Reviewerly/exHarmony huggingface dataset:
PENGARUH PEMBELAJARAN DARING BERPRAKTIKUM MENGGUNAKAN MEDIA TRACKER BERBASIS INKUIRI TERBIMBING TERHADAP PENINGKATAN KEMAMPUAN INTERPRETASI GRAFIK

This study aims to determine the effect of practicing online learning using tracker analysis media on the ability to interpret graphs. The sample of this research is the students of class X MIPA 3 SMA Negeri 1 Tanjung Bintang. The research design used is the One Group Pretest-Posttest Design. Online learning is carried out by conducting partly elastic collision-based practicum activities based on guided inquiry. The experimental data were processed using tracker analysis media. Data on students' graph interpretation abilities were obtained from the pretest and posttest scores. The analysis technique uses Paired Sample T-Test. Based on the results that practicing online learning using tracker analysis media can improve the ability to interpret graphs well with a significance value of 0.00. The results of the analysis of the average score of graph interpretation skills increased with the acquisition of an N-gain of 0.39 in the medium category.
Autonomous Software Requirement Specifications towards AI Programming

Autonomous software requirement specifications and code generation are not only an ultimate goal of AI Programming (AIP), but also a persistent challenge to theories and technologies of software engineering. A cognitive system is demanded to autonomously elicit and rigorously refine software requirements in order to generate a set of formal specifications as the front-end of AIP. This paper presents a novel methodology for the design of an Intelligent Tool for Autonomous Software Specifications (ITASS) based on latest advances in software science and intelligent mathematics. ITASS is implemented as an interactive system for capturing software requirements and generating mathematic-based specifications for code generation in the back-end of the AIP system. The ITASS methodology and experiments are demonstrated for solving real-world and complex software engineering problems enabled by the AIP theories underpinned by intelligent mathematics.
Analysis of the problems generated by the adaptation of teaching caused by Covid-19: Proposal for a new learning framework

After the pandemic crisis caused by Covid-19, we can affirm that the future will increasingly rely on remote methodologies and will most likely continue with hybrid or blended teaching, combining online and face-to-face classes. Article 9 of Spanish Royal Decree 463/2020 ordered the suspension of presential educational activity at all levels in Spanish territory, as it was recommended that distance and online teaching be used whenever possible. Due to these quarantines, education authorities have been forced to modify teaching methods and methodologies in a short period, using existing technological and human resources. Students and teachers have found themselves in a new educational scenario very quickly and traumatically, without being prepared on most occasions. This paper presents a systematic literature review of the main national and international journals in the Web of Sciences, with the aim of finding out the main problems that the academic community has faced with the rapid adaptation of presential teaching to online or blended teaching. We can conclude that this expeditious change has caused problems that can be derived from (1) technical or infraestructure, (2) psychological and/or (3) formative and normative aspects. We are talking about a new learning framework that involves changes in the knowledge to be applied -pedagogical and technological-, in the didactics used -more collaborative and active- and in the context in which it is carried out -virtualisation of the environment and use of social networks-.
USING NEURAL MACHINE TRANSLATION FOR DETECTING AND CORRECTING GRAMMATICAL ERRORS

Computer assisted language learning can help ESL/EFL learners facilitate their writings in multiple ways such as spell checking, grammar checking, and style checking.Owning to the complexity of various linguistic errors intertwining in a sentence, it is still a challenging task to detect and correct grammatical errors automatically.Different from previous studies on using pattern matching or statistical language models on this task, we design a Transformer-based neural sequence transduction model to detect and correct grammatical errors.Neural language models are often data-hungry and their performance is also data-dependent.Given the limited size of standard learner corpora and its enormous annotating cost, we employ another Transformer-based encoder-decoder structure to back-translate an error-free sentence into an erroneous one, automating data augmentation for training neural models.We first design some artificial rules to produce a noisy learner dataset to train the back-translation model.The model can then generate more synthesized learner data for training the Transformer-based correction model.In addition to that we also propose an iterative training scheme that unifies the process of error generation and correction.Our state-of-the-art model can reach F0.5, the harmony vale of precision and recall, at 64.3% in the shared task of CoNLL-2014, surpassing all the participating systems.
A Study in Search Pattern Efficiency Using Under-Actuated Aircraft

Area coverage algorithms or Search Patterns have always been useful in many real life problems such as search and rescue missions, crime prevention, forest fire detection and many others. This means knowing which algorithms to apply in the different cases in order to obtain the best results is immensely valuable. The goal of this paper is to evaluate mission performance and with it discover the best approach for various possible problems. In order to achieve this goal, multiple metrics were created to have some indicators that can be used to calculate search pattern performance. Various simulations were conducted, with different strategies and parameters, to compare distinct approaches to the various missions. These experiments consisted in applying different search patterns in a search mission with distinct delimitation areas and parameters. The results obtained allow the user to easily adopt the most efficient strategy in the scope of the simulations executed. For example, the larger the ratio between height and width of the delimitation area, the better the Parallel Track pattern is, when compared to the others. On the other hand, the closer the area is to a circle, the better the Spiral search pattern is, when compared to the others.
An Ontology for Supporting Digital Forensics Controlled Experiments: Early Stages of Development

