Reviewerly/exHarmony huggingface dataset:
Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing

To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive research has been conducted on RCC-based computation offloading, while the studies on VCC-based computation offloading are relatively rare. In fact, due to the dynamic and uncertainty of on-board resource, the VCC-based computation offloading is more challenging then the RCC one, especially under the vehicular scenario with expensive inter-vehicle communication or poor communication environment. To solve this problem, we propose to leverage the VCC’s computation resource for computation offloading with a perception-exploitation way, which mainly comprise resource discovery and computation offloading two stages. In resource discovery stage, upon the action-observation history, a Long Short-Term Memory (LSTM) model is proposed to predict the on-board resource utilizing status at next time slot. Thereafter, based on the obtained computation resource distribution, a decentralized multi-agent Deep Reinforcement Learning (DRL) algorithm is proposed to solve the collaborative computation offloading with VCC and RCC. Last but not least, the proposed algorithms’ effectiveness is verified with a host of numerical simulation results from different perspectives.
Metamodeling and metaquerying in OWL2QL

OWL 2 QL is a standard profile of the OWL 2 ontology language, specifically tailored to Ontology-Based Data Management. Inspired by recent work on higher-order Description Logics, in this paper we present a new semantics for OWL 2 QL ontologies, called Metamodeling Semantics (MS), and show that, in contrast to the official Direct Semantics (DS) for OWL 2, it allows exploiting the metamodeling capabilities natively offered by the OWL 2 punning. We then extend unions of conjunctive queries with both metavariables, and the possibility of using TBox atoms, with the purpose of expressing meaningful metalevel queries. We first show that under MS both satisfiability checking and answering queries including only ABox atoms, have the same complexity as under DS. Second, we investigate the problem of answering general metaqueries, and single out a new source of complexity coming from the combined presence of a specific type of incompleteness in the ontology, and of TBox axioms among the query atoms. Then we focus on a specific class of ontologies, called TBox-complete, where there is no incompleteness in the TBox axioms, and show that general metaquery answering in this case has again the same complexity as under DS. Finally, we move to general ontologies and show that answering general metaqueries is coNP-complete with respect to ontology complexity, Π2p-complete with respect to combined complexity, and remains AC0 with respect to ABox complexity.
WITHDRAWN: An empirical observation of factors leading to subscription of EdTech services

Universities in developed nations introduced new courses to enhance industry orientation for students. These courses required novel approach instead of traditional mode of delivery. Technology interference was considered for a creative course content delivery. Edtech services transformed traditional education model in developed countries. Indian education sector is ripe with opportunities. Edtech service vendors ventured into the sector predicting growth. Edtech companies connected with Indian education establishments. Unlike western scenario the subscription rate is not impressive in India. Free market competition led to attractive offers. Consumers clustered around selected vendors. Validity and recognition of these courses in majority of developed countries symbolized value proposition. Champion edtech companies sustained the journey in rough seas. Edtech is a tool for updation and up gradation of student skills. Largely consumers are hesitant in subscribing for edtech services. Edtech subscriptions behavior in India is unexplained. There is a need to create knowledge in this area. The study aims to explain factors leading to edtech subscriptions. Literature review resulted in selection of variables and subsequently a conceptual model. Survey approach was made to collect data. Factors analysis was conducted to find factors-based item loading. Study identified the factors as items loaded in their respective columns. Regression analysis revealed nature and strength of relationship of factors leading to subscription of edtech services. Except device requirement, all factors led to subscription of edtech services. External factors and user friendliness of a technology were strong influencers of edtech subscription. Further, ANOVA led us to find employment status and educational qualification means significantly different from identified factors.
History-Based Anomaly Detector: An Adversarial Approach to Anomaly Detection

Anomaly detection is a difficult problem in many areas and has recently been subject to a lot of attention. Classifying unseen data as anomalous is a challenging matter. Latest proposed methods rely on Generative Adversarial Networks (GANs) to estimate the normal data distribution, and produce an anomaly score prediction for any given data. In this article, we propose a simple yet new adversarial method to tackle this problem, denoted as History-based anomaly detector (HistoryAD). It consists of a self-supervised model, trained to recognize ‘normal’ samples by comparing them to samples based on the training history of a previously trained GAN. Quantitative and qualitative results are presented evaluating its performance. We also present a comparison to several state-of-the-art methods for anomaly detection showing that our proposal achieves top-tier results on several datasets.
A Framework for Interactive Exploration of Clusters in Massive Data Using 3D Scatter Plots and WebGL

