Reviewerly/exHarmony huggingface dataset:
Gibbs sampling the posterior of neural networks

Abstract In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an efficient Gibbs sampler. For small models, the Gibbs sampler attains similar performances as the state-of-the-art Monte Carlo Markov chain methods, such as the Hamiltonian Monte Carlo or the Metropolis adjusted Langevin algorithm, both on real and synthetic data. By framing our analysis in the teacher-student setting, we introduce a thermalization criterion that allows us to detect when an algorithm, when run on data with synthetic labels, fails to sample from the posterior. The criterion is based on the fact that in the teacher-student setting we can initialize an algorithm directly at equilibrium.
Software Managers' Guide to Operational Excellence

The secret to being a great engineering leader? Setting up the right checks and balances.
Automated and interactive selective collection system in the educational context of the early years of elementary education

Striving for a more sustainable future, selective waste collection is essential to minimize environmental impacts and promote societal awareness. This project aims to raise awareness among children in the early years of elementary school. It teaches them interactively about the importance of selective waste collection through the reinforcement and practice of sustainable actions in a playful manner. The automated trash bin, constructed using electronic components, is controlled through the Arduino platform with the assistance of sensors and actuators. Thus, when a child approaches the automated trash bin, a sound is emitted to indicate the type of material the bin receives. This solution aims to teach non-literate children how to perform selective waste collection in an interactive and playful manner, instructing them on the separation and disposal of waste using the trash bin. The final prototype underwent preliminary evaluation by 64 individuals through an online questionnaire comprising nine closed-ended questions. The results were positive regarding the effectiveness of this tool's proposal.
Participatory Action Research in the Development of Higher Education with Digital Media

The digitalisation of higher education has versatile potential for students and especially for pre-service teachers. Unfortunately, this potential is insufficiently used in higher education institutions. This article displays a project focusing on developing university teacher education with digital media. For this purpose, the applied model of Participatory Action Research for Higher Education will be illustrated, reflected, and discussed to share the findings and experiences of the process and facilitate future development projects. Furthermore, conditions for success, challenges, and recommendations based on this project will be provided.
Reviewing <i>Maple Viewing</i> (<i>Momijigari</i>, 1899)

Abstract Maple Viewing (1899) is considered the earliest surviving Japanese film. Photographed by the cinematographer Shibata Tsunekichi, the film records Kabuki actors Ichikawa Danjurō IX and Onoe Kikugorō V performing in a play of the same name. There is no doubt that Maple Viewing is an early “surviving” work. Yet can we call Maple Viewing “Japanese” simply because it was photographed in Japan by a Japanese cinematographer? If we imagine that it was produced for non-Japanese viewers in the context of an Orientalist imagination, what would its classification as “Japanese” amount to? Moreover, we can also question its status as a “film” in turn-of-the-century discourse. What was Maple Viewing understood to be when it was first exhibited? Focusing on the work of Shibata, this chapter examines early Japanese filmmaking in terms of the influence of Japanese art and culture on European fine art, as well as Japanese reactions to “Japonisme.”
Harnessing AI and Gut Microbiome Research for Precision Health

The gut microbiome's impact on physiological processes, influenced by diet and lifestyle, is profound. Dysbiosis, an imbalance in microbiota composition, is associated with diseases like obesity. This review explores the gut microbiome's role in metabolism and calorie intake, alongside recent AI advancements impacting personalized nutrition. AI has revolutionized microbiome research, especially in multi-omics data analysis. AI-driven approaches enable the integration and interpretation of diverse omics datasets, including genomics, metabolomics, and proteomics, providing comprehensive insights into the gut microbiome's functional dynamics and its impact on host metabolism. These facilitate the identification of microbial biomarkers associated with disease states and dietary interventions, paving the way for personalized nutrition strategies tailored to individual gut microbiome profiles. AI platforms can also offer tailored dietary recommendations based on microbiome composition and health objectives. Healthcare professionals leverage AI to optimize dietary interventions, promoting gut microbiome modulation and preventing chronic diseases. Challenges like data standardization and privacy persist, yet addressing them is vital for maximizing AI's benefits in health outcomes and precision medicine. Ongoing AI and microbiome research promise to revolutionize personalized nutrition and metabolic health worldwide.
AI-Powered Energy Management System for Industry

