Reviewerly/exHarmony huggingface dataset:
Dialog Intent Induction with Deep Multi-View Clustering

We introduce the dialog intent induction task and present a novel deep multi-view clustering approach to tackle the problem.Dialog intent induction aims at discovering user intents from user query utterances in humanhuman conversations such as dialogs between customer support agents and customers. 1Motivated by the intuition that a dialog intent is not only expressed in the user query utterance but also captured in the rest of the dialog, we split a conversation into two independent views and exploit multi-view clustering techniques for inducing the dialog intent.In particular, we propose alternating-view k-means (AV-KMEANS) for joint multi-view representation learning and clustering analysis.The key innovation is that the instance-view representations are updated iteratively by predicting the cluster assignment obtained from the alternative view, so that the multi-view representations of the instances lead to similar cluster assignments.Experiments on two public datasets show that AV-KMEANS can induce better dialog intent clusters than stateof-the-art unsupervised representation learning methods and standard multi-view clustering approaches. 2
DESIGN AND DEVELOPMENT OF HUMAN COMPUTER INTERFACE USING ELECTROOCULOGRAM WITH DEEP LEARNING

Today’s life assistive devices were playing significant role in our life to communicate with others. In that modality Human Computer Interface (HCI) based Electrooculogram (EOG) playing vital part. By using this method we can able to overcome the conventional methods in terms of performance and accuracy. To overcome such problem we analyze the EOG signal from twenty subjects to design nine states EOG based HCI using five electrodes system to measure the horizontal and vertical eye movements. Signals were preprocessed to remove the artifacts and extract the valuable information from collected data by using band power and Hilbert Huang Transform (HHT) and trained with Pattern Recognition Neural Network (PRNN) to classify the tasks. The classification results of 92.17% and 91.85% were shown for band power and HHT features using PRNN architecture. Recognition accuracy was analyzed in offline to identify the possibilities of designing HCI. We compare the two feature extraction techniques with PRNN to analyze the best method for classifying the tasks and recognizing single trail tasks to design the HCI. Our experimental result confirms that for classifying as well as recognizing accuracy of the collected signals using band power with PRNN shows better accuracy compared to other network used in this study. We compared the male subjects performance with female subjects to identify the performance. Finally we compared the male as well as female subjects in age group wise to identify the performance of the system. From that we concluded that male performance was appreciable compared with female subjects as well as age group between 26 to 32 performance and recognizing accuracy were high compared with other age groups used in this study.
Light-Weight Spliced Convolution Network-Based Automatic Water Meter Reading in Smart City

Automatic reading for water meter is one of the practical demands in smart city applications. Due to the high cost, it is not feasible to replace the old mechanical water meter with a new embedded electronic device. Recently, image recognition based meter reading methods have become research hotspots. However, illumination, occlusion, energy and computational consuming in IoT environment bring challenges to these methods. In this paper, we design and implement a smart water meter reading system to handle this issue. Specifically, we first propose a novel light-weight spliced convolution network to recognize the meter number, which simplifies standard 3 x 3 convolutions by splicing a certain number of 1 x 1 and 3 x 3 size kernel. We then prove the superiority of our network by theoretical analysis. Second, we have implemented the prototype which can handle huge real-time data base on the distributed cloud platform. Base on this system, our system can provide industrial service. Finally, we conduct real-world dataset to verify the performance of the system. The experimental results demonstrate that our proposed light-weight spliced convolution network can reduce nearly 10x computational consuming, 7x model space, and save 3x running time comparing with standard convolution network.
Low dimensional mid-term chaotic time series prediction by delay parameterized method

How to predict the future behavior of complex systems with insufficient information, i.e., low dimensional mid-term chaotic time series prediction in mathematical terms, is not only a significant theoretical problem, but a more intricate practical problem. To address this issue, a Delay Parameterized Method (DPM) for low dimensional mid-term chaotic time series forecasting is presented. The correlation function, which immerses the low dimensional information into reconstructed space, is introduced to bridge time series and hidden order of system in DPM. Traversal algorithm and intelligent algorithm including particle swarm optimization or genetic algorithm, are used to obtain the optimal parameters for prediction. In addition, the applications of the proposed method on Lorenz chaotic time series, stress-strain signals and stock K-line maps show that it produces high quality predictions.
On the Weaknesses of Reinforcement Learning for Neural Machine Translation

