Reviewerly/exHarmony huggingface dataset:
Multi-Domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution

Over the past few years, image-to-image (I2I) translation methods have been proposed to translate a given image into diverse outputs. Despite the impressive results, they mainly focus on the I2I translation between two domains, so the multi-domain I2I translation still remains a challenge. To address this problem, we propose a novel multi-domain unsupervised image-to-image translation (MDUIT) framework that leverages the decomposed content feature and appearance adaptive convolution to translate an image into a target appearance while preserving the given geometric content. We also exploit a contrast learning objective, which improves the disentanglement ability and effectively utilizes multi-domain image data in the training process by pairing the semantically similar images. This allows our method to learn the diverse mappings between multiple visual domains with only a single framework. We show that the proposed method produces visually diverse and plausible results in multiple domains compared to the state-of-the-art methods.
Research and examination on implementation of super-resolution models using deep learning with INT8 precision

Fixed-point arithmetic is a technique for treating weights and intermediate values as integers in deep learning. Since deep learning models generally store each weight as a 32-bit floating-point value, storing by 8-bit integers can reduce the size of the model. In addition, memory usage can be reduced, and inference can be much faster by hardware acceleration when special hardware for int8 inference is provided. On the other hand, when inferences are carried out by fixed-point weights, accuracy of the model is reduced due to loss of dynamic range of the weights and intermediate layer values. For this reason, inference frameworks such as TensorRT and TensorFlow Lite, provide a function called "calibration" to suppress the deterioration of the accuracy caused by quantization by measuring the distribution of input data and numerical values in the intermediate layer when quantization is performed. In this paper, after quantizing a pre-trained model that performs super-resolution, speed and accuracy are measured using TensorRT. As a result, the trade-off between the runtime and the accuracy is confirmed. The effect of calibration is also confirmed.
Long-term Human Video Activity Quantification of Student Participation

Research on video activity recognition has been primarily focused on differentiating among many diverse activities defined using short video clips. In this paper, we introduce the problem of reliable video activity recognition over long videos to quantify student participation in collaborative learning environments (45 minutes to 2 hours).Video activity recognition in collaborative learning environments contains several unique challenges. We introduce participation maps that identify how and when each student performs each activity to quantify student participation. We present a family of low-parameter 3D ConvNet architectures to detect these activities. We then apply spatial clustering to identify each participant and generate student participation maps using the resulting detections.We demonstrate the effectiveness by training over about 1,000 3-second samples of typing and writing and test our results over ten video sessions of about 10 hours. In terms of activity detection, our methods achieve 80% accuracy for writing and typing that match the recognition performance of TSN, SlowFast, Slowonly, and I3D trained over the same dataset while using 1200x to 1500x fewer parameters. Beyond traditional video activity recognition methods, our video activity participation maps identify how each student participates within each group.
Comparative Analysis of Various Task Scheduling Algorithms in Cloud Environment

Internet is indispensable in all of the activities of today's society. Cloud computing emerges as a demanded recent technology to make the use of internet in a simplified, cost-affordable and timely available form to the user. The primary purpose of cloud computing is to utilize the resource efficiently, allocate the tasks to the resources, balance their load and satisfy the agreements of user without fail. This research paper includes a brief analysis and comparison of the traditional task scheduling algorithms like FCFS, RR, and MCT with recent algorithms such as IWD, TSCSA, LBMPSO. All of the recent time algorithms are satisfying multi-objective qualities and ensures economic feasibility.
A Multi-level Morphological and Stochastic Tagalog Stemming Template

Tagalog is the basis of the Philippine language, that is widely spoken throughout the majority of Philippine regions. Currently, various Filipino language morphological investigations employs a language dependent methodology that is evidently efficient. However, due to its substantial morphological traits and emergent evolution, the native language is regarded to be morpho syntactically rich. This paper proposed a stochastic and multi-level morpho-tactical system for stemming the Filipino language that can cover limited frameworks. Numerous word forms were gathered and examined to create a stochastic template. It derives morphological weights from multi-layer systems, emphasizing the importance of language-dependent and language-independent approaches. Additionally, the study incorporates unusual and novel data such as slang words, borrowed words, street jargon in “Taglish,” and colloquial phrases used in formal and informal interactions, works, and literature.
Blockchain Based Secured Multipurpose Identity (SMID) Management System for Smart Cities