The industrial sector, serving as a linchpin of global economic growth, continually shapes innovation, job creation, and wealth accumulation. However, its indispensable role is juxtaposed with substantial energy consumption, contributing significantly to the world’s energy usage and environmental footprint. In their pursuit of heightened competitiveness and profitability, industries confront a pressing challenge: the judicious management of energy resources. Striking a harmonious equilibrium between cost-efficiency, operational excellence, and environmental sustainability presents a formidable complexity. Compounding this complexity is the reluctance of traditional energy management methodologies to adapt to the dynamic and data-rich landscapes characterizing modern industrial domains. This research paper ventures into the heart of these challenges, exploring the convergence of the industrial sector, energy management, and technological advancements. Within this multifaceted landscape, it scrutinizes the intricacies of contemporary industrial environments, exposing the inherent complexities of responsible energy management. With data-driven technologies and advanced analytics as guiding lights, this study illuminates a transformative path forward—transcending conventional paradigms to forge a new era of efficiency, sustainability, and profitability
The Role of Explainable AI in Building Trust and Confidence in Automated Business Processe

Computer-based intelligence (AI) has shown significant potential in various applications, but it requires a clear understanding of the local area. This issue falls within the Reasonable simulated intelligence (XAI) field, which is crucial for the practical application of AI models. This article discusses the current work in XAI, defining explainability in machine learning and proposing a new definition. It also discusses ongoing commitments related to AI models, including Profound Learning strategies. The article suggests Dependable Computerized reasoning, a method for large-scale AI execution in real-world contexts with reliability, model logic, and responsibility at its core. The goal is to provide a comprehensive taxonomy for newcomers and professionals to embrace AI's benefits.
Video Action Recognition Using Depth Motion Maps

&lt;p&gt;This thesis presents an algorithm for modifying Depth Motion Map (DMM) pyramid analysis based action recognition. It extends the work presented by Chengwu Liang to better handle incoming data. Morphological image transforms are used to remove incoming noise created by the inaccuracies in a Microsoft Kinect when the subject is stationary. This avoids the problem of motion being registered in frames where there is no motion. It also uses a non-linear cost metric on the motion energy to ensure some degrees of shift invariance in the Z axis, which is towards or away from the camera. In addition, advanced information fusion methods are adopted to improve feature representation, leading to better performance. Experiments are conducted on video data containing a variable number of stationary frames at the start and end of an action. The experiments are conducted with many different classifiers. The results show that a performance benefit is realized due to the use of these enhancement methods.&lt;/p&gt;
Image Enhancement for the Improved Extraction of Local Image Features Using Image Offsets

&lt;p&gt;In this thesis, an algorithm for improved feature extraction is presented using enhancement offsets that are created dynamically with adaptive parameter selection. The algorithm operates by analyzing an input image and building image offsets to improve colour contrast, non-uniform illumination and lack of detail which allows for additional keypoints to be detected by numerous different detectors. Comparative keypoint detection testing on the enhanced images is conducted to test if more keypoints can be extracted using the method. Keypoint strength experiments are also conducted as well as quality assessment experiments to test the validity of the proposed algorithm as an image enhancement method. Image matching experiments though SIFT and SURF are also conducted. It is quantitatively shown that the proposed algorithm results in visual improvements, as well as in additional, stronger keypoints being detected in all images irrespective of the detector used. Matching experiments are conducted using the Webcam, Heinly, and Oxford/EF datasets wherein the proposed algorithm consistently outputs more correct matches and achieves higher or similar matching accuracy compared with related enhancement algorithms using SIFT and SURF.&lt;/p&gt;
Efficient Detection of Shillings Attacks in Collaborative Filtering Recommendation Systems using Hybrid Deep Learning: A CNN-based Model and Architecture

&lt;p&gt;Recommendation systems are widely used in various areas to personalize recommendations to users. However, they are vulnerable to shilling attacks in which malicious users try to promote their products or diminish their competitors. Therefore, detecting shilling attacks can significantly improve the quality of recommender systems. With the increasing complexity of attacks and changes in attackers' behaviour, more advanced approaches are required to find the hidden patterns in data. This thesis proposes a CNN-based hybrid model and architecture that integrates self-learning and flexible aspects of CNN with other supervised learning methods to enhance shillings attacks detection on collaborative filtering recommendation systems. We also introduce a new metric, the F-compatible score, to measure the compatibility ratio of merging the CNN with any other classifier in a given aggregate. This measure helps in monitoring the enhancement level of the detection results of shilling attacks. Two different approaches are proposed in this thesis, user-based, and item-based. The experimental analysis used three benchmark datasets: the Movie-Lens 100K, Netflix, and Movie-Lens 1M. A comprehensive comparative analysis is also completed to assess the performance of both individual-based and hybrid-based detection models. Experimental results show that the proposed hybrid models performed well on most attack profiles and reached an F1-score of up to 99%. We concluded that the superiority of hybrid models over individual models depends on the sparsity level of data and the divergence of results.&lt;/p&gt;
PENERAPAN MODEL PEMBELAJARAN DISCOVERY LEARNING BERBASIS POWERPOINT PADA MATA PELAJARAN EKONOMI UNTUK MENINGKATKAN MINAT BELAJAR SISWA DI SMP NEGERI 3 SATU ATAP ARAMO

