Reviewerly/exHarmony huggingface dataset:
Digital Writing and Labor-based Grading: An Equitable and Inclusive Approach to Undergraduate Writing Instruction

The tradition of digital writing instructional practices is nearly 50 years old in the US (Handa, 2004).Sometimes, students fully online digital writing courses may fail to engage with and finish the course because they do not feel competent in handling the technologies in the academic context; thus, it is important to find out what equitable practices and what factors influence student success in these courses.Therefore, this study aims to examine digital writing assignments requested in a writing course, with the goal of demonstrating an inclusive and equitable practice: the Labor-based grading contract, in a way that promotes equal and fair grades.This practice is proposed as a solution to the shortcomings detected, since it has been observed that students inexperienced in pre-college education in the delivery of online writing assignments persist in their difficulties with technological educational platforms in college.The creation of a contract between the teacher and the students -at first-, fosters knowledge, motivation, involvement, or engagement with the task; thus, digital writing assignments go from being an unattainable goal to being a feasible task to engage in.Also, the cooperative creation of this online writing with an easy-to-use platform (Eli Peer Review) stimulates them to persist in the following tasks, as they have already reflected on them and have already found out among their peers what they consist of and how to deal with such tasks.In our research we present a case study of a course based on online writing instruction.Therefore, this study aims to examine a particular course in the United States in which multimedia writing assignments and labor-based grading allowed for deep student engagement and success (Dickson,1974;Inoue, 2019).The data comes from the use of mixed methods that combine qualitative information collected through document analysis (teacher materials: syllabi, guidelines, instructions; student materials: personal research, blog entries, and final letter); classroom observation field diaries and the semi-structured teacher interview, with the quantitative methods of a student survey.The results show that there is a high degree of alignment between the course and the best practices of online instruction, and that the nature of the assignments and the Labor-based Grading Contract (Dickson,1974, Inoue, 2019) appear to play key roles in student engagement and success in the course.Likewise, the most highlighted aspect by the students has been the emotional factor, since the tasks have allowed them to get involved and enjoy writing in the digital support.The systematic observation of this writing course aims to deeply understand its provenance, objectives, taxonomy and functionality, with the final purpose of highlighting the capabilities of this methodology in order to offer it as a model in other contexts to promote a fairer and more equitable education.
Research on Remote Regional Collaborative Intelligence Platform Based on Blockchain

The 19th and 20th National Congresses of the Communist Party of China continued to stress the need to vigorously promote coordinated development among regions, making it an important strategic task in building a great modern socialist country in the new era. At present, the economic circle vigorously developed is based on the traditional regional cooperation based on the geographical adjacency relationship, while the regional cooperation obstacles will make the cooperation fail. Therefore, this paper builds the remote regional cooperation platform based on the virtual and real integration of game subject mirroring technology, collaborative contract execution technology of smart contract and collaborative credit management technology of blockchain, so as to realize the remote regional cooperation.
Teaching History in Non-Humanitarian Universities in Russia: on the Issue of a New Concept

A new concept of history is being developed for university students today. The authors of the article set the task to reveal the main problems of the modern stage of teaching history at the university and suggest areas for improvement. The article is based on published materials of conferences, discussions of the professional community and on my own long experience in the university. The authors consider it necessary to unify the name of the discipline in history, which is mandatory for non-humanitarian universities in Russia, to develop a new state standard, real, corresponding to the time allocated in the curricula of universities. A number of the most important changes in the content and methodological part of the course are proposed in the conclusions. The authors propose the main changes: a combination of the problem-chronological principle, the connection of the history of Russia with world history, the obligatory study of the history of Russia in the post-Soviet period and its place in the post-Soviet space, the development of studentsʼ understanding of the general and particular in the history and modern development of Russia. The need to improve the legal regulation of the work of a teacher, taking into account its complication in the context of digitalization, is substantiated in the article.
Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) dalam Peramalan Data Curah Hujan di Kota Makassar

