Reviewerly/exHarmony huggingface dataset:
Expressing attention requirement of a floor cleaning robot through interactive lights

Cleaning robots are used to cater to the demands in both domestic and industrial premises. Much of research work is being conducted to improve functionalities and efficiency of cleaning robots. Users of these robots prefer human-friendly interactive features in these functional robots. Therefore, the focus of the research work in the area of cleaning robots has been drifted from improving core cleaning related functionalities to implementing human-friendly interactive features in recent years. The existing floor cleaning robots do not possess the ability to express their state to users. Lack of expressiveness is one of the major factors that degrade human-robot interaction. Therefore, this paper proposes a method to express a floor cleaning robot's attention requirement to its users. The attention requirement of the robot in a situation is determined by a fuzzy inference system that analyzes bumping level and experience of attention level requirement. The requirement of attention is expressed to users by varying the color of lights attached to the robot. Based on the color variation, users can identify the robot's situation and users could take corrective measures. Experimental results confirm that the robot can express its attention requirement to a user based on the state. A user study has also been conducted to evaluate the performance improvement of the proposed method. According to the outcomes, the proposed method can remarkably improve human-friendliness of a floor cleaning robot.
End-to-End Learning for Graph Decomposition

Deep neural networks provide powerful tools for pattern recognition, while classical graph algorithms are widely used to solve combinatorial problems.In computer vision, many tasks combine elements of both pattern recognition and graph reasoning.In this paper, we study how to connect deep networks with graph decomposition into an end-to-end trainable framework.More specifically, the minimum cost multicut problem is first converted to an unconstrained binary cubic formulation where cycle consistency constraints are incorporated into the objective function.The new optimization problem can be viewed as a Conditional Random Field (CRF) in which the random variables are associated with the binary edge labels.Cycle constraints are introduced into the CRF as high-order potentials.A standard Convolutional Neural Network (CNN) provides the front-end features for the fully differentiable CRF.The parameters of both parts are optimized in an end-to-end manner.The efficacy of the proposed learning algorithm is demonstrated via experiments on clustering MNIST images and on the challenging task of real-world multi-people pose estimation.
Multimodal Spatiotemporal Networks for Sign Language Recognition

Different from other human behaviors, sign language has the characteristics of limited local motion of upper limb and meticulous hand action.Some sign language gestures are ambiguous in RGB video due to the influence of lighting and background color, which affects the recognition accuracy.We propose a multimodal deep learning architecture for sign language recognition which effectively combines RGB-D input and two-stream spatiotemporal networks.Depth videos, as an effective compensation of RGB input, can supply additional distance information about the signer's hands.A novel sampling method called ARSS (Aligned Random Sampling in Segments) is put forward to select and align optimal RGB-D video frames, which improves the capacity utilization of multimodal data and reduces the redundancy.We get the hand ROI by joints information of RGB data for local focus in spatial stream.D-shift Net is proposed as depth motion feature extraction in temporal stream, which fully utilizes three dimensional motion information of the sign language.Both streams are fused by convolutional fusion layer to get complementary features.Our approach explored the multimodal information and enhanced the recognition precision.It obtains the state-the-of-art performance on the datasets of CSL (96.7%) and IsoGD (63.78%).
Practical Attribute-Based Multi-Keyword Ranked Search Scheme in Cloud Computing

Attribute-based keyword search (ABKS) has a broad developing prospect in providing search service for users and realizing fine-grained access control over ciphertext in the background of cloud computing. However, two open problems prevent further development and application of ABKS. First, most of ABKS schemes suffer from inside keyword guessing attack (KGA) inherently, which is a great threat to the security of the scheme. Second, the existing ABKS schemes focus on single or conjunctive keyword search, these inflexible retrieval modes may lead to efficiency loss caused by inaccurate positioning of user’s interest and greatly reduce user search experience. In this article, we introduce a semi-trusted server and build a dual server model. Based on the dual server model and our proposed techniques, we are the first to put forward an attribute-based multi-keyword ranked search scheme against inside keyword guessing attack (ABKRS-KGA) to solve the mentioned two problems simultaneously. In our scheme, the queries of users contain weighted keywords and the returned files can be ranked according to user’s query interest. We provide strict security definitions for two types of adversaries and we are the first to prove that the construction is adaptively secure against both chosen-keyword attack (CKA) and KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the simulation results show that the efficiency of the proposed scheme is acceptable.
Unsupervised Face Domain Transfer for Low-Resolution Face Recognition