This paper presents a rendering framework for the visualization of massive point datasets in the web. It includes highly interactive point rendering, cluster visualization, basic interaction methods, and importance-based labeling, while being available for both mobile and desktop browsers. The rendering style is customizable, as shown in figure 1. Our evaluation indicates that the framework facilitates interactive visualization of tens of millions of raw data points even without dynamic filtering or aggregation.
Reliable SoC Design and Implementation of SHA-3-HMAC Algorithm with Attack Protection

The Keyed-Hash Message Authentication Codes (HMAC) is a widely used method to ensure the integrity and authentication of data and its security is based on the Hash encryption algorithm. As part of HMAC, the secure hash algorithm (SHA)-3 was selected as the winner of the hash function competition in 2012. Though SHA-3 has been implemented individually in hardware, reliable SoC (system on a chip) design with multiple attack protection is still unsolved to support SHA-3-HMAC. In this paper, we propose our design of a SHA-3-HMAC SoC module which has the ability to defend against common side channel attacks and error injection attacks. Our design also supports different digest values of 256/384/512 according to configuration, with automatic padding operation inside SHA-3. Furthermore, through application-specific integrated circuit (ASIC) analysis of the implementation of our design, the maximum clock frequency of our implementation can reach 300MHz. By adding a clock randomization module and a circuit design based on time redundancy, the security and reliability of the SoC module has been greatly improved.
ZoomTouch: Multi-User Remote Robot Control in Zoom by DNN-based Gesture Recognition

We present ZoomTouch, a breakthrough technology for multi-user and real-time control of robot from Zoom by DNN-based gesture recognition. The users can have a video conferencing in a digital world and at the same time to perform dexterous manipulations with tangible objects by remote robot. As the scenario, we proposed the remote robotic COVID-19 test Laboratory to substitute medical assistant working in protective gear in close proximity with infected cells and to considerably reduce the time to receive the test results. The proposed technology suggests a new type of reality, where multi-users can jointly interact with remote object, e.g. make a new building design, joint cooking in robotic kitchen, etc, and discuss/modify the results at the same time.
Comparison of Khasi speech representations with different spectral features and hidden Markov states

In this paper, we present a comparison of the Khasi speech representations with four different spectral features and novel extension towards the development of Khasi speech corpora. These four features include linear predictive coding (LPC), linear prediction cepstrum coefficient (LPCC), perceptual linear prediction (PLP), and Mel frequency cepstral coefficient (MFCC). The 10-h speech data was used for training and 3-h data for testing. For each spectral feature, different hidden Markov model (HMM) based recognizers with variations in HMM states and different Gaussian mixture models (GMMs) were built. The performance was evaluated by using the word error rate (WER). The experimental results showed that MFCC provides a better representation for Khasi speech compared with the other three spectral features.
A Light-Weight Text Summarization System for Fast Access to Medical Evidence

As the volume of published medical research continues to grow rapidly, staying up-to-date with the best-available research evidence regarding specific topics is becoming an increasingly challenging problem for medical experts and researchers. The current COVID19 pandemic is a good example of a topic on which research evidence is rapidly evolving. Automatic query-focused text summarization approaches may help researchers to swiftly review research evidence by presenting salient and query-relevant information from newly-published articles in a condensed manner. Typical medical text summarization approaches require domain knowledge, and the performances of such systems rely on resource-heavy medical domain-specific knowledge sources and pre-processing methods (e.g., text classification) for deriving semantic information. Consequently, these systems are often difficult to speedily customize, extend, or deploy in low-resource settings, and they are often operationally slow. In this paper, we propose a fast and simple extractive summarization approach that can be easily deployed and run, and may thus aid medical experts and researchers obtain fast access to the latest research evidence. At runtime, our system utilizes similarity measurements derived from pre-trained medical domain-specific word embeddings in addition to simple features, rather than computationally-expensive pre-processing and resource-heavy knowledge bases. Automatic evaluation using ROUGE-a summary evaluation tool-on a public dataset for evidence-based medicine shows that our system's performance, despite the simple implementation, is statistically comparable with the state-of-the-art. Extrinsic manual evaluation based on recently-released COVID19 articles demonstrates that the summarizer performance is close to human agreement, which is generally low, for extractive summarization.
Twitter Data Sentimental Analysis Using Multiple Classifications

