Reviewerly/exHarmony huggingface dataset:
Use of Body Sensors for Implementation of Human Activity Recognition

The biomedical field has developed to a greater extent since digital technology has emerged. Digital communication can connect to everything. In this project, the activities of the patient can be detected by using body sensors, and the data collected by the sensor will be processed with the help of Node-MCU. The processed data will be sent to the cloud server, and the data can be viewed through an android application by the user. The main objective of this paper is to calculate patients' activities based check their body temperature, pulse rates, and if patients are very critical, then what type of environment can we provide for them? For that, we can check the room temperature and the room humidity also. The activities of patients, like sitting, resting, standing, and so on, can be viewed on the Android app.
Product Sumarrization with Pointer Generator Network

In this paper, product information extraction and product information abstract generation are studied in E-commerce field. This study implemented a method for automatically generating commodity summaries by using the Pointer Generator Network model. In this paper, Pointer Generator Network model and LSTM model are used on the same dataset, and ROUGE metric is used to evaluate the advantages of Pointer Generator Network in the field of summary generation.
Update from the Standards Vice President: Cloud

<fig orientation="portrait" position="float" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <graphic orientation="portrait" position="float" xlink:href="bruce-2935882.tif"/> </fig> During a recent meeting discussing cloud workflows, someone commented that disruption was the new normal. We will never be in a state of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ruption</i> again. Apart from getting a laugh at the invention of a new word that really should exist but doesn’t, the point is well made. Cloud is more a way of thinking than any single technology. It’s a different way of paying for services that require rare resources to create (i.e., special humans who write code) but have applications that are probably global in scale.
Effects of Pairing Methods Based on Digital Textbook Logs and Learner Artifacts in Conceptual Modeling Exercises

This paper describes the effects of a pairing method based on digital textbook logs and learners’ artifacts in conceptual modeling exercises. We developed a digital textbook system called Smart E-textbook Application (SEA) and a conceptual modeling tool called KIfU 3.0 to collect conceptual modeling activity logs in exercises. This study proposes a method that makes pairs of learners for group work by considering the characteristics of the artifacts created by them and digital textbook logs. An initial evaluation was conducted to evaluate the learning effects of our proposed pairing method compared to the random pairing method. From its results, this study found the discussion patterns and digital textbook browsing status in the maximum value of improvement and deterioration points of learners’ artifacts in the conceptual modeling.
Improved Reversible Data Hiding Scheme Based on Mean Preservation and Difference Expansion

In this paper, the erroneous scheme proposed by S. Agrawal and M. Kumar, [“Mean value based reversible data hiding in encrypted images,” Optik, Vol. 130, pp. 922–934, 2017.] has been identified, as well as new and improved reversible data hiding (RDH) scheme for digital images has been subsequently proposed. In S. Agrawal and M. Kumar, [“Mean value based reversible data hiding in encrypted images,” Optik, Vol. 130, pp. 922–934, 2017.], they have proposed an RDH scheme based on a floating mean value of a particular row preserved in the embedded image itself. As per the data type standards followed in such technologies, it is impossible to preserve a floating mean value in the embedded image. In this article, by using certain mod operations and offset addition to ensure an integer average value for a specific row, a realizable RDH scheme is proposed. Rows may further be divided into sub-rows of a specific size on the basis of payload required and in such a way that it is a perfect divisor of row size. Corresponding integer mean value is preserved by replacing the first pixel location of the corresponding sub-row. Subsequently, difference expansion scheme is applied further to store the mod value as well as the watermark. Hence, the proposed scheme improves on embedding capacity significantly. Experimental results and performance comparison prove that the proposed method is better than certain existing methods.
EFFECTS OF COMPUTER-BASED INTERACTIVE MATERIALS IN LEARNING ENGLISH

The use of technology to strengthen English language teaching and learning is still less observable even now that it is almost everywhere.As a result, teachers often resort to the traditional methods and approaches in the lesson development, assessment, and enrichment activities.Guided by Situated Conditioning learning Theory, this study aims to find out the effect of pupils' exposure to computer-based interactive materials utilized in English classes and find out the challenges experienced by the pupils in learning English.This study is descriptive phenomenological, employing Colaizzi's method of data analysis.It utilized focus group discussions, documentary analysis, and informal interviews to gather data.Participants were randomly selected coming from different grade levels.This study revealed that using technology-based materials in teaching English affects the pupils positively.Moreover, pupils experienced challenges using computer-based interactive materials regarding access and technology support.The results of this study will provide valuable inputs in the effective delivery of instruction and will, in turn, improve pupils' interest and performance in learning English.
Adolescent test-taker characteristics

