Reviewerly/exHarmony huggingface dataset:
A Multifunctional and Robust Learning Approach for Human Motion Modelling

Human motion modelling plays an important role in the automation of the robot systems, including human motion recognition and generation. The problem is often intractable since it requires high-dimensional sequential data modelling as well as capturing both spatial and temporal correlations from motion videos. In the paper, a multifunctional and robust learning approach called broad conditional restricted Boltzmann machine (BCRBM) for human motion modelling is introduced, which employs CRBM for motion generation and broad learning system (BLS) for motion recognition. The proposed hybrid broad CRBM model has many advantages from four aspects. (1) This one-off trained model can be simultaneously employed to recognize and generate human motions with outstanding performance; (2) Without deep architecture and with the help of broad learning system, it shows out better capability in different motion generation and recognition over deep models; (3) It is more efficient as its learning process no longer need the time-consuming two-phases learning, namely pre-training and fine-tuning for deep models; (4) It is still a probabilistic graph model that has better robustness for noisy samples compared with deterministic models. These advantages are both verified and evaluated with a number of experiments, where 2D MOCAP datasets are employed.
A Journey into Ontology Approximation: From Non-Horn to Horn

We study complete approximations of an ontology formulated in a non-Horn description logic (DL) such as ALC in a Horn DL such as EL. We provide concrete approximation schemes that are necessarily infinite and observe that in the ELU-to-EL case finite approximations tend to exist in practice and are guaranteed to exist when the source ontology is acyclic. In contrast, neither of this is the case for ELU_bot-to-EL_bot and for ALC-to-EL_bot approximations. We also define a notion of approximation tailored towards ontology-mediated querying, connect it to subsumption-based approximations, and identify a case where finite approximations are guaranteed to exist.
Data Security of SaaS Platform based on Blockchain and Decentralized Technology

Data security in the SaaS platform based on the Blockchain and decentralized technology is well analyzed in this paper. The unified SaaS service platform based on cloud computing needs to realize functions of application virtualization, automatic deployment and delivery, data format standardization, information sharing and effective user and genera; operation management to support core SaaS services. The unified service support platform is essentially a cloud computing platform. This paper proposes two aspects of innovations. First, The unified SaaS service platform based on cloud computing needs to realize the functions of application virtualization, automatic deployment and delivery, data format standardization, information sharing and effective user and the operation management to support SaaS services. The unified service support platform is essentially a cloud computing platform. Second, In order to better adapt to the block chain system, our systems use P2P network for data dissemination. The results are satisfactory.
Domain Aggregation Networks for Multi-Source Domain Adaptation

In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e.g., recognizing characters of a new font using a set of different fonts. While most recent research has considered ad-hoc combination rules to address this problem, we extend previous work on domain discrepancy minimization to develop a finite-sample generalization bound, and accordingly propose a theoretically justified optimization procedure. The algorithm we develop, Domain AggRegation Network (DARN), is able to effectively adjust the weight of each source domain during training to ensure relevant domains are given more importance for adaptation. We evaluate the proposed method on real-world sentiment analysis and digit recognition datasets and show that DARN can significantly outperform the state-of-the-art alternatives.
APS: An Active PubMed Search System for Technology Assisted Reviews

Systematic reviews constitute the cornerstone of Evidence-based Medicine. They can provide guidance to medical policy-making by synthesizing all available studies regarding a certain topic. However, conducting systematic reviews has become a laborious and time-consuming task due to the large amount and rapid growth of published literature. The TAR approaches aim to accelerate the screening stage of systematic reviews by combining machine learning algorithms and human relevance feedback. In this work, we built an online active search system for systematic reviews, named APS, by applying an state-of-the-art TAR approach -- Continuous Active Learning. The system is built on the top of the PubMed collection, which is a widely used database of biomedical literature. It allows users to conduct the abstract screening for systematic reviews. We demonstrate the effectiveness and robustness of the APS in detecting relevant literature and reducing workload for systematic reviews using the CLEF TAR 2017 benchmark.
Evaluating Natural Language Generation via Unbalanced Optimal Transport

Embedding-based evaluation measures have shown promising improvements on the correlation with human judgments in natural language generation. In these measures, various intrinsic metrics are used in the computation, including generalized precision, recall, F-score and the earth mover's distance. However, the relations between these metrics are unclear, making it difficult to determine which measure to use in real applications. In this paper, we provide an in-depth study on the relations between these metrics. Inspired by the optimal transportation theory, we prove that these metrics correspond to the optimal transport problem with different hard marginal constraints. However, these hard marginal constraints may cause the problem of incomplete and noisy matching in the evaluation process. Therefore we propose a family of new evaluation metrics, namely Lazy Earth Mover's Distances, based on the more general unbalanced optimal transport problem. Experimental results on WMT18 and WMT19 show that our proposed metrics have the ability to produce more consistent evaluation results with human judgements, as compared with existing intrinsic metrics.
Activity recognition through interactive machine learning in a dynamic sensor setting