Reinforcement learning (RL) is frequently used to increase performance in text generation tasks, including machine translation (MT), notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN). However, little is known about what and how these methods learn in the context of MT. We prove that one of the most common RL methods for MT does not optimize the expected reward, as well as show that other methods take an infeasibly long time to converge. In fact, our results suggest that RL practices in MT are likely to improve performance only where the pre-trained parameters are already close to yielding the correct translation. Our findings further suggest that observed gains may be due to effects unrelated to the training signal, concretely, changes in the shape of the distribution curve.
Towards Best Experiment Design for Evaluating Dialogue System Output

To overcome the limitations of automated metrics (e.g.BLEU, METEOR) for evaluating dialogue systems, researchers typically use human judgments to provide convergent evidence.While it has been demonstrated that human judgments can suffer from the inconsistency of ratings, extant research has also found that the design of the evaluation task affects the consistency and quality of human judgments.We conduct a between-subjects study to understand the impact of four experiment conditions on human ratings of dialogue system output.In addition to discrete and continuous scale ratings, we also experiment with a novel application of Best-Worst scaling to dialogue evaluation.Through our systematic study with 40 crowdsourced workers in each task, we find that using continuous scales achieves more consistent ratings than Likert scale or ranking-based experiment design.Additionally, we find that factors such as time taken to complete the task and no prior experience of participating in similar studies of rating dialogue system output positively impact consistency and agreement amongst raters.
DIANet: Dense-and-Implicit Attention Network

Attention networks have successfully boosted the performance in various vision problems. Previous works lay emphasis on designing a new attention module and individually plug them into the networks. Our paper proposes a novel-and-simple framework that shares an attention module throughout different network layers to encourage the integration of layer-wise information and this parameter-sharing module is referred to as Dense-and-Implicit-Attention (DIA) unit. Many choices of modules can be used in the DIA unit. Since Long Short Term Memory (LSTM) has a capacity of capturing long-distance dependency, we focus on the case when the DIA unit is the modified LSTM (called DIA-LSTM). Experiments on benchmark datasets show that the DIA-LSTM unit is capable of emphasizing layer-wise feature interrelation and leads to significant improvement of image classification accuracy. We further empirically show that the DIA-LSTM has a strong regularization ability on stabilizing the training of deep networks by the experiments with the removal of skip connections (He et al. 2016a) or Batch Normalization (Ioffe and Szegedy 2015) in the whole residual network.
Mini-batch learning of exponential family finite mixture models

Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectation–maximization (EM) algorithm framework, we demonstrate how mini-batch (MB) algorithms may be constructed, and propose a scheme for the stochastic stabilization of the constructed mini-batch algorithms. Theoretical results regarding the convergence of the mini-batch EM algorithms are presented. We then demonstrate how the mini-batch framework may be applied to conduct maximum likelihood (ML) estimation of mixtures of exponential family distributions, with emphasis on ML estimation for mixtures of normal distributions. Via a simulation study, we demonstrate that the mini-batch algorithm for mixtures of normal distributions can outperform the standard EM algorithm. Further evidence of the performance of the mini-batch framework is provided via an application to the famous MNIST data set.
Combining Interactive Spatial Augmented Reality with Head-Mounted Display for End-User Collaborative Robot Programming

This paper proposes an intuitive approach for collaborative robot end-user programming using a combination of interactive spatial augmented reality (ISAR) and headmounted display (HMD). It aims to reduce user's workload and to let the user program the robot faster than in classical approaches (e.g. kinesthetic teaching). The proposed approach, where user is using a mixed-reality HMD - Microsoft HoloLens - and touch-enabled table with SAR projected interface as input devices, is compared to a baseline approach, where robot's arms and a touch-enabled table are used as input devices. Main advantages of the proposed approach are the possibility to program the collaborative workspace without the presence of the robot, its speed in comparison to the kinesthetic teaching and an ability to quickly visualize learned program instructions, in form of virtual objects, to enhance the users' orientation within those programs. The approach was evaluated on a set of 20 users using the within-subject experiment design. Evaluation consisted of two pick and place tasks, where users had to start from the scratch as well as to update the existing program. Based on the experiment results, the proposed approach is better in qualitative measures by 33.84% and by 28.46% in quantitative measures over the baseline approach for both tasks.
Joint embedding of structure and features via graph convolutional networks

