Reviewerly/exHarmony huggingface dataset:
Workbook as a mandatory component in independent physical education classes

According to the Federal State Educational Standard, the Department of Physical Culture plans to allocate a certain number of academic hours to independent work. In this regard, the independent work of students in connection with other types of educational activities, such as basic and elective practical classes, should be a prerequisite for obtaining a credit in the subject. The task for performing this type of educational section and monitoring the implementation is assigned to the teacher. The peculiarity of physical education lessons is its diversity. During one fixed time, students can simultaneously engage in various types of physical training. In one gym there can be a class according to the basic program and elective courses in parallel in another gym. One study group, divided into elective subgroups, masters several different sports and giving a single task for independent work is a difficult task to solve. In addition, independent practical classes cannot take place without the accompaniment of a teacher and therefore the opportunity to give practical tasks for independent work is completely excluded. Another feature of physical education lessons is the division of students into medical groups - basic, special medical (SMG) and a group of students completely exempt from practical classes. Choosing a single algorithm of tasks for independent work with such a variety of activities, various medical and sports groups is a certain difficulty. In order for the tasks and monitoring of their implementation to be the most effective and complete, it is proposed to develop "Workbooks for students in physical education".
Cross-Modal Recurrent Semantic Comprehension for Referring Image Segmentation

Referring image segmentation aims to segment the target object from the image according to the description of language expression. Due to the diversity of language expressions, word sequences in different orders often express different semantic information. The previous methods focus more on matching different words to different visual regions in the image separately, ignoring the global semantic understanding of language expression based on the sequence structure. To address this problem, we redesign a new recurrent network structure for referring image segmentation, called Cross-Modal Recurrent Semantic Comprehension Network (CRSCNet), to obtain a more comprehensive global semantic understanding through iterative cross-modal semantic reasoning. Specifically, in each iteration, we first propose a Dynamic SepConv to extract relevant visual features guided by language and further propose Language Attentional Feature Modulation to improve the feature discriminability, then propose a Cross-Modal Semantic Reasoning module to perform global semantic reasoning by capturing both linguistic and visual information, and finally updates and corrects the visual features of the predicted object based on semantic information. Moreover, we further propose a Cross-Modal ASPP to capture richer visual information referred to in the global semantics of the language expression from larger receptive fields. Extensive experiments demonstrate that our proposed network significantly outperforms previous state-of-the-art methods on multiple datasets.
Keyword-Based Processing for Assessing Short Answers in the Educational Field

When grading open-ended engineering exam responses, it is assessed to what extent its content and quality suit the requirements and accomplish the objectives of the test. This is a time consuming and subjective task. The support of a software tool that identifies the correctness of the response and provides useful feedback to both student and teacher may alleviate its complexity. In this work, a semi-automatic evaluation method based on augmented Spanish keyword recognition is presented. This assessment is based on the occurrence of a set of keyterms that the teacher expects to appear in a good response. The evaluation is based on an augmented catalogue of terms, automatically created from the teacher selected keyterms, resulting in an ad hoc thesauri. The method uses state-of-the-art techniques, but also ad hoc procedures developed from the Spanish corpus Wikicorpus, for pre-processing the texts. The results, tested using real anonymized data from engineering exam topics such as database techniques and bigdata, indicate good performance in the thesauri creation and keyword matching. Besides, the keyterms strategy allows simple individualized feedback. However, the relationship found between automatic and human grading indicates that further research is required.
Naive Bayes Classifier Algorithm for Spam Detection of Email to Improve Accuracy and in Comparison with Decision Tree Algorithm

Aim:The aim of the research is to detect spam in email using the Novel Naive Bayes Classifier (NB) and the Decision Tree algorithm (DT).Material and Methods: We'll need two groups of 40 samples each to classify spam.The Decision Tree technique (DT) includes a sample size of 20, whereas the Novel Naive Bayes Classifier (NB) includes a sample size of 20 and G-power (value = 0.8).Results: The accuracy of the Novel Naive Bayes Classifier is 98.05 %, which is higher than the Decision Tree algorithm with 91.80 %.All of us identified that the 2-tailed significant value of accuracy is 0.022 (p<0.05) in the Independent Sample T-Test analysis.Conclusion: The Novel Naive Bayes Classifier has higher accuracy than the Decision Tree algorithm.
Local Roughness Binary Pattern for Texture Classification

