Reviewerly/exHarmony huggingface dataset:
Joint Deep Model with Multi-Level Attention and Hybrid-Prediction for Recommendation

The Recommender System (RS) has obtained a pivotal role in e-commerce. To improve the performance of RS, review text information has been extensively utilized. However, it is still a challenge for RS to extract the most informative feature from a tremendous amount of reviews. Another significant issue is the modeling of user–item interaction, which is rarely considered to capture high- and low-order interactions simultaneously. In this paper, we design a multi-level attention mechanism to learn the usefulness of reviews and the significance of words by Deep Neural Networks (DNN). In addition, we develop a hybrid prediction structure that integrates Factorization Machine (FM) and DNN to model low-order user–item interactions as in FM and capture the high-order interactions as in DNN. Based on these two designs, we build a Multi-level Attentional and Hybrid-prediction-based Recommender (MAHR) model for recommendation. Extensive experiments on Amazon and Yelp datasets showed that our approach provides more accurate recommendations than the state-of-the-art recommendation approaches. Furthermore, the verification experiments and explainability study, including the visualization of attention modules and the review-usefulness prediction test, also validated the reasonability of our multi-level attention mechanism and hybrid prediction.
Matrix completion as a post-processing technique for probabilistic roadmaps

Inspired by the recent literature on matrix completion, this paper describes a novel post-processing algorithm for probabilistic roadmaps (PRMs). We argue that the adjacency matrix associated with real roadmaps can be decomposed into the sum of low-rank and sparse matrices. Given a PRM with n vertices and only [Formula: see text] collision-checked candidate edges, our algorithm numerically computes a relaxation of this decomposition, which estimates the status of all [Formula: see text] possible edges in the full roadmap with high accuracy, without performing any additional collision checks. Typical results from our experiments on problems from the Open Motion Planning Library indicate that after checking [Formula: see text] of the possible edges, the algorithm estimates the full visibility graph with [Formula: see text] accuracy. The practical utility of the algorithm is that the average path length across the resulting denser edge set is significantly shorter (at the cost of somewhat increased spatial complexity and query times). An ancillary benefit is that the resulting low-rank plus sparse decomposition readily reveals information that would be otherwise difficult to compute, such as the number of convex cells in free configuration space and the number of vertices in each. We believe that this novel connection between motion planning and matrix completion provides a new perspective on sampling-based planning and may guide future algorithm development.
A fuzzy logic‐based method for solving the scheduling problem in the cloud environments using a non‐dominated sorted algorithm

Summary Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta‐heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non‐dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta‐heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution. Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms.
A Bayesian Approach to Mixed Gamma Regression Models

Gamma regression models are a suitable choice to model continuous variables that take positive real values. This paper presents a gamma regression model with mixed effects from a Bayesian approach. We use the parametrisation of the gamma distribution in terms of the mean and the shape parameter, both of which are modelled through regression structures that may involve fixed and random effects. A computational implementation via Gibbs sampling is provided and illustrative examples (simulated and real data) are presented.
Intelligent Electromagnetic Compatibility Management of Cell Phones by Using Knowledge Graphs

In this paper, a new electromagnetic compatibility (EMC) management method for cell phones is presented. The interference and sensitive characteristics of components in printed circuit board (PCB) of cell phones are analyzed, and the knowledge graph for these interference and sensitive units is established for the first time. In the knowledge graph, the interference and sensitive units are stored as entities and new triple functions are defined to associate these entities. The EMC management report is intelligently generated based on the knowledge graph from interactive sessions, in which long short-term memory and multilayer perceptron methods are used to extract the entities and key parameters of the triple function. The accuracy of the entity extraction model is 75.6%, and F1 measure is 70.9%. Two experiments, which validated the feasibility of the established model, were executed using the proposed method. The method is useful and practical for intelligent EMC management in the industry.
An Effective Diffeomorphic Model and Its Fast Multigrid Algorithm for Registration of Lung CT Images

