Reviewerly/exHarmony huggingface dataset:
A Cross-modal Attention Model for Fine-Grained Incident Retrieval from Dashcam Videos

Dashcam video has become popular recently due to the safety of both individuals and communities. While individuals can have undeniable evidence for legal and insurance, communities can benefit from sharing these dashcam videos for further traffic education and criminal investigation. Moreover, relying on recent computer vision and AI development, a few companies have launched the so-called AI dashcam that can alert drivers to near-risk accidents (e.g., following distance detection, forward collision warning) to improve driver safety. Unfortunately, even though dashcam videos create a driver’s travel log (i.e., a traveling diary), little research focuses on creating a valuable and friendly tool to find any incident or event with few described sketches by users. Inspired by these observations, we introduce an interactive incident detection and retrieval system for first-view travel-log data that can retrieve fine-grained incidents for both defined and undefined incidents. Moreover, the system gives promising results when evaluated on several public datasets and popular text-image retrieval methods. The source code is published at https://github.com/PDD0911-HCMUS/Cross_Model_Attention
AWARENESS OF CREATIVE TASKS AS A CONDITION FOR THE PROFESSIONAL DEVELOPMENT OF A FUTURE TEACHER-MUSICIAN

The article discusses the new approaches to the process of improving the professional training of future teachers-musicians. The author of the article, who has been leading the pedagogical practice course and lecture-seminar courses in music pedagogy and performance for many years, pays special attention to the awareness of future teachers of their own artistic intentions and the ways of their pianistic embodiment. Unconsciousness of all the components of the process of mastering a piece of music leads to the inability of the future teacher to transfer his creative experience to the student. Some reasons for ineffective pedagogical activity of music students are analyzed, one of which is the inability to understand that while giving recommendations to their students they are guided by their own inner holistic musical-auditory perception, but do not explain what exactly they are proceeding from. In the literature on the psychology of musical and performing arts, the problem of awareness of methods of work on a piece does not always receive the coverage and scientific justification that it deserves. Meanwhile, this is one of the most important problems both for pedagogy and performing ― without a deep awareness of all the components of the performing process, it is impossible to achieve a real artistic maturity or to develop pedagogical skill.
Health-Oriented Multimodal Food Question Answering

Health-oriented food analysis has become a research hotspot in recent years because it can help people keep away from unhealthy diets. Significant progress has been made in recipe retrieval, food recommendation, nutrition and calorie estimation. However, existing works still cannot well balance the individual preference and the health. Multimodal food question and answering (Q &A) has a great potential in practical applications, but it is still not well studied. In this paper, we build a health-oriented multimodal food Q &A dataset (MFQA) with 9K question and answer pairs based on a multimodal food knowledge graph collected from a food-sharing website. In addition, we propose a knowledge-based multimodal food Q &A framework, which consists of three important parts: encoder module, retrieval module, and answer module. Extensive experimental results on the MFQA dataset demonstrate the effectiveness of our method. The code and dataset are available at https://github.com/Wjianghai/HMFQA .
BDMasker: Dynamic Data Protection System for Open Big Data Environment

Big data has become a national basic strategic resource, and the opening and sharing of data is the core of China's big data strategy.Cloud native technology and lake-house architecture are reconstructing the big data infrastructure and promoting data sharing and value dissemination.The development of the big data industry and technology requires stronger data security and data sharing capabilities.However, data security in an open environment has become a bottleneck, which restricts the development and utilization of big data technology.The issues of data security and privacy protection have become increasingly prominent both in the open source big data ecosystem and the commercial big data system.Dynamic data protection system under the open big data environment is now facing challenges in regards such as data availability, processing efficiency, and system scalability.This paper proposes the dynamic data protection system BDMasker for the open big data environment.Through a precise query analysis and query rewriting technology based on the query dependency model, it can accurately perceive but does not change the original business request, which indicates that the whole process of dynamic masking has zero impact on the business.Furthermore, its multi-engine-oriented unified security strategy framework realizes the vertical expansion of dynamic data protection capabilities and the horizontal expansion among multiple computing engines.The distributed computing capability of the big data execution engine can be used to improve the data protection processing performance of the system.The experimental results show that the precise SQL analysis and rewriting technology proposed by BDMasker is effective.The system has good scalability and performance, and the overall performance fluctuates within 3% in the TPC-DS and YCSB benchmark tests.
Security Analysis of Visual based Share Authentication and Algorithms for Invalid Shares Generation in Malicious Model

