Reviewerly/exHarmony huggingface dataset:
Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization

Due to the influence of the shooting environment and inherent image characteristics, there is a large amount of interference in the process of image stitching a geological borehole video. To accurately match the acquired image sequences in the inner part of a borehole, this paper presents a new method of stitching an unfolded borehole image, which uses the image generated from the video to construct a large-scale panorama. Firstly, the speeded-up robust feathers (SURF) algorithm is used to extract the image feature points and complete the rough matching. Then, the M-estimator sample consensus (MSAC) algorithm is introduced to remove the mismatched point pairs and obtain the homography matrix. Subsequently, we propose a local homography matrix offset optimization (LHOO) algorithm to obtain the optimal offset. Finally, the above process is cycled frame by frame, and the image sequence is continuously stitched to complete the construction of a cylindrical borehole panorama. The experimental results show that compared with those of the SIFT, Harris, ORB and SURF algorithms, the matching accuracy of our algorithm has been greatly improved. The final test is carried out on 225 consecutive video frames, and the panorama has a good visual effect, and the average time of each frame is 100 ms, which basically meets the requirements of the project.
MA-TEECM: Mutual Anonymous Authentication-Based Credential Migration Technology for Mobile Trusted Execution Environments

ARM TrustZone is the most widely used mobile trusted execution environment (TEE) technology today. Its hardware-enabled isolated execution environment provides reliable assurance of secure storage of credentials in mobile devices. However, the research on managing credentials stored in the TEE throughout the lifecycle of mobile devices has received little attention in recent years, and the credentials in TEE generally face usability problems caused by the mobile device lifecycle events. Aiming at the risk of information disclosure caused by the third-party service providers in the traditional credential migration scheme, this paper presents a mutual anonymous authentication-based credential migration framework for mobile trusted execution environments. First, we propose a peer-to-peer credential migration model between mobile terminals based on TrustZone and SGX, which solves the single point of failure caused by attacks on trusted third parties that act as credential transfer stations and managers in traditional solutions; Second, we propose an identity authentication protocol between TEEs based on mutual anonymous authentication, and a detailed authentication process is designed based on the universal mobile TEE model; Third, we build a formal verification model using High-Level Protocol Specification Language (HLPSL). Finally, the formal and informal security analysis indicate that the improved scheme meets the expected security requirements and is secure against several known attacks.
Recommender System with User-Based and Item-Based Collaborative Filtering on Twitter using K-Nearest Neighbors Classification

Netflix is one of the most widely used applications for watching movies online. There are various movie titles that can be watched by users, so a recommendation system is needed to help users who feel confused in choosing movie titles. Twitter is a social media used to express ideas, thoughts, and feelings. Not a few Twitter users who conduct movie discussions, with the movie discussion can be converted into a rating that can be used in the recommendation system. Collaborative Filtering is one of the methods of the recommendation system, by recommending based on the similarity between users (user-based) and based on items that have similarities with user-selected items (item-based). In this research, the Collaborative Filtering method is combined with K-Nearest Neighbors classification which obtains an RMSE value for user-based 1.8244 and item-based 0.5449. K-Nearest Neighbors gets 91.22% precision and 91.07% recall for user-based, while item-based gets 89.44% precision and 91.22% recall with the optimal K as a parameter is 3.
Voice Privacy in Biometrics

Attempts to spoof an ASV (voice biometric system) have been successful in the past due to the advent of technologies. However, despite the development of various countermeasures for each spoofing attack, there is an urgent need for a versatile countermeasure. Hence, designing a voice privacy system has become crucial. Moreover, the energy losses in a speech production model contain speaker-specific information and thus provide acoustic cues for voice privacy. In this chapter, the design of 2nd-order resonator and the linear prediction modeling of speech production are exploited to design voice privacy system. The performance of the proposed system is compared with the secondary baseline system of the INTERSPEECH 2020 voice privacy challenge. Improved performance-wise EER and WER are achieved for various subsets of the corpora, furthermore, while we may achieve anonymization by cryptography, which have limitations in complexity and implementation costs, discussed in detail for privacy preservation.
Online Writing Instruction in Indonesia: Causes of anxiety problems and strategies of solutions

