Reviewerly/exHarmony huggingface dataset:
A New Eye-tracking Method with Image Feature Based Model for Mobile Devices

In order to improve the computational efficiency and accuracy of eye-tracking methods for mobile devices, this paper proposes an eye-tracking method based on face features and head pose estimation. The method first uses image processing to detect human eye feature points, and then establishes an eyepoint mapping model based on Gaussian process regression; on this basis, the head pose estimation is combined to compensate for the eye-point coordinates; finally, a prototype eye-tracking system based on mobile devices is designed and developed. The experimental results show that the system has high accuracy and robustness, thus verifying the feasibility and effectiveness of the method in this paper.
Secure Transaction Mechanism of Blockchain Digital Assets Based on Distributed Identity

With the development of the new industrial revolution, data has become an important factor of production. Many physical assets and value data will appear in the form of digital original ecology. Traditional digital asset trading schemes have security problems such as difficulty in confirming the ownership of assets, difficulty in tracing the transaction process, and leakage of identity information. Aiming at the problems of digital asset confirmation and identity information leakage, we propose a secure transaction mechanism of blockchain digital assets based on distributed identity. Aiming at the problems of data leakage and anti-counterfeiting traceability in the process of digital asset transaction, we propose a digital fingerprint embedding scheme based on homomorphic encryption. Finally, it is verified in the digital collection transaction scenario, and deployed on the “Spark Chain Digital Native Asset Service Network” to verify the effectiveness of this mechanism. The scheme ensures the secure and credible sharing of digital assets in the transaction process, and provides a technical path for the future transaction of digital assets in the meta universe.
Deep Learning and Data Randomness based Blind Recognition of Channel Codes

Channel coding is an efficient approach for data transmission in the wireless network, blind recognition of channel codes plays a pivotal role in non-cooperative communication and spectral scrutiny. But there are some difficulties in the low accuracy and robustness of the current research, meanwhile, most of them analyze this problem to take the coding type as the prior knowledge, which may be unknown in the actual condition. This paper proposes a multi-dimensional feature extraction algorithm, that identifies channel coding types and parameters by the neural network. A data randomness based multi-dimensional feature extraction algorithm and efficient channel attention for deep convolution neural network (ECA-Net) are used in the proposed recognizer, it had an outstanding performance in the identification of coding types, coding lengths, and coding rates. Simulation experiments showed that the proposed recognizer could realize the recognition of coding types, when the signal-to-noise ratio (SNR) is not lower than 10dB, it could achieve above 70% of identification accuracy. On the other hand, it can improve the probability of detection by 10% to 60% compared with previous work for coding lengths and coding rates, which testifies the availability of this approach.
Deep Reinforcement Learning Based Bitrate and Redundance Ratio Adaption for Panoramic Video Transmission

User experience of panoramic video is difficult to guarantee for the conflict between limited bandwidth resources and ultra high encoding bitrate, simultaneously, existing research neglects an important indicator called black area ratio in their quality of experience (QoE) models, and traditional bitrate adaptive (ABR) algorithms appear less intelligent in the complicated QoE optimization problem. In this paper, we first establish a novel QoE model taking black area ratio into account. Next, a bitrate and redundance ratio adaptive algorithm based on deep reinforcement learning (DRL) is proposed. Via continuous interaction with the underlying environment, the DRL agent can learn an effective policy to control video encoding bitrate and redundance ratio given current system state, which includes the predicted FoV, available wireless bandwidth, etc. With an open user viewpoint dataset, simulation results demonstrate that our proposal outperforms buffer-based (BB) and rate-based (RB) adaptive algorithms, and achieves competitive performance compared to model predictive control (MPC) that provides a performance upper bound in our case.
Bi-TWD: A Unified Attack Detection Framework in Recommender Systems Based on BiLSTM and Three-way Decision

The emergence of shilling attacks has threatened the security of recommender systems, which can seriously affect the trustworthiness of recommendation results and reduce the stickiness of users. The state-of-the-art detection methods have showed the effectiveness by manually or automatically extracting detection features. However, these methods lack universality because they tend to focus on detecting individual attacks or group shilling attacks. To this end, this paper proposes a unified framework for detecting various shilling attacks based on BiLSTM and three-way decision, which is named Bi-TWD for short. Firstly, each user rating behavior trajectory is divided into multiple subsequences and a window feature vector can be generated from each subsequence. Secondly, all window feature vectors of the same user are integrated into a fused behavior representation by using BiLSTM and the suspicious degree of each user is calculated. Finally, according to user suspicious degree, users are classified into a positive domain, a negative domain and a boundary domain by using the three-way decision theory. Users in the positive domain are regarded as attack users; the ones in the negative domain are referred to genuine users, while users in the boundary domain are further analyzed. The experiments are conducted on the Netflix and the sampled Amazon datasets, and the experimental results show the effectiveness and excellent performance of the proposed method for detecting various attacks.
SCB-GAN: A High-Quality Small Celestial Body Surface Image Synthesis Method