Abstract The advances in Internet of things lead to an increased number of devices generating and streaming data. These devices can be useful data sources for activity recognition by using machine learning. However, the set of available sensors may vary over time, e.g. due to mobility of the sensors and technical failures. Since the machine learning model uses the data streams from the sensors as input, it must be able to handle a varying number of input variables, i.e. that the feature space might change over time. Moreover, the labelled data necessary for the training is often costly to acquire. In active learning, the model is given a budget for requesting labels from an oracle, and aims to maximize accuracy by careful selection of what data instances to label. It is generally assumed that the role of the oracle only is to respond to queries and that it will always do so. In many real-world scenarios however, the oracle is a human user and the assumptions are simplifications that might not give a proper depiction of the setting. In this work we investigate different interactive machine learning strategies, out of which active learning is one, which explore the effects of an oracle that can be more proactive and factors that might influence a user to provide or withhold labels. We implement five interactive machine learning strategies as well as hybrid versions of them and evaluate them on two datasets. The results show that a more proactive user can improve the performance, especially when the user is influenced by the accuracy of earlier predictions. The experiments also highlight challenges related to evaluating performance when the set of classes is changing over time.
Multi-Scale Spatial-Temporal Integration Convolutional Tube for Human Action Recognition

Applying multi-scale representations leads to consistent performance improvements on a wide range of image recognition tasks. However, with the addition of the temporal dimension in video domain, directly obtaining layer-wise multi-scale spatial-temporal features will add a lot extra computational cost. In this work, we propose a novel and efficient Multi-Scale Spatial-Temporal Integration Convolutional Tube (MSTI) aiming at achieving accurate recognition of actions with lower computational cost. It firstly extracts multi-scale spatial and temporal features through the multi-scale convolution block. Considering the interaction of different-scales representations and the interaction of spatial appearance and temporal motion, we employ the cross-scale attention weighted blocks to perform feature recalibration by integrating multi-scale spatial and temporal features. An end-to-end deep network, MSTI-Net, is also presented based on the proposed MSTI tube for human action recognition. Extensive experimental results show that our MSTI-Net significantly boosts the performance of existing convolution networks and achieves state-of-the-art accuracy on three challenging benchmarks, i.e., UCF-101, HMDB-51 and Kinetics-400, with much fewer parameters and FLOPs.
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines

Support Vector Machines (SVMs) are among the most fundamental tools for binary classification. In its simplest formulation, an SVM produces a hyperplane separating two classes of data using the largest possible margin to the data. The focus on maximizing the margin has been well motivated through numerous generalization bounds. In this paper, we revisit and improve the classic generalization bounds in terms of margins. Furthermore, we complement our new generalization bound by a nearly matching lower bound, thus almost settling the generalization performance of SVMs in terms of margins.
Metode Deteksi Intrusi Menggunakan Algoritme Extreme Learning Machine dengan Correlation-based Feature Selection

&lt;p&gt;Deteksi intrusi pada jaringan komputer merupakan kegiatan yang sangat penting dilakukan untuk menjaga keamanan data dan informasi. Deteksi intrusi merupakan proses monitor &lt;em&gt;tra&lt;/em&gt;&lt;em&gt;f&lt;/em&gt;&lt;em&gt;fi&lt;/em&gt;&lt;em&gt;c&lt;/em&gt; pada sebuah jaringan untuk mendeteksi adanya pola data yang dianggap mencurigakan, yang memungkinkan terjadinya serangan jaringan. Penelitian ini melakukan analisis pada &lt;em&gt;traffic&lt;/em&gt; jaringan untuk mengetahui apakah paket tersebut mengandung intrusi atau merupakan paket normal. Data &lt;em&gt;traffic &lt;/em&gt;yang digunakan untuk deteksi intrusi pada penelitian ini diambil dari &lt;em&gt;dataset&lt;/em&gt; KDD Cup. Metode yang digunakan untuk melakukan deteksi intrusi dengan cara klasifikasi yaitu dengan menggunakan metode &lt;em&gt;Extreme Learning Machine&lt;/em&gt; (ELM). Namun, dengan menggunakan metode ELM saja tidak mampu untuk menghasilkan akurasi yang baik maka, pada metode ELM perlu ditambahkan metode seleksi fitur &lt;em&gt;Correlation-Based Feature Selection&lt;/em&gt; (CFS) untuk meningkatkan hasil akurasi dan waktu komputasi. Hasil penelitian yang dilakukan dengan menggunakan metode ELM menunjukkan tingkat akurasi mencapai 81,97% dengan waktu komputasi 3,39 detik. Setelah ditambahkan metode seleksi fitur CFS pada ELM tingkat akurasi meningkat secara signifikan menjadi 98,00% dengan waktu komputasi 2,32 detik.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Intrusion detection of computer networks is a very important activity carried out to maintain data and information security. Intrusion detection is the process of monitoring traffic on a network to detect any data patterns that are considered suspicious, which allows network attacks. This research analyzes the network traffic to find out whether the packet contains intrusion or is a normal packet. Traffic data used for intrusion detection in this study were taken from the KDD Cup dataset. The method used to do intrusion detection by classification is using the Extreme Learning Machine (ELM) method. However, using the ELM method alone is not able to produce good accuracy, so the ELM method needs to be added to the Correlation-Based Feature Selection (CFS) feature selection method to improve the accuracy and computational time. The results of the research conducted using the ELM method showed an accuracy rate of 81.97% with a computation time of 3.39 seconds. After adding the CFS feature selection method to ELM the accuracy level increased significantly to 98.00% with a computing time of 2.32 seconds.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Comparative Analysis and Proposal of Deep Learning Based Colorectal Cancer Polyps Classification Technique

