Reviewerly/exHarmony huggingface dataset:
Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.
Extreme Learning Machine-Based Deep Model for Human Activity Recognition With Wearable Sensors

Human activity recognition (HAR) is a main research field of context-aware computing; the performance of HAR mainly depends on the feature extraction method and classification algorithm. Extreme learning machine (ELM) is a single hidden layer neural network, which has better classification and generalization ability. However, ELM is not suitable for feature extraction. Deep learning is a hot research field as it can automatically extract significant features from raw data. In this paper, we propose an approach: an ELM-based deep model, which combined convolutional neural network (CNN), multilayer ELM (ML-ELM) as feature extractor, and used kernel ELM (KELM) as classifier. We used CNN and ML-ELM to extract significant features, and used KELM to achieve stable performance. The performance of proposed approach is validated on two public HAR datasets, and the experimental results show that the proposed approach is effective.
On isogeny graphs of supersingular elliptic curves over finite fields

We study the isogeny graphs of supersingular elliptic curves over finite fields, with an emphasis on the vertices corresponding to elliptic curves of j-invariant 0 and 1728.
Quantum algorithms for training Gaussian processes

Gaussian processes (GPs) are important models in supervised machine learning. Training in Gaussian processes refers to selecting the covariance functions and the associated parameters in order to improve the outcome of predictions, the core of which amounts to evaluating the logarithm of the marginal likelihood (LML) of a given model. LML gives a concrete measure of the quality of prediction that a GP model is expected to achieve. The classical computation of LML typically carries a polynomial time overhead with respect to the input size. We propose a quantum algorithm that computes the logarithm of the determinant of a Hermitian matrix, which runs in logarithmic time for sparse matrices. This is applied in conjunction with a variant of the quantum linear system algorithm that allows for logarithmic time computation of the form $\mathbf{y}^TA^{-1}\mathbf{y}$, where $\mathbf{y}$ is a dense vector and $A$ is the covariance matrix. We hence show that quantum computing can be used to estimate the LML of a GP with exponentially improved efficiency under certain conditions.
On Multiplicative Dependence of Values of Rational Functions and a Generalization of the Northcott Theorem

In this paper, we study multiplicative dependence of values of polynomials or rational functions over a number field. As an application, we obtain new results on multiplicative dependence in the orbits of a univariate polynomial dynamical system. We also obtain a generalization of the Northcott theorem replacing the finiteness of preperiodic points from a given number field by the finiteness of algebraic integers having two multiplicatively dependent elements in their orbits.
Reducing the effort for systematic reviews in software engineering

Context: Systematic Reviews (SRs) are means for collecting and synthesizing evidence from the identification and analysis of relevant studies from multiple sources. To this aim, they use a well-defined methodology meant to mitigate the risks of biases and ensure repeatability for later updates. SRs, however, involve significant effort. Goal: The goal of this paper is to introduce a novel methodology that reduces the amount of manual tedious tasks involved in SRs while taking advantage of the value provided by human expertise. Method: Starting from current methodologies for SRs, we replaced the steps of keywording and data extraction with an automatic methodology for generating a domain ontology and classifying the primary studies. This methodology has been applied in the Software Engineering sub-area of Software Architecture and evaluated by human annotators. Results: The result is a novel Expert-Driven Automatic Methodology, EDAM, for assisting researchers in performing SRs. EDAM combines ontology-learning techniques and semantic technologies with the human-in-the-loop. The first (thanks to automation) fosters scalability, objectivity, reproducibility and granularity of the studies; the second allows tailoring to the specific focus of the study at hand and knowledge reuse from domain experts. We evaluated EDAM on the field of Software Architecture against six senior researchers. As a result, we found that the performance of the senior researchers in classifying papers was not statistically significantly different from EDAM. Conclusions: Thanks to automation of the less-creative steps in SRs, our methodology allows researchers to skip the tedious tasks of keywording and manually classifying primary studies, thus freeing effort for the analysis and the discussion.
Optimised Lambda Architecture for Monitoring Scientific Infrastructure

Within scientific infrastructuscientists execute millions of computational jobs daily, resulting in the movement of petabytes of data over the heterogeneous infrastructure. Monitoring the computing and user activities over such a complex infrastructure is incredibly demanding. Whereas present solutions are traditionally based on a Relational Database Management System (RDBMS) for data storage and processing, recent developments evaluate the Lambda Architecture (LA). In particular these studies have evaluated data storage and batch processing for processing large-scale monitoring datasets using Hadoop and its MapReduce framework. Although LA performed better than the RDBMS following evaluation, it was fairly complex to implement and maintain. This paper presents an Optimised Lambda Architecture (OLA) using the Apache Spark ecosystem, which involves modelling an efficient way of joining batch computation and real-time computation transparently without the need to add complexity. A few models were explored: pure streaming, pure batch computation, and the combination of both batch and streaming. An evaluation of the OLA on the CERN IT on-premises Hadoop cluster and the public Amazon cloud infrastructure for the monitoring WLCG Data acTivities (WDT) use case are both presented, demonstrating how the new architecture can offer benefits by combining both batch and real-time processing to compensate for batch-processing latency.
A dynamic linear model for heteroscedastic LDA under class imbalance

