Reviewerly/exHarmony huggingface dataset:
Reliability Study of Embedded Security Systems Integrated in Green Buildings

Green building, as an important development direction of modern architecture, has been increasingly recognized and valued for its importance in energy saving, environmental protection, and sustainable development. However, safety issues have always been a concern during the lifecycle of buildings, especially in today’s world where safety issues are rampant. Therefore, ensuring the safety of green buildings throughout their lifecycle has become an urgent issue that needs to be addressed. Embedded security systems, as an important technical means, are also increasingly being applied in green buildings. Embedded security systems can guarantee the safety of buildings through intelligent monitoring and control of the building’s interior. At the same time, embedded security systems have the characteristics of strong integration, high real-time performance, and good reliability, which can ensure the safety of buildings while achieving sustainable development of green buildings. Therefore, this paper aims to study the reliability of embedded security systems integrated in green buildings, in order to provide a certain reference and inspiration for the sustainable development of the construction industry. The main research content of this paper includes an overview of embedded security systems, an overview of green buildings, the application of embedded security systems in green buildings, and the reliability of embedded security systems in green buildings. Through analysis and discussion of practical cases, this paper provides theoretical and practical support for building safety and sustainable development. To achieve the above research objectives, this paper uses various research methods such as literature review, case analysis, and experimental research, in order to obtain scientific, objective, and reliable conclusions.
Exploring the Core Competence of Cultivating Applied Talents in Artificial Intelligence under the Background of Industry Education Integration

Education, technology, and talent are the fundamental and strategic support for the comprehensive construction of a socialist modernized country. Artificial intelligence is currently a hot research topic in the field of information technology, greatly affecting and even changing people's production, life, and thinking methods. However, artificial intelligence is still in the weak stage of artificial intelligence. Therefore, our school is actively promoting and continuously innovating the deep integration of industry and education, promoting the reform of applied artificial intelligence talent cultivation mode through school enterprise collaboration, constructing an applied course system, creating a resource sharing platform, and establishing a long-term cooperation mechanism between schools and enterprises. This article provides a deep analysis of the construction of the artificial intelligence major in universities. It should analyze the job requirements of domestic artificial intelligence industry enterprises, clarify the level of talent needs that can be met in the artificial intelligence industry chain, and based on the concept of industry education integration, combine artificial intelligence technology with applied professional skills and professional qualification certification to construct our university's talent training plan and professional curriculum system for the artificial intelligence undergraduate major, Based on the premise of cultivating the core professional abilities of college students, cultivating the needs that meet the development of social economy and the improvement of scientific and technological level, and meeting the needs of college students to achieve self-worth and sustainable development, providing useful reference for the construction and development of artificial intelligence majors.
The Fallacy of Borda Count Method - Why it is Useless with Group Intelligence and Shouldn’t be Used with Big Data including Banking Customer Services

Borda Count Method is an important theory in the field of voting theory. The basic idea and implementation methodology behind the approach is simple and straight forward. Borda Count Method has been used in sports award evaluations and many other scenarios, and therefore is an important aspect of our society. An often ignored ground truth is that online cultural rating platforms such as Douban.com and Goodreads.com often adopt integer rating values for large scale public audience, and therefore leading to Poisson/Pareto behavior. In this paper, we rely on the theory developed by Wang from 2021 to 2023 to demonstrate that online cultural rating platform rating data often evolve into Poisson/Pareto behavior, and individualistic voting preferences are predictable without any data input, so Borda Count Method (or, Range Voting Method) has intrinsic fallacy and should not be used as a voting theory method.
Utilizing Digital Technology to promote the transformation and upgrading of the utilization of ancient literature in the new era based on the ancient literature digitization project of Southwest Minzu University Library

Based on the ancient literature digitization project of Southwest Minzu University Library, the paper summarizes the current status of the digital utilization of ancient literature from four aspects: staff allocation, basic requirements, environment construction and digital service platform, and promotes the future planning in the digital utilization of ancient literature from six aspects:the construction of digitization staff team, the environment construction of standardization digitization of ancient literature, the intelligent transformation of digitization service platform, strengthening publicity and promotion, strengthening cooperation and exchange and setting up special funds for digitization of ancient literature.
Psychological Content Effective Professional Activity of A Teacher, in the Context of Modeling Image Potential

