Reviewerly/exHarmony huggingface dataset:
OpenSoundLab – A virtual sound laboratory for the arts

How can a virtual sound laboratory allow for new and exciting ways of sonic interaction in the context of the arts? Our project addresses this question by developing the virtual sound lab ‘OpenSoundLab’ (as an open-source fork of ‘SoundStage VR’ by Logan Olson) that introduces to the artistic and musical production of sonic media with the help of the VR goggles ‘Meta Quest 2’. The aim is to combine the physical experience of working on spatial experimental systems, which is often perceived as positive and productive, with the advantages of digital tools and thus to enable independent learning and experimentation. The virtual lab allows to become familiar with the basics of creative sound generation and processing. Specially produced video tutorials play a central role here, which can be viewed at any time within the virtual environment and thus make it possible to study in individual lab environments independently of time and place. Furthermore, ‘OpenSoundLab’ may serve as an open-source tool for the professional and academic community of musicians, performers, and artists alike. In our reflection, we develop the notion of ‘cooking’ sound while ‘flowing’ in a mixed environment and apply this to experimental work in a virtual sound laboratory.
3D Face Recognition Neural Network for Digital Human Resource Management

Digital human resource management can improve the organizational and operational efficiency of enterprises.In order to improve the efficiency of enterprise digital management and solve the problems of low security level and insufficient stability of 2D face recognition, we introduce 3D face recognition into the digital human resource management system. We propose a face recognition method based on a multistream convolutional neural network and local binary pattern and build a digital face recognition management system. We first build the system computer vision scene. Then a local binary mode facial expression feature extraction scheme is designed according to the depth camera image extraction method. Considering that face 3D features are easy to be missed, we build a multistream convolutional neural network to learn facial 3D features. Finally, we validate the effectiveness of the method in selecting a public face dataset. Experiments prove that our method can reach 98% face recognition accuracy, which is significantly better than other methods.
A Personalized Recommendation Method for Ancient Chinese Literary Works Based on a Collaborative Filtering Algorithm

The works of ancient Chinese literature that rely on Internet technologies are developing quickly. Through mobile phone inspection, ancient Chinese literary masterpieces are becoming more well-known, which encourages readers to read them more regularly. Providers of output literary works are confronted with a conundrum and a challenge, enabling users to quickly discover and attract their own works in the vast body of ancient Chinese literature. Providers of output literature can rely on personalized recommendation technology to find a solution to this issue. The production of works of literature is one of the most significant signs of human civilisation. As Internet technology becomes more widespread, the suggestion of the platform will become a more important factor in determining how the general audience reacts to works of literature. A collaborative filtering (CF) algorithm is offered as a method for making the recommendation algorithm for ancient Chinese literature more accurate. The personalized recommendation system’s technological support helps to increase the accuracy of recommendations. This system predicts and scores the reading preferences of the readers in a thorough manner, which helps to improve the recommendation’s accuracy. It is hoped that the user community will find the analysis and discussion contained in this article to be useful as a reference. The compelling experimental findings show that the recommendation algorithm suggested in this study greatly improves the accuracy of the intelligent recommendation system. These results were found by analyzing the final experimental data.
Analysis of Performance Improvement of Real-time Internet of Things Application Data Processing in the Movie Industry Platform

The goal of this study is to plan and develop complete strategies to improve the performance of film industry. The primary objectives of this study are to investigate a dataset generated by a IoT application and the nature of the data forms obtained, the speed of the data arriving rate, and the required query response time and to list the issues that the current film industry faces when attempting to handle IoT applications in real time. Finally, in film industry platforms, high performance with varied stream circulation levels of real-time IoT application information was realized. In this study, we proposed three alternative methods on top of the Storm platform, nicknamed Re-Storm, to improve the performance of IoT application data. Three different proposed strategies are (1) data stream graph optimization framework, (2) energy-efficient self-scheduling strategy, and (3) real-time data stream computing with memory DVFS. The work proposed a methodology for dealing with heterogeneous traffic-aware incoming rate of data streams Re-Storm at multiple traffic points, resulting in a short response time and great energy efficiency. It is divided into three parts, the first of which is a scientific model for fast response time and great energy efficiency. The distribution of resources is then considered using DVFS approaches, and successful optimum association methods are shown. Third is self-allocation of worker nodes towards optimizing DSG using hot swapping and making the span minimization technique. Furthermore, the testing findings suggest that Re-Storm outperforms Storm by 20-30% for real-time streaming data of IoT applications. This research focuses on high energy efficiency, short reaction time, and managing data stream traffic arrival rate. A model for a specific phase of data coming via IoT and real-time computing devices was built on top of the Storm platform. There is no need to change any software approach or hardware component in this design, but only merely add an energy-efficient and traffic-aware algorithm. The design and development of this algorithm take into account all of the needs of the data produced by IoT applications. It is an open-source platform with less prerequisites for addressing a more sophisticated big data challenge.
Learning Geometry by Using Virtual Reality

