Reviewerly/exHarmony huggingface dataset:
Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.
The Effect of the Jigsaw Cooperative Learning Model on Beginning Reading for Grade 3 Students in Elementary Schools in Sidoarjo

This study aims to determine whether or not there is an effect of the Jigsaw Cooperative learning on Beginning Reading for grade III students at SDN Siwalanpanji Buduran Sidoarjo. This study uses a quantitative approach. This type of research is pre-experimental design with saturates sampling technique sampling. This study used a sample of 20 students. The instrument is a test in the form of a description question. The results of the hypotesis test using the paired sample t test. In the pretest-postest hypotesis test, the sig (1-tailed) value was 0,000 &gt; 0,5 and 21,032 &gt; 2,093. So it can be concluded that from the result of the analysis with the t test, which shows that Ho is rejected and Ha is accepted. So that there is an effect of the Jigsaw Cooperative Learning model on beginning reading for grade III students at Siwalanpanji Elementary school Buduran Sidoarjo.
Evaluation of Leet Speak on Password Strength and Security

Making secure passwords is one of the biggest challenges in everyday life. There are numerous rules and requirements making passwords complex and hard to remember, and keeping track of which password is for which account is a major hassle. According to an article from HelpNetSecurity, statistics show that an alarming “78% of respondents required a password reset in their personal life within the last 90 days” ("78% of people"). In recent years, leet speak has become increasingly popular as a way to create memorable passwords. Leet speak is a convenient method for users to create passwords that meet password requirements in many services. But there has been increasing debate on whether this approach is a secure and safe method or not. This paper aims to solve this debate by effectively evaluating the strength of ordinary passwords and leet passwords using various means. With the help of password cracking or recovery tools and password strength classifiers, this paper will compare the cracking time and strength scores of ordinary passwords and leet converted passwords. The paper will begin with a background information section explaining important concepts discussed in the paper, followed by the methodology of the experiment, a presentation of the data along with the evaluation of the results, and a conclusion at the end.
Evaluating Science in the News

Book Review| December 01 2022 Evaluating Science in the News Evaluating Science in the News (https://www.biointeractive.org/classroom-resources/evaluating-science-news) Jeffrey D. Sack Jeffrey D. Sack Ed.D. Science Education Consultant/Writer, Westbrook, CT 06498, sack.jeffrey@comcast.net Search for other works by this author on: This Site PubMed Google Scholar The American Biology Teacher (2022) 84 (9): 577. https://doi.org/10.1525/abt.2022.84.9.577 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Jeffrey D. Sack; Evaluating Science in the News. The American Biology Teacher 1 December 2022; 84 (9): 577. doi: https://doi.org/10.1525/abt.2022.84.9.577 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentThe American Biology Teacher Search Anyone who has picked up a newspaper or watched cable tv news knows that these sources often lack credibility, especially when it comes to reporting science. The internet is home to conspiracy theories, fake news, and just plain bad science, all of which make it very difficult for students to find accurate, reliable resources for their research papers and lab reports. Fortunately, the good folks at HHMI BioInteractive have produced an activity to help students weed through the trash and find the true, accurate, and credible sources they need. Evaluating Science in the News is a teaching tool that guides students through exercises to practice reading comprehension and source evaluation by asking a series of questions about a particular science article. Students then need to synthesize answers to these questions and make conclusions about whether or not the article is valid, accurate, and reliable. There are two versions of the... You do not currently have access to this content.
Generation of microwave photon perfect W states of three coupled superconducting resonators

We propose an efficient method for the generation of perfect W states on three microwave superconducting resonators, of which the two nearest neighbors are coupled by a symmetric direct current superconducting quantum interference device (dc-SQUID). With suitable external magnetic fluxes applied to the dc-SQUID symmetry loops, on-chip tunable interactions between neighboring resonators can be realized, and different perfect W states can be deterministically created on-demand in one step. Numerical simulations show that high-fidelity target states can be generated and our scheme is robust against imperfect parameter tuning and environment-induced decoherence. The present work may have potential applications for implementing quantum computation and quantum information processing based on microwave photons.
An Approach for Building a Framework for Applying Natural Language Queries on RDF Database