Low-resolution face recognition suffers from domain shift due to the different resolution between a high-resolution gallery and a low-resolution probe set. Conventional methods use the pairwise correlation between high-resolution and low-resolution for the same subject, which requires label information for both gallery and probe sets. However, explicitly labeled low-resolution probe images are seldom available, and labeling them is labor-intensive. In this paper, we propose a novel unsupervised face domain transfer for robust low-resolution face recognition. By leveraging the attention mechanism, the proposed generative face augmentation reduces the domain shift at image-level, while spatial resolution adaptation generates domain-invariant and discriminant feature distributions. On public datasets, we demonstrate the complementarity between generative face augmentation at image-level and spatial resolution adaptation at feature-level. The proposed method outperforms the state-of-the-art supervised methods even though we do not use any label information of low-resolution probe set.
An Intelligent Video Tag Recommendation Method for Improving Video Popularity in Mobile Computing Environment

Big data generated from social media and smart mobile devices has been regarded as a key to obtain insights into human behavior and been extensively utilized for launching marketing activities. A successful marketing activity requires attracting high social popularity to their contents, since higher popularity usually indicates stronger influence, more fame and higher revenue. In this paper, we focus on the question of how to improve popularity of videos sharing on websites like YouTube in mobile computing environment. Obviously, composing high quality titles and tags is beneficial for viewers to discover videos of their interests and increase their tendency to watch more videos. However, it is not an easy task for uploaders, which is especially true since the screen is tight for most mobile devices. To this end, this paper proposes a novel hybrid method based on multi-modal content analysis that recommends keywords for video uploaders to compose titles and tags of their videos and then to gain higher popularity. The method generates candidate keywords by integrating techniques of textual semantic analysis of original tags and recognition of video content. On one hand, taking the original keywords of a video as input, the method obtains most relevant words from WordNet and related video titles gathered from the three top video sharing sites (YouTube, Yahoo Video, Bing Video). On the other hand, through recognizing video content with deep learning technology, the method extracts the entity name of video content as candidate keywords. Finally, a TF-SIM algorithm is proposed to rank the candidate keywords and the most relevant keywords are recommended to uploaders for optimizing the titles and tags of their videos. The experimental results show that the proposed method can effectively improve the social popularity of the videos as well as extend the length of video viewing time per playback.
SPLBoost: An Improved Robust Boosting Algorithm Based on Self-Paced Learning

It is known that boosting can be interpreted as an optimization technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which proves to be very sensitive to random noise/outliers. Therefore, several boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robustness of AdaBoost by replacing the exponential loss with some designed robust loss functions. In this article, we present a new way to robustify AdaBoost, that is, incorporating the robust learning idea of self-paced learning (SPL) into the boosting framework. Specifically, we design a new robust boosting algorithm based on the SPL regime, that is, SPLBoost, which can be easily implemented by slightly modifying off-the-shelf boosting packages. Extensive experiments and a theoretical characterization are also carried out to illustrate the merits of the proposed SPLBoost.
Improved Multi-Stage Training of Online Attention-Based Encoder-Decoder Models

In this paper, we propose a refined multi-stage multi-task training strategy to improve the performance of onli <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ne</sup> attention-based encoder-decoder (AED) models. A three-stage training based on three levels of architectural granularity namely, character encoder, byte pair encoding (BPE) based encoder, and attention decoder, is proposed. Also, multi-task learning based on two-levels of linguistic granularity namely, character and BPE, is used. We explore different pre-training strategies for the encoders including transfer learning from a bidirectional encoder. Our encoder-decoder models with online attention show ~35% and ~10% relative improvement over their baselines for smaller and bigger models, respectively. Our models achieve a word error rate (WER) of 5.04% and 4.48% on the Librispeech test-clean data for the smaller and bigger models respectively after fusion with long short-term memory (LSTM) based external language model (LM).
Three-step action search networks with deep Q-learning for real-time object tracking

