Reviewerly/exHarmony huggingface dataset:
Development of a video re-editing tool for on-demand learning materials

On-demand lectures have become common in Japan. We are now developing a video re-editing tool for the purpose of utilizing the lecture videos used in such lectures. This tool is designed to shorten the playback time of lecture videos as much as possible without reducing the lecture content and to create digest videos by picking out the important parts from multiple lecture videos. In developing the tool, we conducted a survey of lecture videos actually used in on-demand lectures. We also confirmed that the time required for video playback can be reduced by about 30% by removing silent time. In addition, we conducted a basic investigation to see if it is possible to detect the parts of the lecture that the lecturer considers particularly important by using voice emotion recognition AI.
A system that supports learners' discovery of lack of knowledge by subdividing knowledge

A system based on the concept of utilizing such relationships between knowledge was developed. This system aims to identify the lack of knowledge of learners by systematizing the relationships between quizzes and replacing complex problems with a set of simple questions. The educational effectiveness of the system was measured by tests, which showed a higher increase in scores than when the system was not used. The questionnaire also showed that users of the system had positive impressions of the system, such as being able to save the learning time.
Classification Model for Class-imbalanced Encrypted Traffic Based on Deep Learning

The development of encrypted traffic technology poses a huge challenge to the field of cyber security. Machine learning suffer from poor classification of a few classes when faced with class imbalance traffic. Therefore, this paper combines deep learning methods to propose a parallel neural network classification model for class imbalanced encrypted traffic. It uses convolutional neural networks for spatial feature acquisition. A parallel Convolutional Block Attention Module is also introduced to assign corresponding weights to the important features extracted by the convolutional neural network in both spatial channel dimensions, so that the model ignores features irrelevant to classification as much as possible. Meanwhile, Bidirectional long and short-term memory network to extract temporal features. The attention mechanism module is also used to make it more focused on the main information of the current task by assigning weights to enhance the classification accuracy. To reduce the parameters of the network and to improve the generalisation of the model, a convolutional layer plus a maximum pooling layer is used replacing the traditional fully connected layer. The experimental results shows that the accuracy, precision, recall and fl-score of the model classification achieves 95.31 percent, 94.57 percent, 94.55 percent and 94.56 percent, respectively. And there is also a significant improvement in the identification of different classes of encrypted traffic.
Design and Implementation of Machine Learning Algorithm in Question Bank System

The construction of an online marking question bank system aims to provide teachers and student workers with a standardized and scientific platform that covers intelligent paper grouping, online mock exams, intelligent online marking, and online practice for students to achieve efficient and effective personalized learning needs. The question bank system is designed with B/S structure and developed with advanced JSP technology, JAVA technology, MYSQL database design, and other technologies. The question bank system, which has three role settings for the system administrator, teacher, and student, enables the creation of intelligent groups of papers, intelligent marking, and other functions. To implement the scoring judgment of similar answers other than correct answers in subjective questions, this system simultaneously calls third-party natural language analysis libraries and text similarity detection libraries through APIs. This ensures the relative fairness of the system's marking. The system is additionally enhanced with humanized features, such as the topic score analysis and online student practice functions.
Stacked Mosaic Detection based on Line Expansion

When uploading multimedia data such as photos or videos on network, they might be covered with mosaic blocks since some private information is not suitable for exposing. In some situation, mosaic images or video frames need to be restored or trimmed from them, it is necessary to detect the mosaic so as to discriminate these data for subsequent processing. Up to now, most mosaic detection methods are based on edge detection. However, mosaic regions with similar colors may cause several similar mosaic blocks to be stacked together, which makes it difficult to extract sufficient edge features, and lead to hardly detecting the subsequent mosaic region. In this paper, a line expansion- based mosaic detection method is proposed. First, find the upper left point of a block, expand downward to obtain a vertical line. Second, expand to the right based on vertical line to get the whole rectangular block. Third, repeat above steps until the entire mosaic region is extracted, ignoring the regions that have been marked during extraction. The proposed method is compared with several representative methods under different sizes and stacked mosaic. The experimental results show that the proposed method outperforms the state-of-the-art methods on stacked mosaic.
MultiFDF: Multi-Community Clustering for Fairness-Aware Recommendation

