Reviewerly/exHarmony huggingface dataset:
An improved algorithm for computing hitting probabilities of quantum walks

Quantum walks are the quantum corresponding version of classical random walks, providing polynomial and even exponential acceleration for some classical difficult problems. In recent years, there have been numerous researches on quantum walks, among which the hitting probabilities problem is another crucial problem. In 2021, Guan et al. have proposed an HHL-based algorithm to calculate the hitting probabilities of quantum walks, which can effectively transform the original problem into an inverse matrix problem, then achieve an acceleration effect on the corresponding classical algorithm under some specific conditions. However, we propose a new algorithm consisting of an improved quantum matrix inversion algorithm with complexity O κ 2 log 1 . 5 ( κ / ε ) p o l y log ( n ) , where κ is the condition number of the matrix, n + 1 is the number of positions of the one-dimensional quantum walks, and ε is the expected accuracy of the output state. Compared to the HHL-based algorithm, our improved algorithm achieves an exponential improvement in the dependence on accuracy.
Hairstyle Transfer between Face Images

We propose a neural network which takes two inputs, a hair image and a face image, and produces an output image having the hair of the hair image seamlessly merged with the inner face of the face image. Our architecture consists of neural networks mapping the input images into a latent code of a pretrained StyleGAN2 which generates the output high-definition image. We propose an algorithm for training parameters of the architecture solely from synthetic images generated by the StyleGAN2 itself without the need of any annotations or external dataset of hairstyle images. We empirically demonstrate the effectiveness of our method in applications including hair-style transfer, hair generation for 3D morphable models, and hair-style interpolation. Fidelity of the generated images is verified by a user study and by a novel hairstyle metric proposed in the paper.
Deep Learning Approach For Facial Age Recognition

Age estimate using facial images is a fascinating and challenging issue. The characteristics from the face images are utilized to assess people&#x2019;s age, gender, ethnic origin, and emotion. Among this group of characteristics, age estimates can be beneficial in numerous possible real-time applications. Deep learning has recently achieved great success. Hence, we are using the Generative Adversarial Network (GAN) based method for automatic aging of faces. GAN produces images by altering facial attributes, and we create them to preserve the original person&#x2019;s identity in any age version. The deep generative networks have exhibited a remarkable capability in image generation. To the end, we introduced an approach for Identity-Preserving and GAN&#x2019;s Latent vector optimization. The evaluation of the objective of the proposed method demonstrates the following results proposed framework produced more realistic by comparing the state-of-art and ground truth. It can also be used for cross-age verification. We will be using the Dataset of MORPH and CACD to train our GAN model as it requires much data to learn. Moreover, an adversarial learning technique is presented to train a generator and parallel discriminators simultaneously, resulting in smooth continuous face aging sequences.
Voice to Action: Spoken Language Understanding for Memory-Constrained Systems

Spoken Language Understanding (SLU) is the task of extracting semantic information from speech. Traditional architectures of SLU involve a two-stage pipeline consisting of an Automatic Speech Recognition (ASR) module that converts speech into text hypotheses, followed by a Natural Language Understanding (NLU) module that extracts semantic information from the text hypotheses. The increasing demand for light-weight modules, especially in on-device applications, promulgated end-to-end SLU, where a single module directly converts speech into semantic information. This work pro-poses a low resource, lightweight end-to-end SLU model that treats SLU as a sequence-to-sequence task and is optimized similar to the ASR task. The proposed model utilizes transfer learning from a heavy-resource, general domain ASR model to achieve an 10.8% absolute improvement in the Word Error Rate (WER) over the base model. We then utilize Knowledge Distillation to achieve a further 31.9% reduction in the model size with 22.7% relative WER improvement. In addition, we compress our models by more than 4 times using Low-Rank Approximation (LRA) method with minimum degradation in accuracy.
Research on Blockchain Reconciliation Applications of Intelligent Charging Pile Platforms

To solve the problem of low reconciliation efficiency of city-level charging pile platforms, we propose a homomorphic encryption-based blockchain smart contract reconciliation application. Blockchain technology is used to achieve order recording, bill reconciliation, and synchronization of data. At the same time, homomorphic encryption technology is used to protect the data privacy in the blockchain system so that the order data can be not only open and transparent but also privacy preserved. In this study, three different kinds of fully homomorphic encryption technologies are used for performance comparison, including a new polynomial homomorphic encryption algorithm based on coefficient mapping transformation. Experimental data show that the new algorithm has great performance improvement in all operations.
ShieLD: Shielding Cross-Zone Communication Within Limited-Resourced IoT Devices Running Vulnerable Software Stack

