Reviewerly/exHarmony huggingface dataset:
Video Image Stabilization Algorithm for Remote Control Driving

Under the condition of remote control driving, the vehicle jitter caused by bumpy road condition causes the camera to acquire the picture to shake violently. In order to solve this problem, this paper adopts the method of combining vehicle-mounted inertial navigation and electronic image stabilization to stabilize the image and then compensate the image, so as to realize the stable and reliable display of the image. Firstly, vehicle inertial derivative data and video image data are synchronized in time to determine the base frame. Then Shi-Tomasi corner detection is carried out on the video image, and then the corner is tracked by optical flow method. The affine transformation matrix between adjacent frames is estimated by RANSAC algorithm, and the original affine transformation parameters are obtained. After smoothing the original affine parameters by Kalman filter, the optimized affine parameters are obtained. Finally, synchronous inertial derivative data is used for motion compensation to eliminate the black edge of image stabilization, so as to obtain a stable video, which is further output through the ROS system. The experimental results show that, after image stabilization, the MSE decreases by 996.499 and PSNR increases by 7. 199dB compared with the original video, and the average processing time of a single frame is 21. 855ms.
Ensembles for Text-Based Sarcasm Detection

Sarcasm is a big challenge for text related classification such as sentiment analysis and opinion summarization. The nature of sarcasm is to express opinions in a way that carries the opposite sentiment as a way to insult or make fun of the situation. Because of its nature, it poses a very difficult challenge in text classification task as they will be classified according to words used and not the meaning implied. Therefore, the accuracy of classification will be affected significantly. Sarcasm is also a problem for the task of sentiment analysis and emotion detection, as they reflect opposite sentiments of the author. So, sarcasm detection is needed to find the sarcasm text and revert the sentiment of the text. In the recent works seen in sarcasm detection, machine learning methods and deep learning methods are more commonly used to perform the task. Although deep learners are efficient learners, machine learner are still widely used and can perform as well as deep learners with proper training. This work seek to compare the different ensemble settings to evaluate the performance of ensembles against simple learners. The results show that ensembles can improve the performance of simple learners and even deep learners.
Research on Mnist Handwritten Numbers Recognition based on CNN

Abstract In view of the increasing demand for handwritten digit recognition, a handwritten digit recognition model based on convolutional neural network is proposed. The model includes 1 input layer and 2 convolutional layers (5*5 convolution Core), 2 pooling layers (2*2 pooling core), 1 fully connected layer, 1 output layer, and use the mnist data set for model training and prediction. After a lot of training and participation, the accuracy rate of the training set was finally reached to 100%, and the accuracy rate of 99.25% was also achieved on the test set, which can meet the requirements of recognizing handwritten digits.
New direction in Cryptography: Homomorphic Encryption

Homomorphic Encryption was firstly introduced in 1978, but even nowadays it can be named as a &#x201D;new direction&#x201D; in cryptography due to the limited researches in this area. The main idea of homomorphic encryption is to give users the possibility to change encrypted data without its decryption.In this work the main mathematical concept of homomorphic encryption will be shortly described, the new method for obtaining homomorphism in an encryption system based on set theory will be illustrated.
Shape Estimation of Negative Obstacles for Autonomous Navigation

Obstacle detection and avoidance plays a crucial role in autonomous navigation of unmanned ground vehicles. This becomes more challenging in off-road environments due to the higher probability of finding negative obstacles (e.g., holes, ditches, trenches, etc.) compared with on-road environments. One approach to solve this problem is to avoid the candidate path with a negative obstacle, but in off-road avoiding negative obstacles all the time is not possible. In such cases, the path planner may need to choose a candidate path with a negative obstacle that causes the least amount of damage to the vehicle. To deal better with these types of scenarios, this study introduces a novel approach to perform shape estimation of negative obstacles using LiDAR 3D point cloud data. The dimensions (width, diameter, and depth) and the location (center) of negative obstacles are calculated based on estimated shape. This approach is tested on different terrain types using the Mississippi Autonomous Vehicle Simulation (MAVS).
Delivering a rapid digital response to the COVID-19 pandemic

Lessons learned from South Korea's contact-tracing system.
Hybrid Quantum Approximate Optimization Using Enhanced Ant Colony Optimization to Solve Large-Scale Combinatorial Optimization Problems

