Reviewerly/exHarmony huggingface dataset:
Designing an English Teaching Resource’s Information Management System Using Collaborative Recommendation

In the modern world, English education institutions are more concerned about how to recommend the required resources to all types of learners and how to improve the application value of the resources. Therefore, this study introduces and combines the collaborative recommendation algorithm to recommend resources to the learners. The development and design of an English teaching resource information management system based on collaborative recommendation technology are in the best interests of English learners. It establishes an English teaching resource information management system to recommend appropriate teaching resources for different English learners and provide personalized recommendations. English teaching resources are used to improve efficiency and make it easier for English learners to find English teaching resources that meet their specific requirements. Firstly, this study briefly introduces the collaborative algorithm and establishes how the score of similar items or users is calculated by the user-based collaborative filtering algorithm and the item-based collaborative filtering algorithm. Based on similarity, the proposed approach recommends the architectural framework of the English teaching system to other users, describes the functions of each module of the system in detail, and detects the system’s application performance through simulation experiments. The result shows that the learning resources are sufficiently accurate. The design system, in comparison with the general information management system, can reduce the time required for users to obtain English teaching resources.
Malaria Prevalence and Anemia Status of Children Attending a Health Facility in Yola

Background: Malaria and anemia among children especially those that are &lt;5 years of age are still diseases of public health concern. This study was designed to determine the effect of malaria on the anaemia status of under five children attending a health facility in Yola, Adamawa state, Nigeria.&#x0D; Methods: Blood samples were collected from out-patient under five children reporting at the paediatric unit of the hospital. The blood samples were screened for the presence of malaria parasites by microscopy while their anaemia status was determined by checking their packed cell volume (PCV). The prevalence rate of malaria, anemia, and malaria-anemia was calculated. The level of significance was determined using chi-square (χ2) statistics.&#x0D; Results: Of the 200 samples, 124 (62%) were positive for malaria, with the prevalence being higher in males. 103 of the patients had PCV values below 33% giving an overall anaemia prevalence of 51.5% of anemia cases. The results obtained show that the three forms of anaemia – mild, moderate and severe were seen in the sampled population with moderate anaemia being the most prevalent (29.0%); the results also show that malaria parasite density has an effect on the PCV levels of the children at p= .0001, and patients with severe anaemia had the highest parasite loads.&#x0D; Conclusion: Malaria remains a common infection among under five children in this environment and predisposes them to having anaemia. Moderate malaria is also the most common form of anaemia seen in malaria positive children.
Dynamic Gesture Recognition Model Based on Millimeter-Wave Radar With ResNet-18 and LSTM

In this article, a multi-layer convolutional neural network (ResNet-18) and Long Short-Term Memory Networks (LSTM) model is proposed for dynamic gesture recognition. The Soli dataset is based on the dynamic gesture signals collected by millimeter-wave radar. As a gesture sensor radar, Soli radar has high positional accuracy and can recognize small movements, to achieve the ultimate goal of Human-Computer Interaction (HCI). A set of velocity-range Doppler images transformed from the original signal is used as the input of the model. Especially, ResNet-18 is used to extract deeper spatial features and solve the problem of gradient extinction or gradient explosion. LSTM is used to extract temporal features and solve the problem of long-time dependence. The model was implemented on the Soli dataset for the dynamic gesture recognition experiment, where the accuracy of gesture recognition obtained 92.55%. Finally, compare the model with the traditional methods. The result shows that the model proposed in this paper achieves higher accuracy in dynamic gesture recognition. The validity of the model is verified by experiments.
Study on the effectiveness of online and offline hybrid teaching

In the post-epidemic era, in order to meet the learning needs of students at different stages, it is crucial to build a student-centered, teacher-led online and offline hybrid teaching system. Therefore, on the basis of understanding the content of online and offline hybrid teaching, this paper proposes effective online and offline hybrid teaching scheme according to the requirements of practical education, so as to improve the teaching efficiency of practical classroom.
Leveraging Locality in Abstractive Text Summarization

Neural attention models have achieved significant improvements on many natural language processing tasks. However, the quadratic memory complexity of the self-attention module with respect to the input length hinders their applications in long text summarization. Instead of designing more efficient attention modules, we approach this problem by investigating if models with a restricted context can have competitive performance compared with the memory-efficient attention models that maintain a global context by treating the input as a single sequence. Our model is applied to individual pages which contain parts of inputs grouped by the principle of locality during both encoding and decoding. We empirically investigated three kinds of locality in text summarization at different levels of granularity, ranging from sentences to documents. Our experimental results show that our model has a better performance compared with strong baselines with efficient attention modules, and our analysis provides further insights into our locality-aware modeling strategy.
ADDIE Method for implementation Virtual Reality in Online Course Using Model Project Based Learning

