Reviewerly/exHarmony huggingface dataset:
Gemini<sup>2</sup>: Generating Keyframe-Oriented Animated Transitions Between Statistical Graphics

Complex animated transitions may be easier to understand when divided into separate, consecutive stages. However, effective staging requires careful attention to both animation semantics and timing parameters. We present Gemini <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , a system for creating staged animations from a sequence of chart keyframes. Given only a start state and an end state, Gemini <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> can automatically recommend intermediate keyframes for designers to consider. The Gemini <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> recommendation engine leverages Gemini, our prior work, and GraphScape to itemize the given complex change into semantic edit operations and to recombine operations into stages with a guided order for clearly conveying the semantics. To evaluate Gemini <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ’s recommendations, we conducted a human-subject study in which participants ranked recommended animations from both Gemini <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and Gemini. We find that Gemini <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ’s animation recommendation ranking is well aligned with subjects’ preferences, and Gemini <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> can recommend favorable animations that Gemini cannot support.
Learning Interactive Driving Policies via Data-driven Simulation

Data-driven simulators promise high data-efficiency for driving policy learning. When used for modelling interactions, this data-efficiency becomes a bottleneck: small underlying datasets often lack interesting and challenging edge cases for learning interactive driving. We address this challenge by proposing a data-driven simulation engine† that uses inpainted ado vehicles for learning robust driving policies. Thus, our approach can be used to learn policies that involve multi-agent interactions and allows for training via state-of-the-art policy learning methods. We evaluate the approach for learning standard interaction scenarios in driving. In extensive experiments, our work demonstrates that the resulting policies can be directly transferred to a full-scale autonomous vehicle without making use of any traditional sim-to-real transfer techniques such as domain randomization.
Amalgamation of Blockchain and AI to Classify Malicious Behavior of Autonomous Vehicles

Blockchain is a prevalent technology whose applications are aimed towards security, privacy, traceability and trust. One such application is autonomous vehicles (AVs). The biggest concern of AVs is their safety. A malicious AV can cause accidents that may be life-threatening. We have proposed a blockchain and ensemble learning-based system to classify the vehicles as malicious to address the aforementioned safety issue. Smart contracts for AVs transaction verification have been designed to count the number of malicious activities performed by any AV. Finally, results show that the proposed model achieved the goal of this paper with an accuracy of 97.5 %.
Building a Question Answering System for the Manufacturing Domain

The design or simulation analysis of special equipment products must follow the national standards, and hence it may be necessary to repeatedly consult the contents of the standards in the design process.However, it is difficult for the traditional question answering system based on keyword retrieval to give accurate answers to technical questions.Therefore, we use natural language processing techniques to design a question answering system for the decision-making process in pressure vessel design.To solve the problem of insufficient training data for the technology question answering system, we propose a method to generate questions according to a declarative sentence from several different dimensions so that multiple question-answer pairs can be obtained from a declarative sentence.In addition, we designed an interactive attention model based on a bidirectional long short-term memory (BiLSTM) network to improve the performance of the similarity comparison of two question sentences.Finally, the performance of the question answering system was tested on public and technical domain datasets.
Biometric System De-identification: Concepts, Applications, and Open Problems

This chapter advances information security research by integrating privacy concepts with the most recent biometric developments. Analytical discussions on how physiological, behavioral and social behavioral biometric data can be protected in various authentication applications will be presented. The chapter starts with introducing new de-identification classification, including complete de-identification, soft biometric preserving de-identification, soft biometric preserving utility retained de-identification, and traditional biometric de-identification. It then proceeds to introduce additional types of de-identification, related to emerging biometric research domains which include social behavioral biometrics, aesthetic identification, sensor-based biometrics, spatial and temporal patterns, and psychological user profiles. This chapter also provides some insights into current and emerging research in the multi-modal biometric domain and proposes for the first time multi-modal biometric system de-identification based on deep learning. It concludes with formulating open questions and investigating future directions in this vibrant research field. Answers to those questions will assist not only in the establishment of the new methods in the biometric security and privacy domains, but also provide insights into the future emerging topics in big data analytics and social network research.
Quantum Simulation of Molecules in Solution

