the routing, spectrum, and core assignment (RSCA) problem for the MCF optical network. An integer linear programming (ILP) model and an auxiliary graph (AG) based heuristic algorithm are developed to optimize network spectrum resource utilization. Simulation studies show the effectiveness of the proposed core counter-propagation strategy, which can significantly outperform its counterpart, i.e., the co-propagation scheme, in terms of the total number of MCFs used and average inter-core crosstalk. In addition, the proposed RSCA heuristic algorithm is efficient to perform close to the ILP model, which can minimize the number of MCFs used and crosstalk between neighboring cores.
Forecasting Supply in Voronoi Regions for App-Based Taxi Hailing Services

In this paper, we deal with the problem of supply forecasting in the context of an application based taxi hailing service. We first propose a method to optimally partition the city space using a Voronoi tessellation. The generating points of the Voronoi regions are obtained as demand density cluster centers, from the taxi demand dataset. We also identify the optimal temporal resolution to use for forecasting supply in these Voronoi regions. We use a linear time-series based algorithm to forecast supply in each Voronoi region. Using this methodology for the city of Bengaluru, India, we obtained a supply forecast accuracy of about 90% for the heavily used Voronoi regions. This represents a substantial improvement in the forecast accuracy compared to similar time-series based approaches, employed over rectangular ‘geohashes ’.
EBG Enhanced Broadband Dual Antenna Configuration for Passive Self-Interference Suppression in Full-Duplex Communications

A full-duplex system is realised using dual EBG isolated rectangular spiral antennas and its performance is compared with the same full-duplex system using a circulator and a single spiral antenna element. The new antenna system consists of two antennas one with RHCP and the another one with LHCP implemented on a single substrate. Two columns of EBG is placed between the two antennas to improve the isolation. At the operating frequency of 3.2 GHz, the antenna configuration has nearly 31 dB isolation. For the identical baseband input power, the full-duplex system utilising dual spiral antenna configuration exhibits 9 dB higher isolation than the circulator based full-duplex system.
Different Machine Learning Models to Predict Dropouts in MOOCs

Massive Open Online Courses have emerged as an alternative to the traditional educational system because of the flexibility in timings and also it overcomes the economic and geographical barriers for the users. MOOCs also help learners from diverse background to communicate and exchange knowledge in MOOCs forums. The number of learners enrolling in such courses is very high. Despite the unrestricted accessibility, the completion rate is very low. Various factors affect the completion of the course by the students such as interest in the subject, the purpose of enrolling in the subject, whether the lecturer is able to convey his knowledge to the students or not. EDM (Educational Data Mining) and LA (Learning Analytics) are the fields in which data of students learning activity is analyzed to obtain certain vital information or can be used in prediction using EDM tools and techniques. Data analysis shows that there is a strong relationship between the number of events such as click event, video watched, forum post and the successful learner's outcome. Machine Learning algorithms are applied on the dataset from HarvardX and the result shows that Random Forest gives an optimum result with the highest performance.
High-Quality Facial Keypoints Matching with Motion Smoothness Constraint and 3D Model Constraint

Pore-scale facial features, similar to fingerprints and irises, are effective to distinguish human identities. Nonetheless, there is a few of pore-scale facial feature database, which constrains deep learning methods to be employed of generating pore-scale facial features. In this paper, we propose a novel method by merging motion smoothness constraint and 3D model constraint, to generate a large and complex pore-scale facial feature database. The proposed method uses a powerful motion smoothness constraint in feature matching, rather than the standard ratio-test, and utilizes 3D model constraint to eliminate the incorrect matching. In the experiment, by using the proposed method, the matched numbers into high matched quality are two times higher than state-of-the-art with the same local features.
Tile-Based Qoe-Driven Http/2 Streaming System For 360 Video

Recently, the 360-degree video has become a hot topic in multimedia area. However, the requirements of high bitrate, low Internet interactive latency and high perceived quality limit its further applications. So in this paper, we design a tile- based QoE-driven HTTP/2 streaming system for 360 video. It uses a new projection method, Sinusoidal Projection (SP), to reduce the bitrate of tiles. A novel cross-user's behavior learning method is also used to predict viewpoint. To improve bandwidth utilization, it pushes multiple tiles in one request by HTTP/2. Besides, by using a QoE-driven framework, our approach can significantly improve users' perceived quality. The numerous experiment results have demonstrated the efficiency of the proposed system. Compared with the legacy methods, the transmission bitrate drops about 17%, the view- port prediction accuracy improves 30%, the Viewport-PSNR improve 22% and the transmission latency drops about 30%.
Comparative Study of Short-Term Wind Speed Forecasting Techniques Using Artificial Neural Networks