This qualitative validation study investigates the characteristics and needs of adolescent test-takers sitting the reading paper of the Singapore-Cambridge General Certificate of Education Ordinary-Level Chinese Language Examination (GCE 1160). Specifically, evidence gathered through semi-structured interview and document analysis was organised around the following three issues: adolescence and adolescent literacy; reading motivation, interests and authenticity; and new technologies and reading literacy. Findings from the analysis suggest that Singaporean adolescents are generally becoming less motivated to read extensively in the Chinese language, seemingly the direct result of digital technologies and heavy homework loads. It also appeared that motivation to read is connected to the relevance and appeal of texts available as well as the perceived value of reading in securing better education and job opportunities. Another pattern illustrated by the study was that the internet is the defining technology for literacy and learning for Singaporean adolescents. Whilst evidence suggested that the GCE 1160 reading examination is designed with adequate knowledge of adolescence and adolescent literacy, several threats to validity were identified, including the relatively low appeal of the passages to test-takers, and their relevance, authenticity and sensitivity to new technologies and reading literacy. Drawing on a socio-cognitive validation framework, the study analyses these threats to validity and outlines practical directions whereby policy-makers and test designers might develop the reading examination.
Deterministic policy optimization with clipped value expansion and long-horizon planning

Model-based reinforcement learning (MBRL) approaches have demonstrated great potential in handling complex tasks with high sample efficiency. However, MBRL struggles with asymptotic performance compared to model-free reinforcement learning (MFRL). In this paper, we present a long-horizon policy optimization method, namely model-based deterministic policy gradient (MBDPG), for efficient exploitation of the learned dynamics model through multi-step gradient information. First, we approximate the dynamics of the environment with a parameterized linear combination of an ensemble of Gaussian distributions. Moreover, the dynamics model is equipped with a memory module and trained on a multi-step prediction task to reduce cumulative error. Second, successful experience is used to guide the policy at the early stage of training to avoid ineffective exploration. Third, a clipped double value network is expanded in the learned dynamics to reduce overestimation bias. Finally, we present a deterministic policy gradient approach in the model that backpropagates multi-step gradient along the imagined trajectories. Our method shows higher sampling efficiency than the state-of-the-art MFRL methods while maintaining better convergence performance and time efficiency compared to the SOAT MBRL.
Prototype of an Intelligent Multimedia System for Physiotherapeutic Rehabilitation in a Geriatric Center

Physical activity in older adults is of great importance according to the statements of studies that have been conducted at the School of Computing Science, University of Glasgow, UK demonstrating the existence of muscle ailments and chronic diseases in the short term. Based on this, through the Ministry of Economic and Social Inclusion (MIES) in the geriatric center Sagrada Familia, La Maná, Cotopaxi, rehabilitation services are provided by specialized personnel allowing adequate monitoring and control of the patient. This research is oriented to the creation of a physiotherapeutic prototype through three stages of development: 1) Use of wireless technologies and mobile applications, 2) Embedded Electronic Circuit and 3) Structural Design. In addition, its verification and validation is based on the results obtained by the three disciplines involved: 1) Software, 62% user experience effectiveness (usability–functionality), 2) Electronics, experimental error percentage (ε) less than 1% (high reliability) and 3) Electromechanics, 30% overload (viability of the structural prototype).
Research on Robust Navigation Algorithm for Soft Landing of Small Celestial Body

Based on the application background of autonomous navigation for soft landing of small celestial bodies, this paper proposes a robust navigation algorithm for the difficult problems of soft landing of small celestial bodies with strong uncertainty and high requirements for the accuracy and real-time performance of autonomous navigation algorithms. Small celestial bodies are far away from the earth, and the observation information is insufficient. It is difficult to establish an accurate gravitational field model. The robust navigation algorithm integrates optical navigation information and inertial navigation information to improve the accuracy and robustness of the navigation system. Experimental results show that the algorithm can reduce the impact of model uncertainty, effectively improve the accuracy of navigation and positioning, and provide a feasible method for the soft landing of the probe on the surface of small celestial bodies.
A Weighted Online Recurrent Extreme Learning Machine-Based Method for Disease Names Recognition

Automatic disease recognition from biomedical texts is of crucial importance to various areas of healthcare research such as safety surveillance and adverse drug reaction. Although machine learning-based methods have been extensively applied for dealing with this issue, some disease names might not be recognized correctly due to their complex structures. To this end, in this paper, we investigate our proposed method based on the weighted online recurrent extreme learning machine (WOR-ELM) model to effectively recognize disease mentions. It revealed better the overall performance on Adverse drug effect (ADE) mentions extraction. Through a series of experiments, we prove the effectiveness of the proposed method on the BioCreative V Chemical Disease Relation (BC5CDR) corpus. Experimental evaluations show that the proposed method accurately outperforms the current state-of-the-art methods by leading a F1-score of 89%.
DEVELOPING AN INTEGRATED SYSTEM FOR THE CLINICAL SEGMENTATION OF LINER SCHEME CT IMAGES