Quantum computing is a promising technology that may provide breakthrough solutions to today’s difficult problems such as breaking encryption and solving large-scale combinatorial optimization problems. A class of algorithms referred to as Quantum Approximate Optimization Algorithm (QAOA) have been recently proposed. QAOA attempts to approximately solve hard problems using a protocol know as bang-bang. The technique is based on the unitary evolution using a Hamiltonian encoding of the objective function of the combinatorial optimization problem. QAOA has been explored in the context of several optimization problems such as Max-Cut problem, variational Eigenvalue problem etc. Recently, attempts have been made to create QAOA for the Traveling Salesman Problem (TSP). Due to small node size and limited solution capability of the currently available Quantum computers and/or simulators, we develop a hybrid approach where subgraphs of a TSP tour are executed on a Quantum computer and the results from the quantum optimization are combined in further optimization of the whole tour. Since the local optimization of a subgraph is prone to getting stuck in a local minima, we overcome this problem by using a parallel Ant Colony Optimization Algorithm with periodic pheromone exchange between colonies. Our results are encouraging and yield optimum results for benchmarks with less than 50 nodes, and usually within 1% of the optimal solution for benchmarks with around 200 nodes.
Survey on Blockchain Enabled Authentication for Industrial Internet of Things

The Industrial Internet of Things (IIoT) integrates heterogeneous industrial devices, intelligent sensors, and actuators to provide a new dimension to the industrial systems and environments. As the IIoT expands to various aspects of the Industry, devices from different industrial domains need to collaborate without compromising the privacy and security of the data being exchanged and stored. The biggest challenge IIoT is going to face is security and privacy. Any attack against an industrial environment can adversely affect human life, the quality of products, and permanent damage the machines for an extended period. Authentication is considered the first security control in IIoT system to ensure that the right user accesses the right resources. Blockchain is a distributed public ledger that contains blocks of tamper-proof data. Blockchain-based cybersecurity platforms can protect the connected de-vices in an IIoT platform using digital identities and can be used to authenticate the participating entities. A blockchain enabled IIoT platform has a decentralized infrastructure making it impossible for hackers to attack a network because all participants are immediately notified of any alterations. This work presented a critical review of the existing research works on the authentication solutions based on blockchain based technologies. The current research gaps were analyzed and discussed as well.
Descriptive statistics and advanced text analytics: A dual extension

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SOMETIMES LESS IS MORE

Abstract Effects of phonetically variable input (PVI) for processing instruction (PI) training and the number of training items were tested with a picture-selection eye-tracking task. Intermediate second language (L2) learners of French (n = 174) were tested before and after they received either a short (24 items), medium (48), or long (96) training on the causative structure with either single- or multivoice input. PI improved picture-selection accuracy from about 10% to above 50% regardless of the training size. Eye-tracking data showed a reduction in looks to the incorrect picture only after the short and medium training: it surfaced regardless of voice variability after the short training, whereas multivoice training led to a greater reduction after the medium training. Long training did not yield a reliable reduction of incorrect looks regardless of voice variability. Taken together, PVI does not hinder L2 syntactic learning. Learners may benefit more from a relatively shorter training with PVI.
Efficient Classification for Age and Gender of Unconstrained Face Images

Age and gender classification from images is a task which has become relevant to an increasing amount of applications. Conventional methods work well in controlled environments but their performance on in-the-wild or unconstrained face images tends to be unsatisfactory due to the presence of the larger number of variations present in such images. Convolutional Neural Network has been used in the recent past to alleviate some of the difficulties, but the problem of achieving reliable performance on age estimation still exists. In this paper, we present a new CNN approach based on the architecture of efficientnet which exhibits better results and generalization ability than the previous approaches on adience and other unconstrained face images dataset for age and gender classification. The experiments performed shows that the proposed method outperforms other models when tested and validated on the unfiltered and in-the-wild dataset.
Off-chain Anonymous Payment Channel Scheme based on Mimblewimble

Mimblewimble, proposed by an anonymous author in 2016, combines several privacy protection technologies to enhance the Bitcoin blockchain. However, Mimblewimble does not support fast micropayments, and there are still problems such as high transaction fees and poor scalability. The payment channel allows all parties to perform transactions off-chain while maintaining the security of transactions on-chain to reduce the burden of the blockchain. It has been used in the Lightning Network to expand the blockchain. On the basis of Mimblewimble, our paper exploits a method of adding a random blinding factor to the amount of additively homomorphic commitments, and describes a scheme of using payment channels for fast off-chain transactions. The scheme can implement fast off-chain payments and improve scalability while protecting transaction privacy.
Research on Opinion Targets Extraction of Travel Reviews Based on RoBERTa Embedded BILSTM-CRF Model