This research aims at implementation virtual reality (VR) in online learning for the Chinese language of learning. Model learning in this research is project-based learning to support media in VR. The development of VR in online learning used the ADDIE method because this research developed a media and for using it, students have model learning as project-based learning. The target of this research is the effectiveness of using VR in online learning to improve the cognition of students. In this research, VR tested by 20 students to get data of student’s cognitive. The result of this research, VR had effective around 78% compared to without using VR.
ASTER GLOBAL DEM (GDEM) VERSION 3

Abstract. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a 14-channel imaging instrument operating on NASA’s Terra satellite since 1999. ASTER’s visible–near infrared (VNIR) instrument, with three bands and a 15 m Instantaneous field of view (IFOV), is accompanied by an additional VNIR band using a second, backward-looking telescope. Collecting along-track stereo pairs, the geometry produces a base-to-height ratio of 0.6. In 2009, the ASTER Science Team released Version 1 of the global DEM (GDEM) based on stereo correlation of 1.2 million ASTER scenes. The DEM has 1 arc-second latitude and longitude postings (∼30 m) and employed cloud masking to avoid cloud-contaminated pixels. The GDEM covers all of the Earth’s land surface from 83 degrees north to 83 degrees south latitude. Version 2 was released in 2011, with notable improvements in coverage and accuracy. In 2019, the final, Version 3, was released; again improving on coverage and removing almost all artifacts. Th GDEM is a unique, global high spatial resolution digital elevation dataset available to all users at no cost. In addition, a second unique dataset was produced and released. The raster-based ASTER Global Water Body Dataset (ASTWBD) identifies the presence of permanent water bodies, and marks them as ocean, lake, or river. An accompanying DEM file indicates the elevation for each water pixel. To date, over 110+ million 1×1 degree GDEM tiles have been distributed.
Effects and Perceptions of Mobile Learning in Higher Education

Objectives: This study aimed to investigate the effect of mobile learning on students’ achievement in the grammar of the Arabic language, and their attitude towards the use of mobile learning. Methods/Analysis: The study was conducted using a quasi-experimental design, case study. The sample comprised 170 students, divided into two groups: an experimental group (n = 87) and a control group (n = 83). An achievement test and a questionnaire were used to collect data, which was analyzed using SPSS. Findings: The findings revealed statistically significant differences between the two groups, with the experimental group showing better scholastic achievement on the test. The results also show that the students of the experimental group were more positive towards mobile learning. Novel contribution: The study recommends further studies on the use of mobile learning in universities and schools. It is necessary to emphasize and focus on further studies on the use of mobile learning within universities and schools to confirm and provide evidence of the significance of mobile learning in terms of facilitating, promoting, and enhancing the educational process. Doi: 10.28991/ESJ-2022-SIED-06 Full Text: PDF
Reconciling Cognitive Modeling with Knowledge Forgetting: A Continuous Time-aware Neural Network Approach

As an emerging technology of computer-aided education, cognitive modeling aims at discovering the knowledge proficiency or learning ability of students, which can enable a wide range of intelligent educational applications. While considerable efforts have been made in this direction, a long-standing research challenge is how to naturally integrate the forgetting mechanism into the learning process of knowledge concepts. To this end, in this paper, we propose a novel Continuous Time based Neural Cognitive Modeling(CT-NCM) approach to integrate the dynamism and continuity of knowledge forgetting into students' learning process modeling in a realistic manner. To be specific, we first adapt the neural Hawkes process with a specially-designed learning event encoding method to model the relationship between knowledge learning and forgetting with continuous time. Then, we propose a learning function with extendable settings to jointly model the change of different knowledge states and their interactions with the exercises at each moment. In this way, CT-NCM can simultaneously predict the future knowledge state and exercise performance of students. Finally, we conduct extensive experiments on five real-world datasets with various benchmark methods. The experimental results clearly validate the effectiveness of CT-NCM and show its interpretability in terms of knowledge learning visualization.
Digital Media-based Quantum Learning: Improving Students’ German Writing, Critical Thinking and Learning Motivation

The quantum learning model is a combination of various interactions that occur during the learning process. This learning model can be combined with the Assoziogram technique or also with audio-visual media as learning media. This research aims at investigating the implementation of a digital media-based quantum learning model with the Assoziogramm technique/concept map to improve students’ German writing, critical thinking, and learning motivation. This research uses one group pre-test and post-test experimental research design with a total sample of 20 students. The research data were collected using test and non-test (questionnaire) instruments. The research data were analysed using descriptive statistics and paired t-tests. The study results show an increase in the ability to write German, critical thinking, and learning motivation after the implementation of the digital media-based quantum learning model with the Assoziogramm technique. This study contributed to showing how variations in learning are crucial in providing a more engaging learning climate than the conventional learning done by lecturers.
Private Datapods: Web 3.0 does not Automatically Mean Blockchain Decentralization