The information hidden in an image is worth more than a thousand words. Proper analysis of a medical image can help in timely detection and diagnose of a disease which increases the rate of survival of cancer patients. Analysis of images manually is subjective and time consuming. On the other hand, automated analysis of a medical image has a lot of challenges due to the architecture and colors of the medical images. This paper, gives a survey on detection, classification and diagnosis of colorectal cancer and proposes a deep learning based techniques to differentiate between healthy tissues and cancerous polyps in histology images. It also compares the accuracy of three different classification frameworks namely Convolutional Neural Network (CNN), Fully Convolutional Network (FCN) and Recurrent Neural Network (RNN). It also presents the overview of the work done in this field. It first discusses basic deep learning methods and then the known techniques used for detection, classification and diagnosis of colorectal cancer followed by the comparative analysis of all the surveyed paper. Finally, it talks about the conclusion, challenges and the future scope of the progress in this field.
Training Physical and Geometrical Mid-Points for Multi-person Pose Estimation and Human Detection Under Congestion and Low Resolution

This paper introduces the design and evaluation of NeoPose which is developed for multi-person pose estimation and human detection. The design of NeoPose is targeting the issue of human detection under congested situation and with low resolution in the image. Under such situations, we compared the performance of different versions of NeoPose as well as other existing algorithms in a human detection task. Throughout the task, the usefulness of two kinds of mid-point (physical and geometrical mid-points) and a deconvolution structure was discussed. Experiment results indicated that NeoPose which applied geometrical mid-points and deconvolution structure performed the best in terms of both precision and recall in the evaluation.
Features of Roof Rainwater Runoff Pollution in a Northern Coastal City under the Effects of Multiple Factors

This paper attempts to disclose the features of roof rainwater runoff pollution under the effects of air pollution, season, rainfall intensity, and antecedent dry period.Targeting an inclined tile roof in Yantai, China, this paper samples rainwater multiple times from March to November, 2018.The quality of roof rainwater samples in 12 representative natural rainfalls was monitored, and the curves of the main pollutants in these samples were plotted.On this basis, the authors analyzed the change features and outflow laws of the main pollution indices of roof runoff rainwater under the effects of different factors.The results show that: The degree of air pollution directly bears on rainwater quality.The rainfall also has a certain impact on air pollution indices.Season is the leading influencing factor of the quality of roof runoff rainwater.The best rainwater quality was observed in autumn, followed by that in spring and then that in summer.In the first 10-20min of each rainfall, the pollutant contents decreased at a growing rate with the increase of rainfall intensity.The increments of the initial pollutant contents reached the maximum at the antecedent dry period of 10d.The changes of all pollution indices of roof runoff rainwater could be divided into the sudden change phase, the transition phase, and the stable phase.Roof rainwater runoff has an obvious initial washing effect, and exerts a secondary washing effect.The research results provide a good reference for northern coastal cities to rationalize the parameters of rainwater purification facilities.
Adaptive Language Processing Based on Deep Learning in Cloud Computing Platform