Online learning brings EFL students the new experience of writing instruction.On the one hand, the new experience in online learning may lead to excitement; on the other hand, it may also result in problems such as anxiety.The current study aims at tracing the students' writing anxiety levels, identified the causes of anxiety problems, and put forward strategies of solutions.The results of the data analyses show that out of 149 students involved in this study, 115 students had a high level of writing anxiety and the other 35 students were of medium and low levels of anxiety.The results of the study also indicated that anxiety problems were predominantly caused by time constraints (3.92), linguistic factors (3.70), and feeling afraid of negative evaluation from peers and the teacher (3.63).Furthermore, students also reported conditions that made their writing skills stagnant, for instance, limited writing practice (3.57), insufficient writing technique (3.57), and shortage of feedback (2.81).The qualitative data revealed that numerous strategies implemented by highly anxious students to reduce their writing anxiety in online writing instruction include doing more linguistics activities, doing self-anxiety treatment, implementing writing techniques, building resources, and increasing their self-confidence.Understanding the numerous causes of anxiety, further research may investigate teachers' strategies to help students reduce their anxiety.
Funnel Control Under Hard and Soft Output Constraints

This paper proposes a funnel control method under time-varying hard and soft output constraints. First, an online funnel planning scheme is designed that generates a constraint consistent funnel, which always respects hard (safety) constraints, and soft (performance) constraints are met only when they are not conflicting with the hard constraints. Next, the prescribed performance control method is employed for designing a robust low-complexity funnel-based controller for uncertain nonlinear Euler-Lagrangian systems such that the outputs always remain within the planned constraint consistent funnels. Finally, the results are verified with a simulation example of a mobile robot tracking a moving object while staying in a box-constrained safe space.
Reflections of an ancient document processor

The bulk of the documents that affect our lives are digital or born digital. Our laborious investigations of layout, script, font and graphics, are turning into mere exercises with little influence on pursuits outside the Document Analysis and Recognition (DAR) community. Recent performance improvements on such tasks, even if based on deep learning and AI, are as much the result of advances in computer hardware as of breakthroughs in document research. It is time to automate tasks beyond transcription. This Commentary addresses our mission, our approach to some technical issues, and the role of AI in DAR. Opportunities for a wider role for document analysis include more pervasive application of statistical decision theory, integrated genre analysis, summarization, interpretation and information extraction, bolder goals in content analysis, and alternative modalities, induced by the open source movement, for sharing research results. Importantly, expanding the scope of our research incurs increased responsibility for retaining human prerogatives in critical decision making and preserving essential human skills like good writing and discriminative reading.
Reform and practice of vocational basic skills training course under the concept of curriculum ideology and politics

The curriculum ideology and politics of practice courses in the undergraduate specialty of food quality and safety were explored by taking the Vocational Basic Skills Training course as an example. The teaching content, teaching methods, assessment system and also other aspects of this course were reformed. Our practice and exploration have obtained some good results. The preliminary practical teaching was integrated and developed with ideological and political elements. To improve the training quality of innovative and applied talents of undergraduate specialty of food quality and safety in our university, we need to carry out long-term and continuous exploration and practice.
Constrained Load Transportation by A Team of Quadrotors

Cable-suspended load transportation by multiple unmanned aerial vehicles (UAVs) has many applications. However, system nonlinearities and uncertainties as well as load dimensions are usually ignored by the literature, while the performance and safety constraints during the cooperative transportation operation are not discussed by existing works. In this work, we develop a new adaptive constrained formation control architecture for a team of UAVs that are collaboratively transporting a load, subject to multiple performance and safety constraint requirements. Universal barrier functions are used to address these time-varying constraints, and adaptive estimators are employed to deal with system uncertainties. In the end, a simulation study further demonstrates the effectiveness of the proposed framework.
Seeking Information with a More Knowledgeable Other

This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. --- Juho Kim and Henriette Cramer, Editors
CoFHE: Software and hardware Co-design for FHE-based machine learning as a service

