Reviewerly/exHarmony huggingface dataset:
Variational quantum classifiers through the lens of the Hessian

In quantum computing, the variational quantum algorithms (VQAs) are well suited for finding optimal combinations of things in specific applications ranging from chemistry all the way to finance. The training of VQAs with gradient descent optimization algorithm has shown a good convergence. At an early stage, the simulation of variational quantum circuits on noisy intermediate-scale quantum (NISQ) devices suffers from noisy outputs. Just like classical deep learning, it also suffers from vanishing gradient problems. It is a realistic goal to study the topology of loss landscape, to visualize the curvature information and trainability of these circuits in the existence of vanishing gradients. In this paper, we calculate the Hessian and visualize the loss landscape of variational quantum classifiers at different points in parameter space. The curvature information of variational quantum classifiers (VQC) is interpreted and the loss function's convergence is shown. It helps us better understand the behavior of variational quantum circuits to tackle optimization problems efficiently. We investigated the variational quantum classifiers via Hessian on quantum computers, starting with a simple 4-bit parity problem to gain insight into the practical behavior of Hessian, then thoroughly analyzed the behavior of Hessian's eigenvalues on training the variational quantum classifier for the Diabetes dataset. Finally, we show how the adaptive Hessian learning rate can influence the convergence while training the variational circuits.
Improved Bidirectional RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mi>∗</mi> </msup> </math> Path Planning Method for Smart Vehicle

In this paper, an improved bidirectional RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow /> <mi>∗</mi> </msup> </math> vehicle path planning method for smart vehicle is proposed. In this method, the resultant force of the artificial potential field is used to determine the search direction to improve the search efficiency. Different kinds of constraints are considered in the method, including the vehicle constraints and the vehicle driving environment constraints. The collision detection based on separating axis theorem is used to detect the collision between the vehicle and the obstacles to improve the planning efficiency. The cubic B-spline curve is used to optimize the path to make the path’s curvature continuous. Both simulation and experiment are implemented to verify the proposed improved bidirectional RRT <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow /> <mi>∗</mi> </msup> </math> method. In the simulation analysis, this paper’s method can generate the smoothest path and takes the shortest time compared with the other two methods and it can be adaptive to the complicated environment. In the real vehicle experiment, we can see from the test results that this paper’s method can be applied in practice on the smart electric vehicle platform; compared with others’ algorithm, this paper’s algorithm can generate shortest and smoothest path.
Argumentative Conversational Agents for Online Discussions

Artificial Intelligence is revolutionising our communication practices and the ways in which we interact with each other. This revolution does not only impact how we communicate, but it affects the nature of the partners with whom we communicate. Online discussion platforms now allow humans to communicate with artificial agents in the form of socialbots. Such agents have the potential to moderate online discussions and even manipulate and alter public opinions. In this paper, we propose to study this phenomenon using a constructed large-scale agent platform. At the heart of the platform lies an artificial agent that can moderate online discussions using argumentative messages. We investigate the influence of the agent on the evolution of an online debate involving human participants. The agent will dynamically react to their messages by moderating, supporting, or attacking their stances. We conducted two experiments to evaluate the platform while looking at the effects of the conversational agent. The first experiment is a large-scale discussion with 1076 citizens from Afghanistan discussing urban policy-making in the city of Kabul. The goal of the experiment was to increase the citizen involvement in implementing Sustainable Development Goals. The second experiment is a small-scale debate between a group of 16 students about globalisation and taxation in Myanmar. In the first experiment, we found that the agent improved the responsiveness of the participants and increased the number of identified ideas and issues. In the second experiment, we found that the agent polarised the debate by reinforcing the initial stances of the participant.
MatRiCT<sup>+</sup>: More Efficient Post-Quantum Private Blockchain Payments

We introduce MatRiCT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , a practical private blockchain payment protocol based on “post-quantum” lattice assumptions. MatRiCT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> builds on MatRiCT due to Esgin et al. (ACM CCS’19) and, in general, follows the Ring Confidential Transactions (RingCT) approach used in Monero, the largest privacy-preserving cryptocurrency. In terms of the practical aspects, MatRiCT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> has 2-18× shorter proofs (depending on the number of input accounts, M) and runs 3-11× faster (for a typical transaction) in comparison to MatRiCT. A significant advantage of MatRiCT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> is that the proof length’s dependence on M is very minimal (only O(logM)), while MatRiCT has a proof length linear in M. To support its efficiency, we devise several novel techniques in our design of MatRiCT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> to achieve compact lattice-based zeroknowledge proof systems, exploiting the algebraic properties of power-of-2 cyclotomic rings commonly used in practical latticebased cryptography. Along the way, we design a family of “optimal” challenge spaces, using a technique we call partition-and-sample, with minimal $\ell_{1}$-norm and invertible challenge differences (with overwhelming probability), while supporting highly-splitting power-of-2 cyclotomic rings. We believe all these results to be widely applicable and of independent interest.
An improved traffic lights recognition algorithm for autonomous driving in complex scenarios