This paper focuses on the importance of wind forecasting and comparison of two different forecasting schemes using artificial neural network approach. Types of forecasting include feed-forward network models using standard back propagation technique and recurrent neural network models with inherent memory for any given data. In this study, how local memory and relevant inputs make recurrent neural networks more suitable for time-series prediction than normal feed-forward networks is shown. And also for accurate forecasting and better energy trading, fine tuning of present techniques is required. Therefore, LSTM models are implemented which are a part of recurrent neural networks. Finally, the results are measured in terms of mean-squared error, an error function which calculates the difference between actual and model outputs. It was found that LSTM models were more suitable for short as well as long term time-series forecasting as compared to RNN model.
Online Multi-Object Tracking Using Selective Deep Appearance Matching

In this paper, we focus on designing appearance matching network and solving computational bottleneck problem of it. From the development of deep neural network and graphic device(GPU), many research topics in computer vision (e.g. detection, classification) achieved state-of-the-art performance using convolutional neural network(CNN). In multi-object tracking, also, there have been several works which used CNN for extracting appearance feature of targets. Although, deep appearance feature improved an accuracy of tracking, it increased processing time and made an algorithm hard to be applied in real-world situation. So, we propose a simple technique to improve speed by removing redundant appearance matchings. Also, we propose a structure of joint-input siamese network and method to train it. We verify the performance of our work by comparison with recent online trackers.
Induction Motor Fault Detection and Classification Using Current Signature Analysis Technique

The induction motor (IM) faults detection utilizing current signature analysis strategies break even with presented and the upside of these systems are illuminated and checking the present, voltage, temperature of the electrical engines we can viably diminish the working cost of help by allowing the basic discovery of flaws, which could be intemperate to repair. The induction motors utilized for home apparatus and also the modern reason. With the modern development, it has turned out to be important to screen the state of the machine. This paper displays the evaluation of recognizing the individual sorts of faults in the induction motor by controlling the current, voltage, temperature. The flag preparing methods measure up to connected to condition checking and fault detection of the induction motor. The flag handling strategies have points of interest this isn’t computationally costly, and these are easy to actualize.
A Survey on Crack Detection Technique in Railway Track

As you know railway transportation is mostly used since it is economical and fast method for public transportation. Due to some minor failures such as track failures, obstacles and improper maintenances causes accidents.In order to make it safe rail inspections can be done properly.An efficient crack detection system is needed for better security and inspections. Human inspections are done periodically. But it needs skilled persons walk through the track for inspection. It takes much time. In order to overcome these situations a visual inspection system (VIS) is proposed. We propose a visual based crack detection system that provides accurate result than other existing systems.It makes the things smooth and fast. This system use images captured by a digital scan line camera.VIS provide better performance and quality based on the capturing device.The digital data is preprocessed and then it cut the track images into sub images using some track extraction algorithm. The image contrast can be enhanced using Otsu method for analysis and then crack is identified if any in the track. This system automatically detect crack and generate appropriate warning to nearest station to avoid risk of accidents. This system doesn’t disturb the running of train while the system is working.
Developing a New Model of Maturity Stages of Organizational Information Systems: An Action Design Research

Information systems planning (ISP) contributes to the success or failure of organizational information systems (IS). One of the critical actions in ISP is the assessment of current and future use of IS which is based on effectively identifying the maturity stages of organizational IS. Although researchers and practitioners had developed many useful models of maturity stages, it is hard to determine which existing model can be suitable for assessment of contemporary use of IS. However, organizations must leverage an appropriate model to assess the use of IS. We fill the gap by conducting an action design research (ADR), a form of design research that incorporates action research and supports the emergence of ensemble IT artifacts and allows researchers to respond to unexpected consequences. We partnered with a Chinese IT/IS consulting company to implement the assessment of IS for a large Chinese government institution. Initially, our research focused on how to leverage an appropriate existing model, but the feedback from intervention revealed that the existing models are unsuitable, which significantly hindered the assessment. Thus, we shifted our research question to how to develop a new model of maturity stages of organizational IS. Our ADR study is in progress and the preliminary findings unveil a model framework of IS maturity stages and three design principles for developing the model.
Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks

Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. Re-assigned spectrogram of measured radar signal and detected outliers of its instantaneous phases filtered by a special function are used for training multiple convolutional neural networks. Automatically extracted features from the networks are fused to distinguish frequency and phase modulated signals. Simulation results show that the proposed FF-CNN (Feature Fusion based Convolutional Neural Network) technique outperforms the current state-of-the-art alternatives and is easily scalable among broad range of modulation types.