In clinical picture handling, the division of the liver in figured tomography pictures is of huge importance. To acquire liver segmentation, there is an examination of strategies for dividing the liver and methods utilizing processed tomography pictures. Isolating plans into two classes are self-loader and fully automated programs. The two classes have a few techniques, estimation, related questions; a few downsides will be depicted and explained. Following the similar review for liver division plans, different assessments and scoring are given; we will cautiously highlight the benefits and inconveniences of procedures. A few deficiencies and hardships of the proposed strategies are still focused around.
ISmart: Protecting Smart Contract Against Integer Bugs

Blockchain technology is known as a decentralized, distributed ledger that records digital asset.It has been applied in numbers of aspects of society, including finance, judiciary and commerce.Ethereum is referred to as the next generation decentralized application platform.It is one of the most popular blockchain platforms that supports smart contracts.Smart contract is a set of codes that sored on blockchain and can be called and created as turing-complete programs running on the blockchain.Developers use smart contracts to build decentralized applications (Dapp) which has widely used cryptocurrency related project.As smart contracts become more popular and more valuable, they are faced with more risk of being hacked.As a result that smart contracts cannot be modified once deployed on the blockchain, it is a great challenge to fix and update deployed vulnerable contract which can lead to a huge loss of cryptocurrency and financial disorder.In this paper, we focus on Integer Bugs in Ethereum Smart Contracts and present ISmart, which protects deployed smart contracts against attacks caused by Integer Bugs.We implemented ISmart based on go-ethereum, a Ethereum client written in Go, by adding a simplified taint analysis component.In our preliminary, ISmart can prevent attacks accurately with little runtime overhead.
LYRASIS Research and an Inclusive Approach to Open Access in the United States

In 2020, LYRASIS Research conducted a member survey of predominantly United States (U.S.) higher education libraries to understand the spectrum of attitudes and actions related to Open Access (OA). The results indicated that the U.S. approach to OA is decentralised, lacking the focused trends that are apparent in other areas of the world. The diversity among types of colleges and universities in the U.S. is revealed through discussions about support or lack thereof for APCs, crowdfunding models, preprint repositories, the Subscribe to Open approach, and more. The array of OA approaches that garner support in the U.S. may appear confusing as we strive for scale in our efforts. LYRASIS has used its research findings, in combination with our deep understanding of U.S. higher education libraries, to develop a collaborative approach towards OA that provides multiple incentives and opportunities for libraries serving all types of institutions to engage. This article, expanding on the LIBER 2021 Conference Presentation of the same name, will outline the results of the survey, the conclusions LYRASIS has drawn, and our work to develop an inclusive approach to a variety of OA initiatives. Our understanding of the landscape of U.S. higher education has led us to develop or support several significant recent OA initiatives, including a fund for OA ebooks focused on United Nations Sustainable Development Goals topics and the establishment of the LYRASIS Open Access Community Investment Program (OACIP).
Research on the path of fitness strategic planning based on mobile internet

The purpose of fitness is to exercise and make people healthier. This paper proposes a research process based on the mobile internet environment. The proposed algorithm mainly combines mathematical statistics with practical operations, taking cities as an example to understand the current strategy for national fitness. The experimental results show that this method has important research value for the development of modern fitness education.
Building a Digital Competences Scale: An Evaluation Experience in Elementary Education

The objective of this study was to design and validate a scale for evaluating digital skills for primary school students. The scale has five dimensions organized into subscales that add up to 15 items. A sample of 158 Peruvian primary school students participated (70 males (44.3%) and 88 females (55.7%)). Validity was evaluated with the Exploratory Factor Analysis (EFA) and the Kaiser-Meyer-Olkin test (KMO) achieving a value of .80 and the Bartlett Sphericity Test was significant (χ2 = 843.610, gl = 105, p < .001). In the reliability analysis, the information and information literacy subscale obtained a Cronbach’s Alpha coefficient of .79, communication and collaboration of .68, digital content creation of .72, security of .62 and resolution of technological problems of .75. Allowing concluding that the scale is valid and reliable.
An Augmented Embedding Spaces approach for Text-based Image Captioning

