Miracle-dz/UniFAR-FTUs huggingface dataset:
ImmunoAdept – bringing blood microbiome profiling to the clinical practice

Microbiome research aims to understand environmental features, relationships and microorganisms’ composition and their genomes. In the context of healthcare, microbiomics has unveiled that health status of the patient is more dependent on the microbiota than ever before thought. Here we present a pipeline designed to bring microbiome analysis to clinical practitioners in order to offer it as a service for diagnostic support. The Immunology Assay Development Platform (ImmunoAdept) enables the analysis of the whole-metagenome, therefore it can identify viruses, bacteria and archaea at species level. With the objective of making the ImmunoAdept platform broadly available, a cloud-based infrastructure was used, combined with an easy to use input and results interface. This was achieved by implementing it as part of Simplicity, TM a bespoke bioinformatics platform.
Building rating system: an instrument for building accessibility measurement for better indoor navigation by blind people

Purpose The purpose of this paper is to create a building rating system (BRS) with its bottom-up design model that can be carried out manually and in the future automatically. Design/methodology/approach The BRS is built on the basis of the structure of spatial representation framework for indoor navigation by people with visual impairment, which was validated with visually impaired people, and incorporated with building design standards and regulations from around the world. The BRS was afterwards validated by three groups of five experts in the related fields such as research and development, accessibility, and building and interior designs. Finally, the user evaluation was carried out by three focus groups of three experts in risk assessment to verify the usability of the system. Findings This paper provides the design and methodology of the BRS used for classifying the accessibility in buildings into four levels of classification for people with visual impairment navigating around the buildings. This system is evaluated with system usability scales (SUS), which is found to be in a “Good” level on average (72.2 SUS scores). Research limitations/implications Success criteria used in the space classification are mainly created for people with visual impairment at this stage; other disabilities requirements must be taken into account for the next stage of the development. Practical implications The system can be carried out in the future automatically in the form of standalone software or plugins that can be integrated in buildings and interior design software to seek recommendations toward a creation of inclusive built environment. Originality/value This paper presents a design architecture of BRS with its details, description and success criteria used in the space classification.
One- Dimensional Convolutional Neural Networks Based on Exponential Linear Units for Bearing Fault Diagnosis

Rolling bearings are one of the most commonly used components in rotating machinery which is mainly operated in complex working environment. Therefore, it is of great theoretical value and practical significance to study the state monitoring and fault diagnosis technology of rolling bearing to avoid sudden accidents and make a better system maintenance. In this paper, we propose a one-dimensional convolutional neural network to identify rolling bearing fault. Furthermore, we adopt a novel activation function: exponential linear units in the task of rolling bearing fault diagnosis. Simulation results show that one-dimensional convolutional neural network has a prominent generalization ability and high accuracy rate. Exponential linear units can make neural network more robust and stable when we diagnose the rolling bearing fault.
Research on User Consumption Behavior Prediction Based on Improved XGBoost Algorithm

This paper is to propose an improved algorithm in modeling user consumption behavior, which combined Logistic regression and XGBoost algorithm to predict users’ purchasing behavior in an e-commerce website.XGBoost, as a feature transformation, is used to make sample prediction. According to the prediction results, the information of each regression tree will construct the new feature vector, which will be the input data of the logistic regression model. The previous improved clustering algorithm [1] will be involved to cluster the different user divisions for further comparative analysis with the three predictive models in this paper.Specifically, more than 50 million original data are collected and preprocessed for correlation mining. 60% are selected randomly to be the training set and 20% to be the verification set and the rest 20% as the test set. Logistic regression and XGBoost algorithm are used respectively to set up two models based on making use of the advantages of each. The research shows that Logistic regression on XGBoost method is feasible and the evaluation index of the model is better than any methods being used alone.
Scheduling Stream Processing Tasks on Geo-Distributed Heterogeneous Resources

Low-latency processing of data streams from distributed sensors is becoming increasingly important for a growing number of IoT applications. In these environments sensor data collected at the edge of the network is typically transmitted in a number of hops: from devices to intermediate resources to clusters of cloud resources. Scheduling processing tasks of dataflow jobs on all the resources of these environments can significantly reduce application latencies and network congestion. However, for this schedulers need to take the heterogeneity of processing resources and network topologies into account.This paper examines multiple methods for scheduling distributed dataflow tasks on geo-distributed, heterogeneous resources. For this, we developed an optimization function that incorporates the latencies, bandwidths, and computational resources of heterogeneous topologies. We evaluated the different placement methods in a virtual geo-distributed and heterogeneous environment with an IoT application. Our results show that metaheuristic methods that take service quality metrics into account can find significantly better placements than methods that only take topologies into account, with latencies reduced by almost 50%.
Sample Size and Reproducibility of Gene Set Analysis