Introduction: Privacy concerns arise whenever sensitive data is outsourced to untrusted Machine Learning as a Service (MLaaS) platforms. Fully Homomorphic Encryption (FHE) emerges one of the most promising solutions to implementing privacy-preserving MLaaS. But prior FHE-based MLaaS faces challenges from both software and hardware perspectives. First, FHE can be implemented by various schemes including BGV, BFV, and CKKS, which are good at different FHE operations, e.g., additions, multiplications, and rotations. Different neural network architectures require different numbers of FHE operations, thereby preferring different FHE schemes. However, state-of-the-art MLaaS just naïvely chooses one FHE scheme to build FHE-based neural networks without considering other FHE schemes. Second, state-of-the-art MLaaS uses power-hungry hardware accelerators to process FHE-based inferences. Typically, prior high-performance FHE accelerators consume <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mo>&gt;</mml:mo><mml:mn>160</mml:mn></mml:math> Watt, due to their huge capacity (e.g., 512 MB) on-chip SRAM scratchpad memories. Methods: In this paper, we propose a software and hardware co-designed FHE-based MLaaS framework, CoFHE. From the software perspective, we propose an FHE compiler to select the best FHE scheme for a network architecture. We also build a low-power and high-density NAND-SPIN and SRAM hybrid scratchpad memory system for FHE hardware accelerators. Results: On average, under the same security and accuracy constraints, on average, CoFHE accelerates various FHE-based inferences by 18%, and reduces the energy consumption of various FHE-based inferences by 26%. Discussion: CoFHE greatly improves the latency and energy efficiency of FHE-based MLaaS.
Formalising the h-Principle and Sphere Eversion

In differential topology and geometry, the h-principle is a property enjoyed by certain construction problems. Roughly speaking, it states that the only obstructions to the existence of a solution come from algebraic topology.
A Study on Security Schemes in Blockchain Technology

Block-chain is widely praised as an astounding advancement because of its recent development in security. Many believe that information stored in a block-chain is secure because of its decentralization and cryptographic methods. Consensus is the most important layer in block-chain architecture, and it is responsible for the network’s security and reliability. Block-chain schemes’ stabilization relies heavily on consensus-algorithms. The article provides an in-depth explanation of the most common consensus algorithms, including Proof-of-Work (PoW), Proof-of-Stake (PoS), Proof-of-Burn (PoB), Proof-of-Activity (PoA), Proof-of-Vote (PoV) and Proof-of-Elapsed-Time (PoET). An in-depth evaluation of these consensus mechanisms has been conducted, and the results have been presented. These algorithms can be measured using a variety of metrics, including security, efficiency, and scalability. A secured system that functions properly needs to identify the algorithm and its strength. According to various parameters, such as decentralization, POA has a lower level of decentralization than other protocols.
Construction and Development of Chemistry Teaching Platform in Higher Vocational Colleges Under the Background of Artificial Intelligence

Artificial intelligence technology has set off a wave of industrial clusters in digital economy, accelerated the evolution of social industries and occupations, and promoted the transformation and upgrading of higher vocational colleges. Under the background of artificial intelligence, the teaching of chemistry teaching platform in higher vocational colleges, as a new teaching form, has become a hot issue of network teaching. Starting with the characteristics of network teaching in higher vocational colleges, this paper gives a construction and system management mode of chemistry teaching resource platform in higher vocational colleges; This paper analyzes how teachers and students share teaching and learning through this platform; Try to solve the double problems of teaching resource construction and management of chemistry teaching platform software.
Realities of modern art education (according to the results of the teaching community survey)

The issues of art education that have occurred and intensified in the last six months in connection with the large-scale of the Russian Federation’s aggression against Ukraine have been updated. The purpose of the article is to outline the state and prospects of art education for students in the progress of Ukraine towards the post-war reconstruction implemented by the author in the tasks of systematizing the results of the pedagogical community survey and the corresponding ranking of the problem in its perception. It has allowed us to define more clearly the methods of its psychological and pedagogical solution. The methods of fact-finding survey of respondents with further description of the obtained data, analysis of identified problems with the theoretical explanation of their essence and causes, determination of predicted ways of eliminating identified contradictions in art education of students during the survey were used. The process of the experimental survey is described. Systematization of the results is based on six blocks that reflect the need for a valuable rethinking of the content of art education, the difficulties of distance art learning for students, the teachers’ worries for the destruction of national heritage, objects of education and culture because of military actions and the organization of education in temporarily occupied territories. Moreover, psychological problems among children, their parents and teachers are growing. The deepening inferior attitude towards artistic disciplines against the background of military actions has caused to anxiety and specific methodical questions regarding the teaching of various types of art. During the analysis of the results of the experiment, the emphasis was placed on the first block of identified problems, the connection between the rethinking of values and the de-Russification of the content as well as the strengthening of the national content of the educational content, bringing to the fore the task of patriotic education through art. The key role of this value reflection in relation to the rest of the actualized issues, which consists of scientific novelty, is presented. The article is addressed to teachers of art and other disciplines of institutions of general secondary, higher art and art-pedagogical, out-of-school, specialized art education.
An augmented reality-based science book for junior high school students: Case study on environmental pollution and global warming topics