Blockchain, the decentralized architecture and distributed public ledger technology intended to provide a secure, irreversible and transparent information management platform for practically any application which also plays a vital role in establishing the future of cities. In today's digital era, identifying an individual is one of the most critical concerns for business and many other public institutions. Digital identity plays a vital role in this sector as the data may be used to identify an individual and his fundamental qualities like name, address, health status, credit score and others. In this paper, we will look at how Blockchain can be used to secure digital identity which is a trustworthy idea that describes how people can use or what type of attribute or parameter can be used to identify someone's identity. The identity is used for different kind of application which is typically decentralized and any or-ganization can take control over the identity data which reasons a large number of identity fraud. The idea is to build a single multipurpose identity that will be secured using Blockchain technology. Instead of several identification documents, Users can use this single multipurpose identity to prove their identity in various sectors such as the government and other private sectors to access particular services. Automated and real-time identity verification through smart contracts can verify the identity data. The Blockchain will provide a tamper-proof method of storing documents or data that can be audited by the government and ensures the User's transparency through Blockchain by showing the record of activity.
Fake Data Generation for Medical Image Augmentation using GANs

This paper uses WGAN-GP to generate fake data that can be used as augmented data for strabismus classification and analyze the results. In the introduction of this paper, the general diagnostic technique for strabismus disease is described and the diagnostic technique using deep learning is described. And the reason for generating fake data is described. Main subject describes the WGAN-GP, data set used for data generation and evaluation metrics of GAN. In the experimental result, the data generated by the GAN is visually checked, and the performance of the fake data is evaluated with the FID that is one of the evaluation metrics of the GAN. And in the conclusion, evaluation of the proposed GAN and future work are described.
Exploring the facilitating and obstructing factors of using virtual reality for 5S training: An exploratory qualitative study from students' perspectives in an industrial engineering undergraduate course

Abstract Virtual reality (VR) learning is still a new challenge in the e‐learning domain. This paper is one of the early studies bringing forward the concept of learning 5S principles (a core technique in Industrial Engineering) using VR and performed an exploratory qualitative study in an undergraduate course to discover factors that would facilitate or hinder students from accepting this idea—learning 5S principles using VR. Four factors and 14 subfactors were identified based on 47 valid responses. They were human attributes, human–machine interaction, technology characteristics, and training contents. The results should help teachers, schools, VR device manufacturers to understand the benefits and challenges of teaching 5S principles using VR.
Edge-Computing based Secure E-learning Platforms

Implementation of Information and Communication Technologies (ICT) in E-Learning environments have brought up dramatic changes in the current educational sector. Distance learning, online learning, and networked learning are few examples that promote educational interaction between students, lecturers and learning communities. Although being an efficient form of real learning resource, online electronic resources are subject to threats and vulnerabilities on the internet. Authentication, access and storage of data is a major concern among many organizations implementing E-learning platforms. This study provides a literature review of past five-year research studies, and proposes Edge-computing based solution to the currently existing authentication and data access problems that prevail in the current E-learning management systems using cloud services for data storage. The study guides researchers towards enabling Edge-computing based E-learning platforms to support low power computing devices running Elliptic Curve Cryptography for secure access and authentication.
An Accurate Visual Navigation Method for Wheeled Robot in Unstructured Outdoor Environment Based on Virtual Navigation Line

For the navigation problem in unstructured outdoor environment such as the field without artificial marks, a precise vision navigation method for wheeled robot based on virtual navigation line is proposed. Virtual navigation line is a virtual line based on a small amount of visual information, which can be used in many complex environments. Firstly, the robot needs to design the shape of the walking route. And the general robot’s walking routes can be divided into linear route and curve route. Secondly, different virtual navigation line strategies are generated for different walking routes. In the linear navigation part, the virtual navigation line is determined according to the tracking target and the reference target generated. In the curve navigation part, the curve virtual navigation line is fitted according to the multiple targets. Then, the virtual calibration line is determined based on the camera pitch angle, field of view (FOV) and the horizon position in the image, the offset angle and offset distance are computed by using the geometric relationship between virtual calibration line and virtual navigation line. Finally, the fuzzy PID control method is applied to correct robot's direction. The Pioneer3-DX robot is used to do experiments in the outdoor field. The results show that our method can make the robot walk along the designed route in the unstructured outdoor environment accurately, and the navigation accuracy is within 10 cm.
Evaluation and Prediction of Train Communication Network Performance