With the continuous advancement of technology, the amount of information and knowledge disseminated on the Internet every day has been developing several times. At the same time, a large amount of bilingual data has also been produced in the real world. These data are undoubtedly a great asset for statistical machine translation research. Based on the dual-sentence quality corpus screening, two corpus screening strategies are proposed first, based on the double-sentence pair length ratio method and the word-based alignment information method. The innovation of these two methods is that no additional linguistic resources such as bilingual dictionary and syntactic analyzer are needed as auxiliary. No manual intervention is required, and the poor quality sentence pairs can be automatically selected and can be applied to any language pair. Secondly, a domain adaptive method based on massive corpus is proposed. The method based on massive corpus utilizes massive corpus mechanism to carry out multidomain automatic model migration. In this domain, each domain learns the intradomain model independently, and different domains share the same general model. Through the method of massive corpus, these models can be combined and adjusted to make the model learning more accurate. Finally, the adaptive method of massive corpus filtering and statistical machine translation based on cloud platform is verified. Experiments show that both methods have good effects and can effectively improve the translation quality of statistical machines.
Handwritten Bengali Character Recognition Using Deep Convolution Neural Network

Recognition of Handwritten Character had been one of the promising area of research for its applications in diverse field, it appear to be a challenging research. In our paper, we focus specifically on offline handwritten character recognition of regional language (Bengali) by first detecting individual characters. The principal approaches for offline handwritten character recognition may be divided into two classes, segmentation and holistic based. In our method we applied segmentation based handwritten word recognition and to identify individual characters neural network have been used. We have used 15,000 instances of Bengali alphabets to create a recognition model, which when provided with images of physical pieces of handwritten texts, it is able to segment and extract characters from the said image of a physical handwritten text with 65% accuracy, and recognize the properly segmented alphabets with 99.5% accuracy.
Classification of malicious insiders and the association of the forms of attacks

Purpose The authors argue that the current studies about malicious insiders confuse the fact that malicious attacks belong to two different categories, namely, those that launch instrumental attacks and expressive attacks. The authors collect malicious insider data from publicly available sources and use text-mining techniques to analyze the association between malicious insiders’ characteristics and the different types of attack. Design/methodology/approach The authors investigated the relationship between personality characteristics and different types of malicious attacks. For the personality characteristics, the authors use the same method as Liang et al. (2016), which extracted these characteristics based on a keyword-characteristic dictionary. For different types of malicious attacks, two raters rated each case based on criteria modified from criminology research to determine the degree of expressiveness and instrumentality. Findings The results show that malicious insiders who are manipulative or seeking personal gain tend to carry out instrumental attacks. Malicious insiders who are arrogant tend to conduct expressive attacks. Research limitations/implications This study uses third party articles to identify the personality characteristics of known malicious insiders. As such, not all personality characteristics may have been reported. Data availability was an issue. Practical implications Understanding if different personality characteristics lead different types of attacks can help managers identify employees who exhibit them and mitigate an attack before it occurs. Social implications Malicious insider attacks can have devastating results on businesses and employees. Help to identify potential malicious insiders before they act, may prevent undue harm. Originality/value This study used 132 cases of none malicious insiders to examine their attack objectives. No other study that the authors know of used that many cases.
Nature-inspired and hybrid optimization algorithms on interval Type-2 fuzzy controller for servo processes: a comparative performance study

In this paper, performance evaluations of six well-known nature-inspired algorithms have been reported containing genetic algorithm, cuckoo search, particle swarm optimization, differential evolution, bee colony, and combined particle swarm optimization and differential evolution (CPSODE) algorithms. Based on these optimization algorithms, input and output scaling factors of an interval Type-2 fuzzy PID controller (IT2-FLC) are chosen for closed-loop servo tracking. Optimal values of the scaling factors are chosen by minimization of the objective function which is defined based on the closed-loop controller performance criteria. A detailed comparative analysis is reported based on the simulation and experimental results. Performance analysis reveals that improved performance, reliability, robustness, and lesser noise sensitivity are reported by IT2-FLC with the optimal values obtained by the hybrid algorithm CPSODE.
Adversarial Robustness of Flow-Based Generative Models

Flow-based generative models leverage invertible generator functions to fit a distribution to the training data using maximum likelihood. Despite their use in several application domains, robustness of these models to adversarial attacks has hardly been explored. In this paper, we study adversarial robustness of flow-based generative models both theoretically (for some simple models) and empirically (for more complex ones). First, we consider a linear flow-based generative model and compute optimal sample-specific and universal adversarial perturbations that maximally decrease the likelihood scores. Using this result, we study the robustness of the well-known adversarial training procedure, where we characterize the fundamental trade-off between model robustness and accuracy. Next, we empirically study the robustness of two prominent deep, non-linear, flow-based generative models, namely GLOW and RealNVP. We design two types of adversarial attacks; one that minimizes the likelihood scores of in-distribution samples, while the other that maximizes the likelihood scores of out-of-distribution ones. We find that GLOW and RealNVP are extremely sensitive to both types of attacks. Finally, using a hybrid adversarial training procedure, we significantly boost the robustness of these generative models.