Securing IoT devices is gaining attention as the security risks associated with these devices increase rapidly. TrustZone-M, a Trusted Execution Environment (TEE) for Cortex-M processors, ensures stronger security within an IoT device by allowing isolated execution of security-critical operations, without trusting the entire software stack. However, TrustZone-M does not guarantee secure cross-world communication between applications in the Normal and Secure worlds. The cryptographic protection of the communication channel is an obvious solution; however, within a low-power IoT device, it incurs high overhead if applied to each cross-world message exchange. We present ShieLD, a framework that enables a secure communication channel between the two TrustZone-M worlds by leveraging the Memory Protection Unit (MPU). ShieLD guarantees confidentiality, integrity and authentication services without requiring any cryptographic operations. We implement and evaluate ShieLD using a Musca-A test chip board with Cortex-M33 that supports TrustZone-M. Our empirical evaluation shows, among other gains, the cross-zone communication protected with ShieLD is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5 times</i> faster than the conventional crypto-based communication.
Blockchain-based digital trust mechanism: a use case of cloud manufacturing of LDS syringes for COVID-19 vaccination

Trust is essential in the digital world. It is a critical task to build digital trust for the ongoing digital engineering transformation. Aiming at developing a blockchain-based digital trust mechanism for Cloud Manufacturing or Manufacturing-as-a-Service (MaaS), in this paper, we use the manufacturing of low dead space (LDS) medical syringes through Cloud Manufacturing as a motivating scenario to develop a basic framework. To meet the need of optimally saving COVID-19 vaccine doses to save more lives, the medical device manufacturing community needs to make a swift move to meet the surged need for LDS syringes. Cloud Manufacturing is a form of emerging Digital Manufacturing facilitated with Cloud/Edge Computing, the Internet of Things, and other digital technologies. Cloud manufacturing allows quickly establishing a digital virtual enterprise that pools together various manufacturing resources worldwide to meet the surged needs of products and save cost and time. Trusting the product quality and safety is a significant challenge when using Cloud Manufacturing to manufacture the products. This paper proposes a schema of blockchain-based digital trust mechanisms with examples of using Cloud Manufacturing of medical LDS syringes for the urgent needs of catering COVID-19 vaccination.
Multiport Impedance Quantization For a Class of Annihilation Operator Linear Quantum Systems

This paper provides a procedure for building a multiport impedance quantization involving annihilation operators only for a class of linear quantum systems having a positive real impedance transfer function matrix. Based on the positive real properties of these quantum systems, it is shown that it is possible to use the Brune algorithm in order to find an electrical circuit that can physically implement these quantum systems. This theory, illustrated for multiport circuits may be useful for the implementation of superconducting microwave circuits used in quantum filters found in the field of quantum computing.
Information Sharing and Banking Efficiency in Africa: A Disaggregated Panel Data Analysis

The study assesses the how information sharing by means of mobile phones affects banking system efficiency in Africa with particular emphasis on income levels (Middle income versus Low income countries) and legal origins (English Common law versus French Civil law countries). The focus is on 53 African countries with data for the period 1996-2019 and the empirical evidence is based in Quantile regressions which enable the study to assess the nexus throughout the conditional distribution of banking system efficiency. The following findings are established: (i) mobile phone penetration promotes banking system efficiency in the 25th quantile and the median of banking system efficiency in low income countries while for middle income countries; it is significant exclusively in the bottom quantile (i.e. 10th quantile). (ii) With the exception of the highest (i.e. 90th) quantile in which the effect of the mobile phone is not significant in English Common law countries, the impact is significant throughout the conditional distribution of banking system efficiency in Common law countries. (iii) As for French Civil law countries, the nexus is only significant in the median and highest (i.e. 90th) quantile of the conditional distribution of banking system efficiency. Policy implications are discussed.
Improving HS-DACS Based Streaming Transformer ASR with Deep Reinforcement Learning