The article presents a mechanism for modeling the structure of the professional image potential of a modern primary education teacher, in the process of preparing and developing an empirical study. The article presents the results of organizing and conducting an expert assessment of generalized individual psychological characteristics characterizing the professional image potential identified in the framework of the study, which allowed us to prepare a structural model consisting of four components including twelve relevant structural components, as well as their classification and ranking by degree of significance.
Characterizing Common Quarterly Behaviors in DeFi Lending Protocols

The emerging decentralized financial ecosystem (DeFi) is comprised of numerous protocols, one type being lending protocols. People make transactions in lending protocols, each of which is attributed to a specific blockchain address which could represent an externally-owned account (EOA) or a smart contract. Using Aave, one of the largest lending protocols, we summarize the transactions made by each address in each quarter from January 1, 2021, through December 31, 2022. We cluster these quarterly summaries to identify and name common patterns of quarterly behavior in Aave. We then use these clusters to glean insights into the dominant behaviors in Aave. We show that there are three kinds of keepers, i.e., a specific type of users tasked with the protocol’s governance, but only one kind of keeper finds consistent success in making profits from liquidations. We identify the largest-scale accounts in Aave and the highest-risk kinds of behavior on the platform. Additionally, we use the temporal aspect of the clusters to track how common behaviors change through time and how usage has shifted in the wake of major events that impacted the crypto market, and we show that there seem to be problems with user retention in Aave as many of the addresses that perform transactions do not remain in the market for long.
The Intercultural Communication Analysis of Chinese Documentary Films from the Perspective of Empathy

The New Year is the most solemn traditional festival of the Chinese nation, not only carrying the collective consciousness and spiritual world of the Chinese nation, but also an important symbol for the external dissemination of Chinese culture, with a wide influence worldwide. Using Python to obtain and analyze barrage comments of the documentary Chinese New Year in Bilibili with a focus on Chinese themes. Through text analysis of the barrage comments, it is essential to investigate its dissemination effectiveness, uncover successful cross-cultural communication experiences within the realm of empathy, and provide insights for effectively telling the Chinese story through documentaries, fostering empathy between Chinese culture and the world.
An Automated Market Maker Minimizing Loss-Versus-Rebalancing

The always-available liquidity of automated market makers (AMMs) has been one of the most important catalysts in early cryptocurrency adoption. However, it has become increasingly evident that AMMs in their current form are not viable investment options for passive liquidity providers. This is large part due to the cost incurred by AMMs providing stale prices to arbitrageurs against external market prices, formalized as loss-versus-rebalancing (LVR) (Milionis et al. 2022). In this paper, we present Diamond, an automated market making protocol that aligns the incentives of liquidity providers and block producers in the protocol-level retention of LVR. In Diamond, block producers effectively auction the right to capture any arbitrage that exists between the external market price of a Diamond pool, and the price of the pool itself. The proceeds of these auctions are shared by the Diamond pool and block producer in a way that is proven to remain incentive compatible for the block producer. Given the participation of competing arbitrageurs to capture LVR, LVR is minimized in Diamond. We formally prove this result, and detail an implementation of Diamond. We also provide comparative simulations of Diamond to relevant benchmarks, further evidencing the LVR-protection capabilities of Diamond. With this new protection, passive liquidity provision on blockchains can become rationally viable, beckoning a new age for decentralized finance.
Working with Artboards and Saving Files

In this chapter, I will now describe how to link your digital mock-up to the artboard so that it will display for tracing over should you need to and then conclude with how to save your Illustrator file and other design considerations.
OSPT: European Portuguese Paraphrastic Dataset with Machine Translation

We describe OSPT, a new linguistic resource for European Portuguese that comprises more than 1.5 million Portuguese-Portuguese sentential paraphrase pairs. We generated the pairs automatically by using neural machine translation to translate the non-Portuguese side of a large parallel corpus. We hope this new corpus can be a valuable resource for paraphrase generation and provide a rich semantic knowledge source to improve downstream natural language understanding tasks. To show the quality and utility of such a dataset, we use it to train paraphrastic sentence embeddings and evaluate them in the ASSIN2 semantic textual similarity (STS) competition. We found that semantic embeddings trained on a small subset of OSPT can produce better semantic embeddings than the ones trained in the finely curated ASSIN2’s training data. Additionally, we show OSPT can be used for paraphrase generation with the potential to produce good data augmentation systems that pseudo-translate from Brazilian Portuguese to European Portuguese.
Assessing Good, Bad and Ugly Arguments Generated by ChatGPT: a New Dataset, its Methodology and Associated Tasks