Gene set analysis is widely used to gain insight from gene expression data. Achieving reproducible results is a fundamental part of any expression analysis. In this paper, we propose a systematic approach to study the effect of sample sizes on the reproducibility of the results of 10 gene set analysis methods. To do so, we quantity the concept of reproducibility and use real expression datasets of different sizes. Our findings suggest that, as a general pattern, the results of gene set analysis are more reproducible as sample size increases. However, the smallest sample size for achieving reproducible results are variable across gene set analysis methods. Moreover, for some methods, increasing sample size leads to an increase in the number of false positives.
Segment Routing Green Spine Switch Management Systems for Data Center Networks

The datacenter is the core of most IT industries today. As a result, due to the exponential increase of information and services demanded from datacenters, it is projected that 14.1 zeta bytes of bandwidth would be needed to meet the current demands of data by the end of 2020 in USA datacenters alone. This would require a tremendous amount of energy to run those datacenters. Hence, in recent years there is more focus on reducing the energy consumption in a datacenter. This paper approaches this problem by utilizing Segment Routing based Green Spine Switch Management System (SR-GSSMS) to have an efficient bandwidth usage. The proposed approach makes it possible to deactivate some low utilized spine switches and links on the datacenter networks, which results in energy savings. The system also maintains the network performance when congestion or a link failure occurs. Our experimental results yielded up to a 78% energy savings on the spine-links, while maintaining the same traffic requirements.
Adaptive Data Pruning for Support Vector Machines

Support Vector Machine (SVM) is one of the most popular classification algorithms. SVM separates data points into two classes by using the hyper-plane that is maximally distant from the two classes. Since SVM is theoretically based on statistical learning theory and the principle of structural risk minimization, it offers highly accurate classification. However, its training process is computationally expensive. This paper proposes Sahara as an efficient training algorithm for SVM. It identifies data points that have no influence on SVM classification by computing the upper and lower bounds of a parameter that determines the hyper-plane. Our approach can efficiently compute the bounds by using Singular Value Decomposition (SVD) and a sparse data matrix. Theoretically, our approach guarantees to yield the optimal hyper-plane of SVM for any given set of data points. Experiments show that Sahara is significantly faster than previous approaches.
Inter/Intra-Constraints Optimization for Fast Vessel Enhancement in X-ray Angiographic Image Sequence

Automatic vessel enhancement is significant for automatic analysis of the X-ray angiographic image sequence. However, the image is often interrupted by motion artefacts and other anatomical structures. In this paper, we propose an intra-frame and inter-frame constraint principal component analysis model (IFC-PCA) for vessel enhancement in the X-ray angiographic image sequence. The proposed method not only considers temporal information of image sequence by iteratively updating variable value on the basis of previous image information, but also takes the spatial information of each image into consideration. Experiments are conducted on a series of clinical X-ray angiographic image sequences. It demonstrates that our method is effective and robust for vessel enhancement from image sequence.
Recognising Named Entity of Medical Imaging Procedures in Clinical Notes

Information on medical imaging procedures in free-text clinical notes plays a significant role in help diagnosis process. In this paper, we present a named entity of the medical imaging procedure recognition system based on conditional random fields (CRF) model with word-based, part-of-speech, Metamap semantic and et.al features. The system is trained and tested on a manually labelled dataset from I2B2 with the F1 score up to 0.923 for recognizing medical imaging procedure entities. Our system can be customized by simply defining new medical named entities, which has been proved on medication recognition with the F1 score from 0.870 to 0.937.
Distance-Dependent Metric Learning

In most existing metric learning methods, the data pairs are equally treated without considering their diversity. In fact, for pairs with different distances, the main purpose of the metric acted on them has some differences. If the pairs have smaller distance, metric should focus more on expanding the negative pairs, which are more easily misjudged. While if the pairs have larger distance, metric should focus more on shrinking the positive pairs. However, most metric learning methods neglect these differences. In this letter, we propose a distance-dependent metric learning (D<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>ML) method. It partitions data pairs into different clusters according to the <inline-formula><tex-math notation="LaTeX">$\ell _2$</tex-math></inline-formula> distance between them. Each cluster is associated with a Mahalanobis metric that learns the pairs’ distance. This not only allows us to make each metric more targeted and adapt to the data diversity flexibly, but also avoids the problem of computing the distance between points assigned to different clusters, which happens in some local metric learning methods. D<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula>ML is further extended to D<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula>ML to embrace the nonlinear capacity of neural network. Experiments on UCI datasets and speaker recognition i-vector machine learning challenge show that the proposed methods are superior to other metric learning methods.
A Unified Hierarchical Convolutional Neural Network for Fine-grained Traffic Sign Detection