ICT and blockchain professionals must be careful not to equate Web 3.0 automatically with blockchain, or vice versa. That is an incorrect equivalence. Blockchain is certainly one possible architecture on which to base a Web 3.0 implementation. However, the essence of Sir Tim Berners-Lee’s decentralized Web 3.0 concept is nothing automatically or implicitly to do with blockchain, but, rather, is focused on the important idea of Private Datapods. The author’s own Zykme / ZykPass invention is an available working hybrid App (beta test version) compliant with this Web 3.0 Private Datapod fundamental design principle, ushering-in a new era of decentralized user-controlled and user-owned social media based on secure P2P personal data communications, implemented using edge computing. This article describes the architecture and future development of this invention. This includes potential addition of a future blockchain-based ‘social media’ ZykToken, awarded to, and owned and tradeable by, each Zykme / ZykPass user, implementing the author’s novel CapChere (Customer Corporation) IP and business ownership structure. This contends to be a transformative, socially useful re-purposing of traditional industrial capitalism in which all are winners.
Urban crime and livelihood implications among the motorcycle taxi riders in Dar Es Salaam City- Tanzania

The motorcycle taxi business is increasingly becoming one of the key livelihood strategies in the urban settings of Sub-Saharan Africa. However, crime against operators is threatening the subsector and jeopardizes the livelihood of motorcycle riders. This study examines urban crime and its implications on the livelihoods of motorcycle taxi riders in Dar es Salaam Tanzania in a bid to recommend strategies for alleviating the looming crime. Based on the qualitative design, data were collected using in-depth interviews, focus group discussion and indirect observations with a sample of 100 motorcycle taxi-riders. Thematic data analysis was employed based on the five themes [aka assets] pre-determined from the sustainable livelihood framework namely natural, physical, human, social and financial assets. Findings show that crime has negatively impacted the motorcycle taxi riders’ livelihoods through erosion of the livelihood assets. Accordingly, in order to alleviate the crime, relevant authorities should devise a well-defined mechanism of sharing crime information, use of technology such as the GPRS system and security cameras around the city, ensure the availability of the police and prompt response to crime incidence through night security patrols and sharing reliable contacts, provision of security and crime education and awareness to the riders. Others are ensuring an effective, communication system between and among the motorcycle riders e.g. through phones, WhatsApp groups, and other social media, and use of ID cards by the riders for easy identification and traceability whenever they fall victims of crime.
Near-Optimal Search Time in $δ$-Optimal Space, and Vice Versa

Two recent lower bounds on the compressibility of repetitive sequences, $\delta \le \gamma$, have received much attention. It has been shown that a length-$n$ string $S$ over an alphabet of size $\sigma$ can be represented within the optimal $O(\delta\log\tfrac{n\log \sigma}{\delta \log n})$ space, and further, that within that space one can find all the $occ$ occurrences in $S$ of any length-$m$ pattern in time $O(m\log n + occ \log^\epsilon n)$ for any constant $\epsilon>0$. Instead, the near-optimal search time $O(m+({occ+1})\log^\epsilon n)$ has been achieved only within $O(\gamma\log\frac{n}{\gamma})$ space. Both results are based on considerably different locally consistent parsing techniques. The question of whether the better search time could be supported within the $\delta$-optimal space remained open. In this paper, we prove that both techniques can indeed be combined to obtain the best of both worlds: $O(m+({occ+1})\log^\epsilon n)$ search time within $O(\delta\log\tfrac{n\log \sigma}{\delta \log n})$ space. Moreover, the number of occurrences can be computed in $O(m+\log^{2+\epsilon}n)$ time within $O(\delta\log\tfrac{n\log \sigma}{\delta \log n})$ space. We also show that an extra sublogarithmic factor on top of this space enables optimal $O(m+occ)$ search time, whereas an extra logarithmic factor enables optimal $O(m)$ counting time.
Sentiment analysis on vaccine COVID-19 using word count and Gaussian Naïve Bayes