The performance of train communication network (TCN) is one of the most important factors to ensure the safety of train operation, and there is a lack of research for the TCN performance currently. To master the state of TCN, a combined method to evaluate and predict the TCN performance is proposed in the paper. First, we select appropriate evaluation indexes and determine their comprehensive weights. Then, the evaluation model of TCN performance is established based on a multidimensional normal cloud model combined with weights. According to the evaluation results, the artificial neural network (ANN) is used to predict the TCN performance to realize the real-time evaluation. Finally, the effectiveness of the evaluation and prediction model of TCN performance is verified by the simulation result, which is obtained from the simulation platform of TCN. The results show that service quality for TCN has been effectively improved, which provides a theoretical reference for evaluation and prediction of TCN performance.
On the Impossibility of Post-Quantum Black-Box Zero-Knowledge in Constant Round

We investigate the existence of constant-round post-quantum black-box zero-knowledge protocols for NP. As a main result, we show that there is no constant-round post-quantum black-box zero-knowledge argument for NP unless <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{NP} \subseteq \text{BQP}$</tex> . As constant-round black-box zero-knowledge arguments for NP exist in the classical setting, our main result points out a fundamental difference between post-quantum and classical zero-knowledge protocols. Combining previous results, we conclude that unless <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{NP} \subseteq \text{BQP}$</tex> , constant-round post-quantum zero-knowledge protocols for NP exist if and only if we use non-black-box techniques or relax certain security requirements such as relaxing standard zero-knowledge to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> -zero-knowledge. Additionally, we also prove that three-round and public-coin constant-round post-quantum black-box <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> -zero-knowledge arguments for NP do not exist unless <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{NP} \subseteq \text{BQP}$</tex> .
Approaches for migrating non cloud-native applications to the cloud

This paper explores different strategies and tools for migrating non cloud-native or legacy applications to cloud. The approach taken involves studying the existing migration strategies and experimenting them on different types of non cloud-native applications to identify the strengths and challenges associated with each of them. Some of the commonly used platforms, tools and features currently available for supporting these migrations are also studied for their efficacy towards migration of the chosen applications. The paper concludes with a consolidated set of steps for each migration strategy, while factoring in the range of applications they are best suited for, considering their complexity and scope.
A Secure Federated Learning Mechanism for Data Privacy Protection

The combination of big data and machine learning brings more convenience to people, but also brings security risks of data privacy leakage. The services provided by traditional machine learning can no longer meet the needs of privacy protection. The emergence of federated learning technology has alleviated privacy disclosure threats, however adversaries can still infer from the data model or even reconstruct the raw training data, causing the data privacy of the raw training data to be leaked. To solve this problem, we propose a secure federated learning mechanism based on variational autoencoder (VAE) to resist inference attacks. Participants use raw data to generate forged data through a VAE and train a local model with forged data, thereby protecting the data privacy and guaranteeing the quality of the global model. The experimental results show that the proposed secure federated learning mechanism can guarantee the high accuracy of the global model while reducing the probability of the raw data of the participants being reconstructed.
Master equation for the quantum Rabi model in the adiabatic regime

A qubit--harmonic-oscillator open system coupled to thermal baths was considered and the master equation describing the evolution of the open system was deduced when the qubit transition frequency is $\ensuremath{\lesssim}0.1$ the oscillator frequency. The master equation is valid in all the qubit-oscillator state space and holds for all values of the qubit-oscillator coupling including the ultrastrong and deep strong coupling regimes. It only requires an oscillator frequency much larger than the relaxation rates. The qubit-oscillator coupling can enhance or decrease both the relaxation rates and the frequency shifts induced by the thermal baths. It was found that weak, sinusoidal qubit driving forces the qubit-oscillator open system to behave like a driven qubit whose evolution is governed by equations similar to those of the Bloch vector in the optical Bloch equations and whose transition frequency decreases with increasing qubit-oscillator coupling strength. Finally, it was shown how one can reach the adiabatic regime by using sinusoidal qubit driving with large driving frequency and the concepts of $\ensuremath{\pi}$ and $\ensuremath{\pi}/2$ pulses were generalized to manipulate transitions between dressed states.
Distributed Representations of HTML Page