Sliding window and candidate sampling are two widely used search strategies for visual object tracking, but they are far behind real-time. By treating the tracking problem as a three-step decision-making process, a novel tracking network, which explores only three small subsets of candidate regions, is developed to achieve faster (real-time) localization of the target object along the frames in a video. A convolutional neural network agent is formulated to interact with a video over time, and two action-value functions are exploited to learn a favorable policy off-line to determine the best action for visual object tracking. Our model is trained in a collaborative learning way by using action classification and cumulative reward approximation in reinforcement learning. We have evaluated our proposed tracker against a number of state-of-the-art ones over three popular tracking benchmarks including OTB-2013, OTB-2015, and VOT2017. The experimental results have demonstrated that our proposed method can achieve very competitive performance on real-time object tracking.
Information Security Policy Compliance: Systematic Literature Review

The growth of research in information technology security has enlarged in recent years. Investigations in Information security besides discussing technical problems but also consider policies, behavior and user compliance issues. Therefore, a systematic review is needed to inform the development of research in compliance with information security policies. This literature review aims to find the challenges and explore the current state-of-the art of information security policy compliance. We identified 305 research papers published on topics of information security policy compliance between 2014 and 2019 and extract 53 documents from discussing with inclusion and exclusion criteria. This literature review found there is a lack of study about an evaluation of information security policy compliance using specific metric and need to enhance the model of information security policy compliance with organizational theories.
Foundations of Query Answering on Inconsistent Databases

Notwithstanding the traditional view that database instances must respect all integrity constraints imposed on them, it is relevant to develop theories about how to handle database instances that violate some integrity constraints, and more particularly, how to cope with query answering in the presence of inconsistency. Such a theory developed over the past twenty years is currently known as consistent query answering (CQA). The aim of this article is to summarize and discuss some core concepts and theoretical developments in CQA.
FastChain: Scaling Blockchain System with Informed Neighbor Selection

The main advantage of the Bitcoin-style blockchain systems is immutability of data and decentralized authority. Despite its success, Bitcoin suffers from scalability issues. The potential of the blockchain technology cannot be fully realized unless it is made more scalable. We extend previous analysis on a fundamental trade-off between the throughput and forking rate of the blockchain system. We further propose FastChain to increase the throughput of the blockchain system by reducing the block propagation time. FastChain adopts bandwidth-informed neighbor selection. Miners disconnect from bandwidth-limited neighbors and favor the nodes with higher bandwidth. We build a Blockchain Dynamics Simulator (BDSim) to evaluate the performance of FastChain. FastChain improves the throughput by 20%~40% in the normal operations.
Bengali Text generation Using Bi-directional RNN

Current world is growing so fast and communication between nation and different type of people with different language became part of our life. Even from buying product to our social life everything is dependent on communication. Therefore language is the most important part of human life. Though still now there is a language barrier for communication between people. But very soon language will be universal and everyone will be able to communicate in any language worldwide using the NLP technology. For that it is necessary to understand each language individually. This research proposes a new type of text generation of Bangla language using the bi-directional RNN. This technique is used to predict the next possible word in a Bangla text.
SIF: A Framework for Solidity Contract Instrumentation and Analysis

Solidity is an object-oriented and high-level language for writing smart contracts that are used to execute, verify and enforce credible transactions on permissionless blockchains. In the last few years, analysis of smart contracts has raised considerable interest and numerous techniques have been proposed to check the presence of vulnerabilities in them. Current techniques lack traceability in source code and have widely differing work flows. There is no single unifying framework for analysis, instrumentation, optimisation and code generation of Solidity contracts at the source code level. In this paper, we present SIF, a comprehensive framework for Solidity contract analysis, query, instrumentation, and code generation. SIF provides support for Solidity contract developers and testers to build source level techniques for analysis, understanding, diagnostics, optimisations and code generation. We show feasibility and applicability of the framework by building practical tools on top of it and running them on 1838 real smart contracts deployed on the Ethereum network.
Multi-exposure high dynamic range imaging with informative content enhanced network

For High Dynamic Range (HDR) imaging systems, a new multi-exposure HDR imaging method based on Informative Content Enhanced Network (ICEN) is proposed to overcome the disadvantage that the existing deep learning based methods fails to fully exploit the differently exposed Low Dynamic Range (LDR) image contents to recover the details of the under/over-exposed regions. Specifically, the key contents in differently exposed LDR images that contribute to HDR imaging are firstly defined as Informative Contents (ICs). Then, the ICs are enhanced by the proposed Residual Channel Attention Module (RCAM), and then fused to generate HDR image. Furthermore, a generation scheme is designed for constructing HDR image labels and producing a multi-exposure HDR imaging dataset for training the proposed ICEN. The experimental results show that the proposed method is superior to the existing HDR imaging methods in quantitative and qualitative analysis, and can quickly generate high-quality HDR images.
Sentiment Analysis: Usage of Text and Emoji for Expressing Sentiments

