Miracle-dz/UniFAR-FTUs huggingface dataset:
Byte Segment Neural Network for Network Traffic Classification

Network traffic classification, which can map network traffic to protocols in the application layer, is a fundamental technique for network management and security issues such as Quality of Service, network measurement, and network monitoring. Recent researchers focus on extracting features for traditional machine learning methods from flows or datagrams of the specific protocol. However, as the rapid growth of network applications, previous works cannot handle complex novel protocols well. In this paper, we introduce the recurrent neural network to network traffic classification and design a novel neural network, the Byte Segment Neural Network (BSNN). BSNN treats network datagrams as input and gives the classification results directly. In BSNN, a datagram is firstly broken into serval byte segments. Then, these segments are fed to encoders which are based on the recurrent neural network. The information extracted by encoders is combined to a representation vector of the whole datagram. Finally, we apply the softmax function to use this vector for predicting the application protocol of this datagram. There are several key advantages of BSNN: 1) no need for prior knowledge of target applications; 2) can handle both connection-oriented protocols and connection-less protocols; 3) supports multi-classification for protocols; 4) shows outstanding accuracy in both traditional protocols and complex novel protocols. Our thorough experiments on real-world data with different protocols indicate that BSNN gains average F1-measure about 95.82% in multi-classification for five protocols including QQ, PPLive, DNS, 360 and BitTorrent. And it also shows excellent performance for detection of novel protocols. Furthermore, compared with two recent state-of-the-art works, BSNN has superiority over the traditional machine learning-based method and the packet inspection method.
Action Recognition and Localization with Instance FCNN

To cooperate with robots, it is crucial to have them recognize humans action. However, in robotic vision, action recognition task remains challenging since videos are often untrimmed and arbitrary long with multiple action instances and background clutter. To address this issue, we propose an end-to-end fully convolutional spatio-temporal localization framework consisting of a proposal branch and a recognition branch. On the one hand, in the proposal branch, pixel-level score maps are regressed to model temporal boundaries and spatial instance-aware sub-tubes for each action and generate potential action tubes. On the other hand, we regress another category score maps to indicate the confidence of different action type for each potential action tube. Our method is of significant efficiency because we can obtain all proposals by processing each frame once. We evaluated our networks in two well-known public datasets MSR-II [1] and UT-interaction [2]: our result outperform state-of-the-art methods in a large margin at various spatio-temporal threshold α in multiple evaluation metrics.
Current Distribution in Conduction-Cooled NbTi/BSCCO Composite Conductor

In this paper, an NbTi/BSCCO composite conductor made of NbTi/Cu wire and BSCCO/Ag wire cooled by cryocooler were investigated, extending the basic research on current distribution characteristics of low temperature superconductor and high temperature superconductor composite conductor and exploring its potential application. Current distribution characteristics with operating current, temperature, magnetic field, and current frequency were simulated and measured. Current-transfer characteristics between the two superconductors were also obtained.
A Hybrid Model Based on Constraint OSELM, Adaptive Weighted SRC and KNN for Large-Scale Indoor Localization

In this paper, a novel hybrid model based on the constraint online sequential extreme learning machine (COSELM) classifier with adaptive weighted sparse representation classification (WSRC) and k nearest neighbor (KNN) is proposed for the WiFi-based indoor positioning system. It is referred to as A Fast-Accurate-Reliable Localization System (AFARLS). AFARLS exploits the speed advantage of COSELM to reduce the computational cost, and the accuracy advantage of WSRC to enhance the classification performance, by utilizing KNN as the adaptive sub-dictionary selection strategy. The understanding is that the original extreme learning machine (ELM) is less robust against noise, while sparse representation classification (SRC) and KNN suffer a high computational burden when using the over-complete dictionary. AFARLS unifies their complementary strengths to resolve each other’s limitation. In large-scale multi-building and multi-floor environments, AFARLS estimates a location that considers the building, floor, and position (longitude and latitude) in a hierarchical and sequential approach according to a discriminative criterion to the COSELM output. If the classifier result is unreliable, AFARLS uses KNN to achieve the best relevant sub-dictionary. The sub-dictionary is fed to WSRC to re-estimate the building and the floor, while the position is predicted by the ELM regressor. AFARLS has been verified on two publicly available datasets, the EU Zenodo and the UJIIndoorLoc. The experimental results demonstrate that AFARLS outperforms the state-of-the-art algorithms on the former dataset, and it provides near state-of-the-art performance on the latter dataset. When the size of the dataset increases remarkably, AFARLS shows that it can maintain its real-time high-accuracy performance.
Development of adult T-cell leukaemia/lymphoma during the treatment of rheumatoid arthritis

