Reviewerly/exHarmony huggingface dataset:
Opinion spam detection using Deep Learning

Opinions for any product, topic or organization are provided by users on Social networking and E-commerce sites. These opinions have high influence on other users’ purchasing decisions. Spammers post fake opinions so that their organization’s product can be promoted and competitor’s product can be demoted. There is need for detecting spam opinions so that users can have fair information about any product. Review text and reviewer behavior are factors considered to detect spam opinions. Traditional machine learning techniques such as SVM, Logistic Regression and Naive Bayes are applied to distinguish spam opinions from original reviews, but accuracy is not adequate. Furthermore, traditional approaches use only bag-of-words representation which do not consider context of words. In this research work, reviews text are observed and fed into Convolutional Neural Network (CNN) model. Convolutional layers, number of hidden layers, dense layers and filter size are set to build model. Further, essential text features are included in CNN model to improve accuracy. GloVe word embedding is used in proposed model. Experiment analysis on Ott dataset proves that accuracy is improved significantly by proposed model as compared to traditional machine learning approaches.
Proximal policy optimization based dynamic path planning algorithm for mobile robots

For the scenario where the overall layout is known and the obstacle distribution information is unknown, a dynamic path planning algorithm combining the A* algorithm and the proximal policy optimization (PPO) algorithm is proposed. Simulation experiments show that in all six test environments, the proposed algorithm finds paths that are on average about 2.04% to 5.86% shorter compared to the state-of-the-art algorithms in the literature, and reduces the number of training epochs before stabilization from tens of thousands to about 4000.
Do as we do: Multiple Person Video-To-Video Transfer

Our goal is to transfer the motion of real people from a source video to a target video with realistic results. While recent advances significantly improved image-to-image translations, only few works account for body motions and temporal consistency. However, those focus only on video retargeting for a single actor/ for single actors. In this work, we propose a marker-less approach for multiple-person video-to-video transfer using pose as an intermediate representation. Given a source video with multiple persons dancing or working out, our method transfers the body motion of all actors to a new set of actors in a different video. Differently from recent "do as I do" methods, we focus specifically on transferring multiple person at the same time and tackle the related identity switch problem. Our method is able to convincingly transfer body motion to the target video, while preserving specific features of the target video, such as feet touching the floor and relative position of the actors. The evaluation is performed with visual quality and appearance metrics using publicly available videos with the permission of their owners.
FarOut Touch: Extending the Range of ad hoc Touch Sensing with Depth Cameras

The ability to co-opt everyday surfaces for touch interactivity has been an area of HCI research for several decades. Ideally, a sensor operating in a device (such as a smart speaker) would be able to enable a whole room with touch sensing capabilities. Such a system could allow for software-defined light switches on walls, gestural input on countertops, and in general, more digitally flexible environments. While advances in depth sensors and computer vision have led to step-function improvements in the past, progress has slowed in recent years. We surveyed the literature and found that the very best ad hoc touch sensing systems are able to operate at ranges up to around 1.5 m. This limited range means that sensors must be carefully positioned in an environment to enable specific surfaces for interaction. In this research, we set ourselves the goal of doubling the sensing range of the current state of the art system. To achieve this goal, we leveraged an interesting finger "denting" phenomena and adopted a marginal gains philosophy when developing our full stack. When put together, these many small improvements compound and yield a significant stride in performance. At 3 m range, our system offers a spatial accuracy of 0.98 cm with a touch segmentation accuracy of 96.1%, in line with prior systems operating at less than half the range. While more work remains to be done to achieve true room-scale ubiquity, we believe our system constitutes a useful advance over prior work.
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis

Whilst adversarial attack detection has received considerable attention, it remains a fundamentally challenging problem from two perspectives. First, while threat models can be well-defined, attacker strategies may still vary widely within those constraints. Therefore, detection should be considered as an open-set problem, standing in contrast to most current detection approaches. These methods take a closed-set view and train binary detectors, thus biasing detection toward attacks seen during detector training. Second, limited information is available at test time and typically confounded by nuisance factors including the label and underlying content of the image. We address these challenges via a novel strategy based on random sub-space analysis. We present a technique that utilizes properties of random projections to characterize the behavior of clean and adversarial examples across a diverse set of subspaces. The self-consistency (or inconsistency) of model activations is leveraged to discern clean from adversarial examples. Performance evaluations demonstrate that our technique (AUC ∈ [0.92, 0.98]) outperforms competing detection strategies (AUC ∈ [0.30, 0.79]), while remaining truly agnostic to the attack strategy (for both targeted/untargeted attacks). It also requires significantly less calibration data (composed only of clean examples) than competing approaches to achieve this performance.
Parallel-and-stream accelerator for computationally fast supervised learning