The fairness consideration has received increasing attention in artificial intelligence (AI), especially in the significant application, recommender system. One kind of fairness issue is to balance the exposure of popular items and less popular items. Existing works mainly compensate the less popular items when predicting their ratings. However, the compensation for the items may result in a loss of recommendation accuracy, as the compensated items may not match user’s preference. For this problem, we propose a multi-community clustering recommendation with fair decision fusion (MultiFDF) framework to compensate less popular items locally, which can reduce the negative impact on user’s preferred popular item. We first design an experiment and deliver a causal graph-based mathematical proof to demonstrate the feasibility of local compensation. It proves that there is a discrete tendency of ratings predicted by a recommendation algorithm and a recommendation algorithm gives a higher rating to the popular item than the less popular item. The MultiFDF consists of three parts, community exploration module, local recommendation module, and fair decision fusion module. The community exploration module outputs several communities for local recommendation module to generate local recommendation lists, respectively. The fair decision fusion module then computes the discrete ratings of items based on local recommendation lists and designs an edge reranking strategy based on their discrete ratings to obtain the final fair top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$</tex-math> </inline-formula> recommendation list. To verify the superiority of our proposed MultiFDF, we conduct experiments on three real world datasets and the results demonstrate that MultiFDF can improve fairness at the cost of lower accuracy than the state-of the-art algorithms.
Modified Split-Rendering Architecture to enable AI-assisted Application-aware MAC for XR Slice

Supporting Extended Reality (XR) over traditional networks is challenging due to its bursty nature, low latency, and high-reliability requirements. We demonstrate that DiffServ in EPON results in low network utilization, around 10%. Additionally, XR frames are highly correlated due to changes in the application-layer data caused by human activity. By exploiting this correlation, in this letter, we introduce a cross-layer MAC scheduling termed "Application-aware MAC," which provides a manifold increase in network utilization by introducing a small prediction error in the application layer. For enabling this, in this letter, we also suggest a modified split rendering architecture with additional NFVs.
Cryptocurrency the Worldwide Head of Innovation in Investment

Industry 4.0 is the current and developing environment which has led to the ever-growing use of disruptive technology in all areas of life, including finance and investment. Cryptocurrency appeared on the surface of capital markets in 2008, as one of the greatest innovations of our century. The study shows that cryptocurrencies have their own niche in payment systems; they are highly competitive and dependable financial instruments. The growth dynamics of cryptocurrency market capitalisation in the world makes Bitcoin the most successful example of the use of virtual currency in the information economy. Our country's economy should follow the path of innovation in finding solutions to a number of technical, economic and legal issues concerning the development of the cryptocurrency market in India through involving the experience of the leading countries. The study also assesses how the financial industry uses Cryptocurrency to enhance the efficiency and wealth of investors as the alternative for the traditional investment avenues. Cryptocurrency has an enormous propensity to improve an investor's risk-yield profile. The paper substantiates opportunities and perspectives for the development of the future of Indian cryptocurrency market.
An Assessment Model for Prioritizing CVEs in Critical Infrastructures in the Context of Time and Fault Criticality

Assessing vulnerabilities in operational technology (OT) and industrial control systems in a multifaceted manner is challenging, particularly in the context of critical infrastructures. In practice, different standards support vulnerability management and incident response management. Here, it is not enough to understand only the theoretical concepts; the operational efficient models, and tools, which are ultimately responsible for the implementation of the standards, play an essential role. This paper presents a coherence model and its Incident Response Evaluation Tool (IRET) developed and evaluated for OT networks. It is used for time-efficient decision-making for successful defense against cyber-attacks and reduces the acquisition, analysis, and decision effort of Common Vulnerabilities and Exposures (CVEs) by 72% on average, from regularly 19 min to 5 min, and allows to evaluate CVEs in according to objective facts through its mathematical coherence model of vulnerabilities and the modified Observe-Orient-Decide-Act loop as main framework. It also demonstrates the possibility of integrating the coherence model into the context of vulnerability management to evaluate CVEs by reliable determinants in IRET, thus increasing efficiency and simultaneously increasing coverage in complex OT environments.
Realizing peer-to-peer trading in the electricity market in the EU and its Member States