The texture is an essential property of an image that tells about the arrangements of pixels of different intensities in an image matrix. Researchers have used many descriptors for texture classification of the images in computer vision and image processing. Local Binary Pattern (LBP) is one of the descriptors, which is computationally efficient and straightforward for texture classification. In recent years, researchers have made many enhancements to the basic LBP method to improve the quality of extracted features for an image. This paper presents a Local Roughness Binary Pattern (LRBP) descriptor for feature extraction of an image to increase LBP discriminative capability. This new descriptor extracts features from the local region of an image called a partial feature vector and concatenates these local features to obtain a feature vector of an image. This feature vector is used as input to a classifier to classify the class of an image. We tested the proposed descriptor for the images available on CUReT and KTHTIPS2b databases with Support Vector Machine (SVM) and k-nearest Neighbors (KNN) classifiers. We have presented the experimental results obtained from the analysis process regarding classification accuracy and confusion matrix. Finally, we have presented a performance comparison between the proposed method, basic LBP descriptor, and its variants. Results show that the proposed descriptor gives much better results than other descriptors on the KTHTIPS2b Database when we use the KNN classifier.
Design Exploration for Better Security of Recognition-Based Image Authentication in Mobile Environment

Recognition-based image authentication (RbIA) has an advantage in terms of its memory retention of user credentials, which is one of the issues in knowledge-based user authentication (KUA). Therefore, RbIA is expected to serve as an alternative to conventional KUA. However, previous studies have reported that the security associated with RbIA schemes is comparable to that of four-digit personal identification number authentication. Therefore, the usage scene of RbIA schemes is considered to be limited. In this study, we propose two approaches for improving the security of an RbIA scheme without increasing the memory load of user credentials. We also conduct a user experiment to evaluate the login times of the proposed scheme for use in a mobile environment. The results reveal that the proposed scheme demonstrates potential in providing better security with a shorter login time than RbIA schemes proposed previously.
Online State Estimation Using Optical Flow for Passing an Arm Through a Shirt Sleeve

In this paper, we present a learning-based method for estimating the state of cloth during putting an arm through a sleeve of shirt. To capture the dynamic change of cloth during manipulation, we take an approach that uses optical flow extracted from image streams. We adopt a deep neural network for optical flow extraction, then extend that the network to output a cloth state. To evaluate the accuracy of state estimation, we conducted two experiments: (i) putting a cylindrical cloth through an L-shaped stand, and (ii) putting a long-sleeved shirt through an arm of a doll. The experimental results indicated that our method is useful to estimate cloth state more accurately than conventional methods.
The Effect of Think-Pair-Share Technique to Improve Students’ Reading Comprehension on Narrative Text Text

This research is motivated by the lack of reading comprehension among students, which may be due to a lack of student motivation or the technique utilized in the learning process. Therefore, the researcher attempted to incorporate Think Pair Share into the learning process. This study tries to assess whether or not the Think-Pair-Share strategy has a substantial impact on students' reading comprehension. This study was conducted at SMA Syekh Yusuf Tangerang using samples from grade 2. This research is pre-experimental in nature. This study sampled two classes: XI IPS1 as the experimental class and XI IPS2 as the control class. Both groups were given a pre-test at the start of the study, various procedures in the middle, and a post-test at the conclusion. The acquired data were analyzed using version 24 of IBM SPSS statistics. Using the T-test, researchers assessed the test findings. The findings of the data analysis indicate that t count is more than t table (3,775&gt;1,679), so Ha is approved. This indicates that Think Pair Share has a good effect on pupils' reading comprehension.
PASAR — Planning as Satisfiability with Abstraction Refinement

One of the classical approaches to automated planning is the reduction to propositional satisfiability (SAT). Recently, it has been shown that incremental SAT solving can increase the capabilities of several modern encodings for SAT-based planning. In this paper, we present a further improvement to SAT-based planning by introducing a new algorithm named PASAR based on the principles of counterexample guided abstraction refinement (CEGAR). As an abstraction of the original problem, we use a simplified encoding where interference between actions is generally allowed. Abstract plans are converted into actual plans where possible or otherwise used as a counterexample to refine the abstraction. Using benchmark domains from recent International Planning Competitions, we compare our approach to different state-of-the-art planners and find that, in particular, combining PASAR with forward state-space search techniques leads to promising results.
In Focus - hybrid deep learning approaches to the HDC2021 challenge

In this work, we present our contribution to the Helsinki Deblur Challenge 2021.The goal of the challenge was to recover images of sequences of letters from progressively out-of-focus photographs.While the blur model was unknown, a dataset of sharp and blurry images was provided.We propose to tackle this problem in a two-step process: (i) the blur models are first extracted and estimated from the provided dataset, and (ii) then incorporated into the reconstruction process.Here, we present three different ways of integrating the estimated model into learning-based methods: (i) an educated deep image prior employing the estimated model in the loss function, (ii) a learned iterative approach that directly employs the estimated model in the architecture and (iii) a fully learned approach where we used the estimated model to simulate additional training data.These three models are improved versions of our original contributions to the challenge.We compare and benchmark them on the released test set of the HDC2021.
Validation of Photovoltaic Spectral Effects Derived From Satellite-Based Solar Irradiance Products