Abstract The creation of social ties is largely determined by the entangled effects of people’s similarities in terms of individual characters and friends. However, feature and structural characters of people usually appear to be correlated, making it difficult to determine which has greater responsibility in the formation of the emergent network structure. We propose AN2VEC , a node embedding method which ultimately aims at disentangling the information shared by the structure of a network and the features of its nodes. Building on the recent developments of Graph Convolutional Networks (GCN), we develop a multitask GCN Variational Autoencoder where different dimensions of the generated embeddings can be dedicated to encoding feature information, network structure, and shared feature-network information. We explore the interaction between these disentangled characters by comparing the embedding reconstruction performance to a baseline case where no shared information is extracted. We use synthetic datasets with different levels of interdependency between feature and network characters and show (i) that shallow embeddings relying on shared information perform better than the corresponding reference with unshared information, (ii) that this performance gap increases with the correlation between network and feature structure, and (iii) that our embedding is able to capture joint information of structure and features. Our method can be relevant for the analysis and prediction of any featured network structure ranging from online social systems to network medicine.
Impact of consensus on appendable-block blockchain for IoT

The Internet of Things (IoT) is transforming our physical world into a complex and dynamic system of connected devices on an unprecedented scale. Connecting everyday physical objects is creating new business models, improving processes and reducing costs and risks. Recently, blockchain technology has received a lot of attention from the community as a possible solution to overcome security issues in IoT. However, traditional blockchains (such as the ones used in Bitcoin and Ethereum) are not well suited to the resource-constrained nature of IoT devices and also with the large volume of information that is expected to be generated from typical IoT deployments. To overcome these issues, several researchers have presented lightweight instances of blockchains tailored for IoT. For example, proposing novel data structures based on blocks with decoupled and appendable data. However, these researchers did not discuss how the consensus algorithm would impact their solutions, i.e., the decision of which consensus algorithm would be better suited was left as an open issue. In this paper, we improved an appendable-block blockchain framework to support different consensus algorithms through a modular design. We evaluated the performance of this improved version in different emulated scenarios and studied the impact of varying the number of devices and transactions and employing different consensus algorithms. Even adopting different consensus algorithms, results indicate that the latency to append a new block is less than 161ms (in the more demanding scenario) and the delay for processing a new transaction is less than 7ms, suggesting that our improved version of the appendable-block blockchain is efficient and scalable, and thus well suited for IoT scenarios.
Holistic design for deep learning-based discovery of tabular structures in datasheet images

Extracting data from tabular structures contained within product datasheets is crucial in many contexts, particularly in the management and optimization of supply chains that serve various industries. In order to minimize human intervention, table detection and table structure detection form the essential functionality. However, a self-contained holistic solution to extract the tables as well as their columns and rows in not readily available. To address this challenge, This study presents a new formal procedure that consists of the following sequence: table detection, structure segmentation and holistic tabular structure detection on documents. The proposed table detection model outperforms the state-of-the-art solutions by achieving a recall value of 1.0 and a precision of more than 0.99 on public competition datasets. Furthermore, this work introduces a judging mechanism and an agreement-based post-processing procedure to incorporate hand-crafted rules into the deep learning models. Though the individual components achieve a new state-of-the-art F1-Score, when integrated the best achieved F-measure for the holistic system is 0.89.
Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Summarization is a process which efficiently retrieves the relevant information from documents.The objective of the proposed, unsupervised approach is to summarize bug reports (software artefacts) with complete content and diversified information.The proposed approach utilizes Rapid Automatic Keyword Extraction and term frequency-inverse document frequency method to extract meaningful keywords and key-phrases with a relevant score.For sentence extraction, fuzzy C-means clustering is used to extracts sentences having high degree of membership from each cluster above a set threshold value.A ruleengine is used for sentence selection.The rules are generated with the domain knowledge and based on the extracted information by the keywords and sentences selected by the clustering method.Cohesive and coherent summary is generated by the proposed method on apache bug reports.For redundancy removal and to re-rank generated summary, hierarchical clustering is presented to enrich the extracted summary.The proposed approach is evaluated on newly constructed Apache project Bug Report Corpus (APBRC) and existing Bug Report Corpus (BRC).The results are compared on the basis of performance metrics such as precision, recall, pyramid precision and F-score.The experimental results depict that our proposed approach attains significant improvement over other baseline approaches such as BRC and LRCA.It also attains significant improvement over existing state-of-art unsupervised approaches such as Hurried, centroid and others.It extracts significant keyword phrases and sentences from each cluster to achieve full coverage and coherent summary.The results evaluated on APBRC corpus attains an average value of 78.22%, 82.18%, 80.10% and 81.66% for precision, recall, f-score and pyramid precision respectively.
PolyFit