Several (k, n) Secret Image Sharing (SIS) schemes supporting authentication of reconstructed secret image and that of shares are proposed in the past. In these existing schemes, two similar state-of-the-art SIS schemes performing visual based share authentication have following merits compared to other schemes: no restriction on values of k and n to be 2, linear time complexity of share authentication, lossless secret image reconstruction, no pixel expansion, and support for share authentication in both dealer participatory and non-participatory environments. In this paper, we show that respective share authentication in these two similar state-of-the-art SIS schemes is computationally insecure in malicious model. We first identify the vulnerabilities in their respective share authentication through security analysis. Then, we propose two linear time algorithms for generating invalid shares from original shares by exploiting the identified vulnerabilities. These generated invalid shares are capable of passing respective authentication in the two analyzed SIS schemes. In addition, usage of a generated invalid share in place of original share during secret image reconstruction results in distorted secret image. Finally, we provide experimental results that accord with inferences of security analysis and linear time complexity of the proposed algorithms for invalid shares generation.
Video Deduplication using CNN(Conv2d) and SHA-256 hashing

We all know that there is an exponential growth in the production of digital data. The user of the internet approaches the cloud service providers to upload the video data into the cloud. The cost and size of the digital videos is huge. Hence there is great need for techniques which aims at optimizing the cost and size for storing the data. This paper provides a comprehensive overview of video deduplication system by extracting the global and local features of the video. The major techniques employed in the process of video deduplication are Conv2d i.e., CNN algorithm to extract the local features of the video and hashing algorithms like SHA-256 to extract the global features of the video.
Analysis on Role of Quantum Computing in Machine Learning

Quantum machine learning is a process by which quantum computers are used to learn from data. It is still in its begining stages of development, but has the potential to be much more efficient than classical machine learning algorithms. The main advantages of machine learning is that it can exploit the massive parallelism of quantum computers. This means that quantum machine learning algorithms can potentially learn from data much faster than classical algorithms. Another advantage is that quantum machine learning algorithms can deal with data that is too large or too complex for classical algorithms. For example, a quantum algorithm could be used to learn from a dataset that is too large to fit into a classical computer's memory. There are still many challenges to overcome before quantum machine learning can be used in practice, but the potential benefits are huge. If successful, Quantum machine learning could revolutionize the field of machine learning and have a profound impact on many other areas of science and technology.
A New Blockchain Design Decoupling Consensus Mechanisms from Transaction Management

In a traditional blockchain system, the consensus protocol and the transaction management are coupled, i.e., transactions are performed during the consensus process. When the consensus protocol completes its process, the transaction is considered as completed. Such an integrated approach challenges the system scalability and regulatory issues. And is not in line with the regulatory principles such as PFMI (Principles of Financial Market Infrastructures). This paper proposed a new decoupling mechanism that separates consensus protocols from transaction management, and further separate transactions from settlement with regulation compliance. Based on the new scheme, this paper proposed a new block-building protocol for blockchain systems. This protocol allows consensus steps, transaction management, settlement, and regulation compliance can be done concurrently.
The Role of Vocabularies in the Age of Data: The Question of Research Data

The objective of this work is to discuss how vocabularies can contribute to assigning computational semantics to digital research data within the context of Big Data, so that computers can process them, allowing their reuse on large scale. A conceptualization of data is developed in an attempt to make it clearer what would be data, as an essential element of the Big Data phenomenon, and in particular, digital research data. It then proceeds to analyse digital research data uses and cases and their relation to semantics and vocabularies. Data is conceptualized as an artificial, intentional construction that represents a property of an entity within a specific domain and serves as the essential component of Big Data. The concept of semantic expressivity is discussed, and is used to classify the different vocabularies; within such a classification ontologies, are shown to be a type of knowledge organization system with a higher degree of semantic expressivity. Features of vocabularies that may be used within the context of the Semantic Web and the Linked Open Data to assign machine-processable semantics to Big Data are suggested. It is shown that semantics may be assigned at different data aggregation levels.
A New Digital-Currency Model Based on Certificates