Quantum chemical calculations on quantum computers have been focused mostly on simulating molecules in the gas phase. Molecules in liquid solution are, however, most relevant for chemistry. Continuum solvation models represent a good compromise between computational affordability and accuracy in describing solvation effects within a quantum chemical description of solute molecules. In this work, we extend the variational quantum eigensolver to simulate solvated systems using the polarizable continuum model. To account for the state dependent solute-solvent interaction we generalize the variational quantum eigensolver algorithm to treat non-linear molecular Hamiltonians. We show that including solvation effects does not impact the algorithmic efficiency. Numerical results of noiseless simulations for molecular systems with up to 12 spin-orbitals (qubits) are presented. Furthermore, calculations performed on a simulated noisy quantum hardware (IBM Q, Mumbai) yield computed solvation free energies in fair agreement with the classical calculations.
Contrastive attention network with dense field estimation for face completion

Most modern face completion approaches adopt an autoencoder or its variants to restore missing regions in face images. Encoders are often utilized to learn powerful representations that play an important role in meeting the challenges of sophisticated learning tasks. Specifically, various kinds of masks are often presented in face images in the wild, forming complex patterns, especially in this hard period of COVID-19. It's difficult for encoders to capture such powerful representations under this complex situation. To address this challenge, we propose a self-supervised Siamese inference network to improve the generalization and robustness of encoders. It can encode contextual semantics from full-resolution images and obtain more discriminative representations. To deal with geometric variations of face images, a dense correspondence field is integrated into the network. We further propose a multi-scale decoder with a novel dual attention fusion module (DAF), which can combine the restored and known regions in an adaptive manner. This multi-scale architecture is beneficial for the decoder to utilize discriminative representations learned from encoders into images. Extensive experiments clearly demonstrate that the proposed approach not only achieves more appealing results compared with state-of-the-art methods but also improves the performance of masked face recognition dramatically.
Online music education for sustainable development: Analysis of music learning videos in<i>e-Hakseupteo</i>

This study investigates music learning video content at e-Hakseupteo, a government-supported e-learning platform utilized during the COVID-19 pandemic, and provides suggestions for remote music learning in the future. The data from music learning videos in e-Hakseupteo were extracted via each subject to statistically analyze the data in various aspects including provision regions, the content providers, target audiences, video titles, and contents of each music learning video. As a result, this analysis demonstrated that e-Hakseupteo offered students fewer opportunities to learn music than other subjects because of the significant lack of music learning videos. In addition, e-Hakseupteo has provided unequal music educational opportunities and regional discrepancy due to the unintentional access settings. Finally, the music learning videos at e-Hakseupteo were produced by specific agent groups in Seoul with limited experts. Implications and suggestions are provided for the sustainability of remote learning in music education.
Study on Hand Keypoint Framework for Sign Language Recognition

This study looks at the development of a sign language translation system that will serve as a communication link between deaf and hearing people. The Indonesian Language Sign System, called SIBI, is one of the communication techniques used by people with hearing impairment in Indonesia. Communication between deaf persons and non-deaf people has been found to have several issues, including a lack of community awareness of sign language. With this background, the presence of hand keypoint detection is one solution that can be implemented. OpenPose, TensorfowLite, and MediaPipe are just a few of the frameworks that are frequently used to identify hand keypoints. The goal of this research is to compare the best framework for working with the picture dataset generated by the Kinect 2.0 camera. The findings of this study show that the TensorflowLite framework can generate tidy hand keypoints and can be applied effectively to the dataset obtained.
A novel method to generate key-dependent s-boxes with identical algebraic properties