Two dominant distributed computing strategies have emerged to overcome the computational bottleneck of supervised learning with big data: parallel data processing in the MapReduce paradigm and serial data processing in the online streaming paradigm. Despite the two strategies' common divide-and-combine approach, they differ in how they aggregate information, leading to different trade-offs between statistical and computational performance. The authors propose a new hybrid paradigm, termed a Parallel-and-Stream Accelerator (PASA), that uses the strengths of both strategies for computationally fast and statistically efficient supervised learning. PASA's architecture nests online streaming processing into each distributed and parallelized data process in a MapReduce framework. PASA leverages the advantages and mitigates the disadvantages of both the MapReduce and online streaming approaches to deliver a more flexible paradigm satisfying practical computing needs. The authors study the analytic properties and computational complexity of PASA, and detail its implementation for two key statistical learning tasks. PASA's performance is illustrated through simulations and a large-scale data example building a prediction model for online purchases from advertising data.
Text Analytics on Twitter Text-based Public Sentiment for Covid-19 Vaccine: A Machine Learning Approach

The global pandemic of the novel Coronavirus in 2019, known by the World Health Organisations (WHO) as Covid-19, has put various governments in a vulnerable situation around the world. For virtually every nation in the world, the effects of the Covid-19 pandemic, previously experienced by the people of China alone, has now become a matter of great concern. This research highlights its impact on the global economy, in addition to the immediate health consequences associated with the Covid-19 pandemic. The study further discussed the use of Text Analytics and Sentiment Analysis in Natural Language Processing (NLP) based on Twitter text to analyse public sentiment and derive insights regarding Covid-19 vaccines in the healthcare domain. Two machine learning algorithms were employed: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) to classify and evaluate the results. Various pre-processing techniques were adopted to help in detecting the public sentiment based on the three sentiment polarity classes: positive, negative, and neutral. The result of the sentiment class distribution reveals that 31% of the public sentiment regarding Covid-19 vaccines is positive, 22% is negative while the remaining 47% were classified as neutral sentiment. The experimented machine learning algorithms reveals that SVM produced 88% accuracy which surpasses KNN with 78% accuracy.
One Time User Key: A User-Based Secret Sharing XOR-ed Model for Multiple User Cryptography in Distributed Systems

The generation of encrypted channels between more than two users is complex, as it is necessary to share information about the key of each user. This problem has been partially solved through the secret sharing mechanism that makes it possible to divide a secret among several participants, so that the secret can be reconstructed by a well-defined part of them. The proposed system represents an extension of this mechanism, since it is designed to be applied systematically: each user has his/her key, through which temporary keys (One Time User Keys) are generated and are used to divide the secret, corresponding to the real encryption key. The system also overcomes the concept of numerical threshold (i.e., at least n participants are required to reconstruct the secret), allowing the definition, for each encryption, of which users can access and which specific groups of users can access. The proposed model can be applied both in distributed user-based contexts and as an extension of cryptographic functions, without impacting the overall security of the system. It addresses some requirements of the European Union Council resolution on encryption and also provides a wide possibility of applications in user-based distributed systems.
Immunofluorescence-guided segmentation of three-dimensional features in micro-computed tomography datasets of human lung tissue

Micro-computed tomography (µCT) provides non-destructive three-dimensional (3D) imaging of soft tissue microstructures. Specific features in µCT images can be identified using correlated two-dimensional (2D) histology images allowing manual segmentation. However, this is very time-consuming and requires specialist knowledge of the tissue and imaging modalities involved. Using a custom-designed µCT system optimized for imaging unstained formalin-fixed paraffin-embedded soft tissues, we imaged human lung tissue at isotropic voxel sizes less than 10 µm. Tissue sections were stained with haematoxylin and eosin or cytokeratin 18 in columnar airway epithelial cells using immunofluorescence (IF), as an exemplar of this workflow. Novel utilization of tissue autofluorescence allowed automatic alignment of 2D microscopy images to the 3D µCT data using scripted co-registration and automated image warping algorithms. Warped IF images, which were accurately aligned with the µCT datasets, allowed 3D segmentation of immunoreactive tissue microstructures in the human lung. Blood vessels were segmented semi-automatically using the co-registered µCT datasets. Correlating 2D IF and 3D µCT data enables accurate identification, localization and segmentation of features in fixed soft lung tissue. Our novel correlative imaging workflow provides faster and more automated 3D segmentation of µCT datasets. This is applicable to the huge range of formalin-fixed paraffin-embedded tissues held in biobanks and archives.
TeSC: TLS/SSL-Certificate Endorsed Smart Contracts