Image recognition is susceptible to interference from the external environment. It is challenging to accurately and reliably recognize traffic lights in all-time and all-weather conditions. This article proposed an improved vision-based traffic lights recognition algorithm for autonomous driving, integrating deep learning and multi-sensor data fusion assist (MSDA). We introduce a method to obtain the best size of the region of interest (ROI) dynamically, including four aspects. First, based on multi-sensor data (RTK BDS/GPS, IMU, camera, and LiDAR) acquired in a normal environment, we generated a prior map that contained sufficient traffic lights information. And then, by analyzing the relationship between the error of the sensors and the optimal size of ROI, the adaptively dynamic adjustment (ADA) model was built. Furthermore, according to the multi-sensor data fusion positioning and ADA model, the optimal ROI can be obtained to predict the location of traffic lights. Finally, YOLOv4 is employed to extract and identify the image features. We evaluated our algorithm using a public data set and actual city road test at night. The experimental results demonstrate that the proposed algorithm has a relatively high accuracy rate in complex scenarios and can promote the engineering application of autonomous driving technology.
Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks

Continual learning is essential for all real-world applications, as frozen pre-trained models cannot effectively deal with non-stationary data distributions. The purpose of this study is to review the state-of-the-art methods that allow continuous learning of computational models over time. We primarily focus on the learning algorithms that perform continuous learning in an online fashion from considerably large (or infinite) sequential data and require substantially low computational and memory resources. We critically analyze the key challenges associated with continual learning for autonomous real-world systems and compare current methods in terms of computations, memory, and network/model complexity. We also briefly describe the implementations of continuous learning algorithms under three main autonomous systems, i.e., self-driving vehicles, unmanned aerial vehicles, and urban robots. The learning methods of these autonomous systems and their strengths and limitations are extensively explored in this article.
Advanced security and privacy technique for digital text in smart grid communications

In the current era of technology, digital contents copyright protection is a significant issue in the Internet of Things and smart grid. Digital contents are distributed, reproduced, and disclosed with extensive use of communication technologies such as the cloud. It has become quite easy to produce an illegal copy of digital content. Limited techniques are available for text documents ownership verification and copyright protection in the Internet of Things. However, most of the existing techniques produce distortion during watermark insertion, which directly impacts imperceptibility. We propose a robust and secure digital text watermarking technique for Microsoft Word documents in the said perspective. The watermark is extracted with high probability, which illustrates that the proposed technique is robust, imperceptible, and improves the capacity. The results of the proposed technique are compared with the previous techniques of text watermarking. The proposed technique is imperceptible with the Peak signal-to-noise ratio is 33.65, and the similarity percentage is 99.42%. The length of the secret message is improved from 0.2 to 1.24 kilobytes.
Data Sampling Strategies for Click Fraud Detection Using Imbalanced User Click Data of Online Advertising: An Empirical Review

In the pay-per-click online advertisement model, fraudulent publishers’ presence is rare than that of genuine publishers. This high-class imbalance between fraudulent and genuine publishers poses a challenge for the accurate classification of fraudsters due to the bias of automated learning models towards the outnumbered class. In this work, an empirical evaluation of significant popular data sampling methods is carried out using nine state-of-the-art learning models for classifying fraudsters in online advertisement. The main objective of this work is to understand the effect of oversampling, under-sampling, and hybrid sampling methods on the performance of various classifiers in click fraud detection. Extensive experiments are performed on the benchmark FDMA-2012 user-click dataset. The performance of each combination of data sampling method and classifiers is validated using average precision, recall, f1-score, and AUC. The results are also compared with the existing state-of-the-art models. The results suggest that adaptive synthetic sampling (ADASYN) oversampling with a gradient tree boosting (GTB) model performs best with an average precision score of 64.32%.
Cloud and Edge Computing-Based Computer Forensics: Challenges and Open Problems