This paper proposes a blockchain-based digital-currency model based on certificates, and uses stablecoins as an example to illustrate the model. A stablecoin is anchored on a fiat currency, and often backed by 100% fiat such as USD. Unlike traditional cryptocurrency models, the blockchain system using this model does not store the assets directly, but instead store the certificates of these assets. People can trade digital certificates like trading digital currencies, but the value of these certificates in stored in custodian banks. The transaction process is slightly different from cryptocurrency transactions. The advantage of trading certificates is that the loss of these digital certificate does not mean the loss of value. If a private key is lost, a client can recover her assets from the custodian bank with a replacement certificate. Due to this nature, using this model, the center of digital economy remains at banks, rather than the blockchain network, as suggested by Digital-Currency Areas (DCA).
The Importance of Image Interpretation: Patterns of Semantic Misclassification in Real-World Adversarial Images

Adversarial images are created with the intention of causing an image classifier to produce a misclassification. In this paper, we propose that adversarial images should be evaluated based on semantic mismatch, rather than label mismatch, as used in current work. In other words, we propose that an image of a “mug” would be considered adversarial if classified as “turnip”, but not as “cup”, as current systems would assume. Our novel idea of taking semantic misclassification into account in the evaluation of adversarial images offers two benefits. First, it is a more realistic conceptualization of what makes an image adversarial, which is important in order to fully understand the implications of adversarial images for security and privacy. Second, it makes it possible to evaluate the transferability of adversarial images to a real-world classifier, without requiring the classifier’s label set to have been available during the creation of the images. The paper carries out an evaluation of a transfer attack on a real-world image classifier that is made possible by our semantic misclassification approach. The attack reveals patterns in the semantics of adversarial misclassifications that could not be investigated using conventional label mismatch.
Cross-Case Analysis

This chapter conducts a cross-case analysis between the three case studies. The chapter starts with similarities and differences in the blockchain adoption process (Sect. 7.1); followed by the key success factors that influence blockchain implementation (Sect. 7.2); next the common barriers and solutions are summarised in Sect. 7.3; and the performance outcomes of blockchain projects are presented in the end in Sect. 7.4. The theories of the innovation process model (Flint et al., 2005; Su et al., 2011) and the practice-based view (PBV) are applied to analyse the blockchain implementation in the case companies.
Discussion

This chapter discusses and critiques the research findings and prior related research. The innovation process model (Flint et al., 2005; Su et al., 2011) and the practice-based view (Bromiley & Rau, 2014) are applied, respectively. Several propositions are then formulated based on the discussions.
From Participatory to Online: Exploring Contexts of Group Singing and the Role of the Singer

This article discusses the contexts of pre- and COVID-19 impacted group singing. It begins by outlining a purposely composed work, Wordsong, that sought to create organisational identity and foster community through in-person participatory and presentational singing. The subsequent online group singing strategies that are analysed and discussed, also sought to construct new musical identities and, for many, to create or sustain communities. Described as providing a ‘lifeline’ during the COVID-19 pandemic, the context of group singing online, particularly in relation to the multitracking of a group performance, ultimately changes the role of the singer within the group aesthetic. Indeed, like most recordings, the overall aesthetic is often engineered by someone other than the singer or singers. This creates several considerations when analysing the participatory ethos of group singing online. Until latency issues are resolved and appropriate technologies are developed to enable the real-time participatory or presentational singing afforded by works such as Wordsong, then group singing online is restricted in its traditional group singing capability. Instead, it is the singer’s perception of their individual sound that becomes a focus. This is particularly evident in relation to vocal ensemble strategies that were utilised in response to the COVID-19 pandemic. The article concludes with a model of the transformation from participatory singer to online audience member, that delineates the place or role of the singer within this transformation.
The Interpretation of Deixis in Cognitive Linguistics: A Literature Review

This paper studied deixis in cognitive linguistics, and mainly introduced three types of deixis: spatio-temporal; knowledge-related; empathy. On the basis, the literature on this topic in recent years was reviewed relying on China National Knowledge Infrastructure (CNKI) and Web of Science. The findings are: the deixis represented by "这" and "那" and "this" and "that" are related to the distance of psychological space rather than only physical space; the emerging study like children's deixis and the combination of deixis with sign language has been developing gradually. More cross-field or other empirical studies will attract more researchers to conduct in the future.
Analysis of Problems in the Development of Online Teaching and Suggestions for Improvement