The Satellite Application Facility on Climate Monitoring (CM-SAF) Spectral Resolved Irradiance (SRI) and National Renewable Energy Laboratory (NREL) National Solar Radiation Database Spectral on Demand (NSRDB-S) satellite-based spectral irradiance products are tested here against benchmark data and models at seven ground stations: one in Spain for CM-SAF SRI and six in North America for NSRDB-S. Benchmarks include WISER spectroradiometers, spectra modeled from SolarSIM-G measurements and the SMARTS radiative code with two alternate input sources: AErosol RObotic NETwork (AERONET) and the ModernEra Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. The satellite products are tested in terms of their ability to estimate photovoltaic (PV) spectral effects for six PV module technologies. The spectra are also compared directly under clear-sky conditions. Both CM-SAF SRI and NSRDB-S outperformed the simple benchmark of neglecting spectral effects in terms of predicting instantaneous spectral mismatch factors, but only CM-SAF SRI did better at predicting the long-term spectral derate factors. The clear-sky results revealed systematic differences between NSRDB-S and benchmark spectra, likely due to the NSRDB-S treatment of aerosols. Meanwhile, the mean SMARTS spectra with AERONET and MERRA-2 inputs were in good agreement, showing promise for the use of MERRA-2 as input to clear-sky models.
Event Knowledge Graph Extracted from Intelligence Text

For the problem of knowledge extraction and application of battlefield intelligence text, this paper proposes to use Hierarchical Event Knowledge Graph to build a model of battlefield events knowledge, and presents a framework consists of Specific Temporal Graph and Abstract logical Graph. Based on new methods of Event Recognition and Extraction, we obtained information of events and relationships from battle intelligence text using Wargaming data as the original corpus. The mapping between facts and concepts is also achieved by the method of Trigger Recognition. Then we propose a method of assignment of edge weights for the Event logical Graph, and construct the hierarchical structure. Finally, the visualization of knowledge was achieved using Visjs. It is shown that the method can effectively extract event knowledge from specific texts, and support downstream knowledge reasoning tasks for Assisted Decision-making.
Improving Gaming Experience with Dynamic Service Placement in Mobile Edge Computing

Mobile cloud gaming (MCG) can provide users with high-quality gaming services anytime, anywhere, but suffers from long network latency and huge wide-area traffic. In order to solve these problems, mobile edge computing (MEC) is envisioned as a promising approach to enable relevant computing at the edge. Since the quality of experience (QoE) of the game requires high frame rates and low network latency, the placement of service entities can affect the performance of MEC-enabled MCG. In addition, users have a high degree of mobility while enjoying MCG, so service migration is proposed to reduce QoE impairment, and service migration means an increase in system cost. To address these challenges, we investigate the service placement of MEC-enabled MCG. Considering the dynamics of the system, we propose to minimize the QoE impairment according to the constraint cost of migration. We design the ECP algorithm to solve the problem.
Accuracy Analysis of Heart Disease Prediction using Logistic Regression in Comparison with the Linear Regression Algorithm

Aim:The main objective of this research article is to employ the detection of heart disease by using the Logistic Regression (LR) classifier in comparison with the Linear regression (LR) model.Materials & Methods: The dataset used in this paper was collected from the UCI machine learning repository database.The sample size for the detection of heart disease was sample 60 (Group 1=30 and Group 2 =30) and calculation was performed utilizing G-power 0.8 with alpha and beta qualities of 0.05, 0.2 with a confidence interval of 95%.The detection of heart disease is performed by the Logistic Regression (LR) classifier with a number of samples (N=30) and Linear regression (LR) model with a number of samples (N=30).Results: The Logistic Regression (LR) classifier has a 88.68 percent higher accuracy rate when compared to the accuracy rate of the Linear regression (LR) model is 78.56 percent.The study has a significance value of p=0.025.Conclusion: Logistic Regression (LR) classifier provides better outcomes in accuracy rate when compared to Linear regression (LR) model for detection of heart disease.
The Annotation of Liber Abbaci, a Domain-Specific Latin Resource

This paper 1 presents the early stages of the development of a new treebank containing all of Dante Alighieri's Latin works.In particular, it describes the conversion of the original TEI-XML files to CoNLL-U, the creation of a gold standard, the process of training four annotators and the evaluation of the syntactic annotation in terms of inter-annotator agreement and LA, UAS and LAS.The aim is to release a new resource, in view of the celebrations for the 700th anniversary of Dante's death, which can support the development of the Vocabolario Dantesco.
Balancing Competing Interests in the Reidentification of AI-Generated Data