In recent years, there has been a dramatic change in attitude towards computers and the use of computer resources in general. Cloud and Edge computing have emerged as the most widely used technologies, including fog computing and the Internet of Things (IoT). There are several benefits in exploiting Cloud and Edge computing paradigms, such as lower costs and higher efficiency. It provides data computation and storage where data are processed, enables better data control, faster understanding and actions, and continuous operation. However, though these benefits seem to be appealing, their effects on computer forensics are somewhat undesirable. The complexity of the Cloud and Edge environments and their key features present many technical challenges from multiple stakeholders. This paper seeks to establish an in-depth understanding of the impact of Cloud and Edge computing-based environmental factors. Software and hardware tools used in the digital forensic process, forensic methods for handling tampered sound files, hidden files, image files, or images with steganography, etc. The technical/legal challenges and the open design problems (such as distributed maintenance, multitasking and practicality) highlight the various challenges for the digital forensics process.
A Data-Driven Framework for Intention Prediction via Eye Movement With Applications to Assistive Systems

Fast and accurate human intention prediction can significantly advance the performance of assistive devices for patients with limited motor or communication abilities.Among available modalities, eye movement can be valuable for inferring the user's intention, as it can be tracked non-invasively.However, existing limited studies in this domain do not provide the level of accuracy required for the reliable operation of assistive systems.By taking a datadriven approach, this paper presents a new framework that utilizes the spatial and temporal patterns of eye movement along with deep learning to predict the user's intention.In the proposed framework, the spatial patterns of gaze are identified by clustering the gaze points based on their density over displayed images in order to find the regions of interest (ROIs).The temporal patterns of gaze are identified via hidden Markov models (HMMs) to find the transition sequence between ROIs.Transfer learning is utilized to identify the objects of interest in the displayed images.Finally, models are developed to predict the user's intention after completing the task as well as at early stages of the task.The proposed framework is evaluated in an experiment involving predicting intended daily-life activities.Results indicate that an average classification accuracy of 97.42% is achieved, which is considerably higher than existing gazebased intention prediction studies.
A Novel Approach of Symmetric Key Cryptography

Cryptography plays an important role in secured data communication over the network using unknown, untrusted mediums. In the fast digital transformation, traffic on network increasing rapidly where users are always connected, being online, anytime anywhere. As part of data, extortions like altering, spoofing and snooping of are quite common over the network by unauthorized access. Cryptography is well known mechanism where asymmetric or symmetric algorithms using public or private keys provide secure data communication. There are many more cryptography algorithms existing but with emerging technologies and diverse application domains seeking consistent enhancement and better performance with resource restriction. In this paper, we present a novel symmetric cryptography technique based on Caesar cipher symmetric cryptography technique to transform the original text/message into secret text/message. In this technique, the sender transmits hash code instead of symmetric key and Hash code provides the symmetric key to the receiver to decrypt the message of a sender. Proposed method works for all 256 characters having ASCII value from 0 to 255.
Boosting Few-Shot Classification with View-Learnable Contrastive Learning

The goal of few-shot classification is to classify new categories with few labeled examples within each class. Nowadays, the excellent performance in handling few-shot classification problems is shown by metric-based meta-learning methods. However, it is very hard for previous methods to discriminate the fine-grained sub-categories in the embedding space without fine-grained labels. This may lead to unsatisfactory generalization to fine-grained sub-categories, and thus affects model interpretation. To tackle this problem, we introduce the contrastive loss into few-shot classification for learning latent fine-grained structure in the embedding space. Furthermore, to overcome the drawbacks of random image transformation used in current contrastive learning in producing noisy and inaccurate image pairs (i.e., views), we develop a learning-to-learn algorithm to automatically generate different views of the same image. Extensive experiments on standard few-shot learning benchmarks demonstrate the superiority of our method.
An efficient image encryption scheme based on double affine substitution box and chaotic system

Privacy of information is one of the most important and unavoidable issues in our digitally advance era. A huge amount of information transmitted over different servers and networking protocols. The sphere of digitally advanced world is tied with information in different forms of facilitations, which includes online banking systems, ecommerce and many more. Providing the ease of access, anything online makes our confidential information open to different threats. Therefore, to enjoy ease of access and at the same time secure our digital information from theft, we need a robust information security system. In this article, we have designed a novel and an efficient security system, which provides secrecy to our digital information. The designed encryption scheme is fundamentally a combination of chaos and nonlinear confusion components. We have developed a new mechanism of adding confusion, namely S8 permutation of double affine transformation to construct 40320 new substitution boxes (S-boxes) having nonlinearity 112 from a single S-box. Moreover, nonlinear Lorenz dynamical system is utilized to select any three S-boxes from 40320 newly generated nonlinear components. To add diffusion in our proposed algorithm, we have utilized Chirikov discrete iterative map. The excellence of an offered digital image encryption has been examined and evaluated with standard benchmarks. The simulation results reveal that the quality of the image encryption passes all these tests and is comparable to current benchmarks.
General fuzzy C-means clustering algorithm using Minkowski metric