The s-box plays the vital role of creating confusion between the ciphertext and secret key in any cryptosystem, and is the only nonlinear component in many block ciphers. Dynamic s-boxes, as compared to static, improve entropy of the system, hence leading to better resistance against linear and differential attacks. It was shown in Easttom (2018) that while incorporating dynamic s-boxes in cryptosystems is sufficiently secure, they do not keep non-linearity invariant. This work provides an algorithmic scheme to generate key-dependent dynamic n×n clone s-boxes having the same algebraic properties namely bijection, nonlinearity, the strict avalanche criterion (SAC), the output bits independence criterion (BIC) as of the initial seed s-box. The method is based on group action of symmetric group Sn and a subgroup S2n respectively on columns and rows of Boolean functions (GF(2n)→GF(2)) of s-box. Invariance of the bijection, nonlinearity, SAC, and BIC for the generated clone copies is proved. As illustration, examples are provided for n=8 and n=4 along with comparison of the algebraic properties of the clone and initial seed s-box. The proposed method is an extension of Hussain et al. (2012); Hussain et al. (2012); Hussain et al. (2018); Anees and Chen (2020) which involved group action of S8 only on columns of Boolean functions (GF(28)→GF(2) ) of s-box. For n=4, we have used an initial 4 × 4 s-box constructed by Carlisle Adams and Stafford Tavares (Adams and Tavares, 1990) to generated (4!)2 clone copies. For n=8, it can be seen (Hussain et al. (2012); Hussain et al. (2012); Hussain et al. (2018); Anees and Chen (2020)) that the number of clone copies that can be constructed by permuting the columns is 8!. For each column permutation, the proposed method enables to generate 8! clone copies by permuting the rows.
On the Condition of Covering Completeness in Associative Steganography

Abstract Previously, the completeness of the coverage was considered by the authors as an interpretation of a kind of logical interpretation of the criterion of perfect secrecy by K. Shannon – one of the necessary conditions that the concept of associative steganography should satisfy. The article shows that in reality, the completeness of coverage is a completely independent condition for ensuring the required level of data protection when analyzing scenes. It should not be associated with the criterion of K. Shannon.
Bayesian modelling of multivalued power curves from an operational wind farm

Power curves capture the relationship between wind speed and output power for a specific wind turbine. Accurate regression models of this function prove useful in monitoring, maintenance, design, and planning. In practice, however, the measurements do not always correspond to the ideal curve: power curtailments will appear as (additional) functional components. Such multivalued relationships cannot be modelled by conventional regression, and the associated data are usually removed during pre-processing. The current work suggests an alternative method to infer multivalued relationships in curtailed power data. Using a population-based approach, an overlapping mixture of probabilistic regression models is applied to signals recorded from turbines within an operational wind farm. The model is shown to provide an accurate representation of practical power data across the population.
Effects of Image Realism on the Stress Response in Virtual Reality

Safety critical situations, as they occur in professions such as medicine, nursing, and aviation, are often trained in simulators to prevent damages to personnel and material. These jobs often come with a high amount of stress, to which prolonged exposure can have devastating effects. Over the past years, stress inoculation training in conjunction with Virtual Reality has become focus of the research community and software companies. Especially the nursing profession can benefit from it, since stress-related illnesses are often the reason for an early exit from the workforce. However, since training facilities often need to compromise on their simulations due to monetary reasons, trade-offs must be made in the degree of detail of such simulations in order to keep development and acquisition costs low. One such possibility is in using low graphical fidelity. We present a psycho-physiological study on the influence of image realism of virtual environments on the stress response. In a within subject design study, we ask participants to complete nursing related, virtually recreated tasks in an artificial intensive care unit, whilst exposed to different stress factors. We provide our findings in the form of objective and subjective measures. Results show that one can elicit different stress responses by manipulating image realism in a sufficiently drastic manner. However, a life-like reaction does not seem to depend on a highly realistic environment.
A New Approach to the Development of Additive Fibonacci Generators Based on Prime Numbers

Pseudorandom number and bit sequence generators are widely used in cybersecurity, measurement, and other technology fields. A special place among such generators is occupied by additive Fibonacci generators (AFG). By itself, such a generator is not cryptographically strong. Nevertheless, when used as a primary it can be quite resistant to cryptanalysis generators. This paper proposes a modification to AGF, the essence of which is to use prime numbers as modules of recurrent equations describing the operation of generators. This modification made it possible to ensure the constancy of the repetition period of the output pseudorandom pulse sequence in the entire range of possible values of the initial settings–keys (seed) at specific values of the module. In addition, it has proposed a new generator scheme, which consists of two generators: the first of which is based on a modified AFG and the second is based on a linear feedback shift register (LFSR). The output pulses of both generators are combined through a logic element XOR. The results of the experiment show that the specific values of modules provide a constant repetition period of the output pseudorandom pulse sequence in a whole range of possible values of the initial settings–keys (seed) and provide all the requirements of the NIST test to statistical characteristics of the sequence. Modified AFGs are designed primarily for hardware implementation, which allows them to provide high performance.
A study on the use of cryptocurrency wallets from a user experience perspective