With the rapid change in the society, education must evolve to adapt to the digital native students of the 21st century. A change in the current educational paradigm is thus necessary. Information and Communication Technology (ICT) is a vehicle to achieve this objective of paradigm shift in education [ 2 ]. In this paper, we propose gamification approach [ 3 ] via a virtual reality (VR) through a report of a didactic experience. A mixed method research design was adopted for this study in which students’ classification of polyhedral will be reported for the two different learning experiences using either wooden polyhedral or a VR tool within an immersive learning environment. The VR environment allows the learning of geometry in three-dimensional with a friendly interface that offers a multitude of options. It is worth highlighting the benefits of this VR tool in the teaching-learning process of geometry, making explicit the fundamental role played by ICT in meaningful learning of mathematics.
Preliminary Procedures to Develop Storytelling-Based Assessment for Aging and Neurogenic Disorders

Objectives: The purpose of this study was to develop a storytelling-based assessment for aging and neurogenic disorders. We developed a story scenario and picture stimuli to be used in this assessment. Methods: In experiment 1, in order to select the most familiar folk-tale, familiarity and contents’ awareness, surveys were conducted for participants aged from their 20s to 60s. In experiment 2, for the scenario composition of the finally selected folk-tale, stories were collected from published books, and the initial scenarios and picture stimuli were produced based on the core content and Correct Information Unit. In experiment 3, a storytelling-based assessment was conducted for 147 young people and written data was collected. Based on the word-class analysis from the written data, scenarios and pictures were revised. In experiment 4, a total of 115 individuals (young: 60, elderly: 55) participated in the storytelling-based assessment and spoken data were collected. The final scenarios and picture stimuli were completed using the results of word-class analysis from the spoken data. Results: In the results of experiment 1, the Korean folk-tale ‘Heungbu-Nolbu’ was selected as the most familiar story. Through experiments 2 to 4, we completed the final scenario and picture stimuli which consists of eight episodes and contains a total of 24 sentences and 32 CIUs. Conclusion: Our result is meaningful in providing the theoretical evidence for the development of a Korean version of storytelling-based language assessment. It can be applied to various age groups and neurogenic disorders.
Smart System Design for College Physical Education Class Based on Abnormal Audio Detection Algorithm

Based on dual-core technology and the theory of nontransmission and noninterference of information flow, this paper conducts a detailed system trust environment investigation on key issues such as trust routing design and reliability analysis in the embedded structure. At the same time, we will study the method of extracting features from speech data. When dealing with abnormal audio detection, firstly, the preprocessed valid audio clips are framed and windowed and have stable short-term characteristics; after the feature is stable, the frequency feature and time domain feature of audio data are analyzed and compared. And then combined with specific applications, a detailed demand analysis is carried out, and the development plan and implementation method of the college physical education network auxiliary system are proposed. The teaching system follows a three-tier system structure. In order to expand the existing functions, this paper uses the principle of modularity, starting from the following four aspects: educational resources, online question and answer, coursework, and test-related modules. We choose object-oriented and easy-to-expandable modules, such as the development and implementation of programming language environments and database systems. By applying the abnormal audio detection technology to the embedded system of college P.E. classroom, it can effectively optimize the traditional P.E. teaching mode and promote the teaching efficiency and the new development of P.E. teaching in the information age.
Sequences of Events from the Electronic Medical Record and the Onset of Infection

We present a novel model of time-series analysis to learn from electronic health record (EHR) data when infection occurred in the intensive care unit (ICU) by translating methods from proteomics and Bayesian statistics. Using 48,536 patients hospitalized in an ICU, we describe each hospital course as an 'alphabet' of 23 physician actions ('events') in temporal order. We analyze these as k-mers of length 3-12 events and apply a Bayesian model of (cumulative) relative risk (RR). The log2-transformed RR (median=0.248, mean=0.226) supported the conclusion that the events selected were individually associated with increased risk of infection. Selecting from all possible cutoffs of maximum gain (MG), MG>0.0244 predicts administration of antibiotics with PPV 82.0 %, NPV 44.4 %, and AUC 0.706. Our approach holds value for retrospective analysis of other clinical syndromes for which time-of-onset is critical to analysis but poorly marked in EHRs, including delirium and decompensation.
Easy and plain languages as special cases of linguistic tailoring and standard language varieties