Abstract Two patients with rheumatoid arthritis (RA) developed adult T-cell leukaemia/lymphoma (ATLL) in the course of RA treatment. We previously reported another case of ATLL that developed during RA treatment and summarised all three cases of ATLL. The mean age at onset of ATLL was 63.7 ± 9.6 years and treatment regimens included biological disease-modifying anti-rheumatic drugs (bDMARDs), such as abatacept (ABT) and tocilizumab, and conventional synthetic DMARDs (i.e. iguratimod (IGU) and methotrexate [MTX]) and/or glucocorticoids. Two patients were complicated with bronchiectasis and/or interstitial pneumonia and one patient developed ATLL after MTX-related lymphoproliferative disease. All three patients with ATLL were encountered for 5 years of clinical practice at our centre. The prevalence of ATLL that developed from 78 human T-cell leukaemia virus type 1 (HTLV)-1-seropositive RA patients was 3.85%. In conclusion, we described three cases of ATLL development during the treatment for RA. It is suggested that HTLV-1 seropositive RA patients should be managed and treated carefully. A nationwide survey is necessary to elucidate if the progression to ATLL after immunosuppressive treatment for RA occurs at higher in incidence than that in the natural course of an HTLV-1 infection.
Incorporating Syntactic Dependencies into Semantic Word Vector Model for Medical Text Processing

Natural language processing has been split into two sub-fields: semantic and syntactic analysis. Semantic processing deals with creating vector representations for each word. The goal is for the vectors to be dense and that relationships between words are made clear by their vectors. Word2Vec Skip-Gram model is one such semantic word embedding model that processes the words into vectors based on the co-occurrences of the words within a fixed context window. Syntactic processing, on the other hand, deals with gleaning textual information from the structure of the text. A popular method of syntactic processing is dependency parsing, where connections are explicitly drawn between different pairs of words in a phrase or clause, where one word modifies the other. Human beings make use of both semantic and syntactic information when processing text. As such, combining semantic and syntactic processing is important for medical text analysis. The dependencies between words define specific medical concepts, such as diseases and symptoms. The aim of this research paper is to investigate an altered word vector model—the SD Skip-Gram model which incorporates the dependencies between words. The SD Skip-Gram model is compared against the basic Skip-Gram model for analyzing common words and medical disease and symptom concepts. The returned results demonstrate that the SD Skip-Gram model can identify the relationships between the common words as well as the basic Skip-Gram model and perform better than the basic Skip-Gram model in identifying the related disease and symptom concepts.
Study on Parameter Measurement of Hole-Punched Steel Strip*

Research on parameter detection method of hole-punched steel strip based on machine vision in this paper. First of all, the image of hole-punched steel strip is extracted and processed based on machine vision, which mainly includes smoothing, threshold segmentation and holes edge detection of the image. So that to obtain more details and information from the image. Then the least square method is chosen for the parameter extraction of the hole-punched steel strip. Finally, the feasibility of the parameter measurement method based on machine vision is demonstrated by experiments.
Comparision of String Matching Algorithms on Spam Email Detection

Email is one of the most expedient approach to transfer messages among people all over the world. Its features, specifically reliability, quickness, and low cost makes it popular and useful among people in most parts of businesses and society. On the other hand, this popularity also created new harmful actions, such as email attacks (spam) in cyberspace. Spam is arguably one of the main reasons of drowning the WWW with many copies of similar messages generated through anonymous senders, which yields to time/space wasting of the email account holder and also a large virus and malware threat to Email providers. In spite of employing various filters to handle spam problem such as machine learning and content-based filtering, spammers are still able to bypass these defense mechanisms. In this paper, we investigate the use of string matching algorithms for spam email detection. Particularly this work examines and compares the efficiency of six well-known string matching algorithms, namely Longest Common Subsequence (LCS), Levenshtein Distance (LD), Jaro, Jaro-Winkler, Bi-gram, and TFIDF on two various datasets which are Enron corpus and CSDMC2010 spam dataset. We observed that Bi-gram algorithm performs best in spam detection in both datasets.
CAM: A Combined Attention Model for Natural Language Inference

Natural Language Inference (NLI) is a fundamental step towards natural language understanding. The task aims to detect whether a premise entails or contradicts a given hypothesis. NLI contributes to a wide range of natural language understanding applications such as question answering, text summarization and information extraction. Recently, the public availability of big datasets such as Stanford Natural Language Inference (SNLI) and SciTail, has made it feasible to train complex neural NLI models. Particularly, Bidirectional Long Short-Term Memory networks (BiLSTMs) with attention mechanisms have shown promising performance for NLI. In this paper, we propose a Combined Attention Model (CAM) for NLI. CAM combines the two attention mechanisms: intra-attention and inter-attention. The model first captures the semantics of the individual input premise and hypothesis with intra-attention and then aligns the premise and hypothesis with inter-sentence attention. We evaluate CAM on two benchmark datasets: Stanford Natural Language Inference (SNLI) and SciTail, achieving 86.14% accuracy on SNLI and 77.23% on SciTail. Further, to investigate the effectiveness of individual attention mechanism and in combination with each other, we present an analysis showing that the intra- and inter-attention mechanisms achieve higher accuracy when they are combined together than when they are independently used.
Big Data Augmentation with Data Warehouse: A Survey