This research aims to investigate the influence of implementing the Discovery Learning model based on PowerPoint on students' interest in learning economics, specifically on economic activities such as production, distribution, and consumption, at SMP Negeri 3 Satu Atap Aramo. This study employs the Classroom Action Research (CAR) approach with a qualitative method aimed at exploring the learning process in addressing specific issues. The research was conducted at SMP Negeri 3 Satu Atap Aramo with a population of seventh-grade students, totaling 20 students. The results of the research indicate that out of the total sampled students, 25% of them obtained scores ranging from 50 to 56, another 25% of students scored between 66 and 75, and the remaining 50% of students scored between 76 and 98. This research aims to provide a deeper understanding of the effectiveness of specific teaching methods on students' learning interests in the context of economics.
Scattering amplitude from quantum computing with reduction formula

Utilizing the Lehmann-Symanzik-Zimmermann reduction formula, we present a new general framework for computing scattering amplitudes in quantum field theory with quantum computers in a fully nonperturbative way. In this framework, one only has to construct one-particle states of zero momentum, and no wave packets of incoming particles are needed. The framework is able to incorporate scatterings of bound states, and is ideal for scatterings involving a small number of particles. We expect this framework to have particular advantages when applied to exclusive hadron scatterings. As a proof of concept, by simulations on classical hardware, we demonstrate that in the one-flavor Gross-Neveu model, the fermion propagator, the connected fermion four-point function, and the propagator of a fermion-antifermion bound state obtained from our proposed quantum algorithm have the desired pole structure crucial to the implementation of the Lehmann-Symanzik-Zimmermann reduction formula.
Online Teaching Effect Evaluation and Analysis Using Combined Weighting Technique

An evaluation index system of online teaching satisfaction was established based on the data of a questionnaire survey. In addition, an evaluation index system of an online teaching effect with four first-level indexes and 14 second-level indexes was constructed from the aspects of pre-class preparation, video production, classroom teaching, and after-class guidance. Then, a combination weighting–Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model was established using the analytic hierarchy process (AHP), Criteria Importance Through Inter-criteria Correlation method, and TOPSIS model. The objective is to evaluate the online teaching effect of 10 engineering technology universities in Henan Province. Results show that the online teaching effect of these universities is generally satisfactory. In addition, video production and classroom teaching are important factors affecting online teaching, which, however, is slightly influenced by pre-class preparation. On this basis, this study proposes to enhance online assessment, improve students’ attention in class, strengthen teacher–student communication, actively perfect teaching resources, and create a good learning environment.
Enhanced Privacy for Health Records Using Blockchain Technologies

This project deals with the security of digital medical history by implementing advanced measures such as encryption using blockchain methods, access control. Thanks to the integration of encryption, blockchain, dynamic access control and security even after decryption, a robust privacy framework is built to meet evolving cyber threats and strengthen security and availability of health data. This conventional method of storage deprives doctors and researchers of capitalizing on modern technologies and healthcare engines to perform clinical tests, improve patient care, and contribute to future clinical research [1]. By empowering patients using blockchain technology, this project is transforming healthcare data management to improve patient care and promote the development of a reliable healthcare environment, which means remarkable progress towards improving Privacy for Health Records Using Blockchain Technologies. Key Words: Cloud Storage Security, Blockchain Technology, Health Records, Privacy and Security, Blockchain Algorithms, Keccak, Keras, Tesseract
Perceived Effects of Language and Culture Suppression in the Catalan Region of Spain