As one of the most commonly used clustering methods, fuzzy clustering technique such as the Fuzzy C-means (FCM) has undergone a rapid development. In this paper, a general FCM clustering algorithm based on contraction mapping (cGFCM) is proposed for more general cases of using Minkowski metric (Lp-norm distance) as the similarity measure, and the analytical method for calculating the parameters of the proposed algorithm is given. The core of the proposed cGFCM algorithm lies on constructing a contraction mapping to update the prototypes when an arbitrary Minkowski metric is used to measure the closeness of data points. Subsequently, mainly guided by the Banach contraction mapping principle, the algorithm and implementation approaches are discussed in detail, and the correctness and feasibility of the proposed method are proved. Moreover, the convergence of the proposed algorithm is also discussed. Experimental studies carried out on both synthetic data sets and real-world data sets show that the proposed cGFCM algorithm extends FCM to more general cases without extra time and space costs. Compared with another generalized FCM clustering strategy and other five state-of-the-art clustering methods, the proposed algorithm can not only reach better performance in both clustering accuracy and stability, but reduce the running time several-fold.
Towards Diverse Paragraph Captioning for Untrimmed Videos

Video paragraph captioning aims to describe multiple events in untrimmed videos with descriptive paragraphs. Existing approaches mainly solve the problem in two steps: event detection and then event captioning. Such two-step manner makes the quality of generated paragraphs highly dependent on the accuracy of event proposal detection which is already a challenging task. In this paper, we propose a paragraph captioning model which eschews the problematic event detection stage and directly generates paragraphs for untrimmed videos. To describe coherent and diverse events, we propose to enhance the conventional temporal attention with dynamic video memories, which progressively exposes new video features and suppresses over-accessed video contents to control visual focuses of the model. In addition, a diversity-driven training strategy is proposed to improve diversity of paragraph on the language perspective. Considering that untrimmed videos generally contain massive but redundant frames, we further augment the video encoder with keyframe awareness to improve efficiency. Experimental results on the ActivityNet and Charades datasets show that our proposed model significantly outperforms the state-of-the-art performance on both accuracy and diversity metrics without using any event boundary annotations. Code will be released at https://github.com/syuqings/video-paragraph.
Multi-Sensor Track-to-Track Association and Spatial Registration Algorithm Under Incomplete Measurements

Spatial registration and track-to-track association (which are mutually coupled) are essential parts in the process of multi-sensor information fusion. The quality of the spatial registration and track association algorithm directly influences the subsequent fusion performance. Aiming to solve the spatial registration and track association problem in the case where incomplete measurements are provided by different sensors, this paper proposes a residual bias estimation registration (RBER) method based on maximum likelihood and the sequential m-best track association algorithm based on the new target density (SMBTANTD). The RBER method realizes the update of incomplete measurements by sequential filtering technology and eliminates the systematic bias of sensors by using information on the significant targets. The SMBTANTD method introduces a new target density in the correlation matrix, which effectively solves the association problem in the scenarios where the numbers of targets measured by multiple sensors are inconsistent. The reported simulation results demonstrate that the proposed algorithm can not only accurately estimate the systematic bias of the sensors, but also significantly improve the performance of track association.
Transforming the Reading Preferences of Today’s Youth in the Digital Age: Intercultural Dialog

The article deals with the transformation of readers’ preferences and the formation of a “new reader” at the end of the 20th and beginning of the 21st century. In the study, the authors draw attention to the shift in the priority of the format of youth reading in favor of digital, to the loss of the former role of libraries as centers of non-formal education, as well as to the need to provide assistance to children and young people on the part of “teacher of literature”. An international study undertaken in Russia, the U.S., and the Czech Republic touches upon socio-cultural changes that influenced the reading process among the youth of these countries and the particular interest of the analyzed age group in the fantasy genre, which indicates a change in the reading preferences of young people and the need for understanding this process both in theoretical aspects and taking into account the results of empirical research. The authors conclude that it is advisable to determine the scale of values of a modern person by means of literary pedagogy, to bring it to a common denominator in the intercultural space. As the leading genre of revealed reader preferences, fantasy is becoming a multimedia phenomenon and is shifting the age boundaries of potential readers around the world.