Tourism is a form of cultural expression. The key step of fine-grained sentiment analysis of travel review evaluation text is opinion targets extraction. It aims to analyze travel review text and extract the opinion targets contained in it, and targets extraction’s accuracy rate directly affects the accuracy rate of sentiment analysis. Due to the outstanding performance of pre-training language models in the field of natural language processing in recent years, in order to improve the accuracy of opinion targets extraction, we propose a RoBERTa pre-training language model and Chinese pre-training word embedding vector, combined with BiLSTM (bidirectional long short-term memory) and CRF (conditional random field) opinion targets extraction model. The experimental results on the travel review data set of the Mafengwo travel software show that the extraction effect of this model is improved to varying degrees compared with other existing opinion targets extraction models based on deep learning.
Synthesis of a Controller for a System with a Delay

The task of controlling some systems is complicated due to the fact that real technical plants may contain delay links. That is, there is a certain time period when there is no reaction from the regulated plant to the control action. Usually, the delay link presence negatively affects the quality of such system management. There are various ways to synthesize a control system for such systems. These include: Smith predictors, specialized control tuning algorithms, the use of self-adjusting systems with active adaptation. However, they impose additional requirements on the system dynamics or are complex in technical implementation and configuration. Within the framework of this article, an attempt is made to calculate the controller by the polynomial method for a plant with a delay. The delay mathematical model is obtained by approximating the delay link by Pade series. To ensure the necessary dynamics of the transition process, we will require the system to preserve the poles of the delay link. Then the controller, calculated for a system with a delay link in the form of a Pade series, is applied to a system with an &#x201C;ideal&#x201D; delay. For clarity of the calculations carried out, an plant in the form of a aperiodic combination and integrating links connected in different ways is taken as an example. The integrating link is necessary to give the system astatic properties. As a delay, we will use the approximation of a number of Pads of different orders. The lag link gives the system an unstable character.
Convolutional Neural Network for Predicting Sentiment: Case Study in Tourism

As a country with much natural wealth, Indonesia tries to utilize beach tourism in Bali to attract tourists. One of the websites in the tourism sector that is widely used by the world community today is TripAdvisor. Through TripAdvisor, tourists can find information about the beaches in Bali. Each beach has reviews written by tourists who have visited. However, reviews on TripAdvisor are unreliable and even biased. Therefore, Sentiment Analysis of Beach Reviews in Bali on the TripAdvisor Website can be a solution. This study uses real datasets from the TripAdvisor website in tourist reviews of the five most favorite beaches in Bali: Seminyak, Nusa Dua, Double Six, Kelingking, and Canggu. The research used the Convolutional Neural Network (CNN) architecture to produce positive and negative label predictions. The sentiment analysis results are visualized into a graph that describes tourist opinions on the five most favorite beaches in Bali. This study also measures the performance of the CNN model in making predictions. The accuracy obtained is 88% on Seminyak beach, 90% on Nusa Dua beach, 90% on Double Six beach, 87% on Kelingking Beach, and 85% on Canggu Beach. The CNN model performance measurement also produces precision, recall, and ROC curve on each beach.
Construction of an Imitation Learning Agent Using Game Records of Unspecified Players

If it is possible to make a machine learn how a human learner behaves in a certain situation, it can be used as an agent that behaves like a human. Such an imitation learning agent can be used in various educational applications. We describe a learning method for constructing an imitation learning agent from a small number of game records of a specific individual, based on the game records of unspecified players.
Cognitive, Virtual and Physical Context in Smart Learning

This edition brings together experiences that have been conducted mostly in a non-face-to-face teaching context due to the Covid-19 pandemic. Perhaps for this reason, the learning context has set a common line of research within this field. Four papers have been accepted in this edition and they deal with the learning context in very different ways: the physical context where the learning process takes place, the virtual context and the usability of the learning tools, and the cognitive context related to the skills, concepts and attitudes of the learners.
Tool-based Asymmetric Interaction for Selection in VR

Mainstream Virtual Reality (VR) devices on the market nowadays mostly use symmetric interaction design for input, yet common practice by artists suggests asymmetric interaction using different input tools in each hand could be a better alternative for 3D modeling tasks in VR. In this paper, we explore the performance and usability of a tool-based asymmetric interaction method for a 3D object selection task in VR and compare it with a symmetric interface. The symmetric VR interface uses two identical handheld controllers to select points on a sphere, while the asymmetric interface uses a handheld controller and a stylus. We conducted a user study to compare these two interfaces, and found that the asymmetric system was faster, required less workload, and was rated with better usability. We also discuss the opportunities for tool-based asymmetric input to optimize VR art workflows, and future research directions.
Subsumption reduces dataset dimensionality without decreasing performance of a machine learning classifier