The natural language query becomes an extension of the existing World Wide Web. Also searching the web become so difficult due to the huge increase of the unstructured data, and this leads to the increase of knowledge needed to be captured/answered from the available data. Furthermore, many proposed systems had been introduced to get relevant and accurate answers to the given natural language queries. In this paper a new proposed model will be introduced, which includes ontology, and applied NLP algorithms that are capable to generate query graphs and enhancing the conversion of paragraphs into triples form. The answers are based on large paragraphs and any types of complex questions. Moreover, we used Stanford CoreNLP for converting sentences into the form of triples. The used dataset is Stanford SQuAD dataset. The output results show relevant data with an accuracy of 82.27%.
Design and R&amp;D of MIS System and C# Backstage for MIS of Colleges in the Era of Mobile Internet

This article analyzes the background of MIS system development and the development status of management information systems in universities at home and abroad, and makes a more detailed analysis of the business needs of university information management and the needs of network office. This article uses the Web Services environment on the.NET platform The SOAP mechanism is introduced into the B/S structure, and the tasks on the Server side are allocated to different machines, covering the six functional modules of system management, student information management, course selection management, attendance management, score management, and announcement management. The final design is based on C# Interaction and management of background data.
Online allocation algorithm of digital story course resources based on deep learning

The online allocation of course resources is a part of the field of cloud computing. Studying the allocation of digital story course resources through cloud computing architecture can not only meet the requirements of cloud computing resource allocation, but also improve the utilization efficiency of digital story course resources. This paper mainly proposes an application-oriented resource allocation algorithm based on deep learning. This online allocation algorithm mainly quantifies the characteristics of the data, and more accurately creates the server resources required by the digital story course. In addition, the workload prediction model is integrated into the online allocation algorithm, which can Ensure that curriculum resources and teaching are more matched, so as to give full play to the value of resource use.
Robust object detection in remote sensing imagery with noisy and sparse geo-annotations

Recently, the availability of remote sensing imagery from aerial vehicles and satellites constantly improved. For an automated interpretation of such data, deep-learning-based object detectors achieve state-of-the-art performance. However, established object detectors require complete, precise, and correct bounding box annotations for training. In order to create the necessary training annotations for object detectors, imagery can be georeferenced and combined with data from other sources, such as points of interest localized by GPS sensors. Unfortunately, this combination often leads to poor object localization and missing annotations. Therefore, training object detectors with such data often results in insufficient detection performance. In this paper, we present a novel approach for training object detectors with extremely noisy and incomplete annotations. Our method is based on a teacher-student learning framework and a correction module accounting for imprecise and missing annotations. Thus, our method is easy to use and can be combined with arbitrary object detectors. We demonstrate that our approach improves standard detectors by 37.1% AP50 on a noisy real-world remote-sensing dataset. Furthermore, our method achieves great performance gains on two datasets with synthetic noise. Code is available at https://github.com/mxbh/robust_object_detection.
Preface to the Special Issue on “Fuzzy Natural Logic in IFSA-EUSFLAT 2021”

In [1], we gave reasons to focus on one of the possible future directions of fuzzylogic towards the concept of fuzzy natural logic (FNL). [...]
Polarization Switching in VCSELs Subject to Current Modulation for Quantum Random Number Generation

We report an experimental study of the excitation of the linearly polarized modes of a gain-switched VCSEL in order to obtain random bits. The generated bit strings pass all tests in the standard test suite for random number generators provided by NIST.
Research on knowledge graph technology and application scenarios

In recent years, the extensive application of knowledge atlas in China has attracted the attention of the industry and plays an important role in knowledge production and management. By using knowledge graph technology, knowledge can be organized more orderly and organically. Combined with certain knowledge mining and analysis algorithms, users can obtain their own knowledge information more quickly and accurately, so as to realize intelligent reasoning and decisionmaking. At present, the knowledge graph has been widely used in the commercial field, and there are corresponding products in intelligent search, machine Q & A, intelligent recommendation, etc., such as the new generation search engine, Siri and other chat robots, community recommendation systems, etc. By studying the basic types of knowledge atlas technology, this paper analyzes the application of knowledge atlas in knowledge problem, recommendation system and military intelligent decision-making system, and points out the universality and universality of the application of knowledge atlas technology in modern intelligent field.
Meningkatkan Minat dan Keaktifan Belajar Siswa Melalui Game Ranking 1 di SD Negeri 105363 Desa Kesatuan