Since the &lt;span&gt;Coronavirus disease 2019 (COVID-19) pandemic hit the world, it had a significant negative impact on individuals, governments, and the global economy. One way to reduce the negative impact of COVID-19 is to vaccinate. Briefly, vaccination aims to enable the formed immune system to remember the characteristics of the targeted viral pathogen and be able to initiate an immune response that is rapid and strong enough to defeat future live viral pathogens. However, there are still many people in the world who are anti-vaccine. This certainly greatly hampers the process of accelerating the formation of the body's immune system widely in the community. Anti-vaccine people can be found on various social media platforms. Twitter was chosen as the data source because twitter is a common source of text for sentiment analysis. This study aims to analyze public sentiment on the COVID-19 vaccine through twitter in the form of tweets and retweets. This study uses the Gaussian Naïve Bayes method to see the results of the classification of sentiment analysis. The results obtained based on experiments prove that the Gaussian Naïve Bayes method can produce an average accuracy of 97.48% for each vaccine dataset used.&lt;/span&gt;
Securing Color Video When Transmitting through Communication Channels Using DT-CWT-Based Watermarking

In this paper, a color video watermarking system based on SVD in the complex wavelet domain is proposed. The process of inserting copyright information in video bit streams is known as video watermarking. It has been advocated in recent years as a solution to the problem of unlawful digital video alteration and dissemination. An effective, robust, and invisible video watermarking algorithm is proposed in this paper. The two-level dual tree complex wavelet transform (DT-CWT) and singular value decomposition are used to create this approach, which was built on a cascade of two powerful mathematical transforms. This hybrid technique demonstrates a high level of security as well as various levels of attack robustness. The proposed algorithm was used to the test for imperceptibility and robustness, and this resulted in excellent grades. We compared our suggested method to a DWT-SVD-based technique and found it to be far more reliable and effective.
AUTOMATING THE UNDERGROUND CADASTRAL SURVEY: A PROCESSING CHAIN PROPOSAL

Abstract. In order to ensure the proper functioning and evolution of underground networks (water, gas, etc.) over time, municipal services need to maintain accurate and up-to-date maps. Such maps are generally updated using traditional data acquisition methods (total station or GNSS), which are time-consuming, expensive, and require several teams of surveyors in the field. In this context, an important topic of research is the automation of the updating of the underground cadastre in order to save time, money, and human effort. In this paper, we present a new method that we developed ranging from the choice of the acquisition system, the tests carried out in the field to the detection of objects and the automatic segmentation in a 3D point cloud. We have chosen to use a convolutional neural network on images for the detection of objects that are part of the underground cadastre. As the next step, objects are projected to obtain a 3D point cloud segmented based on the object type. The vectorization step is still under development so that objects can be converted to vector format and therefore be used for updating the cadastre. The results based on excavation sites with well-represented objects in our training database are excellent, approaching 96% accuracy. However, the detection of rare objects is much less good and thus remains a topic for future research. Overall, the complete processing chain allowing to automate as much as possible the update of an underground cadastre is presented in this paper.
CLOUD FORENSICS FRAMEWORK FOR LAW ENFORCEMENT AGENCIES

Internet-based cloud technology is a network of remote data centers often placed beyond the country's legal frontiers worldwide. Contrary to the benefits of cloud computing, it is also a target of cybercriminals who may affect its resources on a larger scale by a single exploit. For protecting the cloud resources and increasing the confidence of cloud users, it is necessary to make one accountable for disrupting its services based on relevant evidence that proves someone's guilt in a court of law. In the literature, various frameworks have been presented for evidence collection against the attack on the cloud service for Cloud Service providers (CSP), but there is no framework for LEAs. Unfortunately, the evidence of a security breach in the cloud resides under the control of CSP, which is the sole custodian of cloud resources. However, the CSP does not fully cooperate with the investigators due to various legal, technical, and operational reasons. Hence the entire prosecution is dependent on the provision of evidence by the CSP, which is a great challenge for law enforcement around the world. The study's objective is to design a framework that mitigates the dependency of CSP by collecting the evidence of a security incident outside the cloud by colluding the Internet Service Providers (ISPs) and law Enforcement for a particular cloud service. The framework integrates the components that can detect the attack on a cloud service earlier at ISP and store the logs of the incident in a forensic server which can be used for forensics purposes as and when required.
The Development of Learning Media of 2-Stroke Engine Manufactured by 3D Print for Distance Learning

The Covid19 pandemic that has hit the world requires educational media innovations that are portable, lightweight, and easy to operate for online learning purposes. This study aims to develop learning media for students of the Light Vehicle Engineering Vocational School on the topic of a 2 stroke engine using a engine model made using 3D printing technology with Polylactic Acid as the base material. This is carried out to develop media to support teachers of related subjects in the implementation of online learning. This study uses the Design and Development (DD Research) method which consists of identifying the problem, describing the objectives, design and development, testing, evaluating and finally communicating the results. Data analysis was carried out quantitatively and qualitatively to explain the obtained results. The checklist evaluation was performed to assess the produced learning media. The results indicated that the utilization of 3D print to create lightweight, portable and representable learning media has been successfully achieved. The constructed learning media is aimed to be utilized to explain the competency of two-strokes engine in a distance learning.