Reviewerly/exHarmony huggingface dataset:
Symmetry properties and asymmetry evaluation of Bayesian Confirmation Measures

Abstract Bayesian Confirmation Measures (BCMs) are used to assess the degree to which an evidence (or premise) E supports or contradicts an hypothesis (or conclusion) H , making use of prior probability $$Pr(H)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:mi>r</mml:mi> <mml:mo>(</mml:mo> <mml:mi>H</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , posterior probability $$Pr(H|E)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:mi>r</mml:mi> <mml:mo>(</mml:mo> <mml:mi>H</mml:mi> <mml:mo>|</mml:mo> <mml:mi>E</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> and of probability of evidence $$Pr(E)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:mi>r</mml:mi> <mml:mo>(</mml:mo> <mml:mi>E</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . In the literature many BCMs have been defined with the consequent need for their comparison. For this purpose, various criteria have been proposed and some of these refer to symmetry properties. We relate the set of possible symmetries of BCMs, via an isomorphism, to the dihedral group of symmetries of the square. In this way it is possible to identify 10 subsets of symmetries that can coexist, for each subset we suggest a representative BCM, defining at this aim two new BCMs. The structure of the subgroups of the dihedral group allows also to provide an algorithm that simplifies the verification of the symmetry properties. Addressing the debate on which symmetry properties should be considered as desirable and which should not, we define asymmetry measures for BCMs. In fact, different BCMs that do not satisfy a specific symmetry property may exhibit different levels of asymmetry, this way resulting more (less) desirable. The evidence for the practical use of the approach is given through the numerical evaluation of the asymmetry degrees of some BCMs, showing this way how it is possible to discover some of their characteristics, similarities and differences.
Exploring factors that influence the behavioural intention of medical students to use 3D gastroscopic model to learn how to operate gastroscope using UTAUT Model

The application of virtual reality (VR) in gastroscopic operation teaching can be safe and effective, but the advantages can be realized only when students accept and use it. This study aims to identify the factors influencing Chinese clinical medical postgraduates on their intention to use the 3D gastroscopic model constructed based on VR technology using Unified Theory of Acceptance and Use of Technology (UTAUT) model. Students' demographic factors are also taken into consideration.All methods were carried out in accordance with relevant guidelines. Data were collected from clinical medical postgraduates students in China using stratified sampling. A total of 292 questionnaires including valid responses were used in this study. Data were processed using Amos 24.0 and SPSS 26.0 software and the statistical analysis technique was based on structural equation modeling (SEM).The results showed that different from the mediator of home location and year of clinical learning, mediator of gender, university kind and graduate degree did not affect the behavioral intention. In addition, performance expectancy, facilitating condition, and social influence directly and indirectly have effect on behavioral intention. Also, the significance between social influence and performance expectancy, social influence and effort expectancy were verified.This study manifested that the proposed framework based on the UTAUT had explanatory power to identify the factors influencing the students' behavioral intention to use the 3D gastroscopic model constructed based on VR technology. Whereas, an important variable of effort expectancy in the frame of the SEM were not certified, thereby indicating that particular attention should be paid to this variable by universities and teachers before applying 3D gastroscopic model constructed based on VR technology in teaching. Added preparatory work is required such as explaining the basic knowledge of the operating steps of VR model and make students adequately understand its accessibility, which can probably improve the intentions of them to use it. The positive effects of social influence on performance expectancy and effort expectancy we proposed was also verified in this study, which provided a direction for future research.
A robust sparse identification method for nonlinear dynamic systems affected by non-stationary noise

In the field of science and engineering, identifying the nonlinear dynamics of systems from data is a significant yet challenging task. In practice, the collected data are often contaminated by noise, which often severely reduce the accuracy of the identification results. To address the issue of inaccurate identification induced by non-stationary noise in data, this paper proposes a method called weighted ℓ1-regularized and insensitive loss function-based sparse identification of dynamics. Specifically, the robust identification problem is formulated using a sparse identification mathematical model that takes into account the presence of non-stationary noise in a quantitative manner. Then, a novel weighted ℓ1-regularized and insensitive loss function is proposed to account for the nature of non-stationary noise. Compared to traditional loss functions like least squares and least absolute deviation, the proposed method can mitigate the adverse effects of non-stationary noise and better promote the sparsity of results, thereby enhancing the accuracy of identification. Third, to overcome the non-smooth nature of the objective function induced by the inclusion of loss and regularization terms, a smooth approximation of the non-smooth objective function is presented, and the alternating direction multiplier method is utilized to develop an efficient optimization algorithm. Finally, the robustness of the proposed method is verified by extensive experiments under different types of nonlinear dynamical systems. Compared to some state-of-the-art methods, the proposed method achieves better identification accuracy.
Generative appearance replay for continual unsupervised domain adaptation