The recent success of Large Language Models (LLMs) has sparked concerns about their potential to spread misinformation. As a result, there is a pressing need for tools to identify “fake arguments” generated by such models. To create these tools, examples of texts generated by LLMs are needed. This paper introduces a methodology to obtain good, bad and ugly arguments from argumentative essays produced by ChatGPT, OpenAI’s LLM. We then describe a novel dataset containing a set of diverse arguments, ArGPT. We assess the effectiveness of our dataset and establish baselines for several argumentation-related tasks. Finally, we show that the artificially generated data relates well to human argumentation and thus is useful as a tool to train and test systems for the defined tasks.
Covariance Kernel Learning Schemes for Gaussian Process Based Prediction Using Markov Chain Monte Carlo

Probabilistic supervised learning within the Bayesian paradigm typically use Gaussian Processes (GPs) to model the sought function, and provide a means for securing reliable uncertainties in said functional learning, while offering interpretability. Prediction of the output of such a learnt function is closed-form in this approach. In this work, we present GP based learning of the functional relation between two variables, using various kinds of kernels that are called in to parametrise the covariance function of the invoked GP. However, such covariance kernels are typically parametric in the literature, with hyperparameters that are learnt from the data. Here, we discuss a new nonparametric covariance kernel, and compare its performance against existing non-stationary and stationary kernels, as well as against Deep Neural Networks. We present results on both univariate and multivariate data, to demonstrate the range of applicability of the presented learning scheme.
Tweet2Story: Extracting Narratives from Twitter

Topics discussed on social media platforms contain a disparate amount of information written in colloquial language, making it difficult to understand the narrative of the topic. In this paper, we take a step forward, towards the resolution of this problem by proposing a framework that performs the automatic extraction of narratives from a document, such as tweet posts. To this regard, we propose a methodology that extracts information from the texts through a pipeline of tasks, such as co-reference resolution and the extraction of entity relations. The result of this process is embedded into an annotation file to be used by subsequent operations, such as visualization schemas. We named this framework Tweet2Story and measured its effectiveness under an evaluation schema that involved three different aspects: (i) as an Open Information extraction (OpenIE) task, (ii) by comparing the narratives of manually annotated news articles linked to tweets about the same topic and (iii) by comparing their knowledge graphs, produced by the narratives, in a qualitative way. The results obtained show a high precision and a moderate recall, on par with other OpenIE state-of-the-art frameworks and confirm that the narratives can be extracted from small texts. Furthermore, we show that the narrative can be visualized in an easily understandable way.
Gamified CollectiveEyes: A Gamified Distributed Infrastructure for Collectively Sharing People’s Eyes

This paper presents the design and evaluation of Gamified CollectiveEyes that is a digital infrastructure to collectively share human eyes. Gamified CollectiveEyes collects people’s viewpoints in the world anywhere at all times, and a user sees several collected viewpoints simultaneously in a 3D virtual space. For navigating human viewpoints collected by Gamified CollectiveEyes, we propose a novel abstraction named topic channel in the paper, where a user can choose appropriate viewpoints and hearings that he/she wants to see. After presenting an overview of Gamified CollectiveEyes, we show two user studies to investigate potential opportunities and pitfalls of Gamified CollectiveEyes: the first user study is to investigate the human motivation mechanism to offer their viewpoints and the second user study is to investigate the configuration to present multiple viewpoints. We also show the limitation and future work of the current development of Gamified CollectiveEys.
Socio-psychological and socio-cultural features of communication in an inclusive educational space and in the conditions of multiculturalism

PROBLEMS AND PROSPECTS OF TRAINING IN HIGHER SCHOOL: PEDAGOGICAL, PHILOLOGICAL, PSYCHOLOGICAL AND INTERCULTURAL ASPECTS The chapter is devoted to the study of the features, essence, nature, functional role of communication in modern society in the context of the development of inclusion, inclusive society and environment, intercultural communication and multiculturalism. The construction of effective communication depends on the means and methods of overcoming barriers, socio-psychological adaptation of the individual, the peculiarity of social communication is studied, which is a means of existence and transmission of worldview socio-cultural features, values and norms of culture. The leading place in modern studies of adaptation belongs to theoretical approaches of biological and socio-psychological orientation. The chapter is devoted to the study of the essence and features of inclusive education in the educational environment, the main processes and problems of intercultural communication and multiculturalism.
Predictive Analysis of Road Accidents Using Data Mining and Machine Learning