Transformer-based systems, though have achieved state-of-the-art performance on a wide range of automatic speech recognition (ASR) tasks, are subject to severe latency issues during inference that limit their deployment in real world applications. To enable online decoding, we recently proposed Decoder-end adaptive computation steps (DACS) and the head-synchronous version (HS-DACS) algorithms, which were shown to reduce the computational cost for decoding and close the gap in performance between offline and streaming Transformer ASR. In DACS/HS-DACS based systems, the halting position that triggers the ASR output was determined with an accumulation threshold that was set arbitrarily. In this paper, we propose a deep reinforcement learning approach to improve HS-DACS where an external agent is utilised to optimise the halting position. Experiments on AIShell-1, Tedlium-2 and Librispeech datasets show that the proposed method can further cut down the computation cost in inference with relative gains of 40.0%, 32.8% and 53.4% respectively, and still maintain similar ASR performance when compared with HS-DACS.
Using AES Encryption Algorithm to Optimize High-tech Intelligent Platform

AES encryption algorithm is a universal algorithm, which can be used in many fields, including intelligent marketing. In the field of modern intelligent marketing, every enterprise adopts the platform model to carry out marketing activities, which gives birth to the high-tech intelligent marketing platform. The use of this platform does bring great convenience to marketing activities and effectively improve the success rate and efficiency of marketing. However, with the in-depth use of the intelligent marketing platform, people find that there are many security risks in this platform, including Marketing information may be damaged by people, indicating that the platform needs further optimization. At this time, AES encryption algorithm has become the main method of platform optimization. In order to understand the optimization performance of AES encryption algorithm in the high-tech intelligent marketing platform, this paper will analyze the concept of the algorithm, the existing defects of the platform, the optimization scheme of the platform under the algorithm, and finally simulate the results of the optimization scheme to verify the feasibility of the application of the algorithm.
Estimating the Variance of Return Sequences for Exploration

This paper introduces a method for estimating an upper bound for an exploration policy using either the weighted variance of return sequences or the weighted temporal difference (TD) error. We demonstrate that the variance of the return sequence for a specific state-action pair is an important information source that can be leveraged to guide exploration in reinforcement learning. The intuition is that fluctuation in the return sequence indicates greater uncertainty in the near future returns. This divergence occurs because of the cyclic nature of value-based reinforcement learning; improved estimates of the value function result in policy changes which in turn modify the value function. Although both variance and TD errors capture different aspects of this uncertainty, our analysis shows that both can be valuable to guide exploration. We propose a two-stream network architecture to estimate weighted variance/TD errors within DQN agents for our exploration method and show that it outperforms the baseline on a wide range of Atari games.
Astronomical image denoising based on Convolutional Neural Network

Astronomical images captured using optical telescopes usually suffer from severe noise effects which makes the denoising step inevitable for image analysis. This paper proposes a denoising framework for astronomical images based on Convolutional Neural Network (Astro U-net). The modified Astro U-net model has been learned in four ways, the first method is using astronomical images from the Hubble Space Telescope data set with three types of noise (dark noise, read- out noise, shot noise) added, the second method is learned using the same data set with the dark noise (dn) added only, the third method is using the same data set with the read-out noise (ron) overlaid, the fourth method is using the same data set with the shot noise (sn) added. The proposed framework for denoising the astronomical images is based on a fusion of the image that was improved by the model learned in the first method with the image that was improved by the three models that were learned by the second, third and fourth methods sequentially. Experimentally, the proposed framework shows a significant improvement in both the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) as compared to the Astro U-net model on different exposure time ratios.
Action Recognition of Excavator with Data Augmentation of Simulator-Generated Training Data

In construction sites, construction machinery such as excavators plays a critical role. The management of such equipment, notably the monitoring of actions conducted by each construction machinery, is, therefore, key to high productivity and efficiency. This time-consuming and laborious task is currently conducted manually by humans and thus, its automation is highly sought after. Previous works on this issue have achieved high performance using deep learning-based approaches and cameras. However, the investments needed to obtain the training data critical to such approaches are often prohibitive. Using a simulator to generate the training data appears therefore as an alternative to allow fast and easy gathering of training data. However, models trained using such training data perform poorly on real data. The purpose of this study is therefore to increase the performance of action recognition of construction machinery such as excavators using simulator-generated training data. A data augmentation process using background images gathered from actual construction sites is used to reduce the gap between simulator-generated data and real-world data. Experiments with data collected in an actual construction site showed the effectiveness of the proposed method.
The Behavior of Some Finite Automata in a Non-stationary Fuzzy Environment