In the past few decades human beings have knowledgeable tremendous intensification in the interaction in particular micro blogging websites and various social media as online resources. Many kinds of data have been used and classification data to group and store are challenging in this real world scenario. Various machine and Natural Language Processing (NLP) were being applied to analysis the sentiment. A major concentration of this work was on using several machine learning algorithms to perform sentimental analysis and comparing various machine learning models for the sentiment classification. This work analysed various sentimental using multiple classifications. From the evaluation of this experiment, it can be concluded that NLP and machine learning Techniques are efficient for sentimental analysis.
Neural Wireframe Renderer: Learning Wireframe to Image Translations

In architecture and computer-aided design, wireframes (i.e., line-based models) are widely used as basic 3D models for design evaluation and fast design iterations. However, unlike a full design file, a wireframe model lacks critical information, such as detailed shape, texture, and materials, needed by a conventional renderer to produce 2D renderings of the objects or scenes. In this paper, we bridge the information gap by generating photo-realistic rendering of indoor scenes from wireframe models in an image translation framework. While existing image synthesis methods can generate visually pleasing images for common objects such as faces and birds, these methods do not explicitly model and preserve essential structural constraints in a wireframe model, such as junctions, parallel lines, and planar surfaces. To this end, we propose a novel model based on a structure-appearance joint representation learned from both images and wireframes. In our model, structural constraints are explicitly enforced by learning a joint representation in a shared encoder network that must support the generation of both images and wireframes. Experiments on a wireframe-scene dataset show that our wireframe-to-image translation model significantly outperforms the state-of-the-art methods in both visual quality and structural integrity of generated images.
Simpler Statistically Sender Private Oblivious Transfer from Ideals of Cyclotomic Integers

We present a two-message oblivious transfer protocol achieving statistical sender privacy and computational receiver privacy based on the RLWE assumption for cyclotomic number fields. This work improves upon prior lattice-based statistically sender-private oblivious transfer protocols by reducing the total communication between parties by a factor $$\mathcal {O}(n\log q)$$ for transfer of length O(n) messages. Prior work of Brakerski and Döttling uses transference theorems to show that either a lattice or its dual must have short vectors, the existence of which guarantees lossy encryption for encodings with respect to that lattice, and therefore statistical sender privacy. In the case of ideal lattices from embeddings of cyclotomic integers, the existence of one short vector implies the existence of many, and therefore encryption with respect to either a lattice or its dual is guaranteed to "lose" more information about the message than can be ensured in the case of general lattices. This additional structure of ideals of cyclotomic integers allows for efficiency improvements beyond those that are typical when moving from the generic to ideal lattice setting, resulting in smaller message sizes for sender and receiver, as well as a protocol that is simpler to describe and analyze.
Imbalanced Data Handling in Multi-label Aspect Categorization using Oversampling and Ensemble Learning

In sentiment analysis, aspect based sentiment analysis (ABSA) provides detailed information of user sentiment for a product rather than document level and sentence level. Aspect categorization is one of ABSA tasks, which focuses on categorizing which aspects are related to a review text. This task working on multilabel data that usually have uneven distribution of aspect occurrences or imbalanced data condition. This paper uses 9284 data from user review text in the hotel domain. We employ 3 techniques to address imbalanced multilabel data, namely cross-coupling aggregation (COCOA), multilabel synthetic minority oversampling technique (MLSMOTE), and multilabel synthetic oversampling approach based on the local distribution of labels (MLSOL). Convolutional Neural Network (CNN)-Classifier Chain (CC)-Extreme Gradient Boosting (XGBoost) is employed as a baseline and base architecture to be applied into those 3 techniques of handling imbalanced multilabel dataset. COCOA and MLSMOTE are the best performers. COCOA achieved F1-Macro of 0.9272, F1 macro MLSMOTE is 0.9276 and F1-Macro baseline is 0.9261. The best performer of COCOA is configured using 4 parameters: binary relevance mode is smote-oversampling, multiclass mode is smote-oversampling, random state =10, and binary relevance ratio =0.5. The best performer of MLSMOTE is configured using 2 parameters: number of neighbors =5, and random state =42.
RETRACTED: Development of cultural tourism platform based on FPGA and convolutional neural network