Modeling of rainfall data using time series data involving location elements has not been widely carried out. One model that involves elements of time and location is Space Time Autoregressive (STAR). The development of the STAR model which assumes that each location has heterogeneous characteristics is the Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) model. The purpose of this research is to get the best GSTARIMA model and forecast rainfall data in Makassar City based on the best GSTARIMA model. This model incorporates time and geographic dependencies with different parameters for each location. The data used is Makassar city's monthly rainfall data at the Bawil IV/Panaikang, Biring Romang/Panakkukang and Stammar Paotere rain stations from January 2017 to September 2021. Autoregressive (AR) and Moving Average (MA) orders were identified using the Space Time Autocorrelation plot. Function (STACF) and Space Time Partial Autocorrelation Function (STPACF). The spatial order used in this study is spatial order 1 with an inverse distance weighting matrix and normalized cross-correlation. Parameters were estimated using the Generalized Least Squares (GLS) method. The best model for predicting rainfall in the city of Makassar is the GSTARIMA (1,0,0) (1,1,0)12 model using an inverse distance weighting matrix with the smallest average Root Mean Square Error (RMSE) of 132.9661.
Communication-efficient decision-making of digital twin assisted Internet of vehicles: A hierarchical multi-agent reinforcement learning approach

The connected autonomous vehicle is considered an effective way to improve transport safety and efficiency. To overcome the limited sensing and computing capabilities of individual vehicles, we design a digital twin assisted decision-making framework for Internet of Vehicles, by leveraging the integration of communication, sensing and computing. In this framework, the digital twin entities residing on edge can effectively communicate and cooperate with each other to plan sub-targets for their respective vehicles, while the vehicles only need to achieve the sub-targets by generating a sequence of atomic actions. Furthermore, we propose a hierarchical multiagent reinforcement learning approach to implement the framework, which can be trained in an end-to-end way. In the proposed approach, the communication interval of digital twin entities could adapt to time-varying environment. Extensive experiments on driving decision-making have been performed in traffic junction scenarios of different difficulties. The experimental results show that the proposed approach can largely improve collaboration efficiency while reducing communication overhead.
Deep whole brain segmentation of 7T structural MRI

7T magnetic resonance imaging (MRI) has the potential to drive our understanding of human brain function through new contrast and enhanced resolution. Whole brain segmentation is a key neuroimaging technique that allows for region-by-region analysis of the brain. Segmentation is also an important preliminary step that provides spatial and volumetric information for running other neuroimaging pipelines. Spatially localized atlas network tiles (SLANT) is a popular 3D convolutional neural network (CNN) tool that breaks the whole brain segmentation task into localized sub-tasks. Each sub-task involves a specific spatial location handled by an independent 3D convolutional network to provide high resolution whole brain segmentation results. SLANT has been widely used to generate whole brain segmentations from structural scans acquired on 3T MRI. However, the use of SLANT for whole brain segmentation from structural 7T MRI scans has not been successful due to the inhomogeneous image contrast usually seen across the brain in 7T MRI. For instance, we demonstrate the mean percent difference of SLANT label volumes between a 3T scan-rescan is approximately 1.73%, whereas its 3T-7T scan-rescan counterpart has higher differences around 15.13%. Our approach to address this problem is to register the whole brain segmentation performed on 3T MRI to 7T MRI and use this information to finetune SLANT for structural 7T MRI. With the finetuned SLANT pipeline, we observe a lower mean relative difference in the label volumes of ~8.43% acquired from structural 7T MRI data. Dice similarity coefficient between SLANT segmentation on the 3T MRI scan and the after finetuning SLANT segmentation on the 7T MRI increased from 0.79 to 0.83 with p<0.01. These results suggest finetuning of SLANT is a viable solution for improving whole brain segmentation on high resolution 7T structural imaging.
Dragonfly Algorithm Strategy Parameters Analysis on Swarm Robot Multi-Target Search Efficiency

Dragonfly Algorithm (DA) is a Swarm Intelligence (SI) based optimization strategy. Since its development in 2016, DA has been widely utilized in many technology and engineering applications. Target searching by the swarm robots (SR) is one of the applications that take advantage of the DA strategy advancement. Multi-target search task is the latest inquiry in the target search problem domain whereby the objective is to search all available targets within the minimum time possible. To achieve this purpose, the DA parameters need to be optimized to ensure the search outcome is in the optimum and efficient condition. This article presented the DA parameters analysis within the scope of multi-target search problems based on the swarm robots. There is a total of six parameters included in this analysis which are inertia weight ($\omega$), alignment weight (a), cohesion weight (c), separation weight (s), food attraction factor (f), and enemy distraction factor (e). The analysis results are presented in detail with a focus on the trends and effects of each parameter on the search efficiency outcomes with the least iteration (most efficient) recorded of 19.9 iterations. The results data can be benchmarks or references not only in the multi-target search problem but also in other research domains that are based on the DA strategy.
Full Issue