The behavior of two types of finite automata in a non-stationary fuzzy environment is considered, which, depending on the states of the automata, encourages or punishes them with some fixed membership functions. It is assumed that the behavior of automata in a fuzzy environment is described by generalized ergodic Markov chains and, using the property of such chains, it is shown that the considered automata, under certain conditions on fuzzy punishment functions, are learners and predominantly perform the action for which the sum of fuzzy functions of belonging to punishment is minimal.
Static Analysis Tools Applied to Smart Contracts

Smart contracts are one of the most important innovations of the second generation of the Blockchain. They are widely used in various contexts, including financial, insurance, gaming, and betting. Once a smart contract is deployed on a Blockchain, due to its code immutability, residual vulnerabilities cannot be patched. Static analysis is an efficient method for vulnerability detection. This paper addresses the security evaluation and improvement of Solidity smart contracts through the use of static analysis tools, discussing: (i) the motivation and background, (ii) the evaluation of how good the tools are for improving security, (iii) their combination, and (iv) main results.
EXPERIMENTAL WORK ON ORGANIZATIONAL AND METHODOLOGICAL SUPPORT OF THE ACTIVITIES OF THE CURATORS OF THE ACADEMIC GROUPS AT THE TECHNICAL UNIVERSITY

В статье отмечена значимость института кураторства в технических вузах в развитии профессиональных и универсальных компетенций, личностного роста, самореализации, а также адаптации студентов; выделены существующие проблемы в реализации кураторской деятельности.Определено, что кураторская деятельность в техническом университете нуждается в организационно-методическом сопровождении на уровнях целей, содержания, организации и деятельности для повышения эффективности деятельности кураторов академических групп. Проведена опытно-экспериментальная работа по реализации разработанной системы организационно-методического сопровождения деятельности кураторов академических групп в современном техническом университете.Представленные итоги проведенной работы подтверждают результативность разработанной системы, реализация которой позволяет повысить эффективность кураторской деятельности. The article notes the importance of the supervising in technical universities institution in development of professional and universal competencies, personal growth, self-realization, as well as adaptation of students, highlights the existing problems in the implementation of curatorial activities. The main problems of the curators institute at a technical university include: insufficient qualification of curators; the majority of them have no humanitarian, psychological and pedagogical education; the difficulty in implementing systematic refresher courses for all curators; insufficient replication of curatorial experience, the use of network communication tools in curatorial activities; the need to increase the level of socio-psychological adaptation of students to study at a technical university; difficulties in implementing a personality-oriented approach in the activities of curators; lack of humanitarization of the educational process. Solving the existing problems of the curators at a technical university institution requires the creation of an appropriate organizational and methodological support system for the curators of academic groups activities. Organizational and methodological support for the academic groups curators activities at a technical university is a systemically organized process of comprehensive, continuous professional development of curators, methodological training and curators experience replication. Experimental work was carried out to implement the developed system of organizational and methodological support for the academic groups curators activities at a modern technical university. The presented results confirm the developed system effectiveness, which implementation makes it possible to increase the curatorial activities effectiveness.
Knowledge Transformation via Different Structures of Concepts and its Application in Argumentation Theory and Case-Based Reasoning

The article is devoted to solving some problems of argumentation theory and case-based reasoning. For this purpose, we develop methods for transforming knowledge presented in various structures of concepts. The correspondence rules between concepts of different levels of generality are investigated. We use formal concept lattices to assess knowledge and test the equivalence of knowledge transformations. Based on Melchuk's theory “Meaning <=> text”, formal methods of extracting knowledge from natural language texts and representing the extracted knowledge are proposed. To formally represent domain case structures, we modify the predicates by adding special situation-constants to them. The concept of ontological homomorphism of algebraic systems is introduced, on the basis of which the similarity of cases is defined. These constructions are used to develop argumentation structures that use case-based reasoning.
Enhanced Dyna-QPC model with Fuzzy logic to train gaming models

This paper presents an automated learning process to train the mountain car game model. It proposes an Enhanced Dyna-QPC model to effectively train the mountain car model in the stipulated time, based on their perceived environmental conditions. Decision Tree (DT) classification model along with Neural Network (NN)) model is used in this research to frame decision rules and self-train the game model respectively. Discrete Finite Deterministic Automata (DFA) concepts are included to finalize the state transition of the training model. Moreover, the Erdos-Renyi Random graph-generating model is used to generate dynamic state transition graphs to minimize the number of states. To increase the range of conditions and to derive meaningful decision rules, fuzzy concepts are used in this paper. Various simulation experiments have been conducted to evaluate the efficiency of the proposed training process. Simulation results reveal better performance over 3 popular models in the literature.