Linear Discriminant Analysis (LDA) yields the optimal Bayes classifier for binary classification for normally distributed classes with equal covariance. To improve the performance of LDA, heteroscedastic LDA (HLDA) that removes the equal covariance assumption has been developed. In this paper, we show using first and second-order optimality conditions that the existing approaches either have no principled computational procedure for optimal parameter selection, or underperform in terms of the accuracy of classification and the area under the receiver operating characteristics curve (AUC) under class imbalance. Using the same optimality conditions, we then derive a dynamic Bayes optimal linear classifier for heteroscedastic LDA that is optimised via an efficient iterative procedure, which is robust against class imbalance. Experimental work is conducted on two artificial and eight real-world datasets. Our results show that the proposed algorithm compares favourably with the existing heteroscedastic LDA procedures as well as the linear support vector machine (SVM) in terms of the error rate, but is superior to all the algorithms in terms of the AUC under class imbalance. The fast training time of the proposed algorithm also encourages its use for large-data applications that show high incidence of class imbalance, such as in human activity recognition.
Another Look at Anonymous Communication

Anonymous communication is desirable for personal, financial, and political reasons. Despite the abundance of frameworks and constructions, anonymity definitions are usually either not well defined or too complicated to use. In between are ad-hoc definitions for specific protocols which sometimes only provide weakened anonymity guarantees. This paper addresses this situation from the perspectives of syntax, security definition, and construction. We propose simple yet expressive syntax and security definition for anonymous communication. Our syntax covers protocols with different operational characteristics. We give a hierarchy of anonymity definitions, starting from the strongest possible to several relaxations. We also propose a modular construction from any key-private public-key encryption scheme, and a new primitive-oblivious forwarding protocols, of which we give two constructions. The first is a generic construction from any random walk over graphs, while the second is optimized for the probability of successful delivery, with experimental validation for our optimization. Anonymity is guaranteed even when the adversary can observe and control all traffic in the network and corrupt most nodes, in contrast to some efficient yet not-so-anonymous protocols. We hope this work suggests an easier way to design and analyze efficient anonymous communication protocols in the future.
Facial landmarks localization using cascaded neural networks

The accurate localization of facial landmarks is at the core of face analysis tasks, such as face recognition and facial expression analysis, to name a few. In this work, we propose a novel localization approach based on a deep learning architecture that utilizes cascaded subnetworks with convolutional neural network units. The cascaded units of the first subnetwork estimate heatmap-based encodings of the landmarks locations, while the cascaded units of the second subnetwork receive as input the output of the corresponding heatmap estimation units, and refine them through regression. The proposed scheme is experimentally shown to compare favorably with contemporary state-of-the-art schemes, especially when applied to images depicting challenging localization conditions.
Rao–Blackwellized Gaussian Smoothing

In this paper, we consider Rao–Blackwellization of linear substructures in sigma-point-based Gaussian assumed density smoothers. We derive marginalized prediction, smoothing, and update steps for the mixed linear/nonlinear Gaussian state-space model as well as for a hierarchical model for both conventional and iterated posterior linearization Gaussian smoothers. The proposed method is evaluated in a numerical example and it is shown that the computational complexity is reduced considerably compared to non-Rao–Blackwellized Gaussian smoothers for systems with high-dimensional linear subspaces.
Semantics Mining&amp;Indexing-Based Rapid Web Services Discovery Framework

Web services, as an effective realization technology of SOA, have been deployed largely on the Internet. Comparing with the traditional Web pages, the information island problem existing in Web services field is more serious. Web services with specific functions not only submerge in the Web services library, but also submerge in the traditional Web pages library. As a result, finding web service rapidly and accurately becomes a problem needing solving, and this promots the research on the field of web services discovery. In this paper, we propose a semantics mining&indexing-based rapid Web services discovery framework. First, focus on the Web services formalizing model with the basic information of Web service or composition. Then propose a Web services matching engine with high accurency, and there is no semantics ontology dependence in matching engine, also, we propose a Web services discovery framework basing index library, which can help providing a low processing time in request searching. When creating the index library, we apply semantics mining to overcome the shortcoming of low searching accurancy in traditional index framework. Finally, the experiments are tested that the Web service discovery framework shows high precision rate and recall rate, which can provides a solution to the problem of persuiting both low searching processing time and high search accurancy in Web service discovery field.
Accelerating an FHE Integer Multiplier Using Negative Wrapped Convolution and Ping-Pong FFT

This brief proposes a novel hardware structure for large integer multiplication in fully homomorphic encryption. We propose a method based on negative wrapped convolution to avoid zero padding in Strassen's algorithm, which can cut down half of the Fourier transform length. In addition, we also optimize the ping-pong fast Fourier transform algorithm by doubling the transform throughput and generating the round constant on the fly. Based on our proposed method and optimized algorithm, we design and implement a 768 k-bit integer multiplier on Altera Stratix V field-programmable gate array (FPGA). Implementation results on FPGA show that our structure outperforms the current competitors in area efficiency.
Discovery and recognition of motion primitives in human activities