Although nearly all information regarding smart contract addresses is shared via websites, emails, or other forms of digital communication, blockchains and distributed ledger technologies are unable to establish secure bindings between web sites and the corresponding smart contracts. A user cannot differentiate between a website link to a legitimate smart contract set up by a reputable business owner and that to an illicit contract aiming to defraud the user. Surprisingly, current attempts to resolve this issue are based mostly on information redundancy, e.g., displaying contract addresses multiple times in varying forms of images and text. These verification processes are burdensome because the user is responsible for verifying the accuracy of an address. More importantly, these measures do not address the core problem because the contract itself does not contain information on its authenticity. To resolve such limitations and to increase security, we propose a solution that leverages publicly issued Transport Layer Security (TLS)/Secure Sockets Layer (SSL) certificates of Fully-Qualified Domain Names (FQDN) to ensure the authenticity of smart contracts and their owners. Our approach combines on-chain endorsement storage that utilizes signatures from the respective certificate and off-chain authentication of the smart contract. The system is open and transparent because the only requirement for usage is ownership of a TLS/SSL certificate. Further, moderate deployment and maintenance costs, a widely accepted public key infrastructure, and a simple interface enable TLS/SSL endorsed smart contracts (TeSC) to bridge the gap between websites and smart contracts.
To the development of intelligent adaptive learning systems

Abstract The article describes the prospects for creating an informational adaptive automated learning system (based on the English Language) using advanced artificial intelligence technologies. In particular, the work shows that the quality of education largely depends on the form of educational content and the parameters of the learning object (a student) associated with its perception of information. The formulated hypothesis of this work indicates that according to the written speech of the learning object, it is possible to classify it (the learning object) by the type of perception of educational content. For the first time, the paper proposes the use of the apparatus of artificial neural networks to solve the problem of automatic classification of learning objects according to the parameter indicated above, while analyzing the text written by this object. For the formation of educational content in the required form, it is proposed to use the system-object method of knowledge representation. The logical architecture of an adaptive teaching automated system is being considered.
Analysis of an Arduino based solar tracking system

Abstract The main purpose of this research is to develop and evaluate a solar tracking system which able to maximize the power output of the solar panel. The design of the solar tracking system consists of some electronic components such as an Arduino Uno R3 microcontroller, four light-dependent resistors (LDRs), two servo motors, and one solar panel. The Arduino microcontroller acts as the main controller of the whole system, light-dependent resistor is used as a light sensor to detect sunlight while the function of the servo motor is to rotate the solar panel to align with the sunlight. The prototype of the solar tracking system was build and tested. The data result obtained are tabulated and the comparison of performance between the static solar panel and solar tracking system is showed using the graphical method. As a result, solar panels with tracking mechanisms can generate more current, voltage, and power than static solar panels. The overall efficiency of the solar panel had increased after the implementation of the tracking system.
Fault Diagnosis of ZD6 Turnout System Based on Wavelet Transform and GAPSO-FCM

Traffic safety has become a primary focus on the rapid development of railway traffic. Failure of railway turnout shall endanger train operations and affect their efficiency. Currently, the fault diagnosis of railway turnout still relies on the experience of maintenance personnel that can introduce several problems, such as low fault diagnosis efficiency and large amounts of required labor. To solve these problems, this paper takes the most widely used ZD6 turnout system in China as the research object, the railway turnout failure modes are summarized into eight typical types. Combined with the actual railway turnout fault data, the wavelet transform is used for the railway turnout action current extracted feature vector. The theory of fuzzy cognitive map (FCM) was designed to diagnose the fault modes of the railway turnout based on extracted feature vector, and the genetic algorithm particle swarm optimization (GAPSO) was selected to learn the weights of FCM. The simulation results indicated that FCM classifier model, based on GAPSO, could effectively classify the faults of the railway turnout. The GAPSO-FCM model could achieve a 97.1% fault diagnosis accuracy rate that showed that the GAPSO-FCM model proposed in this paper had high accuracy. Finally, we discuss further improvements that should be made to the model in the future.
Can Explanations Support Privacy Awareness? A Research Roadmap