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on three datasets with different organs and modalities, where it substantially outperforms existing techniques. Our code is available at: https://github.com/histocartography/generative-appearance-replay.
Dynamic analysis of identifying user roles and evolutionary paths in collective intelligence design community

Collective intelligence design has become a new paradigm for product design. This design pattern breaks the situation of information closure, resource tension, and insufficient innovation caused by traditional product design. Individuals act and contribute in diverse ways in collective intelligence design communities, gradually developing distinct user roles. Identifying the dynamic evolution path of user roles is crucial to properly allocating community traffic and increasing user stickiness. However, the majority of previous research solely discusses the roles that users play and their contributions, omitting the reality that user roles alter dynamically as the community develops. Therefore, to refine existing research, this paper introduces a user role identification model and summarizes the user role evolution paths through time series analysis. The method's effectiveness is demonstrated by its use on Thingiverse, the world's largest online design platform. This paper contributes three points to the study of user role identification and evolution. First, this paper identifies six typical user roles. Secondly, this paper portrays four stages of the design community's evolution. Finally, this paper presents the dynamic evolution paths of different user roles and proposes evolutionary optimization strategies for distinct roles. These conclusions enrich user role research from both theoretical and practical perspectives.
Sequential minimum optimization algorithm with small sample size estimators

Sequential minimum optimization is a machine learning global search training algorithm. It is applicable when the functional dependence of the cost function on a tunable parameter given the other parameters can be cheaply determined. This assumption is satisfied by quantum circuits built of known gates. We apply it to photonic circuits where the additional challenge appears: low frequency of coincidence events lowers the speed of the algorithm. We propose to modify the algorithm such that small sample size estimators are enough to successfully run the machine learning task. We demonstrate the effectiveness of the modified algorithm applying it to a photonic classifier with data reuploading.
Insurtech Revolution in the Insurance Sector: A Comprehensive Review of the Transformational Impact and the Lemonade Case Study

The insurance sector has undergone a transformative shift with the emergence of Insurtech, integrating technological innovations into the industry.This review aims to provide a comprehensive overview of the impact of Insurtech on the insurance sector, focusing on the case study of Lemonade.By placing the study in the context of the broader insurance landscape, the review explores the significance and relevance of this transformation.The paper analyzes Lemonade's unique business model and its departure from traditional insurance practices.Furthermore, it examines the implications of Lemonade's approach on the industry and the challenges it faces.Through this analysis, the review sheds light on how Insurtech and the transformation of the insurance sector are redefining insurance services and consumer experiences.The future implications of these trends for insurance companies, consumers, and the industry as a whole are also explored.In summary, this review provides valuable insights into the transformative power of Insurtech, emphasizing the importance of technological advancements and innovative approaches in shaping the future of the insurance sector.
Leveraging Triplet Loss for Unsupervised Action Segmentation

In this paper, we propose a novel fully unsupervised framework that learns action representations suitable for the action segmentation task from the single input video itself, without requiring any training data. Our method is a deep metric learning approach rooted in a shallow network with a triplet loss operating on similarity distributions and a novel triplet selection strategy that effectively models temporal and semantic priors to discover actions in the new representational space. Under these circumstances, we successfully recover temporal boundaries in the learned action representations with higher quality compared with existing unsupervised approaches. The proposed method is evaluated on two widely used benchmark datasets for the action segmentation task and it achieves competitive performance by applying a generic clustering algorithm on the learned representations.
E-Voting