With the rapid development of information technology, various online learning platforms such as WeChat, MOOC, and Ding Talk are increasingly being used in classrooms, changing the way education is delivered. However, the adoption of online teaching has revealed some problems that have limited its effectiveness. The problems include insufficient network service capacity to support large-scale online learning, a lack of high-quality teaching resources, inadequate learning platform functions, and inadequate online learning habits among teachers and students. Although various means, such as Micro-Course Online Video, MOOC, intelligent teaching platforms, and artificial intelligence technologies can enrich the form of online education, online teaching has not yet maximized the use of information technology to improve education. This paper compares and analyzes online teaching and classroom teaching in terms of teaching resources, activities, evaluation, and outcomes, and proposes suggestions for improving online teaching, including improving network services, optimizing platform construction, promoting the reform of online teaching modes, deepening the integration of disciplines and information technology, and integrating online teaching into curriculum reform.
Anomaly Detection in Machine Failure Situation using CNN-AutoEncoder

Continued digitalization and automation in organizations are being implemented and data science is playing a key role in achieving this goal. One of the key tasks is to develop a model for preventive maintenance. Organizations use various equipment and IT devices to manage the oil and gas business. The breakdown of any device has a huge impact on the overall business process. The aim is to improve the uptime of devices by predicting when preventive maintenance is needed. In this outlier detection, monitored data of equipment plays a key role in identifying the right point for preventive maintenance. For this, we are looking for a model which captures the correct outlier, but at the same time gives a very low false alarm. In this paper, we aimed to develop the model as in [1] and test for accuracy and recall with our data.
Research on the Factors Influencing the UV Detection of Corona Discharge Under Combined AC-DC Voltage

In order to better study the relationship between corona discharge intensity and UV detection image characteristics, this paper investigates the variation characteristics of corona discharge pulse current and its UV photon number under different electrode shapes by building a corona discharge simulation test platform with combined AC-DC voltage. The influence of air pressure and humidity on establishing the relationship between corona current-photon number based on the tip-plate electrode model is also explored. The result shows that the shape of the electrode and the uniformity of the electric field have certain effects on the corona current. Besides, the change of air pressure does not cause significant changes in the relationship between the corona current and the photon number. But the increase of humidity will intensify the UV refraction phenomenon, which will reduce the detected photon number.
An Approach to Mine Low-Frequency Item-Sets

High utility item-set mining (HUIM) is an important mining technique in the Data mining field. HUIM produces more beneficial item-sets and the relationship among these item-sets, to make various decision making activities. However, for effective decision making in businesses, factors like discounts and frequency patterns should also be considered. This paper suggests a method in which a combination of low frequent item-sets with high-frequency item-sets to provide association rules considering factors like discount and frequency patterns. A numerical example is used to clarify the method. Moreover, experimental results on real world data sets are done to prove the utility of the algorithm.
Automation of Curriculum-based Student-Subject Encoding: A Web Application

Timetabling is a procedure of arranging examinations or class events into a specific time slot. Universities carriages many timetable recurring problems, one of which is the demand to schedule subject offerings with no collisions in space, time, and resources. At Caraga State University, scheduling was done by the department chairs, and the faculty carried out the enrolment. Based on the curriculum, the faculty evaluates the students' eligibility to take the specific subject and creates a conflict-free timetable based on the offered subjects and schedule plotted by the department chairs. The heavy involvement of faculty diverts their focus, accommodating enrolment requests instead of preparing their lecture for the upcoming opening of classes. With this, a web-based application using Django was developed to automate the student-subject encoding process.
Abstraction and analogy in AI

Abstract The abilities to form concepts and abstractions, and to make analogies, are key to human intelligence, but AI systems have a long way to go before they can match the abilities of humans in these areas. To develop machines that can abstract and analogize, researchers typically focus on idealized problem domains that are meant to capture the essence of human abstraction abilities without having to deal with the complexity of real‐world situations. This commentary describes why solving problems in these domains remains difficult for AI systems, and discusses how AI researches can make progress on imbuing machines with these essential abilities.
Comparative analysis of quantum circuit implementation for domestic and international hash functions

The advent of quantum computers threatens the security of existing hash functions. In this paper, we confirmed the implementation results of quantum circuits for domestic/international hash functions, LSH, SHA2, SHA3 and SM3, and conducted a comparative analysis. To operate the existing hash function in a quantum computer, it must be implemented as a quantum circuit, and the quantum security strength can be confirmed by estimating the necessary quantum resources. We compared methods of quantum circuit implementation and results of quantum resource estimation in various aspects and discussed ways to meet quantum computer security in the future.