Raster clip-art images, which consist of distinctly colored regions separated by sharp boundaries typically allow for a clear mental vector interpretation. Converting these images into vector format can facilitate compact lossless storage and enable numerous processing operations. Despite recent progress, existing vectorization methods that target such data frequently produce vectorizations that fail to meet viewer expectations. We present PolyFit , a new clip-art vectorization method that produces vectorizations well aligned with human preferences. Since segmentation of such inputs into regions had been addressed successfully, we specifically focus on fitting piecewise smooth vector curves to the raster input region boundaries, a task prior methods are particularly prone to fail on. While perceptual studies suggest the criteria humans are likely to use during mental boundary vectorization, they provide no guidance as to the exact interaction between them; learning these interactions directly is problematic due to the large size of the solution space. To obtain the desired solution, we first approximate the raster region boundaries with coarse intermediate polygons leveraging a combination of perceptual cues with observations from studies of human preferences. We then use these intermediate polygons as auxiliary inputs for computing piecewise smooth vectorizations of raster inputs. We define a finite set of potential polygon to curve primitive maps, and learn the mapping from the polygons to their best fitting primitive configurations from human annotations, arriving at a compact set of local raster and polygon properties whose combinations reliably predict human-expected primitive choices. We use these primitives to obtain a final globally consistent spline vectorization. Extensive comparative user studies show that our method outperforms state-of-the-art approaches on a wide range of data, where our results are preferred three times as often as those of the closest competitor across multiple types of inputs with various resolutions.
Hybrid Machine Learning Model for Rainfall Forecasting

The state of the weather became a point of attraction for researchers in recent days. It control in many fields as agriculture, the country determines the types of crops depend on state of the atmosphere. It is therefore important to know the weather in the coming days to take precautions. Forecasting the weather in future especially rainfall won the attention of many researchers, to prevent flooding and other risks arising from rainfall. This Paper presents a vigorous hybrid technique was applied to forecast rainfall by combining Particle Swarm Optimization (PSO) and Multi-Layer Perceptron (MLP) which is popular kind used in Feed Forward Neural Network (FFNN). The purpose of using PSO with MLP is not just to forecast the rainfall but, to improve the performance of the network; this was proved by comparison with various Back Propagation (BP) an algorithm such as Levenberg-Marquardt (LM) through results of Root Mean Square Error (RMSE). RMSE for MLP based PSO is 0.14 while RMSE for MLP based LM is 0.18.
LiDAR-Based Multi-Task Road Perception Network for Autonomous Vehicles

For autonomous vehicles, it is an important requirement to obtain integrate static road information in real-time in dynamic driving environment. A comprehensive perception of the surrounding road should cover the accurate detection of the entire road area despite occlusion, the 3D geometry and the types of road topology in order to facilitate the practical applications in autonomous driving. To this end, we propose a lightweight and efficient LiDAR-based multi-task road perception network (LMRoadNet) to conduct occlusion-free road segmentation, road ground height estimation, and road topology recognition simultaneously. To optimize the proposed network, a corresponding multi-task dataset, named MultiRoad, is built semi-automatically based on the public SemanticKITTI dataset. Specifically, our network architecture uses road segmentation as the main task, and the remaining two tasks are directly decoded on a concentrated 1/4 scale feature map derived from the main task's feature maps of different scales and phases, which significantly reduces the complexity of the overall network while achieves high performance. In addition, a loss function with learnable weight of each task is adopted to train the neural network, which effectively balances the loss of each task and improves performance of the individual tasks. Extensive experiments on the test set show that the proposed network achieves great performance of the three tasks in real-time, outperforms the conventional multi-task architecture and is comparable to the state-of-the-art efficient methods. Finally, a fusion strategy is proposed to combine results on different directions to expand the field of view for practical applications.
From virtualization security issues to cloud protection opportunities: An in-depth analysis of system virtualization models

Virtualization methods and techniques play an important role in the development of cloud infrastructures and their services. They enable the decoupling of virtualized resources from the underlying hardware, and facilitate their sharing amongst multiple users. They contribute to the building of elaborated cloud services that are based on the instantiation and composition of these resources. Different models may support such a virtualization, including virtualization based on type-I and type-II hypervisors, OS-level virtualization, and unikernel virtualization. These virtualization models pose a large variety of security issues, but also offer new opportunities for the protection of cloud services. In this article, we describe and compare these virtualization models, in order to establish a reference architecture of cloud infrastructure. We then analyze the security issues related to these models from the reference architecture, by considering related vulnerabilities and attacks. Finally, we point out different recommendations with respect to the exploitation of these models for supporting cloud protection.