In order to realise the energy transition towards a more sustainable, inclusive and just energy market, the traditional model of the electricity market, with big corporations producing electricity and passive consumers at the receiving end, is changing. These changes also require the adaptation of the existing legal framework to new market realities. In this regard, peer-to-peer (P2P) electricity trading has emerged as one mode to enable this transition. The International Renewable Energy Agency has defined the P2P model as “creat[ing] an online marketplace where prosumers and consumers can trade electricity, without an intermediary, at their agreed price”. This new innovation poses technical chal-lenges to the market and also requires changes in the underlying regulatory framework of these markets. More concretely, the fields of consumer (protec-tion) law, contract law, platform liability, data law, competition, and property law need to be revised in order to facilitate the innovation. The purpose of this contribution is to examine which legal obstacles P2P trading encounters. The article will start with some general reflections on these hurdles, followed by a specific focus on energy law in two Member States, Germany and the Nether-lands.
High Data Throughput Exfiltration Through Video Cable Emanations

The present work investigates the feasibility to exfiltrate a large amount of data from a computer by leveraging the unintended electromagnetic emanations of an HDMI cable to reconstruct its content. The low signal strength and noise of the leaked signals make difficult to recover any useful information, particularly when the content information is text based, since it suffers from low readability. We consider a targeted attack in which malicious software executed inside the victim’s machine encodes the desired information into QR codes, which are then modulated on the HDMI cable and in turn received and reconstructed by the attacker. The efficiency of this method is evaluated under practical conditions showing that the system is capable of achieving a data exfiltration rate up to 12.67 Kbps under optimal conditions or 2.08 Kbps at 50 m distance. To the best of our knowledge, these results outperform, in terms of distance range and exfiltration rate, previous work in the field of electromagnetic leakage from the literature.
A Goal-Driven Approach to Support Decision-Making with Incomplete Information in Cyber Operations

Semi-autonomous cyber security (“cyber") operations require effective communication between a human operator and the underlying cyber systems that carry out the mission. We show a goal-driven approach to specifying mission objectives of such systems, where the system has controlled autonomy to refine the goals into executable plans. An ontology aligns goal decomposition and data representation with human-level concepts, and probabilistic extensions of Belief-Desire-Intention (BDI) agent architectures allow dealing with uncertainty about the agent’s state and the effects of its actions. Our framework effectively automates parts of cyber operations while conveying the state of operations to a human operator. A case study from the cyber operations domain demonstrates that this approach can outperform the traditional BDI agent approach while requiring minimal additional information by the agent programmer.
An End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information Systems

Decision mining enables the discovery of decision rules from event logs or streams, and constitutes an important part of in-depth analysis and optimisation of business processes. So far, decision mining has been merely applied in an ex-post way resulting in a snapshot of decision rules for the given chunk of log data. Online decision mining, by contrast, enables continuous monitoring of decision rule evolution and decision drift. Hence this paper presents an end-to-end approach for discovery as well as monitoring of decision points and the corresponding decision rules during runtime, bridging the gap between online control flow discovery and decision mining. The approach is evaluated for feasibility and applicability on four synthetic and one real-life data set.
Modeling the Hierarchical Structure of Effective Communication Factors for Cyberattack Responses

Cyberattacks have recently been on the rise. The increasing sophistication and complexity of cyberattacks has made it difficult to prevent such attacks from occurring. Therefore, it must be important to formulate appropriate incident responses to cyberattacks limit their damage. As seen in the case of the Japan Pension Service, where damage is increased due to a lack of communication during the response to cyber incidents, good internal communication practices is essential to ensure an appropriate incident response. To minimize the impact of cyberattacks, this study constructs a hierarchical structure model using interpretive structural modeling. The results reveal that the degree of dependency that the variables have over each another according to the cross-impact matrix multiplication applied to classification analysis regarding effective intraorganizational communications during a cyberattack response. In addition, based on these results, a use case of the results will be proposed to design more effective intraorganizational communications when responding to a cyberattack.
Parsing Causal Models – An Instance Segmentation Approach

The steadily growing number of publications in the field of information systems as well as the confusion arising from the naming of theoretical concepts, complicate the process of literature reviewing. While several knowledge repositories and databases are developed to combat this issue, a considerable amount of manual effort to populate the databases is required. The information these tools seek to present is often compactly summarized in causal models with a graph-like structure (e.g., structural equation models). Our work aims to develop a graph parsing method that reduces the amount of manual effort required and thus builds a foundation towards an augmentation of knowledge extraction from causal models. We contribute to the ongoing efforts in developing graph parsing tools by proposing a novel instance segmentation-based approach that leverages a new method to generate annotated synthetic graph images. Our solution is evaluated on a dataset of 166 images of structural equation models and outperforms existing graph parsing approaches in this use case.
Towards Designing Energy-Aware Cybersecurity Policies