The purpose of holding a ranking 1 game is to increase students' interest in learning, create an active and creative learning atmosphere, develop a love for learning at school and train students to have self-confidence. This 1st ranking game was held at the elementary level, this activity was held at SD Negeri 105363 Unity Village. Group 1 held a ranking game method by giving a competition in the form of general questions. So that the results of the competition got enthusiasm from students, increased interest in learning and a lively spirit in answering these questions.
Evolution of autonomic computing

The major theme in this book deals with identifying and developing techniques to make autonomic computing systems trustworthy. To achieve this, it is important to first understand the level of work that has already gone into the autonomic computing research and how that can be harnessed. This chapter starts with an overview of the autonomic computing architecture and its life cycle. A broad analysis of autonomics research to show the trends in and direction of the autonomic computing research and where the work needs to be concentrated to address open challenges and achieve trustworthiness is presented. It is also important to take a holistic view of the entire field of research in order to gain a clearer picture of the need for and lack of effort towards trustworthy autonomic computing.In this chapter, you will learn the following: the design of autonomic systems; the life cycle of autonomic architecture; key factors of trustworthy autonomic computing design; major trends and direction in the autonomic computing research; about key studies and researchers that have shaped the study of autonomic computing.
Conclusions and future work

This chapter concludes the work and summarises the key points covered in this book. It also discusses the direction of future work. Chapter Contents: 8.1 A case for trustworthy autonomics 8.2 The autonomic computing state of the art 8.3 Techniques that power autonomic computing 8.4 Trustworthy autonomic architecture 8.5 Interoperability 8.6 Level of autonomicity (LoA) 8.7 Future work
Multi-agent interoperability

This chapter has presented an overview of multi-agent systems coordination and the challenges of interoperability between autonomic managers in multi-manager scenarios. The very success of autonomic computing has inevitably led to situations where multiple autonomic managers need to coexist and/or interact directly or indirectly within the same system. Potentially, problems can arise due to conflict of interest when these managers (components) coexist. This is partly as a result of increasing scale and complexity of newer systems. Interoperability challenges stem from the fact that these components could be multi-vendor supplied, upgrades in one component could trigger compatibility issues, increasing scale can introduce bottlenecks, one component may be unaware of the existence of another and components, though tested and perfected in isolation, may not have been wired at design to coexist with other components.An overview of a few interoperability solutions has been presented. These solutions are based on different established mechanisms. An architecture-based interoperability solution that addresses complex interactions between coexisting autonomic managers has also been presented. The interoperability solution, which is based on the TrAArch architecture, builds on the Stigmergy mechanism - this mechanism allows for indirect coordination, through the operating environment, between coexisting autonomic managers. In this approach, autonomic managers are designed to sense their environment and dynamically (re)adjust (retune) their behaviour as soon as they notice process conflicts. Experimental analyses have been presented to evaluate the performance of the stigmergy-inspired interoperability solution. The importance of considering interoperability capability as an integral part of the autonomic manager design has also been proposed. This is another step towards trustworthy autonomic computing.
The Tensor Brain: A Unified Theory of Perception, Memory, and Semantic Decoding