Small celestial body (SCB) exploration is a research hotspot in deep space exploration technology. However, the scarcity of small celestial body surface images severely hinders the development of probe intelligence systems, and it is challenging to redeploy probes to obtain massive images due to the high cost. In recent years, with the growth of deep learning, generative adversarial networks have shown excellent potential in generating real images. This paper presents a high-quality small celestial body surface image synthesis method SCB-GAN, which can generate a massive amount and large diversity of small celestial body surface images that match human perception from a limited dataset. Specifically, we propose MineAug, which can automatically select appropriate data augmentations from the given SCB surface imageset based on an efficient mining mechanism, improving the robustness of the GAN training process. We also add the vision-aided loss to our network to further improve image quality. Through extensive experiments, our method outperforms recent methods under a variety of metrics in terms of visual quality comparison, no-referenced image quality assessment, and human subjective evaluation, which has significant implications for the development of intelligent systems. The source code and associated data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LUWENL/SCB-GAN</uri>
Comparison and analysis of classical image denoising methods based on convolution neural network

Nowadays, internet and communication are well developed and all parts of the world are interconnected. People receive a lot of data all the time. According to statistics, visual information accounts for more than 60% of the information received by human eyes. As a part of machine learning, deep neural network has greatly promoted the development of artificial intelligence. Due to the imperfection of the equipment and system, the image loses some features due to the influence of noise during the transmission process, which makes the image unclear, which not only affects the normal recognition of people, but also affects the subsequent segmentation, extraction, detection and recognition of the image. At this time, it is necessary to take measures to make the image clearer. Image denoising refers to the process of eliminating the noise in the digital image as much as possible. In short, it is not only to remove the noise pollution, but also to retain the useful feature information of the image itself as much as possible. In addition to improving the image quality, it can also better identify the image. This paper is a research on image denoising methods based on convolution neural network (CNN). Using neural network’s good learning ability of image statistical characteristics, four CNN based methods of DnCNN, FFDnet, CBDnet and SRMD are used to realize image denoising. It is found that DnCNN has the best denoising effect on Gaussian noise, followed by FFDnet and SRMD.
THE EFFECTS OF CARTOON MOVIE AS THE MEDIUM IN IMPROVING STUDENTS’ SPEAKING SKILL THE NINTH OF SMP NEGERI 51 SURABAYA

Speaking skills in English is a unique topic to study because in this modern era we cannot be separated from the language. In our daily life, language has function as a tool to communicate and interact with others. This study aims to identify and reveal improved speaking skills through the media of cartoon movie in class IX B students of SMPN 51 Surabaya in the year of 2016/2017. The researcher uses the Classroom Action Research (CAR) method and collects qualitative and quantitative data. The results of the study show that media can have a good effect on improving students' speaking skills. they can understand and practice directly using English so that this can provide positive motivation and encouragement for students in learning English. This research showed that there was significant improvement in cycle 1 and cycle 2 in various aspects of speaking which included fluency, pronunciation, grammar, vocabulary, and comprehension
FALSE CONSCIOUSNESS IN IMBOLO MBUE’S BEHOL THE DREAMERS

ABSTRACT: The research which entitled as False Consciousness in Imbolo Mbue’s Behold the Dreamers reports a study of false consciousness found in two main characters. The data are taken from the characters in the novel who pursue their dreams in America as an immigrant. This research aims to find how the characters’ false consciousness depicted in the novel. The method used in this research is descriptive qualitative. This research finds the false consciousness depicted toward the characters in pursuing American dream. The research shows that false consciousness is experienced by the two immigrants to achieve their dreams in America but they are failed to gain material wealth since their belief appears only to have a power to compete others’ point of view about their class structure.&#x0D; &#x0D;
Time-stretch optical neural network with time-division multiplexing

As factors such as power consumption and bandwidth constrain the development of electronic computing in deep learning, optical neuromorphic computing has the potential to meet the growing computational demands of artificial intelligence, presenting various opportunities and challenges. In this paper, we propose an all-optical neural network through a combination of time-stretching and time-division multiplexing that can be used for the classification of multiple datasets. The system modulates a large number of parameters into chirp optical pulses by time-stretching and increases operational efficiency in conjunction with time-division multiplexing. This all-optical neural network system proposed is capable of operating at the speed of 1 Tera-OPS/s and can achieve data transfer rate in excess of 800 Gbit/s. The system is tested by the feedforward neural network, and the computational simulation realized very encouraging and considerable results.
APR-ES: Adaptive Penalty-Reward Based Evolution Strategy for Deep Reinforcement Learning