Using systems as support tools for decision-making is a common part of a citizen’s daily life. Systems support users in various tasks, collecting and processing data to learn about a user and provide more tailor-made services. This data collection, however, means that users’ privacy sphere is increasingly at stake. Informing the user about what data is collected and how it is processed is key to reaching transparency, trustworthiness, and ethics in modern systems. While laws and regulations have come into existence to inform the user about privacy terms, this information is still conveyed in a complex and verbose way to the user, making it unintelligible to them. Meanwhile, explainability is seen as a way to disclose information about a system or its behavior in an intelligible manner. In this work, we propose explanations as a means to enhance users’ privacy awareness. As a long-term goal, we want to understand how to achieve more privacy awareness with respect to systems and develop heuristics that support it, helping end-users to protect their privacy. We present preliminary results on private sphere explanations and present our research agenda towards our long-term goal.
Phase-type mixture-of-experts regression for loss severities

The task of modeling claim severities is addressed when data is not consistent with the classical regression assumptions. This framework is common in several lines of business within insurance and reinsurance, where catastrophic losses or heterogeneous sub-populations result in data difficult to model. Their correct analysis is required for pricing insurance products, and some of the most prevalent recent specifications in this direction are mixture-of-experts models. This paper proposes a regression model that generalizes the latter approach to the phase-type distribution setting. More specifically, the concept of mixing is extended to the case where an entire Markov jump process is unobserved and where states can communicate with each other. The covariates then act on the initial probabilities of such underlying chain, which play the role of expert weights. The basic properties of such a model are computed in terms of matrix functionals, and denseness properties are derived, demonstrating their flexibility. An effective estimation procedure is proposed, based on the EM algorithm and multinomial logistic regression, and subsequently illustrated using simulated and real-world datasets. The increased flexibility of the proposed models does not come at a high computational cost, and the motivation and interpretation are equally transparent to simpler MoE models.
Implementation of a personalized and healthy meal recommender system in aid to achieve user fitness goals

Recent research implies that people's urge to stay healthy and fit has drastically improved and currently, many people are in need to maintain their physical fitness incorporating healthy food habits into their lives amidst hectic urban lifestyles. Thus, nutrition applications are mushrooming in the fitness domain to aid people to improve their dietary intake, track weight-related elements, and generate meal plans. Considering the applications that are typically built for meal planning, it was apparent that personalized nutrition incorporated with healthy meal suggestions is not well addressed, and hence the need for a personalized meal recommendation system that assists the users to achieve their fitness goals is identified. Learning users' food preferences and delivering food recommendations that plead to their taste and satisfy nutritional guidelines are challenging. Due to the lack of access to a proper meal planning application or without professional help most users follow ineffective, generic meal plans which hinder them from achieving their fitness goals and often cause long-term and short-term health complications. The proposed implementation aims to bridge the gap between the existing meal planning applications and the potential need for a personalized healthy meal plan. This paper succinctly presents the design and implementation of the proposed personalized and healthy meal recommendation system and further discusses the architecture and the evaluation of the design solution.
Fast A* Anchor Point based Path Planning for Narrow Space Parking

Automatic parking is a useful ADAS for cars. This paper proposes a path planning method for automatic parking in narrow space. First, a fast A* algorithm solves discrete path points between a given pair of initial and target vehicle positions. Then, a screening of discrete path points obtains “anchor points” oriented for narrow space parking, based on which continuous and smooth trajectories are generated. Simulation experiments demonstrate that the proposed method can effectively perform path planning for narrow space parking under various initial conditions, and outperforms baseline methods in terms of success rate and planning time.
A Nonparametric Bayesian Framework for Uncertainty Quantification in Stochastic Simulation

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 15 June 2020Accepted: 06 August 2021Published online: 01 November 2021Keywordsnonparametric Bayesian approach, design of experiments, stochastic simulation, uncertainty quantification, input uncertainty, Dirichlet process mixturesAMS Subject Headings90-10Publication DataISSN (online): 2166-2525Publisher: Society for Industrial and Applied MathematicsCODEN: sjuqa3
Investigation of Size Variations in Optical See-through Tangible Augmented Reality

Optical see-through AR headsets are becoming increasingly attractive for many applications. Interaction with the virtual content is usually achieved via hand gestures or with controllers. A more seamless interaction between the real and virtual world can be achieved by using tangible objects to manipulate the virtual content. Instead of interacting with detailed physical replicas, working with abstractions allows a single physical object to represent a variety of virtual objects. These abstractions would differ from their virtual representations in shape, size, texture and material. This paper investigates for the first time in optical see-through AR whether size variations are possible without major losses in performance, usability and immersion. The conducted study shows that size can be varied within a limited range without significantly affecting task completion times as well as feelings of disturbance and presence. Stronger size deviations are possible for physical objects smaller than the virtual object than for larger physical objects.