With dynamic changes in world’s technology, an increasing growth and adoption observed in the usage of social media, computer networks, internet of things, and cloud computing. Research experiments are also generating huge amount of data which are to be collected, managed and analyzed. This huge data is known as "Big Data". Research analysts have perceived an increase in data that contains both useful and useless entities. In extraction of useful information, data warehouse finds difficulties in enduring with increasing amount of data generated. With shifts in paradigm, big data analytics emerged as promising area of research which supports business intelligence in terms of decision making. This paper provides a comprehensive survey on BigData, BigData problems, BigData Analytics and Big Data Warehouse. In addition, it also explains how the need for augmentation of big data and data warehouse emerged in perspective of decision making, comparing methods and research problems. It also elaborates applications which support Big Data, Data Warehouse, and its challenges.
An Experimental Study of Learning Behaviour in an ELearning Environment

To achieve an adaptive eLearning system, it is essential to monitor the learner behaviour dynamically to diagnose their learning style. Eye tracking can serve that purpose by investigating the eye gaze movement while engaging in the eLearning environment. In this study, an eye tracking experiment was developed to analyse the pattern of learner behaviour to obtain his learning style as a personalisation aspect in an eLearning system. The electroencephalography EEG Emotive Epoc device was used to disclose learners with more accurate data. In the experiment, we have developed a method to test the hypothesis whether the verbal and visual learning styles reflect actual preferences in an eLearning environment based on Felder and Silverman Learning Style Model. The outcome of this experiment can be used as the starting point for further exhaustive experiments. This paper presents the actual state of our experiment, conclusions, and plans for future development.
An Effective Standardization Method for the Lab Indicators in Regional Medical Health Platform Using N-grams and Stacking

Since 2008, a regional medical health platform has been built for managing electronic health records of top public hospitals in Shanghai. However, public hospitals often use different names to present a same laboratory examination item (or lab indicator) in this regional platform, which seriously hinders the interconnection and sharing of medical information among hospitals. In this paper, we propose an effective method to standardize the lab indicators using n-gram features and Stacking mechanism. Our proposed method sequentially combines a clustering model and a binary classification model. More specifically, we first cluster the lab indicators based on character uni-gram similarity distances to reduce the alignment scale, and then leverage a binary classification algorithm through Stacking mechanism based on character n-gram similarity features to generate candidate-standard indicator pairs iteratively. Experimental studies on the clinical data collected from eight top public hospitals in Shanghai show that our proposed method achieves a good performance 88.43% in terms of F1-score in the final binary classification, which is highly competitive performance compared to baseline methods.
Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of surgical planning, postoperative assessment and hepatic diseases. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of the liver. In this paper, we propose a multi-plane integrated fully convolutional neural network to segment the liver from 3D CT volumes. Our network uses multiple layers of dilated convolution filters to replace traditional ones. Residual connections and multi-scale predictions are also employed in the network to improve the segmentation performance. We extensively evaluated our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method outperformed other state-of-the-art methods with an average Dice score of 96.7% on the segmentation results of liver, which only used a single framework without any pre-processing operation on it.
Ferrite Assisted Synchronous Reluctance Motor Design, Manufacturing and Material Influence on Motor Characteristics

Synchronous permanent magnet machines are gaining interest over the last twenty years. The best torque density and efficiency is achieved with synchronous motors using the rare-earth, usually neodymium (NdFeB) magnets. However, the price of a NdFeB magnets is rising, therefore the research in ferrite assisted synchronous motors has began. Ferrite surface and interior permanent magnet (SPM) motors suffer from low air gap flux density, thus low electromagnetic torque. The ferrite assisted synchronous reluctance motor (PMASR) compensate the lower torque created by permanent magnets with reluctance torque, developed by rotor magnetic saliency (difference between inductances in d- and q-axis). This paper deals with basic characteristics of PMASR, electromagnetic design process and results evaluation. This paper also proposes research on causes for unsatisfactory prototype characteristics. Each discovered manufacture drawback is discussed and implemented into either the FEA model or post-processing and compared with the measured data hereafter.
On Maximizing QoE in AVC-Based HTTP Adaptive Streaming: An SDN Approach