Scene text-based Image Captioning is the problem that generates caption for an input image using both contexts of image and scene text information. To improve the performance of this problem, in this paper, we propose two modules, Objects-augmented and Grid features augmentation, to enhance spatial location information and global information understanding in images based on M4C-Captioner architecture for text-based Image Captioning problems. Experimental results on the TextCaps dataset show that our method achieves superior performance compared with the M4C-Captioner baseline approach. Our highest result on the Standard Test set is 20.02% and 85.64% in the two metrics BLEU4 and CIDEr, respectively.
Clustering and Graph Convolution of Sub-regions for Unsupervised Image Segmentation

This paper focuses on the unsupervised segmentation of images, which is an essential topic in the field of computer vision. In the absence of prior knowledge, it is challenging to generate semantic segmentation regions based on image content automatically. In this paper, we consider unsupervised image segmentation from the perspective of sub-region clustering and graph convolution. We over-segment the source image into disjoint sub-regions and generate multiscale representative maps for each sub-region. To explore the potential contextual correlation between different sub-regions, we build a graph model that establishes the dependency relationship and use a graph convolution network to transfer long-range contextual information. The segmentation results are obtained by combining sub-regions clustering and graph convolution training. We conduct extensive experiments on three image datasets, and the results show that the proposed method can provide consistent and meaningful segmentation results.
Leveraging the ‘Dual Layer’ in Enhancing the Data Security Safeguards in Cloud Environment

One of the significant benefits of distributed computing is dividing information between different organizations. Notwithstanding, this benefit itself has a danger to information. It is important to ensure communication to refute likely risks to the data. Encryption is a tremendously powerful instrument concerning securing information. This paper proposes another span of cryptographic double-layer encryption to make the information put away in the cloud safer and dependable. There are promptly accessible numerous different encryption methods accessible at present; however incapable of giving adequate security. This paper intends to propose a new encryption strategy named double encryption. It depends on the well-known encryption analysis, ais a symmetric-key study. We will offer an extra layer of rabbit calculation around encoded information, which will assist with giving greater protection from Brute force and one more sort of attack. Assuming that an interloper recognizes a solitary key of the cryptosystem, it isn't easy to unscramble the first message. This paper plans to recommend a viewpoint that is a twofold layer encryption technique to guarantee security in the cloud. This proposed twofold layer encryption will acquire the information while securing and participating in a cloud environment. This plan utilizes the incredible handling expertise of distributed computing and can proficiently guarantee cloud information protection and security
Dealing with multi-step verification processes for certification issuance in universities

As in any institution, universities have processes defined in an unique way, involving many verification steps. Issuance of end-of-course certificates require multiple signatures from authorities, instructors and in some cases administrative staff. After an initial work of single signature certificates using blockchain for education purposes, we generate a prototype including more than one signature over the infrastructure generated for the Smart Ecosystem for Learning and Inclusion or SELI project. This prototype records certificates in a private non-monetary blockchain network, and is provided as an open source project. Countries like Ecuador, Turkey, Uruguay, and Finland can share certificates from the SELI platform through local nodes. This article provides relevant details about the implementation of the system, always with the aim of re-use existing software to reduce implementation time.
Authenticity of System Users via Mouse Handling Method

The proposed work narrate a behavioural bio-metric approach to confirm authenticated users dynamically based on their mouse motion. A self&#x2013;generated mouse data [9] was used to extract features to categorize the user&#x2019;s mouse handling pattern which is different from other users. The model built is trained using the Gaussian Naive Bayes Classifier for quick and accurate classification of data. The proposed model performs better than previously used models in all evaluation metrics including, accuracy, false accept rate, false reject rate.
On the Experimental Process in Evaluations of Brazilian Conversational Agents in Education

Conversational agents represent applications that interact in a natural language with humans. Due to their ability to process the human language, they have been explored in diverse areas, e.g., e-commerce, health, entertainment, and education. Particularly in the education field, they have been used as mechanisms that support the teaching of a second language, that recommend educational resources, and that resolve students’ doubts, as a learning partner. Brazil is particularly interested in the educational technology community studying such software systems. Despite the strong interests in their development, few studies have addressed the impact such systems exert when they are used by students. Experimental studies have enabled a better understanding of the impact of technology on education based on evidence. This article presents an overview of empirical research on conversational agents in education through a mapping of the Brazilian community, identifying the way researchers systematically evaluate such agents produced in their research work. The results show a lack of experimental methodologies and taxonomies that support the planning, development, and reporting of experimental studies. However, a set of variables commonly employed has been recognized. An initial set of guidelines has been developed towards guiding experiments in this context and supporting the systematization of studies on experimental evaluations of pedagogical conversational agents.