The Catalan language has had a complex history. The suppression of the Catalan language had a profound impact on the Catalan culture and identity. This study addresses the interaction between language and culture according to students in a Catalan university. The purpose was to evaluate their insights and report the perceived connection between language and culture. An experimental study was conducted to investigate the interrelationship between language and culture and whether it is perceived that language loss affects cultural loss, and both are intertwined. 67 undergraduate university students between the ages of 18 and 22 years participated in this study. They responded to a questionnaire that included general questions on history, language, and culture. The results suggest that the participants feel there is an interdependent connection between language loss and culture loss. This research puts into focus people’s identification with their own language, culture, traditions, and history, which reinforces the difficulty of separating one’s cultural and linguistic history.
Cyber Security: Study on Attack, Threat, Vulnerability

Abstract: The main aim of this research is to examine the various aspects of cyber infrastructure, encompassing both hardware and software systems, networks, enterprise networks, intranets, and the occurrence of cyber intrusions. The paper intends to elucidate the significance of network intrusions and cyber-theft, along with delving into the factors contributing to the proliferation of cybercrime. Furthermore, it provides a comprehensive definition of cyber security and its role in addressing network intrusion and cyber theft, while also exploring the drivers behind the surge in cybercrime and their repercussions. The authors conclude by suggesting preventive measures and potential solutions to mitigate the risks associated with cyber attacks. They emphasize that while technological advancements can aid in minimizing the impact of cyber attacks, human behavior and psychological tendencies remain significant vulnerabilities. Despite this, the authors express optimism in reducing cyber attacks through investment in organizational education campaigns that address psychological susceptibilities.
Exploring the Correlation between Students' Attitudes towards AI and Their Learning Outcomes

This research study aims to investigate the intricate connection between students' attitudes toward Artificial Intelligence (AI) and the correlates of their learning outcomes. With the growing integration of AI in various educational settings, understanding how students perceive AI and how these perceptions correlate with their academic performance is of vital importance. This research employs a descriptive and correlational approach, combining surveys and learning outcomes data analysis to delve into the changing aspects of this relationship. By exploring students' attitudes toward AI, their acceptance levels, and their learning outcomes, this study seeks to provide insights that can inform educators, policymakers, and AI developers on optimizing AI's role in education for enhanced student success. Also, the findings indicate a significant relationship between students' attitudes toward AI and their learning outcomes, emphasizing the importance of considering these attitudes in understanding and predicting educational outcomes. One of the key recommendations is that institutions should consider integrating more comprehensive AI education into their curriculum to bridge the gap in actively seeking out information about AI. This proactive approach can contribute to a more informed and adaptive student body, ultimately fostering a positive and constructive environment for AI integration in education.
Optimalisasi Pemahaman Dasar-dasar Fotografi melalui Model Pembelajaran Berbasis Masalah (PBL) dengan Pendekatan Pembelajaran Berdiferensiasi dan Quizziz di SMKIT Robbani Singosari

This research investigates the effectiveness of implementing the Differentiated Learning Approach and the Quizziz platform to enhance the understanding of the fundamentals of photography at the Robbani Singosari Integrated Islamic Elementary School (SDIT) level. Challenges in photography learning, such as passive student engagement, an inadequately conducive learning environment, difficulty comprehending the material, monotonous teaching methods, and the incongruity of media with student characteristics, serve as the backdrop for this study. The incorporation of the Differentiated Learning Approach facilitates the adjustment of material to accommodate diverse learning styles, addressing the challenges encountered in the learning process. Additionally, the study centers on the utilization of Quizziz as an evaluative tool, revealing students' positive responses to the engaging and interactive learning experience facilitated by the platform. The research findings underscore the substantial role of the Differentiated Learning Approach in augmenting learning efficacy, particularly in surmounting obstacles to material comprehension and establishing an inclusive learning milieu. The employment of Quizziz as an assessment tool not only validates its effectiveness in gauging student comprehension but also introduces an entertaining and interactive facet that enriches the overall learning experience. This study aligns with prior research affirming the constructive influence of the Differentiated Learning Approach and the amalgamation of online platforms in fostering heightened student interest and engagement. Implications of these findings encompass recommendations for refining learning strategies at the elementary school level, especially within the context of photography education. The integration of the Differentiated Learning Approach and Quizziz is anticipated to make a significant contribution to enhancing student comprehension and cultivating a more gratifying learning experience in the current digital era.
Cryptanalysis of efficient semi-quantum secret sharing protocol using single particles

Abstract In the paper[Chin. Phys. B 32 (2023) 070308], Xing et al . proposed a semi-quantum secret sharing protocol by using single particles. We study the security of the proposed protocol and find that it is not secure, that is, the three dishonest agents, Bob, Charlie and Emily can collude to obtain Alice’s secret without the help of David.