Biometric fingerprint devices used in electronic voting machines (EVMs) for voter verification and authentication eliminates two main threats towards present-day voting systems, which include illegal voting as well as repetition of votes. This application has extensive use in real world scenarios and helps to conduct fair and free elections. The person at the polling booth need not carry his ID, which contains his required details; only placing his finger on the device would allow the acquisition of an on-spot fingerprint from the voter – an authentic identification. The fingerprint reader would read details from the tag; the data is then passed onto the controlling unit for verification. The controller fetches the data from the reader and compares the data with pre-existing data stored during registration of voters using biometric. This prototype is based on IoT (internet of things), and the microprocessor platform uses Arduino Uno as the microchip and some TFT displays to successfully simulate the architecture for voting systems and consequently improve the traditional EVM-based voting.
Pendekatan Menyenangkan untuk Meningkatkan Aktivitas dan Hasil Belajar IPS pada Siswa Kelas IX SMPN 1 Baradatu, Way Kanan TP 2022/2023

The aim of the research is to describe the increased activity and social studies learning outcomes using a fun approach to Class IX students Junior High School of Baradatu 1, Way Kanan Regency, 2022/2023 Academic Year. The research method uses Classroom Action Research (CAR) through 4 stages of research namely planning, implementation, observation and reflection. Activities for 3 cycles. The research subjects totaled 30 students. The research instruments used observation sheets of learning activities and test sheets. Data analysis uses simple percentages. The results of the study showed an increase in social studies activities and learning outcomes in class IX students of Junior High School of Baradatu 1 in the 2022/2023 academic year.
Improvement of Network Protocol and Analysis of Security Using Aspect of Cryptography

Wireless network protocol is the top most discussed topics and used of quantum cryptography is adding value to the network performance. In our research paper we are going to make a novel approach to create a development of quantum cryptography for wireless key distribution in 802.11 networks. Due to its mobility with fast data sharing in houses, offices, and businesses they are convenient. Users now choose a wireless network over a LAN-based network connected with Ethernet cables due to advancements in contemporary wireless technology. Unfortunately, security problems are a major worry because they frequently aren’t. It is simple for someone else to obtain information sent through websites or mobile apps while someone is linked to a Wi-Fi network. Based on the theory of physics, cryptographic key can be exchanged by quantum cryptography between two remote sites without any conditional security. The origin of quantum cryptography explained in the Heisenberg uncertainty principle, which tells that the certain pairs of physical channels are related in a way that measuring one property restricts the third user from knowing the value of the other at the same time. In our current research paper, we are using quantum cryptography with AOMDV routing protocol and measure the network performance so using quantum cryptography we will have both secured and improved performance in wireless adhoc network.
A Comparative Study of DWT and DCT Along with AES Techniques for Safety Transmission of Digital Bank Cheque Image

Security, copyright protection, and authenticity are important parameters used for data security. Data access can be either legally or illegal. However, many users are in the network & connected to it in different ways. Such as social media, mobile internet, mobile apps, mobile E-commerce, etc. There are varieties of data. Such as audio, image, video, message, text, special case characters, etc. These data can be sent in an encoded format and received at the destination after decoding like a digital bank image through the Cheque Truncation System for faster cheque clearance. An attack type can occur intentionally or unintentionally during the transmission of cheque images. A solution to this problem is to offer security, authenticity, and copyright protection to the cheque image. In this study, the discrete wavelet (DWT) and discrete cosine (DCT) transforms are integrated and implemented for watermarking using the AES technique with a maximum key length of ‘256 bits. In addition, the analysis is done for watermarking, for which AES is used for security against various attack types.
Low-Light Video Enhancement on a Mobile Device Using Illumination-Reflection Model Based Retinex Envelope

A low-light video usually exhibits features like low brightness, poor contrast, a narrow color spectrum and distortion. Low-light videos are also subjected to considerable noise, which significantly affects the viewing experience for humans and limits the effectiveness of computer vision algorithms. The main feature of the paper is to enhance low light videos using an illumination-reflection envelope model corrupted by poor lighting conditions. A luminance function and cost parameter are used to estimate illumination in the proposed method. Piecewise spatial smoothing is applied to the estimated illumination in the low-light videos to remove halo artifacts. Low-pass filters improve details and correct illumination dynamics. In addition to improving low-light videos’ contrast, our method preserves the natural appearance of the original videos. Color shifting is prevented by performing all operations on the hue-saturation-value (HSV) value component. A red-green-blue (RGB) format is then used to display videos. Metrics such as discrete entropy, a measure of enhancement, absolute mean brightness error, and pixel distance are used to estimate the method’s performance. The experimental results presented clearly demonstrate that the proposed method enhances ill-posed videos more effectively and clearly than other existing methods.
Prospective Elementary School Teachers’ Reading Literacy Profile Based on Minimum Competency Assessment with the Implementation of the Discovery Learning Model