The real-world traffic signs are small objects and have hundreds of fine-grained classes, many of them are similar in appearance. This makes difficulties for detection. To solve the problem, this paper presents a novel two-stage fine-grained object detection network which utilizes the hyper-class of the traffic signs. The traffic signs can be divided into several hyper-class according to their appearance and functions, like ‘warning’ and ‘prohibitory’. An object detection network is used to detect the traffic signs with their hyper-class label first, with some adjustments for small objects. Then the fine-grained detection network shares the feature map of the trained hyper-class model and the hyper-class detections are input as proposals. The RoIPooling is conducted with labels. Then the RoIs with labels input to their corresponding hyper-class' classify branch and generate the final results. The experiments in the Tsinghua-Tencent 100K dataset demonstrate that the proposed method outperforms the state-of-the-art methods.
Forecasting Smart Meter Energy Usage Using Distributed Systems and Machine Learning

In this research, we explore the technical and computational merits of a machine learning algorithm on a large data set, employing distributed systems. Using 167 million (10 GB) energy consumption observations collected by smart meters from residential consumers in London, England, we predict future residential energy consumption using a Random Forest machine learning algorithm. Distributed systems such as AWS S3 and EMR, MongoDB and Apache Spark are used. Computational times and predictive accuracy are evaluated. We conclude that there are significant computational advantages to using distributed systems when applying machine learning algorithms on large-scale data. We also observe that distributed systems can be computationally burdensome when the amount of data being processed is below a threshold at which it can leverage the computational efficiencies provided by distributed systems.
Time Series Forecasting by Evolving Deep Belief Network with Negative Correlation Search

The recently developed deep belief network (DBN) has been shown to be an effective methodology for solving time series forecasting problems. However, the performance of DBN is seriously depended on the reasonable setting of hyperparameters. At present, random search, grid search and Bayesian optimization are the most common methods of hyperparameters optimization. As an alternative, a state-of-the-art derivative-free optimizer-negative correlation search (NCS) is adopted in this paper to decide the sizes of DBN and learning rates during the training processes. A comparative analysis is performed between the proposed method and other popular techniques in the time series forecasting experiment based on two types of time series datasets. Experiment results statistically affirm the efficiency of the proposed model to obtain better prediction results compared with conventional neural network models.
Detecting Port Scan Attempts with Comparative Analysis of Deep Learning and Support Vector Machine Algorithms

Compared to the past, developments in computer and communication technologies have provided extensive and advanced changes. The usage of new technologies provide great benefits to individuals, companies, and governments, however, it causes some problems against them. For example, the privacy of important information, security of stored data platforms, availability of knowledge etc. Depending on these problems, cyber terrorism is one of the most important issues in todays world. Cyber terror, which caused a lot of problems to individuals and institutions, has reached a level that could threaten public and country security by various groups such as criminal organizations, professional persons and cyber activists. Thus, Intrusion Detection Systems (IDS) have been developed to avoid cyber attacks. In this study, deep learning and support vector machine (SVM) algorithms were used to detect port scan attempts based on the new CICIDS2017 dataset and 97.80%, 69.79% accuracy rates were achieved respectively.
Classification of G-protein Coupled Receptors Based on Semi-navïe Bayesian Inference

In this paper, we propose a novel protein classification strategy for the hierarchical classification of G-protein Coupled Receptors (GPCRs) based on a feature enhanced protein classification approach considering the rich synthesis of multiple sequences alignment (MSA) and a semi-navïe Bayesian inference algorithm. The output of MSA on the specific protein family used in further processing to extract features is translated into feature vectors that are presented to a semi-navïe Bayesian classifier. MSA identifies positions that are indicative of functional differences by alignment within a group of sequences, thus we can ascertain whether a query sequence belongs to this family according to the alignment result presented to the classifier. The experimental result indicates that the proposed method makes significant improvements over the previous methods, which attains an accuracy of up to 100% at family level and 98.14% at level I sub-family and level II sub-subfamily. The proposed method has valuable implications for future research on the classification of protein sequences.
Dance of the Dragonfly: A Vision-Based Agile Aerial Touch Solution for IARC Mission 7