When features in a high dimension dataset are organized hierarchically, there is an inherent opportunity to reduce dimensionality. Since more specific concepts are subsumed by more general concepts, subsumption can be applied successively to reduce dimensionality. We tested whether sub-sumption could reduce the dimensionality of a disease dataset without impairing classification accuracy. We started with a dataset that had 168 neurological patients, 14 diagnoses, and 293 unique features. We applied subsumption repeatedly to create eight successively smaller datasets, ranging from 293 dimensions in the largest dataset to 11 dimensions in the smallest dataset. We tested a MLP classifier on all eight datasets. Precision, recall, accuracy, and validation declined only at the lowest dimensionality. Our preliminary results suggest that when features in a high dimension dataset are derived from a hierarchical ontology, subsumption is a viable strategy to reduce dimensionality.Clinical relevance- Datasets derived from electronic health records are often of high dimensionality. If features in the dataset are based on concepts from a hierarchical ontology, subsumption can reduce dimensionality.
A practical succinct dynamic graph representation

We address the problem of representing dynamic graphs using k2-trees. The k2-tree data structure is one of the succinct data structures proposed for representing static graphs, and binary relations in general. It relies on compact representations of bit vectors. Hence, by relying on compact representations of dynamic bit vectors, we can also represent dynamic graphs. However, this approach suffers of a well known bottleneck in compressed dynamic indexing. In this paper we present a k2-tree based implementation which follows instead the ideas by Munro et al. (PODS 2015) to circumvent this bottleneck. We present two dynamic graph k2-tree implementations, one as a standalone implementation and another as a C++ library. The library includes efficient edge and neighbourhood iterators, as well as some illustrative algorithms. Our experimental results show that these implementations are competitive in practice.
BM25 Algorithm Driven Search Tool for Linking Exploits to Vulnerabilities

The vulnerability monitoring and prioritizing could be challenging for enterprise security stakeholders. With a number of vulnerabilities published regularly, few of the vulnerabilities result in real-life attacks. However, no matter how slight the possibility of vulnerabilities could result in an exploit, proactive attention is given to vulnerability related information by the cybersecurity community. Researchers aim to consolidate the information regarding vulnerabilities, including but not limited to CVSS, vulnerability description, vulnerability mentioned on a different platform including social media platforms, dark web. The presented work in this paper is motivated to map released vulnerabilities to exposed exploits and vice versa. The mapping could result in one of the features to approximate the possibility of exploiting a vulnerability. The proposed work is accomplished in the form of a search tool to find relevant vulnerabilities for the given exploit title and vice versa
An improved chaotic ideal gas molecular movement algorithm for engineering optimization problems

Abstract Recently, the ideal gas molecular movement (IGMM) optimization algorithm was introduced by the authors. It is inspired by the movement and collision behaviour of the ideal gas molecules in an isolated medium. Although the IGMM could perform a high potential in determining the global optimum, improving its convergence behaviour came to the attention of the present investigation to deal with any type of especially highly nonlinear optimization problems. For that purpose, two actions took place hybridly. One was the simulation of the vibrational motion of gas molecules, especially at the early stages of the optimization process. The second simultaneous move concerned the non‐repetitive nature of chaotic maps which could diversify the molecules, reduce the threat of premature convergence and improve the convergence speed of the IGMM algorithm. Therefore, this article investigates 10 different chaotic map functions and a random number generator along with four different Vibrational‐based Chaotic IGMM (VCIGMM) strategies to still improve the speed of convergence. The results of applying the proposed algorithm to various numerical and engineering benchmark problems, intensely show that the chaotic maps, merged with vibrational motion of gas molecules, significantly improved the performance of the IGMM. It could considerably outperform some of the well‐known meta‐heuristic optimization algorithms in the literature.
CitiusSynapse: A Deep Learning Framework for Embedded Systems

As embedded systems, such as smartphones with limited resources, have become increasingly popular, active research has recently been conducted on performing on-device deep learning in such systems. Therefore, in this study, we propose a deep learning framework that is specialized for embedded systems with limited resources, the operation processing structure of which differs from that of standard PCs. The proposed framework supports an OpenCL-based accelerator engine for accelerator deep learning operations in various embedded systems. Moreover, the parallel processing performance of OpenCL is maximized through an OpenCL kernel that is optimized for embedded GPUs, and the structural characteristics of embedded systems, such as unified memory. Furthermore, an on-device optimizer for optimizing the performance in on-device environments, and model converters for compatibility with conventional frameworks, are provided. The results of a performance evaluation show that the proposed on-device framework outperformed conventional methods.