As a black-box optimization approach, derivative-free evolution strategy (ES) draws lots of attention in virtue of its low sensitivity and high scalability. It rivals Markov Decision Process based reinforcement learning or even can more efficiently improve rewards under complex scenarios. However, existing derivative-free ES still confronts slow convergence speed at the early training stage and limited exploration at the late convergence stage. Inspired from human learning process, we propose a new scheme extended from ES by taking advantage of prior knowledge to guide ES, thus accelerating early exploitation process and improving later exploration ability. At early training stage, Drift-Plus-Penalty (DPP), a penalty-based optimization scheme, is reformulated to boost penalty learning and reduce regrets. Along with DPP-directed evolution, reward learning with Thompson sampling (TS) is increasingly enhanced to explore global optima at late training stage. This scheme is justified with extensive experiments from a variety of benchmarks, including numerical problems, physics environments, and games. By virtue of its imitation of human learning process, this scheme outperforms state-of-the-art ES on the benchmarks by a large margin.
Fast Provisioning of Virtual Machine Based on Intelligent Virtual Machine Image Prefetching

Virtual Machine (VM) provisioning is one of the most critical issues in cloud computing. However, the long delay in VM provisioning significantly degrades the customer experience. Especially, the time-consumption of transferring Virtual Machine Images (VMI) from storage nodes to selected compute nodes is one of the major factors that cause the delay in VM provisioning. Unfortunately, existing approaches usually make their efforts to improve efficiency at the cost of disk performance. In this paper, we propose an intelligent VMI prefetching approach to expedite VM provisioning. First, we identify the data blocks necessary for VMs startup from the whole VMI and record their access order. Then, we design a VMI prefetching mechanism to reduce data transmission, which transfers only a small amount of startup block to compute node during the pre-steps stage (i.e., the short time before startup). Next, we employ a VM request prediction model which leverages historical records to predict the number of requests for each VM type in the future period, so as to perform VMI prefetching in advance, thus reducing VM suspension caused by the preparation of requested blocks. Furthermore, we implement a prototype named PreVMI and perform a series of experiments to evaluate the performance of our approach. Experiments show that our approach can reduce VM provisioning latency by about 35%.
AOP-Net: All-in-One Perception Network for LiDAR-based Joint 3D Object Detection and Panoptic Segmentation

LiDAR-based 3D object detection and panoptic segmentation are two crucial tasks in the perception systems of autonomous vehicles and robots. In this paper, we propose All-in-One Perception Network (AOP-Net), a LiDAR-based multitask framework that combines 3D object detection and panoptic segmentation. In this method, a dual-task 3D backbone is developed to extract both panoptic- and detection-level features from the input LiDAR point cloud. Also, a new 2D backbone that intertwines Multi-Layer Perceptron (MLP) and convolution layers is designed to further improve the detection task performance. Finally, a novel module is proposed to guide the detection head by recovering useful features discarded during down-sampling operations in the 3D backbone. This module leverages estimated instance segmentation masks to recover detailed information from each candidate object. The AOP-Net achieves state-of-the-art performance for published works on the nuScenes benchmark for both 3D object detection and panoptic segmentation tasks. Also, experiments show that our method easily adapts to and significantly improves the performance of any BEV-based 3D object detection method.
Development of creative project activity of students of Bauman Moscow State Technical University

The article considers the solution to the problem of improving the training of modern engineering personnel at the Bauman Moscow State Technical University, starting from the first course. Technological progress is greatly influenced by the use of «artificial intelligence» in the creation and operation of fundamentally new machines. In this regard, in specialized schools at BMSTU, the interest of students in the events taking place at the university and aimed at developing their creative activity has increased. Teachers of the department «Engineering Graphics» take part in the organization and conduct of the so-called «project weeks», the task of which is to develop the creative activity of children, starting from the first grade. For several decades, the Department of Engineering Graphics has been holding the annual student conference «Scientific Spring», at which first-year students present creative projects completed at school, but with some development under the guidance of teachers of the department. The article presents a methodology for organizing such work with students of Bauman Moscow State Technical University.
Running Longer To Slim Down: Post-Quantum Cryptography on Memory-Constrained Devices