Abstract This article aims to introduce new insights to further the understanding of easy language (EL) and plain language (PL) as examples of tailored language and place them within a broader context of linguistic varieties. We examine EL and PL in relation to standard language, and we consider the degree of conscious effort required in tailoring and the compliance with the codified norms of standard language. Both EL and PL are used in asymmetric communication: PL to mediate between specialists and the general public and EL in communication with people with language barriers. We argue that while these varieties have similar purposes and methods, they also have significant differences; for example, the tailoring moves in opposite directions, as PL seeks general comprehensibility and EL aims to reach special and vulnerable groups. The differences between PL and EL are primarily linked to social prestige and the potential risk of stigma related to their use.
A Study on Online Level-differentiated College English Classes: Focusing on the Case of C University

This study aims to investigate how learners’ academic achievement and satisfaction have changed via online level-differentiated college English classes in the last two years. Mock TOEIC tests were conducted at the beginning of the 1SUPst/SUP semester and the end of the 2SUPnd/SUPSUP/SUP semester each year, and the results were compared and analyzed to verify the academic achievement of the learners. The results showed a decline in students’ English proficiency through online education both in 2020 and 2021. The drop in the TOEIC scores in 2021, however, was smaller than that of 2020. Surveys were also carried out to examine the satisfaction of the students with online level-differentiated classes. While low-level students seem to have experienced difficulty in a non-face-to-face environment, high-level learners showed much satisfaction in 2020. On the other hand, students of all levels expressed relatively high satisfaction in 2021, which shows that most students are well-adapted to the online class. Online education is expected to expand even after the covid-19 pandemic is over. Guidance and management from professors are required more than ever to improve learners
Deformable convolution infrared tracking algorithm based on improved SiamRPN++

Infrared images are not affected by visible light and can be used to continuously track pedestrian targets at night and in other complex environments. Pedestrian tracking is of great significance in intelligent monitoring, military investigation and other fields. However, due to the deformation and size changes caused by their changes in the process of night pedestrian tracking, the difficulty of the pedestrian tracking task is increased. Because of the above problems, this paper studies how to extract more feature information from infrared images and proposes an improved SiamRPN++ deformable convolution target deformation infrared tracking algorithm. An improved ResNet-50 deformable convolution network model is constructed to increase the convolution kernel offset. The extracted infrared target features have higher discrimination. At the same time, a weighted loss training method is proposed for classification and regression branches. The classification score is more consistent with the regression accuracy. The experimental results show that the improved SiamRPN++ algorithm improves the success rate and accuracy by 3.7% and 4.0%, respectively, compared with the mainstream SiamRPN++ algorithm. The tracking performance is effectively improved.
Self-Supervised Entity Alignment Based on Multi-Modal Contrastive Learning

Dear Editor, This letter proposes an unsupervised entity alignment method, which realizes integration of multiple multi-modal knowledge graphs adaptively.
A Conceptual Framework of Adaptive AR-Based Digital Guidance System for High-Mix Low-Volume Manual Assembly

In a high-mix, low-volume (HMLV) manufacturing environment, operators had long relied on paper instructions to identify product variations, which have cognitive disadvantages that digital assembly guidance and assistance systems addressed. Together with AR technology, the digitalised systems are used in guiding, training, designing and planning manual assembly. However, their implementation faces challenges arising from the high variation of products and the authoring expertise required. As easy tasks have negligible benefit under AR guidance, it is proposed that a multi-mode adaptive guidance system use AR guidance only at necessary assembly steps. This paper proposes an Adaptive Digital Guidance System for HMLV manufacturing, which includes a multi-mode assembly assist system for varying tasks complexity. The assembly information is shown adaptively based on the type of assembly process, user experience level, and task complexity. The assembly information manager application is proposed to track product variation and complexity in order to recommend appropriate instructional visual assets. The operator’s experience profile will group similar product variations while tracking the user’s improvement in task performance for each variant. Finally, all of these systems will be integrated into a proposed framework to create an adaptive digital guidance system.
Efficient Hardware Approximation for Bit-Decomposition Based Deep Neural Network Accelerators

Recently, an increasing number of embedded devices for neural network acceleration have adopted hardware approximations as they enable an efficient trade-off between computational resources, power consumption, and network accuracy. In this paper, we present novel approximation techniques for architectures based on bit-decomposition of the multiply and accumulate operations, especially In-memory architectures. For the target architecture, we propose seven different approximations with a high degree of freedom. We explore the design space of these approximations and evaluate their effect on architecture area and power as well as network accuracy loss. Our approximation techniques can achieve up to 26.5% power and 20% area reductions at only 1% accuracy loss.
Inconspicuous Data Augmentation Based Backdoor Attack on Deep Neural Networks