The International Aerial Robotics Competition (IARC) aims to move the state-of-the-art in aerial robotics forward through mission challenges. In the IARC Mission 7, the aerial robot will navigate without external navigation aids, interact with autonomous ground robots, and avoid dynamic obstacles in the herding problem. In 2017 competition, our team firstly accomplished to interactively herd one iRobot to one end of the arena through accurate and rapid aerial touch and won the first place in system control. This paper presents the self-localization, control and tracking strategies for a micro unmanned aerial vehicle (UAV) to accurately and agilely touch ground moving robots. A real-time self-localization framework is proposed to estimate the ego motion of the UAV. A computationally efficient visual tracking scheme is designed to detect the ground robot and estimate its direction. Inspired by the dance of the dragonfly, tracking and control schemes are presented for the UAV to achieve 3-D agile interaction with ground autonomous robots. Simulation and flight experimental results validate the effectiveness of the proposed methods.
Acquire, adapt, and anticipate: continuous learning to block malicious domains

We present an automated learning system that continuously gathers domain data from open repositories, develops a deep learning model, uses the model to make detections, publishes unreported malicious domains, leverages threat intelligence to label the detected domains, and periodically updates the detection models. The results presented in this paper show that the system not only extends the detection coverage of threat intelligence feeds, but also that it reduces the delay in detection. We also leverage deep learning models to generate new, unregistered domains that are likely to be used by attackers in the future.
eZiGait: Toward an AI Gait Analysis And Sssistant System

Objectively assessing gait function in lower limb rehabilitation remains a challenge in healthcare. This paper proposed the framework of AI gait analysis and assessment system eZiGait, which is based on seamless smart insoles. The preliminary study of activity recognition using eZiGait is presented. Walking data for five types of activities including slow walking, normal walking, fast walking, climbing upstairs, and walking down stairs have been investigated. Three classifiers were used, including artificial neural network (ANN), k-nearest neighbour (KNN) and random forest, to classify the five exercises. Results shows that a classification accuracy of 80% can be achieved with the ANN or 70% with KNN and random forest. This demonstrates that simple features extracted from smart insoles can be used to classify different types of exercise. This provides for potential development of an AI gait analysis and assistant system to support lower limb rehabilitation at hospital, community or at home using state-of-the-art smart insoles and mobile technologies.
A novel study for MDD detection through task-elicited facial cues

Depression is a common mental disorder worldwide. Individuals with major depressive disorder (MDD) are at increased risk for suicide. Current clinical practice for assessing this psychosomatic state are mainly based on self-report and expert evaluation, which risking a range of subjective biases. We investigate a number of task-elicited facial features from Chinese subjects for MDD detection. Moreover, we collect the data from Kinect, which make us achieve a good detection result with low time and space consumption. Experiments are performed on an age, gender and education level matched clinical dataset of 36 MDD patients and 36 healthy controls (HCs). We can get three points from the experimental results: 1) We have presented a simple and objective means for MDD detection, and the average classification accuracies (female: 71.5%, male: 66.7%) are all much higher than chance level. The best classification accuracies (female: 86.8%, male: 79.4%) are achieved during video watching task. 2) Neutral emotion stimulus is a better choice for data collection than positive and negative valences. 3) Eyebrows and mouth have more contributions than other parts of a face in neutral emotion valence. These findings suggest that detecting MDD from facial indicators is feasible, and we provide effective emotion stimulus and facial features.
MaMPF: Encrypted Traffic Classification Based on Multi-Attribute Markov Probability Fingerprints

With the explosion of network applications, network anomaly detection and security management face a big challenge, of which the first and a fundamental step is traffic classification. However, for the sake of user privacy, encrypted communication protocols, e.g. the SSL/TLS protocol, are extensively used, which results in the ineffectiveness of traditional rule-based classification methods. Existing methods cannot have a satisfactory accuracy of encrypted traffic classification because of insufficient distinguishable characteristics. In this paper, we propose the Multi-attribute Markov Probability Fingerprints (MaMPF), for encrypted traffic classification. The key idea behind MaMPF is to consider multi-attributes, which includes a critical feature, namely “length block sequence” that captures the time-series packet lengths effectively using power-law distributions and relative occurrence probabilities of all considered applications. Based on the message type and length block sequences, Markov models are trained and the probabilities of all the applications are concatenated as the fingerprints for classification. MaMPF achieves 96.4% TPR and 0.2% FPR performance on a real-world dataset from campus network (including 950,000+ encrypted traffic flows and covering 18 applications), and outperforms the state-of-the-art methods.