Abstract Image registration is the process of aligning sets of similar, but different, intensity image functions to track changes between the images. In medical image problems involving lung images, variational registration models are a very powerful tool which can aid in effective treatment of various lung conditions and diseases. However, a common drawback of many variational models, such as the diffusion model and even optic flow models, is the lack of control of folding in the deformations leading to physically inaccurate transformations. For this reason, such models are generally not suitable for real life lung imaging problems where folding cannot occur. There are two approaches offering reliable solutions (though not necessarily accurate). The first approach is a parametric model such as the affine registration model, still widely used in many applications, but is unable to track local changes or yield accurate results. The second approach is to impose an extra constraint on the transformation of registration at the cost of increased non-linearity. An alternative to the second approach, achieving diffeomorphic transforms without adding any constraints, is an inverse consistent model such as by Christensen and Johnson (2001) from computing explicitly both the forward and inverse transforms. However, one must deal with the strong non-linearity in the formulation. In this paper we first propose a simplified inverse consistent model to avoid the inclusion of strong non-linearities and then a fast non-linear multigrid (NMG) technique to overcome the extra computational work required by the inverse consistent model. Experiments, performed on real medical CT images, show that our proposed inverse consistent model is robust to both parameter choice and non-folding in the transformations when compared with diffusion type models.
Real-time long-term correlation tracking by single-shot multibox detection

Long-term robust tracking remains a challenging problem in visual object tracking. Based on the popular tracking-by-detection framework, we propose an approach named real-time long-term correlation tracking by single-shot multibox detection (RLCT-SSD), in which tracking and detection work in parallel and cooperate together. Our algorithm consists of two parts: a tracking module is expected to track in real time and accurately, whereas a detection module is responsible for verifying the tracking results at intervals, and redetecting if necessary. The detector updates the tracking module when the tracking result is unreliable. We introduce a motion model on the basis of fast discriminative scale space tracker to design our tracking module. The detection module is based on the single-shot multibox detector algorithm. To further reduce the computational cost, we use the ShuffleNet as our base network of detection. The experimental results on OTB2013 and OTB2015 demonstrate that our RLCT-SSD performs favorably against most state-of-the-art trackers and achieves long-term accurate tracking running in real time.
A Convolutional Neural Network Based Seam Carving Detection Scheme for Uncompressed Digital Images

Revealing the processing history that a given digital image has gone through is an important topic in digital image forensics. Detection of seam carving, a content-aware image scaling algorithm commonly implemented in commercial image-editing software, has been studied by forensic experts in recent years. In this paper, a convolutional neural network (CNN) architecture is proposed for seam carving detection. Unlike the existing forensic works in detecting seam carving, where the feature selection and the pattern classification are two separated procedures, the proposed CNN-based deep learning architecture learns and then uses more effective features via joint optimization of feature extraction and pattern classification. Experimental results conducted on a large dataset have demonstrated that, compared with the current state-of-the-art, the proposed CNN based deep learning scheme can largely boost the classification rates as the seam carving rate is rather low.
Adding Object Detection Skills to Visual Dialogue Agents

Our goal is to equip a dialogue agent that asks questions about a visual scene with object detection skills. We take the first steps in this direction within the GuessWhat?! game. We use Mask R-CNN object features as a replacement for ground-truth annotations in the Guesser module, achieving an accuracy of 57.92%. This proves that our system is a viable alternative to the original Guesser, which achieves an accuracy of 62.77% using ground-truth annotations, and thus should be considered an upper bound for our automated system. Crucially, we show that our system exploits the Mask R-CNN object features, in contrast to the original Guesser augmented with global, VGG features. Furthermore, by automating the object detection in GuessWhat?!, we open up a spectrum of opportunities, such as playing the game with new, non-annotated images and using the more granular visual features to condition the other modules of the game architecture.
Integrated Learning Approaches Based on Cloud Computing for Personalizing e-Learning Environment

Improving the performance of e-learning services to provide a scalable and effective e-learning system is a big challenge for the educational organizations. The e-learning system faces many challenges in pedagogical services (e.g. learning design and learning content problems), and technical services (e.g. resource provisioning and financial cost). This article presents an e-learning environment based on cloud computing (PCLE) in attempt to enhance the e-learning services' by customizing the contents of the course's material depending on the students' knowledge, experiences, and requirements. Also, the system focuses on supporting and achieving reusability, interoperability, adaptation and personalization in order to overcome the passive role of the student and transfer it into an interactive and useful participation. On the other hand, PCLE is built over the cloud computing environment trying to overcome the traditional web-hosting challenges as scalability, availability, and cost.
TechnoFeminisms: A Conversation About Pasts, Presents, and Futures