Since we are getting closer to the realisation of a quantum computer capable of breaking the currently deployed public key cryptosystems, we need to be ready with the next generation of quantum-safe cryptosystems. In the case of small, low-memory embedded/cyber physical systems frequently used in the IoT world, the adoption of these post-quantum cryptography algorithms (PQC) is far from being straightforward. Currently available implementations are mostly characterized by high memory requirements that make the adoption on constrained devices a difficult challenge. In this work, we explore the feasibility of implementing quantum resistant cryptography on memory-restricted devices and we present the strategies that should be adopted while tackling the problem. We summarize and discuss the most common techniques currently available in literature for trading speed with reduced memory footprint. Discussed techniques range from strategies to minimise the static memory required by an implementation to techniques to deal with large artifact sizes. We show that, by using the appropriated optimization technique, even PQC schemes that require an amount of memory that largely exceeds the memory available on certain devices, can be successfully implemented, while also leaving enough memory for other applications that might reside on the same device.
Detecting and Classifying Adversarial Examples Based on DCT Transform

Perception algorithms are commonly used in ubiquitous computing. Deep neural networks have achieved significant performance in computer vision field, yet were found that exist vulnerabilities which was misclassified by adversarial examples. This issue has attracted great attention to explore methods to defend against adversarial examples. Detecting adversarial examples is a basic work for improving robustness of models. However, how to exploit an efficient feature to distinguish clean examples and adversarial ones is a challenging problem. In this paper, we investigate the adversarial examples from frequency domain and propose a framework to detect and classify adversarial attacks combing with attention mechanism. We first demonstrate that adversarial perturbations are more perceptible in the frequency domain and exploit this to achieve significant detection results. Then, combined with the advantages of the attention mechanism, an integrate network is proposed to achieve a more refined classification of attack methods. Based on the structure of ResNetl8, our results achieve optimal detection rate and high classification rate of 73% among FGSM, PGD-$\ell_{2}$, PGD$-\ell_{\infty}$, Deepfool and MI-FGSM.
REUT: A Retinex-inspired Low-light Image Enhancer for UAV Tracking at Night

In recent years, trackers based on convolutional neural networks have developed rapidly and performed well. These trackers have outstanding performance in daytime scenes, while their performance is often poor at night. Even the robustness of those state-of-the-art (SOTA) trackers is not ideal in the application area of nighttime unmanned aerial vehicle (UAV) tracking. To solve the problem that UAVs are difficult to track in night scenes, this paper proposes a Retinex-inspired low-light image enhancer for UAV tracking, namely REUT, which is used in the pre-processing stage of tracker. The method proposed in this paper trains an illumination estimation module (IEM) and a denoise module (DM), which can estimate illumination maps and remove the noise of reflectance maps effectively. Different SOTA trackers are used for evaluation in numerous UAV low-light scenarios. The evaluation on the public UAVDark135 benchmark shows that the method proposed in this paper has significant advantages over other SOTA lowlight image enhancement approaches for UAV nighttime tracking scenarios.
Sentiment Analysis using Optimal Transport loss function

Social networks have become an integral part of modern society, allowing users to express their thoughts, opinions, and feelings, and engage in discussions on various topics. The vast amount of user-generated content on these platforms provides a valuable source of data for sentiment analysis (SA), which is the computational analysis of opinions and sentiments expressed in text. However, most existing deep learning models for SA rely on minimizing the cross-entropy loss, which does not incorporate any knowledge of the sentiment of labels themselves. To address this limitation, a novel approach that utilizes an optimal transport-based loss function to improve sentiment analysis performance was proposed. Optimal transport (OT) metrics are fundamental theoretical properties for histogram comparison, and the proposed loss function uses the cost of the OT plan between ground truth and outputs of the classifier. The experimental results demonstrate that this approach can significantly reduce miss detections between positive and negative classes and suggest that using an OT-based loss function can effectively overcome the deficiency of existing SA models and improve their performance in real-world applications.
Intervention Analysis for Modeling and Forecasting Exchange Rates Rupiah Against Yen

The Rupiah exchange rate against the Yen is one of the most important exchange rates in Indonesia since the agreement between the two countries to conduct investment and trade transactions using local currency. Exchange rate movements tended to strengthen during 2019. In 2020 there was a COVID-19 intervention and there was a significant weakening. An intervention is an event that causes a sharp increase or decrease in time series data. Intervention analysis is an analysis used on the data affected by the intervention by measuring the magnitude of the change in value and the duration of the intervention. Intervention analysis research on data on the rupiah exchange rate against the yen is still very rarely done. This study aims to apply intervention analysis in modeling and forecasting the Rupiah against the Yen exchange rate by considering the impact of the COVID-19 intervention. Research shows that the COVID-19 intervention on the Rupiah exchange rate against the Yen has had a long impact with the best intervention model being ARIMA (4,2,0) with an order of intervention (0,1,0). The level of forecasting accuracy using the model is very good with a MAPE value of 2.69%.