Cybersecurity is a fundamental aspect of socio-technical systems. Unfortunately, the price to pay for a secure system is an overhead of computational power and, therefore, of energy consumption. With the last global events, energy consumption is not anymore a secondary effect but should become a relevant driver of decisions on the security measures to implement. Thus, the design of security policies for socio-technical systems should consider energy consumption to carefully choose security measures to deploy. Security policies identify the security objectives of actors in socio-technical systems, the security measures that enforce them, and the business processes affected by these measures. In this direction, the goal of this paper is to propose a method for the energy-aware design of security policies. An estimation of the energy consumption of security goals will be calculated to allow the definition of security policies considering their energy consumption.
Approach to The Development of a Conceptual Model of a Sociotechnical System Based on Digital Technologies

Data show that only a third of the total volume of IT projects are successful, and that more than 80% of developers of large-scale projects will fail. The main reason for this is the ignorance of the social and human factors at play during the development of the conceptual model of the project. The aim of this article is to present an approach for designing a conceptual model of an information processing system based on digital technologies that respects the paradigms of sociotechnical systems. In order to explore the attitudes of young specialists towards such an approach, a Likert-type five-point survey was implemented, the results of which explained the reasons for possible failures of IT projects. The lessons learned in this work will be useful for customers, investors and project developers.
Attacks against Machine Learning Models in 5G Networks

Artificial intelligence and machine learning are revolutionising almost every industry with a seemingly endless list of applications. This list also includes mobile networking for which employing machine learning algorithms can improve efficiency, latency, and reliability of services and applications. For this reason, functionality of future 5G networks is expected to depend on accurate and timely performance of its artificial intelligence components, and disturbance in the functionality of these components may have negative impact on the entire network. This study focuses on analysing adversarial example generation attacks against machine learning based frameworks that may be present in the next generation mobile networks. In particular, we study transferability of adversarial example attacks to the 5G domain and evaluate their negative impact on the network performance in several realistic use case scenarios.
Performance analysis of a solar tracker driven by a microcontroller-embedded computer program

The objective of this paper is to analyze the performance of a solar tracker, which is a system that rotates a solar photovoltaic panel to position it in front of the sun to optimize its performance. For the design of the system, we use a low-power solar panel, a microcontroller, a DC signal motor, two light sensors, and two resistors. We consider two configurations of the system: a permanently moving solar panel to always face the sun and a periodically rotating solar panel to position itself facing the sun at times when its intensity should be maximum. A computer program in C language embedded in the microcontroller allows us to control the permanent or periodic rotation of the solar panel and thus ensures the operation of our mobile solar panel system. We analyze and compare the power of the solar panel in these two configurations to determine which is more advantageous. We find that the permanently moving solar panel performs better than the periodically rotating moving solar panel. We also find that the power of the mobile solar panel in both configurations is higher than that of the fixed solar panel. This last result is explained by the fact that the power of a solar panel depends on its position relative to the incident light flux.
Research on Personalized Recommendation Technology for Learning Resources Combining Knowledge Graph and Interest Diffusion Mechanism

Under the background of Internet technology maturing gradually, the acquisition of learning resources has become increasingly convenient. However, the accompanying “Information Explosion” makes it easy for learners to get lost in the ocean of learning materials and also make it difficult to find the resources they really need. In the face of this reality, it is urgent to explore the implementation and optimization of personalized recommendation technology. In order to make the personalized recommendation technology play a greater role in the field of education, this paper studies a learning resource recommendation algorithm which combines knowledge graph and interest diffusion mechanism, and makes a structure analysis of the recommendation system. Because the proposed technology considers the domain features of the recommended object, it reflects the combination of technology and education, and is of certain reference significance for further research on personalized recommendation of learning resources and personalized learning.
Research on State Monitoring of Ship Power System Based on Multi-channel Data Acquisition System

The multi-channel high real-time data acquisition and processing system is a key component of the normal and reliable operation of the modern intelligent ship electric power system, which is crucial to the real-time monitoring of the power quality of the electric power system and the operation status information of the electric power system equipment. By analyzing the architecture of multi-channel data acquisition system and the principle of data acquisition and data processing, this paper designs a multi-channel data acquisition and processing system, and builds a physical system test platform. The actual operation proves that this system can meet the needs of power system data acquisition in high real-time and high synchronization.