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Erni Yulianti, Vita Ria Mustikasari, Nurhadi Muhlisin, Nor Farahwahidah Abdul Rahman, Fatin Aliah Phang; An augmented reality-based science book for junior high school students: Case study on environmental pollution and global warming topics. AIP Conf. Proc. 10 January 2023; 2569 (1): 060010. https://doi.org/10.1063/5.0112204 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Celestial image classification based on deep learning and FGSM attack

With the Images from large telescopes accumulating, more and more unknown celestial bodies are being discovered by humans. However, determining whether an object is a star or a galaxy through manual methods is often time-consuming and inaccurate. Therefore, this paper collects celestial images obtained by astronomical telescopes from SDSS and classifies them using a Convolutional Neural Network. It is clear that when the image is clear, the accuracy of the model on the test set can reach more than 98%, and it can complete the classification well. Furthermore, the performance of the model under noise disturbance is tested for many times and its robustness is found to be poor. Under the attack of Fast Gradient Sign Method, the classification accuracy of the model is relatively lower than expected, and the anti-interference ability is poor, so optimization measures need to be implemented. After adding noisy images to the dataset, the model was reconstructed and retrained, which improved the classification accuracy of the model. The results show that when there are many images with noise in the test set, the accuracy can reach about 86%, which is proved to be an effective means to defend FGSM attacks.
Investigation and analysis of online teaching in Guangdong-Hong Kong-Macao Greater Bay Area

With the rapid development of information technology, online open courses are playing an increasingly important role in university education. By collecting the course resource information and excellent cases of teachers' online teaching in the online open course alliance platform of universities in the Guangdong-Hong Kong-Macao Greater Bay Area, the article conducts research from six dimensions: the number of course sources in the alliance platform, the classification of course subjects in the alliance platform, the number of courses offered by alliance institutions, the number of courses introduced by alliance institutions, the number of learners studying in alliance institutions and the choice of online teaching wisdom tools of teachers in alliance schools, and analyzes the construction and application status of online courses in the alliance platform. Finally, it puts forward the strategic measures to improve the level of online course construction of the alliance platform, and provides some reference suggestions for teachers to carry out online education and teaching.
Cloud-Based Reconfigurable Hardware Accelerator for the KNN Classification Algorithm

The K-Nearest Neighbor algorithm is a supervised machine learning algorithm that is used for classification problems. The execution time of this algorithm could be extremely high, especially for huge and high-dimensional datasets. The objective of this work is to design and implement efficient parallel hardware architectures to accelerate the KNN classifier. The proposed architectures are implemented using FPGA on Intel DevCloud. Experimental results show that the proposed hardware implementation of the algorithm is 10.7 times faster than the software implementation with 96.6% classification accuracy for a benchmark classification dataset.
The development of educational policy acceptance model

This study aims to develop an education policy acceptance model.Policy implementers need to implement education policies in the best possible way.However, due to the uncertainty of such situations, health, politics, and natural disasters, have an impact on the implementation of education policy and the quality of education.Thus, there is a need to determine what indicators can prevent this uncertainty and accept education policies to be implemented smoothly to guarantee the sustainability of the quality of education.This quantitative study was conducted to obtain research data.In this study, the researcher applied the concept of positivism.Finding shows there was a significant influence between the constructs of Attention Towards Use to Behavioral Intention through path analysis at significance level of 0.05 (β= 0.898, p<0.05) ; Perceived Useful to Attention Towards Use (β=0.152,p <0.05); Perceived Useful to Perceived of Ease (β= 0.789, p<0.05), and Perceived of Ease to Attention Towards Use(β=0.69 p <0.05).While the research model explained 75% of the total variance in Behavioral Intention.This suggests that the three sets of exogenous latent variables have collectively explained 80.6% of the variance in Behavioral Intention.This study wraps up various potential concerns and importance for policymakers.Implementers within an organization should assess user opinions towards the proposed modifications to shape the development of the policies.By knowing the acceptance indicators of education policy, even if there are challenges such as health, politics, and natural disasters, stakeholders can focus on indicators involved in adopting policies and preventing uncertainty in implementing educational policies.