HTTP adaptive streaming (HAS) is quickly becoming the dominant video delivery technique for adaptive streaming over the Internet. Still considered as its primary challenges are determining the optimal rate adaptation and improving both the quality of experience (QoE) and QoE-fairness. Recent studies have shown that techniques providing a comprehensive and central view of the network resources can lead to greater gains in performance. By leveraging software defined networking (SDN), the current study proposes an SDN-based approach to maximize QoE metrics and QoE-fairness in AVC-based HTTP adaptive streaming. The proposed approach determines both the optimal adaptation and data paths for delivering the requested video files from HTTP-media servers to DASH clients. In fact, the proposed approach, which includes a set of application modules, is centrally executed by an SND controller in a time slot fashion. We formulate the problem as a mixed integer linear programming (MILP) optimization model in such a way that it applies defined policies, e.g. setting priorities for clients in obtaining video quality. We conduct experiments by emulating the proposed framework in Mininet using Floodlight as the SDN controller. In terms of improving QoE-fairness and QoE metrics, the effectiveness of the proposed approach is validated by a comparison with different approaches.
Disparity Refinement in Depth Discontinuity Using Robustly Matched Straight Lines for Digital Surface Model Generation

Dense image matching using remote sensing images is of particular importance for providing ground geometric data for object modeling and analysis. These methods normally operate in a rectified (epipolar) space producing disparity images that are correlated with the final three-dimensional geometry. However, the resulting digital surface models can be problematic on discontinuities of the terrain object, e.g., building edges, which largely limit their practical use for highly accurate object modeling. Existing works put forth efforts for dealing with this issue by defining better edge constraints and more global energy optimization, while intended to improve the disparity maps, it might be challenging to leverage the result of all the pixels through a single energy minimization, leading to either oversmoothed object boundaries or noisy surfaces. In this paper, we propose an intuitive method that integrates straight line primitives to enhance the disparity map. For each matched line, we perform a local discontinuity analysis and propose an intensity-based weighting method for a local plane fitting using iteratively solved weighted least squares adjustment, such that straightness of the object's edges (e.g., buildings) can be preserved, as the straight lines are detected and matched. Experiments on both aerial and satellite dataset show that the proposed method yields visually clear object edges and has achieved higher accuracy (2–3 pixels improvement) around edges than DSM generated from typical stereo matching result (i.e., from semiglobal matching).
Improving Coding Efficiency of MPEG-G Standard Using Context-Based Arithmetic Coding

The rapid decreasing of DNA sequencing costs, together with the need for exponentially more DNA sequencing in biomedical research, drug discovery, and clinical genomics has led to a burst in the volume of genomic data. The amount of genomic data is now growing at a rate that already far outpaced Moore’s Law. Storage and transmission of genomic data has become a new bottleneck for sequencing applications. In response to this need, the ISO/IEC JTC 1/SC 29/WG 11 (MPEG) standard organization started a new ISO standard project, namely, the MPEG-G standard, for efficient genomic information representation and compression. In this paper, we present an improved method for lossless compression of nucleobase quality value, one of the most challenging parts from genomic data for data compression due to its high entropy. The proposed method has been adopted by MPEG-G as the normative lossless coding method for quality value due to its good performance, and its compatibility with the context-adaptive binary arithmetic coding (CABAC) entropy coding engine currently used in the MPEG-G framework.
Analysis of Information Polarization During Japan’s 2017 Election

Information polarization has the potential of developing into various problems such as opposition conflict. In this study, we analyzed the information polarization was caused in Japan by using Twitter data during the 48th Lower House Election in 2017. We succeeded to separate the opposite opinion such as political criticism (liberal) and political supporting (conservative) by using tweet networks based on users’ retweets. Furthermore, in the result of analysis focusing users who retweet the tweets, it suggests that the liberal users did not retweet conservative tweets although the conservative users retweeted a few liberal tweets. Also, users cannot even access the tweets which have a different opinion. We indicate that the relation of follower-followee causes selective contact since users’ opinions are related to the followees’ them by the correlation coefficient of 0.903.
Multi-Snapshot Spectrum Sensing for Cognitive Radar via Block-Sparsity Exploitation

Two-dimensional (2-D) spectrum sensing is addressed in the context of a cognitive radar to gather real-time space-frequency electromagnetic awareness. Assuming a sensor equipped with multiple receive antennas, a discrete-time sensing signal model formally accounting for multiple snapshots of observations is introduced. Hence, a new signal processing strategy exploiting the inherent block-sparsity of the overall profile is developed to glean a reliable 2-D occupancy awareness. Specifically, the proposed approach resorts to the regularized maximum likelihood estimation paradigm including a term promoting the block-sparsity of the 2-D profile so as to automatically foster this peculiarity in the profile evaluation. Some illustrative examples (both on simulated and measured data) are provided to compare the novel strategy with a relevant counterpart available in the open literature and highlight the effectiveness of the developed approach.