AI systems generate valuable analytical information from (apparently) non-personal data with vast economic consequences. This information generated from non-personal data provides a competitive edge which serves as a key rationale for its appropriation to the exclusion of others. The proliferation of AI has made it possible for non-personal data (including anonymised data) to result in the reidentification of natural persons. There have been propositions from various quarters for the protection of non-personal data by Intellectual Property Rights (IPR). Should AI-generated data be protected by IPR, this can potentially result in data protection concerns in the event of data reidentification that singles out natural persons. This might particularly occur where reidentified data leads to the identification of natural persons in circumstances where the applicability of data protection law had neither been contemplated nor anticipated. This article highlights the concerns that might arise in the event of data reidentification and how this might raise interesting challenges for data protection compliance. Keywords: AI | AI-Generated Data | Reidentification | Intellectual Property Rights
Advances in Blockchain Security

Blockchain, the technology that underpins the great success of Bitcoin and various other cryptocurrencies, has incredibly emerged as a trending research topic in both academic institutes and industry associations in recent years. With great potential and benefits, the blockchain technology can stimulate a new decentralized platform for various applications such that the possibility of censorship, monopoly, and single point of failures can be eliminated. However, the blockchain is still in its early stages and not yet ready to realize that vision, since there are many security vulnerabilities that can be exploited to obstruct blockchain systems. In this chapter, we present fundamental challenges and recent advancements in the blockchain technology, especially in terms of security. In particular, we investigate the security threats of blockchain, effectively capturing the recent attacks, and review some security enhancement solutions for blockchain.
Software Defined Network Function Virtualization Framework for Securing Cloud with Data Fusion and Machine Learning Techniques

Computing in the cloud is one of the platforms that may be used to provide distributed computing resources. Supplying and managing cloud resources most effectively is referred to as resource management. A recent development in technology known as fog computing is an example of an expanded and dispersed infrastructure. This architecture maintains application processes between end devices and the network edge to provide more dependable and efficient services. These services include remote data storage, allowing customers to access their data from a distant location. Providing remote storage service is an advantageous function offered by cloud suppliers. On the other hand, the data stored in the cloud is geographically dispersed and kept in various data centers, significantly increasing the risk to users' privacy and security. One of the problems that might arise with privacy is when many data centers store the same information. Many cloud service providers check their customers' data using a Third-Party Auditor (TPA) to address concerns about client privacy and data integrity. Currently, most trusted TPAs only have one validator, making it impossible to expand the data integrity across several data centers. The various verifiers used by TPAs have been reduced in number in response to Man in the Cloud (MiTC) attacks. As a result, they cannot check and authenticate the integrity of data stored in several data centers. A unique Peer to Peer (P2P) authentication protocol with Certificate Authority (CA) and Data Storage Protocol is presented as a solution to the problem that has been outlined above to check for and go around any issues that may arise (DSP). The efficiency of the proposed protocol is demonstrated by the incorporation of TPAs and Certificate Authorities. The proposed protocol has been tested with a single user and a single storage server, as well as multiple storage servers in ownCloud with one backup server, two storage servers, three clients, and two TPAs. The NoSQL server in an organization's cloud is set up to save data to storage servers in the appropriate format. The Amanda backup server is used to back up the mirror copy of the stored data on the storage servers. Automated Validation of Internet Security Protocols with Data Fusion and Applications, or AVISPA for short, is a technology that may be used to verify data stored in the cloud. The findings make it abundantly evident that the suggested protocol is strong enough to guarantee the authenticity of data kept in several data centers.
Advances in Air Cargo Financing Using a Consortium Blockchain

Blockchain is the foundational technology of various cryptocurrencies. It has features such as nontempering, decentralization, security, anonymity, etc. Its distributed ledger technology has received broad research and industry attention. Cryptography, hash functions, peer-to-peer (P2P) consensus and smart contracts are being used in a wide variety of applications across industries to solve various problems. Supply chain finance is a model in which various financial institutions connect with stakeholders in the supply chain to optimize working capital and liquidity invested in supply chain processes and transactions. The Air Cargo industry is one of the essential parts of various global supply chains. Consisting of multiple nodes, the complexity in its structure makes it difficult for credit risk analysis for the creditors. Information asymmetry leads to a lack of trust between companies and creditors. Frauds and disruptions in the Air Cargo industry make it vulnerable from the perspective of Financial Institutions. A platform for Air Cargo Financing using Blockchain (ACFB) is proposed in this study to solve these problems; this would make a supply chain more transparent, trustworthy, and efficient for all the stakeholders and financial institutions.