Every student needs to have learning and innovation skills, media, information, and technology literacy, as well as life skills so they can work and survive in the 21st century. Reading literacy skill is required for developing 21st-century life skills. According to empirical data from the PIRLS test in 2011 and the PISA test in 2018, Indonesian students’ reading literacy skill is still subpar worldwide. High reading literacy skill is required of prospective elementary school teachers. Utilizing a discovery learning approach that strengthens prospective elementary school teachers and students reading literacy skills can help reduce these issues. The two main objectives of this study are: 1) to profile the reading literacy skill of prospective elementary school teachers based on the Minimum Competency Assessment and 2) to analyze the effectiveness of implementing the discovery learning model on prospective elementary school teachers’ reading literacy skill. The research method used is a mixed method with a sequential explanatory design that sequentially combines quantitative and qualitative research methods. The results showed that: 1) based on the Minimum Competency Assessment, prospective elementary school teachers’ reading literacy profile was still relatively low (i.e., average score: 54.03, average score for each indicator—finding information, understanding, evaluating and reflecting: 48, 51.8, and of 56.3 respectively); 2) based on the average score of the prospective elementary school teachers’ reading literacy skill before and after the implementation of discovery learning model (54.03 and 72.18 respectively), the implementation of the discovery learning model is effective in improving the reading literacy skills of prospective elementary school teachers who are included under the moderate criteria. This outcome is a result of the integration of the discovery learning model of each syntax training with reading literacy activities, which stimulates students’ reading literacy skills.
Web Browser Forensics: A Comparative Integrated Approach on Artefacts Acquisition, Evidence Collections and Analysis of Google Chrome, Firefox and Brave Browser

Web browser is the important application and majority user users use web browsers to access the social media sites, email application, web search engines, ecommerce sites and download the video or photos. Various web browsers are available in the market for this purpose but Google chrome, Mozilla Firefox and Brave are the well-known browser application. These web browsers might be use for normal internet access also use to committee the crime. In such case it is important to use digital forensics techniques to extract evidences which will be produced to court to prove the crime. Literature survey shows that dead forensics were frequently used by researchers but very less work is carried out to use live or RAM forensics to extract the evidences. In this research paper, we created real time scenario with Google Chrome, Mozilla Firefox and Brave browser and use RAM forensics techniques to extract the evidences related to web browser activities.
Design and Dissemination of Blockchain Technologies: The Challenge of Privacy

Abstract Presented as trust technologies, blockchains, by allowing immediate secure peer-to-peer exchanges without a trusted third party, have strong disruptive potential, but raise privacy issues. We illustrate some challenges that this antagonism raises and the sociotechnical compromises made to overcome them, by analysing the design of a mobility service by a consortium of some fifteen operators, and its experimentation with the employees of these operators. The service seeks to respond to the new needs linked to the electrification of company fleets, by tracking the recharging of (personal) electric vehicles at work or (professional) vehicles at home with a view to reimbursing employees’ professional expenses by relying on a blockchain. Privacy management is a skill, based on emerging expertise, distributed across a range of professions and users, which requires compromises between different conceptions of technology and data to be guaranteed. For blockchain designers, these compromises have limited the disruptive potential of blockchain technology by recentralising data management and losing the open nature of blockchain. However, in the eyes of other designers and users, they have allowed unexpected uses and benefits to emerge, such as reinforcing the choice of blockchain technology as a “ privacy solution” .
Surviving the Intellectual Property Maze in Communication Technologies

Knowledge-based economies are now driven by technologies rooted in science, such as communication technologies, which are high on the agenda of every major global economic player. There is therefore a need to protect innovative advances in these technologies through patents. This paper discusses patenting issues in the context of US patent laws. In particular, we discuss two cases: (1) The saga of Bellâ€™s telephone patents; and (2) The patents war between NTP and RIM.