Abstract We present a unified computational theory of an agent's perception and memory. In our model, both perception and memory are realized by different operational modes of the oscillating interactions between a symbolic index layer and a subsymbolic representation layer. The two layers form a bilayer tensor network (BTN). The index layer encodes indices for concepts, predicates, and episodic instances. The representation layer broadcasts information and reflects the cognitive brain state; it is our model of what authors have called the “mental canvas” or the “global workspace.” As a bridge between perceptual input and the index layer, the representation layer enables the grounding of indices by their subsymbolic embeddings, which are implemented as connection weights linking both layers. The propagation of activation to earlier perceptual processing layers in the brain can lead to embodiments of indices. Perception and memories first create subsymbolic representations, which are subsequently decoded semantically to produce sequences of activated indices that form symbolic triple statements. The brain is a sampling engine: only activated indices are communicated to the remaining parts of the brain. Triple statements are dynamically embedded in the representation layer and embodied in earlier processing layers: the brain speaks to itself. Although memory appears to be about the past, its main purpose is to support the agent in the present and the future. Recent episodic memory provides the agent with a sense of the here and now. Remote episodic memory retrieves relevant past experiences to provide information about possible future scenarios. This aids the agent in decision making. “Future” episodic memory, based on expected future events, guides planning and action. Semantic memory retrieves specific information, which is not delivered by current perception, and defines priors for future observations. We argue that it is important for the agent to encode individual entities, not just classes and attributes. Perception is learning: episodic memories are constantly being formed, and we demonstrate that a form of self-supervised learning can acquire new concepts and refine existing ones. We test our model on a standard benchmark data set, which we expanded to contain richer representations for attributes, classes, and individuals. Our key hypothesis is that obtaining a better understanding of perception and memory is a crucial prerequisite to comprehending human-level intelligence.
TeleViewDemo: Experience the Future of 3D Teleconferencing

Recent demonstrations of 3D telepresence provide a glimpse into the future where 2D video communication is replaced with photo-realistic virtual avatars rendered on 3D displays by using dedicated hardware. Our platform lets visitors experience real-time end-to-end 3D teleconferencing using commodity hardware. This demo integrates current state-of-the-art face reconstruction and rendering algorithms into an end-to-end system to illustrate the utility and capabilities of commodity telepresence on a range of 3D displays.
Factors Influencing the Animation Infographic and Augmented Reality Technique in Healthcare Promotion Communication for the Elderly in Thailand

When it comes to the digital age, computers and technological devices have grown more pervasive than in the past. As of now, technology has been considered to have a major role in every aspect of life. In this case, it is the health and cognitive senses of individuals. In terms of human perception, it is often associated with both long-term and short-term memory or active memory, which are recognized patterns by automated systems. When creating user interfaces, it is necessary to account for the cognitive strengths and limitations of humans. People can get an overview of large datasets if appropriately presented in a proper manner, which can help increase the efficacy and ability of information management. However, one of the most frequent data management issues is inadequate design and data collecting, particularly in the focus theme of elderly health and wellbeing. Infographics are a formal way to help people interpret complex health information more easily and avoid possible mistakes in data management. In the healthcare industry, the system of augmented reality (AR) has been shown to facilitate the procedures of healthcare using AR booklet and mobile applications for elderly-health-related content and healthcare management for older individuals.
Spatiotemporal data mining

Spatiotemporal data mining aims to discover interesting, useful but non-trivial patterns in big spatial and spatiotemporal data. They are used in various application domains such as public safety, ecology, epidemiology, earth science etc. This problem is challenging because of the high societal cost of spurious patterns and exorbitant computational cost. Recent surveys of spatiotemporal data mining need update due to rapid growth. In addition, they did not adequately survey parallel techniques for spatiotemporal data mining. This paper provides a more up-to-date survey of spatiotemporal data mining methods. Furthermore, it has a detailed survey of parallel formulations of spatiotemporal data mining.
Planning the Deployment

Planning a deployment of SQL Server 2022, in order to best support the business’s needs, can be a complicated task. You should make sure to consider many areas, including edition, licensing requirements, on-premises vs. cloud hosting, hardware considerations, software configuration, and even if Windows is the best platform. For example, if your new instance will be supporting a PHP Web App, hosted on Linux, then maybe your instance should also be hosted on Linux? And all of this is before you even start to consider which features of SQL Server you may need to install to support the application.
Resource Governor

Resource Governor provides a method for throttling applications at the SQL Server layer by imposing limits on CPU, memory, and physical I/O on different classifications of connection. This chapter discusses the concepts the Resource Governor uses before demonstrating how to implement them. We then look at how to monitor the effect that Resource Governor has on resource utilization.