We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the 'motion flux', a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis.
Fast Failure Recovery in Vertex-Centric Distributed Graph Processing Systems

There is a growing need for distributed graph processing systems to have many more compute nodes processing graph-based Big Data applications, which, however, increases the chance of node failures. To address the issue, we propose a novel recovery scheme to accelerate the recovery process by parallelizing the recomputation. Once a failure occurs, all recomputations are confined to subgraphs that originally reside in the failed compute nodes. When the recovery starts, these subgraphs are reassigned to another set of compute nodes, where the recomputation over these subgraphs are conducted in parallel. To minimize the recovery latency, we also develop a reassignment strategy, from these subgraphs to the replaced compute nodes, by properly leveraging the computation and communication cost. We integrate the proposed recovery scheme into Giraph system, a widely used graph processing system. The experimental results over a variety of real graph datasets demonstrate that our proposed recovery scheme outperforms existing recovery methods by up to 30x on a cluster of 40 compute nodes.
Sustainable CNN for Robotic: An Offloading Game in the 3D Vision Computation

Three-dimensional (3D) scene understanding is of great significance to many robotic applications. With the huge development of the deep learning methods, especially the convolutional neural network (CNN), 3D robotic vision has achieved a satisfactory performance. However, in most scenarios, sustainability becomes a severe problem, and few existing approaches pay enough attention to energy consumption. In this paper, we propose an energy-aware system for sustainable robotic 3D vision. Our contributions mainly include: 1) an effective CNN model for the 3D scene understanding; and 2) an offloading strategy to make the deep model more sustainable. First, we design a deep CNN model to analyze the 3D point cloud data. The proposed model contains 92 layers for a state-of-the-art recognition accuracy, which, however, bring a big burden to the computing hardware. Then, we formulate this deep learning computation problem as a non-cooperative game, and adopt a heuristic algorithm to balance the local computing and cloud offloading, in order to obtain an optimal solution, in which both the efficiency and energy-saving are taken into account. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks.
Cooperative Aerial Payload Transport Guided by an <i>In Situ</i> Human Supervisor

This paper defines and analyzes a cooperative aerial payload transport mission in which an onsite human supervisor provides haptic motion cues. While multiple small aerial robots (e.g., quadcopters) carry the weight of the payload, a human specifies traversal direction changes to the quadcopter team by exerting a pushing force on the payload. We show how the principle of linear momentum can be used for limited human intent recognition in this supervisory control application. This problem is formulated as real-time path planning of the quadcopter team using a continuum deformation cooperative control protocol that assures avoidance of interagent and obstacle collisions as well as safety of the human supervisor.
Private collaborative filtering under untrusted recommender server

Recommender systems play an increasingly vital role in modern E-commerce. However, exploiting users' preferences with recommender algorithms leads to serious privacy risks, especially when recommender service providers are unreliable. To deal with the problem, this paper proposes a Client/Server framework to create a private recommender system (PrivateRS). The system assumes that the Server side is untrustworthy. On the Client side, each user firstly rates the items and randomizes the ratings with a differential privacy mechanism. The ratings are further substituted by private symbols which are autonomously defined by each user to hide the ordinal meaning of the ratings. Using those symbols, the Server applies a private collaborative filtering algorithm to predict the ratings of items for the user. During this process, new similarity metrics are provided to search the nearest neighbours for users or items without knowing the real meanings of those symbols. Experimental results demonstrate that even though the ordinal meaning of the rating is significantly obfuscated, the proposed algorithms can still generate accurate recommendations with acceptable loss.
“A Leopard Cannot Change Its Spots”: Improving Face Recognition Using 3D-Based Caricatures

Caricatures refer to a representation of a person, in which the distinctive features are deliberately exaggerated, with several studies showing that humans perform better at recognizing people from caricatures than using original images. Inspired by this observation, this paper introduces the first fully automated caricature-based face recognition approach capable of working with data acquired in the wild. Our approach leverages the 3D face structure from a single 2D image and compares it with a reference model for obtaining a compact representation of face features deviations. This descriptor is subsequently deformed using a “measure locally, weight globally” strategy to resemble the caricature drawing process. The deformed deviations are incorporated in the 3D model using the Laplacian mesh deformation algorithm, and the 2D face caricature image is obtained by projecting the deformed model in the original camera view. To demonstrate the advantages of caricature-based face recognition, we train the VGG-face network from scratch using either original face images (baseline) or caricatured images and use these models for extracting face descriptors from the LFW, IJB-A, and MegaFace data sets. The experiments show an increase in the recognition accuracy when using caricatures rather than original images. Moreover, our approach achieves competitive results with the state-of-the-art face recognition methods, even without explicitly tuning the network for any of the evaluation sets.