Recently, the cryptocurrency market has been overgrowing, many investors from the global stock market entered the cryptocurrency business. Users interact and manage their investments and transactions through the cryptocurrency wallet, which is the gateway to buying, selling, sending, receiving, and trading. Studies and statistics show sixfold growth in cryptocurrency wallet accounts over the past 4 years (2017–2020). However, the rate of inactive trading that occurs during the same period causes market sluggishness. This paper examines the reasons behind the high adoption and low usage rate of cryptocurrency wallets, highlighting the factors impacting the utilization of cryptocurrency wallets. We aim to testify the impact of user experience (UX) on the usage level and the impact of trust on wallet usage. We designed a new model to understand the actual users' behavior on using the cryptocurrency wallets. This model combined User Experience Questionnaire (UEQ) with usability to comprehend UX and add trust as a significant construct to understand the impact of user trust in cryptocurrency wallets. The examination was performed with an actual user on an online survey. The data were analyzed using Smart-PLS. The results show good outcomes that contribute to developing and enhancing the implementation of cryptocurrency wallets. Highlighting the importance of UEQ, Usability, and Trust in a blockchain-based application and showed vital signs affecting wallet users. These results could be applied to enhance cryptocurrency wallet utilization and improving the adoption of blockchain applications.
Twitter Bot Detection using Machine Learning Algorithms

In nowadays world lots of people like a businessman’s, Media, politicians, etc., uses Twitter daily & have become an important part of life. Twitter is one of the favourite social networking sites which let the individuals to express their sentiment on various topics like politics, sports, stock market, entertainment etc. It is one of the quickest means of transmission information. It extremely affects people’s viewpoint. An increasing number of people on twitter but hide their identity for malignant purpose. It is dangerous for other users hence the necessity for identifying the twitter bots. So it is essential that tweets are sent by authentic users and not by twitter bots. A twitter bot transmits spam subject matters. Therefore detecting of bots helps to determine spam messages.The characteristics of twitter accounts are utilized as Features in machine learning algorithms to label users as genuine or fake. In this paper, we used three machine learning algorithms to detect the account is fake or real, which are Decision Tree, Random Forest, and Multinomial Naive Bayes The classification performance of the algorithms is compared with their accuracy. The accuracy given by the Decision tree algorithm is 93%, the Random Forest algorithm is 90% and the Multinomial Naive Bayes is 89%. Hence it is seen that the Decision tree gives more accuracy as compared to Random Forest and Multinomial Naive Bayes.
Building Covert Timing Channel of the IoT-Enabled MTS Based on Multi-Stage Verification

Although the global shipping industry is experiencing a productivity revolution due to the adoption of IoTs (Internet of Things), the dependence on complex data transmission and interactive centers is also increasing, which makes the IoT-enabled Maritime Transportation Systems (MTS) one of the most valuable but vulnerable industries against network security attacks. To guarantee the transmission security of confidential data, an important alternative in an untrustworthy IoT-enabled MTS is to apply the covert timing channels. This paper mainly introduces the construction of covert timing channel with low bit shifting rate and high reliability by multi-stage verification and error correction. For the covert timing channel schemes realized by active packet loss, the packet loss noise interferes with the channel's reliability. However, due to the constraints of stealthiness, the active packet loss ratio during covert communication is low, so more effective reliable strategies are needed to reduce noise interference. In the excellent scenario, when the bit error rate is lower than 0.08%, the transmission performance is kept at 0.49 bps. In the good scenario with strong network noise, although this method loses some performance, it can still maintain the transmission performance of 0.2 bps under the condition of bit error rate less than 1%, which effectively proves the effectiveness of multi-stage verification and error correction.
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. However, this framework suffers from biased classification due to incomplete feature comparisons. To address this issue, we present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes and thus construct complete sample pairs for learning semantic alignment with query features. The complementary features learning manner effectively enriches feature comparison and helps yield an unbiased segmentation model in the few-shot setting. It is implemented with a two-branch end-to-end network (i.e., a class-specific branch and a class-agnostic branch), which generates prototypes and then combines query features to perform comparisons. In addition, the proposed class-agnostic branch is simple yet effective. In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner. Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ demonstrate the superiority of our method. At no expense of inference efficiency, our model achieves state-of-the-art results in both 1-shot and 5-shot settings for semantic segmentation.