Social media generates massive volume of unstructured data by means of blogs, social networking sites, purchasing sites, etc. This huge amount of data plays a very crucial role in determining the opinions and sentiments of people toward a product, services, popularity of artists, celebrities, etc. The accurate and meaningful analysis of this online media further helps in developing product quality, making strategies, and decision for a company or a personality. The online media consists of both text and emoji. Emoji is pictorial representation of facial expression, objects, weather, animals, etc. In this paper, the web document of various worldwide famous male and female personalities has been examined to determine the amount of usage of emoji and presence of text and emoji both in expressing sentiments. This paper also presents the comparative analysis of the total usage of emoji among the most followed male and female personalities in Twitter. The major application of our paper is that it exhibits that the sentiment analysis is more accurate and complete when done for both text and emoji.
ML-EC2

A multi-label variant of email classification named ML-EC2 (multi-label email classification using clustering) has been proposed in this work. ML-EC2 is a hybrid algorithm based on text clustering, text classification, frequent-term calculation (based on latent dirichlet allocation), and taxonomic term-mapping technique. It is an example of classification using text clustering technique. It studies the problem where each email cluster represents a single class label while it is associated with set of cluster labels. It is multi-label text-clustering-based classification algorithm in which an email cluster can be mapped to more than one email category when cluster label matches with more than one category term. The algorithm will be helpful when there is a vague idea of topic. The performance parameters Entropy and Davies-Bouldin Index are used to evaluate the designed algorithm.
Improving social and behavior recommendations via network embedding

With the rapid development of information technology, information is generated at an unprecedented rate. Users are in great need of recommender systems to provide the potential friends or interested items for them. Social (i.e. friend) recommendation and behavior (i.e. item) recommendation are two types of popular services in real-world applications. Although researchers have proposed various models for each task, a unified model to address both tasks elegantly and effectively is still in demand. In this paper, we propose a model called SBRNE which integrates social and behavior recommendations into a unified framework through modeling social and behavior information simultaneously. Specifically, SBRNE models social and behavior information simultaneously via employing users' latent interests as a bridge, and derives improved performance on both social and behavior recommendation tasks. In addition, by introducing an efficient network embedding procedure, users' latent representations are advanced, and effectiveness and efficiency of recommendation tasks are improved accordingly. Results on both real-world and synthetic datasets demonstrate that: 1). SBRNE outperforms selected baselines on social and behavior recommendation tasks; 2). SBRNE performs stable on recommendation tasks for cold-start users; 3). The network embedding procedure can improve the effectiveness of SBRNE; 4). The hyper-parameter learning procedure can improve both the effectiveness and efficiency of SBRNE.
Structure and Texture-Aware Image Decomposition via Training a Neural Network

Structure-texture image decomposition is a funda-mental but challenging topic in computational graphics and image processing. In this paper, we introduce a structure-aware and a texture-aware measures to facilitate the structure-texture de-composition (STD) of images. Edge strengths and spatial scales that have been widely-used in previous STD researches cannot describe the structures and textures of images well. The proposed two measures differentiate image textures from image structures based on their distinctive characteristics. Specifically, the first one aims to measure the anisotropy of local gradients, and the second one is designed to measure the repeatability degree of signal pat-terns in a neighboring region. Since these two measures describe different properties of image structures and textures, they are complementary to each other. The STD is achieved by optimizing an objective function based on the two new measures. As using traditional optimization methods to solve the optimization prob-lem will require designing different optimizers for different func-tional spaces, we employ an architecture of deep neural network to optimize the STD cost function in a unified manner. The ex-perimental results demonstrate that, as compared with some state-of-the-art methods, our method can better separate image structure and texture and result in shaper edges in the structural component. Furthermore, to demonstrate the usefulness of the proposed STD method, we have successfully applied it to several applications including detail enhancement, edge detection, and visual quality assessment of super-resolved images.