The most common ways to represent documents as fixed length vectors are the bag-of-words model and the Doc2Vec model. The bag-of-words model mainly has two disadvantages. One is that it ignores the sequence and frequency of each word in the document, which makes the vectors lose the original semantic information; another is that the vectors of the bag-of-words model are high-dimensional sparse. Using these vectors as the input of the algorithm will greatly reduce the convergence speed of the algorithm. The Doc2Vec model overcomes the shortcomings of the bag-of-words model, makes the mapped document vectors have real semantic features, and ensures the low-dimensionality and denseness of the vectors. However, the Doc2Vec model is not very suitable for web pages on the Internet. The main reason is that web pages are usually semi-structured HTML format, containing many tags unrelated to specific content, and the meanings represented by each tag are very different. Therefore, based on the Doc2Vec model, this paper proposes a method of mapping HTML pages to fixed-length feature vectors — Html2Vec. The experimental results show that for HTML pages, using the Html2Vec method proposed in this paper to train the embedding vectors is better than using the Doc2Vec model or the bag-of-words model. The trained embedding vector can be applied to web page classification and other fields.
Engineering Kerr-cat qubits for hardware efficient quantum error correction

Stabilized cat qubits that possess biased noise channel with bit-flip errors exponentially smaller than phase-flip errors. Together with a set of bias-preserving (BP) gates, cat qubits are a promising candidate for realizing hardware efficient quantum error correction and fault-tolerant quantum computing. Compared to dissipatively stabilized cat qubits, the Kerr cat qubits can in principle support faster gate operations with higher gate fidelity, benefiting from the large energy gap that protects the code space. However, the leakage of the Kerr cats can increase the minor type of errors and compromise the noise bias. Both the fast implementation of gates and the interaction with environment can lead to such detrimental leakage if no sophisticated controls are applied. In this work, we introduce new fine-control techniques to overcome the above obstacles for Kerr cat qubits. To suppress the gate leakage, we use the derivative-based transition suppression technique to design derivative-based controls for the Kerr BP gates. We show that the fine-controlled gates can simultaneously have high gate fidelity and high noise bias and when applied to concatenated quantum error correction, can not only improve the logical error rate but also reduce resource overhead. To suppress the environment-induced leakage, we introduce colored single-photon dissipation, which can continuously cool the Kerr cats and suppress the minor errors while not enhancing the major errors.
Evaluating Deep Sequential Knowledge Tracing Models for Predicting Student Performance

One of the key priorities in all instructional environments is to ensure that students recognise their learning mechanisms and pathways. Knowledge Tracing (KT), the task of modelling student knowledge from their learning history, is an important problem in the field of Artificial Intelligence in Education (AIEd) and has numerous applications in the development of interactive and adaptive learning technologies. KT can be utilised to understand each student&#x2019;s distinct learning style, particular needs, and ability levels. We trained and evaluated the performance of Knowledge Tracing models on the ASSISTments dataset and EdNet-KT1 dataset. This study revealed that deep learning models for knowledge tracing (Deep Knowledge Tracing (DKT), Dynamic Key-Value Memory Network (DKVMN), and Attentive Knowledge Tracing (AKT)) outperform the Markov process model (Bayesian Knowledge Tracing). We also observed that AKT and DKT go hand in hand with predicting whether or not the following question will be answered correctly or incorrectly by the student.
55 Years of Holographic Non-Destructive Testing and Experimental Stress Analysis: Is there still Progress to be expected?

Holographic methods for non-destructive testing, shape measurement, and experimental stress analysis have shown to be versatile tools for the solution of many inspection problems. Their main advantages are the non-contact nature, the non-destructive and areal working principle, the fast response, high sensitivity, resolution and precision. In contrast to conventional optical techniques such as classical interferometry, the holographic principle of wavefront storage and reconstruction makes it possible to investigate objects with rough surfaces. Consequently, the response of various classes of products on operational or artificial load can be examined very elegantly. The paper looks back to the history of holographic metrology, honors the inventors of the main principles, discusses criteria for the selection of a proper inspection method, and shows exemplary applications. However, the main focus is on modern developments that are inspired by the rapid technological process in sensing technology and digitization, on current applications and future challenges.
An Algorithm with the Even-odd Splitting of the Wavelet Transform of Non-Hermitian Splines of the Seventh Degree

The article investigates an implicit method of decomposition of the 7th degree non-Hermitian splines into a series of wavelets with two zero moments. The system of linear algebraic equations connecting the coefficients of the spline expansion on the initial scale with the spline coefficients and wavelet coefficients on the embedded scale is obtained. The even-odd splitting of the wavelet decomposition algorithm into a solution of the half-size five-diagonal system of linear equations and some local averaging formulas are substantiated. The results of numerical experiments on accuracy on polynomials and compression of spline-wavelet decomposition are presented.