Today, one of the top concerns for governments is road safety. Given the significance of the subject, reducing the harm caused by traffic accidents has shifted its focus to finding the root causes of incidents on the road. On the other hand, it is impractical to analyze the components in this regard due to the exponential growth of data volume. Despite everything that has been done so far to increase road safety in India, many areas continue to be vulnerable to these flaws. Due to the diversity of these vulnerability-causing factors, a thorough analysis is required in order to significantly reduce the worrying numbers. The aim of this paper is to use data mining and machine learning approaches to help develop a model using LR and RF that not only smooths out the heterogeneity of the data by grouping like objects together to identify the regions of the nation that are most likely to experience accidents in relation to various accident-factors, but also assists in establishing the relationship between these parameters, factors, and casualties. The outcomes of data mining will assist businesses like transportation in exploring the police information system’s accident data, finding trends, forecasting future behavior, and making wise judgments to reduce accidents.
Human Eye Fixations Prediction for Visual Attention Using CNN - A Survey

Human attention is closely related to the visual information perceived. Saliency prediction models help to concentrate on the core pixels of an image contributing to attention. Trends and advancements in Artificial Intelligence resulted in many prediction models but not reached the level of prediction as human eye selection. Currently available prediction models concentrate on reflexive or exogenous attention. Published works in the year 2014–2022 are considered for this work and a study on improvements in the path made by deep learning prediction models using Convolutional Neural networks (CNN) in unveiling the pixels which achieve the high visual conspicuousness. This paper shows the avenues which are possible for the saliency models in the future by incorporating voluntary attention to the current models which are beneficial for researchers in the visual attention field like activity recognition, visual question answering etc.
Cone-Traced Supersampling with Subpixel Edge Reconstruction

While signed distance fields (SDFs) in theory offer infinite level of detail, they are typically rendered using the sphere tracing algorithm at finite resolutions, which causes the common rasterized image synthesis problem of aliasing. Most existing optimized antialiasing solutions rely on polygon mesh representations; SDF-based geometry can only be directly antialiased with the computationally expensive supersampling or with post-processing filters that may produce undesirable blurriness and ghosting. In this work, we present cone-traced supersampling (CTSS), an efficient and robust spatial antialiasing solution that naturally complements the sphere tracing algorithm, does not require casting additional rays per pixel or offline prefiltering, and can be easily implemented in existing real-time SDF renderers. CTSS performs supersampling along the traced ray near surfaces with partial visibility – object contours – identified by evaluating cone intersections within a pixel's view frustum. We further introduce subpixel edge reconstruction (SER), a technique that extends CTSS to locate and resolve complex pixels with geometric edges in relatively flat regions, which are otherwise undetected by cone intersections. Our combined solution relies on a specialized sampling strategy to minimize the number of shading computations and correlates sample visibility to aggregate the samples. With comparable antialiasing quality at significantly lower computational cost, CTSS is a reliable practical alternative to conventional supersampling.
Multi-Channel Fused Lasso for Motion Detection in Dynamic Video Scenarios

Motion detection is a fundamental step in analyzing video sequences, capable of enhancing consumer electronics products with increased intelligence, interactivity, and convenience. Structured and fused sparsity has been used in previous works to normalize the foreground signal due to the foreground’s spatial and temporal coherence. As far as we are aware, no previous works have studied the group prior to multi-channels (such as the RGB) to the foreground signals. However, a multi-channel signal is the correct representation of a pixel. Under the condition that one pixel is equal (similar) to its neighboring pixels, it’s reasonable that the three channels of RGB should also be identical (similar). This work investigates the smoothness of multi-channel signals by proposing a novel regularizer named the Multi-Channel Fused Lasso (MCFL). Specifically, we introduce a two-channel structure to implement motion detection. First, low-rank matrix decomposition is performed on the video footage along different planes. Low-rank background and sparse foreground (rough foreground candidate for the second pass) are segmented from the video sequence. Further, MCFL regularization is used for sparse signal recovery to improve the performance of the foreground mask. The proposed method is validated on different challenging videos. Sufficient experimental results show that our method is effective in a variety of challenging scenarios. Compared with the current best sparsely-based method, the performance of F-Measure improves by 0.4, 0.4, and 0.1 respectively on the I2R, BMC, and CDnet2014 datasets. Our approach is also competitive compared to the deep learning models. Our code can be obtained at https://github.com/linuxsino/MCFL.