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 60, Issue 2 p. I-I Full Issue Full Issue First published: 05 April 2023 https://doi.org/10.1111/j.1467-6346.2023.10998.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume60, Issue2April 2023Pages I-I RelatedInformation
Safe campus: An intelligent campus video surveillance system for crowd analysis

Nigerian campuses comprises of diverse culture, ethnicity and religion. Controlling these campuses is a very big deal. Cameras are installed to help security personnel in carrying out these enormous tasks. However, the installed closed circuit television [CCTV] cameras are only used for evidence sourcing rather than prevention of campus vices. With the use of appropriate technique, campus vices can be prevented thus securing the campus. In this research, an Artificial Intelligent (AI) video surveillance system was developed. The system captures, analyses video for any abnormal behaviour and alerts relevant personnel for appropriate required action. The scheme uses crowd surge a crowd analyzer for videos with vulnerability and threats. The result shows that when deployed at strategic flagged locations early campus vices are detected and reported to relevant personnel whom take appropriate actions and measures to curb escalation of the vices.
Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Video is an important carrier of all kinds of information on the Internet. More and more students choose to watch online learning videos from online learning platforms with various forms of intelligent terminals. To allow students to get the videos which they really need and are interested in from the mass videos, it is necessary to improve the existing personalized recommendation algorithm. Content-based recommendation method, collaborative filtering-based recommendation method and hybrid recommendation method often have the problems of loss of feature information or cocoon. To this end, this paper studies the personalized recommendation service of media educational resources based on multi-dimensional user features. This paper constructs a multi-feature candidate set based on student-video interaction, extracts and splices category feature, label feature and semantic feature of Internet media educational videos, and generates the representation of students’ learning preference feature. Based on the improved forward and backward recurrent neural network, this study constructs a recommendation model of Internet media educational videos oriented to students' preference points. The experimental result verifies the validity of the model.
A Chisel Framework for Flexible Design Space Exploration through a Functional Approach

As the need for efficient digital circuits is ever growing in the industry, the design of such systems remains daunting, requiring both expertise and time. In an attempt to close the gap between software development and hardware design, powerful features such as functional and object-oriented programming have been used to define new languages, known as Hardware Construction Languages. In this article, we investigate the usage of such languages—more precisely, of Chisel—in the context of Design Space Exploration, and propose a novel design methodology to build custom and adaptable design flows. We apply an innovative functional approach to define flexible strategies for design space exploration, based on the composition of basic exploration steps, and provide a library of basic strategies along with a proof-of-concept framework—which we believe to be the first Chisel-based DSE framework. This framework fully integrates within the ecosystem of Chisel to allow users to define their DSE processes in the same framework (and language) they use to describe their designs. We demonstrate our methodology through several use cases, illustrating how our functional approach makes it possible to consider various metrics of interest when building exploration processes—in particular, we provide a quality of service -driven exploration example. The methodology presented in this work makes use of designers’ expertise to reduce the time required for hardware design, in particular for Design Space Exploration, and its application should ease digital design and enhance hardware developers’ productivity.
Digital contents in virtual classrooms. An analysis in Ecuadorian education

With the health crisis that humanity went through with the appearance of Covid-19, many countries are facing a great challenge, which is to be able to achieve the continuation of studies while complying with the social distancing and maintaining the same quality in education. In this sense, technological tools are shown as a means that helps and supports through its various educational programs the teaching-learning processes. The new era of technology forces education to change from its foundations in order to achieve in students an integral formation and as part of it, the ability to learn. The research developed for this proposal was of an exploratory qualitative type, sources were collected from different research works, web pages, articles in which the problem that many teachers have when developing new and practical educational resources was described. This research shows that the use of digital tools in the development of educational resources favors education, making classes more practical, interactive and dynamic.
Models, systems, and descriptions

There are many different notions of models in different areas of science that are often not aligned, making it difficult to discuss them across disciplines. In this study, we look at the differences between physical models and mental models as well as the difference between static and dynamic models. Semiotics provides a philosophical underpinning by explaining meaning-making. This allows for identifying a common ground between models in different areas. We use examples from natural sciences and linguistics to illustrate different approaches and concepts and to find commonalities. This study distinguishes between systems, models, and descriptions of models. This distinction allows us to understand the commonalities of mental and physical models in different areas.
Round-up of US copyright developments 2022