Data mining can be described as a typical analysis of large datasets to investigate early unknown types, styles, and interpersonal relationships to generate the right decision information. It improves their markets and today to maintain control over whether these companies are forced into the data mining tools and technologies they use to develop and manage tourism products and services in the market. It is falling out of the favorable situation of the travel and tourism industry. Objective work is to provide and display its application in data mining and tourism. Advances in mobile technology provide an opportunity to obtain real-time information of travelers, such as time and space behavior, at the destination they visit. This study analyzed a large-scale mobile phone data set to capture the mobile phone traces of international tourists who visited South Korea. We adopt the trajectory data mining method to understand tourism activities' spatial structure in three different destinations. The research reveals tourist destinations and multiple "hot spots" (or popular areas) that interact spatially in these places through spatial cluster analysis and sequential pattern mining. Therefore, this article provides the planning of spatial model destinations to integrate important tourism influences, which is based on tourism design. The proposed system is modelled in Field Programmable Gate Array (FPGA) using Xilinx software.
On the modelling of multivariate counts with Cox processes and dependent shot noise intensities

In this paper, we develop a method to model and estimate several, _dependent_ count processes, using granular data. Specifically, we develop a multivariate Cox process with shot noise intensities to jointly model the arrival process of counts (e.g. insurance claims). The dependency structure is introduced via multivariate shot noise _intensity_ processes which are connected with the help of L\'evy copulas. In aggregate, our approach allows for (i) over-dispersion and auto-correlation within each line of business; (ii) realistic features involving time-varying, known covariates; and (iii) parsimonious dependence between processes without requiring simultaneous primary (e.g. accidents) events. The explicit incorporation of time-varying, known covariates can accommodate characteristics of real data and hence facilitate implementation in practice. In an insurance context, these could be changes in policy volumes over time, as well as seasonality patterns and trends, which may explain some of the relationship (dependence) between multiple claims processes, or at least help tease out those relationships. Finally, we develop a filtering algorithm based on the reversible-jump Markov Chain Monte Carlo (RJMCMC) method to estimate the latent stochastic intensities and illustrate model calibration using real data from the AUSI data set.
Attributed Network Embedding with Community Preservation

Network embedding (NE) is a method that maps nodes in a network into a low-dimensional and continuous vector space while maintains inherent features of the network. Most existing algorithms for NE focus on one or two of the aspects of topological structure, node attributes or community structure information, but without integrating the three in a unified framework. In this study, we develop a deep neural network-based framework for Attributed Network Embedding with Community Preservation (ANECP), which simultaneously incorporates the topological structure, node attributes as well as community structure together to obtain the low-dimensional distributed representations of nodes in the network. The use of deep neural networks captures the underlying high non-linearity in both topology and attribute information, while the incorporation of the community structure resolves the issues of data sparsity from microscopic perspective. Consequently, the obtained node representations can preserve proximity and discriminative. We conducted experimental studies using six real-world datasets. The experimental results show that proposed ANECP has superior performance over the existing methods.
Age and Gender Classification using Multiple Convolutional Neural Network

Abstract Since the advent of social media, there has been an increased interest in automatic age and gender classification through facial images. So, the process of age and gender classification is a crucial stage for many applications such as face verification, aging analysis, ad targeting and targeting of interest groups. Yet most age and gender classification systems still have some problems in real-world applications. This work involves an approach to age and gender classification using multiple convolutional neural networks (CNN). The proposed method has 5 phases as follows: face detection, remove background, face alignment, multiple CNN and voting systems. The multiple CNN model consists of three different CNN in structure and depth; the goal of this difference It is to extract various features for each network. Each network is trained separately on the AGFW dataset, and then we use the Voting system to combine predictions to get the result.
An Improved Deep Learning Solution for Object Detection in Self-Driving Cars

Reliable object detection is one of the most important requirements of environment perception in autonomous driving. The goal of this research is to find a convenient solution to detect objects in images from the self-driving car medium. Convolutional neural networks (CNNs) are deep neural networks used in image processing, object classification, and object recognition. Therefore, deep convolution networks are employed in this project to identify objects accurately. In order to train and evaluate the neural network, we used BDD100K dataset which is one of the largest open-source datasets in autonomous driving published by Berkeley University. The approach used in the proposed algorithm is to apply the feature pyramid network along with a single-stage object detector, which enhances the accuracy of object detection. In addition, it improves the detection of different scales, especially small ones compared to those of the previous works, leading to increased safety and security in self-driving cars.
Federated learning on wearable devices

Wearable devices collect user information about their activities and provide insights to improve their daily lifestyles. Smart health applications have achieved great success by training Machine Learning (ML) models on a large quantity of user data from wearables. However, user privacy and scalability are becoming critical challenges for training ML models in a centralized way. Federated learning (FL) is a novel ML paradigm with the goal of training high quality models while distributing training data over a large number of devices. In this demo, we present FL4W, a FL system with wearable devices enabling training a human activity recognition classifier. We also perform preliminary analytics to investigate the model performance with increasing computation of clients.