In September 2017, a group of technofeminist researchers and scholars met for an online discussion about the pasts, presents, and futures of technofeminist work. Participants included Megan Adams, Kris Blair, Lanette Cadle, Dànielle Nicole DeVoss, Radhika Gajjala, Angela Haas, Gail Hawisher, Donna LeCourt, Lisa Nakamura, Jackie Rhodes, Cindy Selfe, Barbi Smyser-Fauble, and Pam Takayoshi. This multivocal piece presents that conversation and captures voices, moments, and themes related to how we came to technology and feminist work, how we participated in the building of the field of computers and writing, what sort of work we engaged related to developing and supporting publishing venues for computers and writing scholars, and what hopes we have for technofeminist scholarly work for the future.
Advanced Machine Learning Techniques in Natural Language Processing for Indian Languages

The paper represents the advanced NLP learning resources in context of Indian languages: Hindi and Urdu. The research is based on domain-specific platforms which covers health, tourism, and agriculture corpora with 60 k sentences. With these corpora, some NLP-based learning resources such as stemmer, lemmatizer, POS tagger, and MWE identifier have been developed. All of these resources are connected in sequential form, and they are beneficial in information retrieval, language translation, handling word sense disambiguation, and many other useful applications. Stemming is first and foremost process of root extraction from given input word, but sometimes it does not produce valid root word. So the problem of stemming has been resolved by developing Lemmatizer, which produces the exact root by adding some rules in stemmed output. Eventually, statistical POS tagger has been designed with the help of Indian Government (TDIL) tagset (Indian Govt. Tagset, [1]). With this POS-tagged file, MWE identifier was developed. However, for developing MWE identifier, some rules are created for MWE tagset and then MWE-tagged file has been developed which in turn produces the automatic extraction of the MWEs from tagged corpora using CRF $${+}{+}$$ tool. Moreover, evaluation of learning resources has been performed to calculate the accuracy, and as a result, the output of corresponding proposed resources such as stemmer, lemmatizer, POS tagger, and MWE identifier are 77.0, 86.8, 73.20, and 43.50% for Hindi and 74.0, 85.4, 84.97 and 47.2% for Urdu, respectively.
Computational creativity infrastructure for online software composition: A conceptual blending use case

Computational creativity (CC) is a multidisciplinary research field, studying how to engineer software that exhibits behavior that would reasonably be deemed creative. This paper shows how composition of software solutions in this field can effectively be supported through a CC infrastructure that supports user-friendly development of CC software components and workflows, their sharing, execution, and reuse. The infrastructure allows CC researchers to build workflows that can be executed online and be easily reused by others through the workflow web address. Moreover, it enables the building of procedures composed of software developed by different researchers from different laboratories, leading to novel ways of software composition for computational purposes that were not expected in advance. This capability is illustrated on a workflow that implements a Concept Generator prototype based on the Conceptual Blending framework. The prototype consists of a composition of modules made available as web services, and is explored and tested through experiments involving blending of texts from different domains, blending of images, and poetry generation.
Incremental models based on features persistence for object recognition

Object recognition has regained a high level of attention in recent years, with the application of deep convolutional neural networks to classification tasks. However, the problem of recognising objects for which a limited number of images is available is still open. In this paper, we propose a view-based object recognition method which can deal with objects represented by a handful of images. Salient points are extracted from the images, and a persistence value is defined for each point and updated as new images are added. An object model is built and refined on the basis of salient point persistence, where points with high persistence have priority over those with low persistence. The model can then be used to match a single image of an object. We demonstrate the efficacy of the proposed methodology on a dataset made of a collection of objects of cultural interest. We show that the recognition performance of the proposed method is superior to that of a competing methodology based on Bag-of-Words.
Sign Prediction on Unlabeled Social Networks Using Branch and Bound Optimized Transfer Learning

Sign prediction problem aims to predict the signs of links for signed networks. Currently it has been widely used in a variety of applications. Due to the insufficiency of labeled data, transfer learning has been adopted to leverage the auxiliary data to improve the prediction of signs in target domain. Existing works suffer from two limitations. First, they cannot work if there is no target label available. Second, their generalization performance is not guaranteed due to that fact that the solution of their objective functions is not global optimal solution. To solve these problems, we propose a novel sign prediction on unlabeled social networks using branch and bound optimized transfer learning (SP_BBTL) sign prediction model. The main idea of SP_BBTL is to use target feature vectors to reconstruct source domain feature vectors based on relationship projection, which is a complicated optimal problem and is solved by proposed optimization based on branch and bound that can obtain global optimal solution. With this design, the target domain label information is not required for classifier. Finally, the experimental results on the large scale social signed networks validate the superiority of the proposed model.
Trajectory Privacy Protection Method Based on Location Service in Fog Computing