... Last year, in Round-up of US copyright developments 2021 (17(4) JIPLP), we considered the impact of the US Supreme Court’s fair use ruling in Google v Oracle and legislative and other developments in US copyright law.1 This article continues the discussion, pointing to various new developments. Fair use continues to be an area where the law may be shifting, as the Supreme Court considers in Andy Warhol Foundation v Goldsmith how Google v Oracle may have changed the fair use doctrine.2 Technology is also impactful, with the lower courts—such as in Thaler (which denied copyright registration to art fully generated by Artificial Intelligence)—as well as the US Copyright Office addressing how changing technology should affect US Copyright Law.3 This article also discusses other relevant cases that have had an impact on US Copyright law.
Sentiment Analysis of User Comment Text based on LSTM

Taking the user-generated Chinese comment dataset on online platforms as the research object, we constructed word2vec word vectors using gensim and built a sentiment analysis model based on LSTM using the TensorFlow deep learning framework. From the perspective of mining user comment data on the platform, we analyzed the sentiment tendency of user comments, providing data support for hotels to understand consumers' real sentiment tendencies and improve their own service quality. Through analysis of the validation dataset results obtained by crawling the website, the accuracy of this LSTM model can reach up to 0.89, but there is still much room for improvement in the accuracy of sentiment analysis for some datasets. In future research, this model needs further optimization to obtain a stable and more accurate deep-learning model.
Quantum Inference for Reliability Assessment

The increased availability of specialized quantum hardware has made possible the exploration of the field by the general scientific community. Enterprises such as IBM, Google, and Microsoft have launched or will launch shortly cloud quantum computing services, which will accelerate forward the development of the field even further. It is, therefore, necessary to prepare the RAMS community to harness the advantages that quantum computing may offer to our discipline. In this paper, we present a proposed application for efficient probabilistic inference in Bayesian networks using gate-based quantum computing and a quantum algorithm called Amplitude Amplification. The proposed methodology is exemplified on a non-trivial Bayesian network, composed of nodes representing discrete probability distributions. To the best of our knowledge, this is the first attempt to use Bayesian networks in combination with Amplitude Amplification for the risk and reliability assessment context. The results indicate that the usage of quantum computing can effectively increase the efficiency of the sampling process, decreasing the number of samples required to obtain estimates of similar quality when compared to a traditional Monte Carlo sampling approach.
A Case Study in Obtaining Freedom from Interference in a Mixed-ASIL Architecture

SummaryIn this paper we describe our experience in identifying and developing safety mechanisms to improve spatial isolation within the AUTOSAR Operating System (OS) in order to allow the usage of cores with mixed integrities. We show that simple write operations on shared OS data structures performed by OS core running on low ASIL cores, e.g. ASIL A core, can impact on the behavior of code running on higher ASIL cores and may lead to AUTOSAR OS malfunctions. In particular, we focus on cross-core AUTOSAR API calls, by which a core executes an AUTOSAR API over some other core, like activating a task or retrieving a task status running on some other core.Therefore, we firstly show that the usage of well-known redundancy mechanisms increases the detectability of such cascading failures. Then, we performed a reformat of some internal data structures of our AUTOSAR OS implementation in order to reduce the possibility for low ASIL core to interfere with higher ASIL cores. In particular, we analyzed the entire OS source code in order to reduce the size and number of data structures that are not needed to be shared among cores. Next, we propose and implement a delegation mechanism coming from the networking domain, i.e., the Remote Procedure Call (RPC). With this mechanism, in case a cross-core API is used by a low ASIL core, all the operations on data structures are performed by the higher ASIL core, so that the spatial isolation among cores is guaranteed.Finally, we evaluated the impact on the OS performance of the redundancy and RPC mechanisms allowing the usage of low ASIL cores on a Tricore architecture. In fact, using the redundancy mechanisms after the data structure reformat increases the overhead for executing a single AUTOSAR OS API function (e.g., ActivateTask). In contrast, we show that the RPC mechanism introduces an acceptable overhead for the high ASIL cores in case cross-core AUTOSAR OS APIs are used. Hence, the overhead introduced by the proposed mechanisms can be acceptable since it allows to increase the computational power by using mixed ASIL cores without cascading failures from the safety point of view.