With new applications made possible by the fusion of edge computing and artificial intelligence (AI) technologies, the global market capitalization of edge AI has risen tremendously in recent years. Deployment of pre-trained deep neural network (DNN) models on edge computing platforms, however, does not alleviate the fundamental trust assurance issue arising from the lack of interpretability of end-to-end DNN solutions. The most notorious threat of DNNs is the backdoor attack. Most backdoor attacks require a relatively large injection rate (≈ 10%) to achieve a high attack success rate. The trigger patterns are not always stealthy and can be easily detected or removed by backdoor detectors. Moreover, these attacks are only tested on DNN models implemented on general-purpose computing platforms. This paper proposes to use data augmentation for backdoor attacks to increase the stealth, attack success rate, and robustness. Different data augmentation techniques are applied independently on three color channels to embed a composite trigger. The data augmentation strength is tuned based on the Gradient Magnitude Similarity Deviation, which is used to objectively assess the visual imperceptibility of the poisoned samples. A rich set of composite triggers can be created for different dirty labels. The proposed attacks are evaluated on pre-activation ResNet18 trained with CIFAR-10 and GTSRB datasets, and EfficientNet-B0 trained with adapted 10-class ImageNet dataset. A high attack success rate of above 97% with only 1% injection rate is achieved on these DNN models implemented on both general-purpose computing platforms and Intel Neural Compute Stick 2 edge AI device. The accuracy loss of the poisoned DNNs on benign inputs is kept below 0.6%. The proposed attack is also tested to be resilient to state-of-the-art backdoor defense methods.
A Novel Combined Correlation Power Analysis (CPA) Attack on Schoolbook Polynomial Multiplication in Lattice-based Cryptosystems

The lattice-based cryptography problems are known to be secure against the quantum computing attacks, till date no known quantum algorithm is able to solve these hard problems in lattices. Their naive implementations on embedded devices are, however, vulnerable to side-channel analysis (SCA) attacks with full key recovery possible via power/EM leakage analysis. This work analyses and attacks the power side channel leakage in the baseline hardware architecture of schoolbook polynomial multiplication, that is an essential component of most of the lattice based cryptography implementations. We first undertake a horizontal correlation power analysis (HCPA) method, optimized to work independent of the precise attack location specification in the schoolbook polynomial multiplier power leakage profile. Inspite of the inherent difficulties in HCPA, the attack is extremely efficient; with an 99.90% accuracy of recovering any one sub secret-key using only a single trace. Next we undertake the vertical correlation power analysis (VCPA) attack on the schoolbook polynomial multiplier power leakage profile, that requires larger number of power traces to analyze the correlation. Finally, we propose a novel combined correlation power analysis (CCPA) method that combines the strengths of both the VCPA and the HCPA to further improve the attacking capability of HCPA. We report a complete secret key recovery with a 100% accuracy by using only 4 power traces.
Low Complexity Reconfigurable-Scalable Architecture Design Methodology for Deep Neural Network Inference Accelerator

Convolutional Neural networks (CNNs) are useful in a wide range of applications such as image recognition, automatic translation and advertisement recommendation. Due to the ever-increasing deep structure, state-of-the-art CNNs are recognized to be computationally and memory intensive. The requirements of neural networks are continuously evolving and a reconfigurable architecture plays a major role in addressing this challenge. In this paper, we propose a Low-Complexity Reconfigurable architecture for implementation of Convolutional Neural Networks. The architecture can be configured as per the requirements of the neural network. The input image size is dependent on the dataset, hence the size varies from network to network. In order to compute any network, the proposed architecture has the flexibility to compute any size of input image. Experimental results shows that the proposed CNN inference accelerator achieves a peak throughput of 0.5 TOPS with an area of 9.58 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> consuming 3.02 Watts in TSMC 40nm technology. The area of the proposed architecture is 50% smaller than the state of the art solutions. An FPGA prototype achieves throughput of 102.4 GOPs consuming 5.057 Watts on ZYNQ Ultrascale+ MPSoC ZCU102 FPGA.
In-depth Analysis of the Effects of Electromagnetic Fault Injection Attack on a 32-bit MCU

As semiconductor technology developing, current microcontrollers (MCUs) contain processor, memory, bus and peripherals, and become a low-power system-on-chip platform supporting various IoT applications. Although providing powerful real-time process capability, MCUs are vulnerable to fault injection attacks (FIAs) which target at the valuable data such as cipher key and personal information. While many research studies concentrated on successful FIAs on MCUs, this paper focuses on the low-cost and high-efficiency FIA technique, electromagnetic fault injection (EMFI), and its effects on the very widespread MCU (STM32F103ZET6) with ARM 32-bit Cortex™-M3 kernel. Interestingly, we observe a number of faults which consistently occur, such as the USART interface errors and the computation errors. By characterizing and reasoning the faults, we identify the vulnerabilities of the MCU and the EMPI fault models. Finally, countermeasures against the EMPI attacks are recommended.