With the development of cloud computing in the Internet of Things and the wide application based on location services, the location trajectory information of mobile users is increasing. The risk of privacy leakage is also increasing. In order to protect the user’s trajectory privacy in the location service, we have designed a trajectory privacy protection method with several properties in the fog-based architecture. Fog computing extends the capabilities of cloud computing to the edge of the network, with local computing and storage capabilities, and a wide geographical mobility. Therefore, this paper uses the fog server instead of the traditional TTP anonymous server in the location-based privacy protection to solve the problem that performance bottleneck and concentrated attack in the TTP server anonymity process, and then completes our design with several properties through the fog server. The TRT algorithm implements k-anonymity of the trajectory. Finally, experimental analysis proves that our method can effectively enhance the user’s trajectory privacy.
Linear mixed-effects model for multivariate longitudinal compositional data

Compositional data analysis is becoming increasing important in economic research, where the variables of interest may be structural indicators, such as the investment proportion of industries. In many applications, the measurements of these indicators are collected from countries/regions on a yearly/monthly basis, which falls into the paradigm of longitudinal data. Typically, data from the same individual may show potential association due to unobserved shared factors. To incorporate the dependence within the individual, we investigate the linear mixed-effects model for multivariate longitudinal compositional data. We develop and implement a maximum likelihood estimation procedure through the expectation maximization algorithm. We also investigate the statistical inferences of fixed effects coefficients and the selection of random effects via a proposed Bayesian information criterion. The proposed method shows desirable properties and performs well in finite samples, as comprehensive numerical studies indicate. We further illustrate the practical utility of the proposed method in a real data study based on China’s industrial structure, and show that it can improve the performance and enhance the interpretability of the regression on multivariate compositional data.
Policy Issues Regarding Implementations of Cyber Attack: Resilience Solutions for Cyber Physical Systems

The Internet of Things (IoT) is dramatically increasing complexity in cities, commerce, and homes. This complexity is increasing the risk of cyber threats. To reduce these risks, resilient cyber-physical systems must be able to respond to different types of disturbances (errors; cyber attacks). Organizational, system, and infrastructure security pose new challenges for policy considerations that aim to reduce cyber risks rather than simply react to cyber attacks. Indeed, policies must be crafted to require anticipatory responses able to discriminate between anomalies caused by errors and those driven by cyber attackers for malicious purposes that may result in obvious damage (e.g., equipment destruction, injury, or death) or subtle control (e.g., Stuxnet). We conclude that anticipatory resilience solutions for cyber-physical systems will require teams of government and commercial organizations to address the consequences of cyber attacks, to detect them, and to defend against them.
Security of generalised Reed–Solomon code‐based cryptosystems

In this study, the authors elaborate on a recently proposed variant of the public-key McEliece and Niederreiter cryptosystems using generalised Reed–Solomon (GRS) codes as private codes. The use of these codes brings known advantages in terms of public key size, but particular care is needed in the choice of parameters not to endanger the system security. In fact, the considered system exploits a strong disguising technique of the private code within the public code. However, it has recently been pointed out that some new attacks exist which may threaten some instances of such a system, therefore the choice of parameters needs to consider some further constraints compared to the original version. After outlining these constraints, the authors propose a new modification of the system achieving greater flexibility in the parameter choice. Moreover, the new system exhibits a lower complexity than the original GRS code-based system. Its very competitive features such as key size and encryption rate are highlighted with respect to classic systems.
Image quality assessment using a novel region smoothness measure

One of the most efficient descriptions of image structure, which has been widely used in image quality assessment (IQA) studies, is the three-components model. Based on this model, the major structural components of an image are edges, textures and flat regions. We found that this model is basically derived from the abstract concept of image region smoothness. Indeed, each of these three components, is a particular region with special smoothness characteristics. Inspired by this fact, we developed an efficient general-purpose full-reference IQA technique, in which the amount of region smoothness degradation is gauged using our efficient MSER (maximally stable extremal region)-based region smoothness measure. For this, we build a block-based smoothness similarity map, and extract the image quality score, using a percentile averaging scheme. Experimental results are provided on popular benchmark databases, which confirm that the proposed approach has a reasonable prediction performance compared to the state-of-the-art image quality metrics.