The experimentation process is one of the main means of science to evaluate theories based on hypothesis. Science evolves most taking into account the performing of controlled experiments, thus providing trust evidence for different research fields. However, for the Digital Forensics (DF) field, formal controlled experimentation has been neglected over the years. In a recent systematic mapping of the literature, we found more than 200 experiments with few formalization of their procedures, thus jeopardizing its evidence reliability and the capacity of reproducibility. Therefore, this paper provides early steps to specify an ontology for supporting proper planning, conducting, and dissemination of DF controlled experiments. The ontology has as a basis a conceptual model created to specify the main elements of an experiment. We adopted the Uschold and King approach to develop such an ontology. We first designed the ontology with the protégé ontology editor, as it organizes knowledge. In addition, the WebVOWL tool was also used from the json file generated in the protegé tool, as this aids at the construction of a dynamic visual identity for the ontology. As general results, we understand that despite the number of ontologies in the literature being relevant, few present a structure that satisfies a relation of similar objects for their properties, in addition do not covering the context of a DF experiment. Therefore, despite the ontology construction is in its early stages, it is expected that it will shad light to the field of experimentation in DF throughout a hierarchy and formalized process.
Integrating geological data in Europe to foster multidisciplinary research

The European Plate Observing System (EPOS, www.epos-eu.org) is a multidisciplinary pan-European research infrastructure for solid Earth science. It integrates a series of domain-specific service hubs (Thematic Core Service, TCS) such as the Geological Information and Modelling, which provides access to data, data products and services on European boreholes, geological maps, mineral occurrences, mines and 3D models. TCS GIM services are hosted by a group of European Geological Surveys and a couple of national research organizations. This paper presents novel data discovery and integration, facilitated using borehole logging information with on-demand web services to produce 3D geological structures. This domain interoperability across EPOS was created for the purpose of research, but it is also highly relevant for the response to societal grand challenges such as natural hazards and climate change. European and international interoperability implementation frameworks are well described and used (e.g., INSPIRE, ISO, OGC, and IUGS/CGI). It can be difficult for data providers to deploy web services that support the full semantic data definition (e.g., OGC Complex Feature) to expose several millions of geological entities through web-enabled data portals as required by pan-European projects. The TCS GIM group implemented and innovatively extended two standardized descriptions, i.e. GeoSciML-Lite and EarthResourceML-Lite, with an important reuse of content from Linked Data Registries. This approach was applied to design and implement the European Borehole Index and associated web services (View-WMS and Discovery-WFS), extended to 3D models, geological maps as well as mineral occurrences and mines. Results presented here apply the Linked Data approach ensuring optimal semantic description and enriching the data graphs, with complex descriptions and contents. In this way, it is now possible to traverse from one Borehole Index instance to linked richer information such as the borehole geological log, groundwater levels, rock sample description, analyses, etc. All this detailed information is served following international interoperability standards (Observations &amp; Measurements, GroundWaterML 2.0, GeoSciML4, amongst others).
Educational Relations in College Students’ Class Participation : Focused on the Conflict

The quality level of university education can be evaluated by various indicators, but the most important thing is whether the university’s educational subjects practice a high level of education in terms of quantity and quality. However, college students form a specific culture related to attitude, norms, and values for class participation and interact with professors, and the quality level and specific appearance of education may vary depending on the characteristics of the culture. Therefore, it is necessary to examine what thoughts and feelings college students have about participating in class, what activities they value in class, and how these perceptions and practices affect educational relations.&#x0D; In this study, professors, assistants, and students of A university were selected as participants to examine the aspects of conflict among college students’ participation in classes and educational relationships. Through the research methods of participation observation, in-depth interviews, and local data surveys, the daily activities and contexts of class participation were specifically understood, and related data were constructed and analyzed. Based on these research results, it was suggested that qualitative indicators showing the characteristics of student culture in class participation should be specified to support university members to share and reflect on their activities, and to provide various channels for professors and students to participate in education. This study aims to contribute to exploring the changes and adjustments necessary for the practice of better educational relationships by specifically understanding the student culture of class participation from an insider perspective.
Optimum Vessel Segmentation

Good blood vessel segmentation in medical imaging is critical during surgery. We propose a parallelized region growth algorithm (pSRG) computing the gradient using Persistence and grid-stride loops. This approach avoids unnecessary memory transfers. As a result, in addition to faster computation, we obtain more accurate segmentation.
Research on Intelligent Community “Housekeeper” System Based on Artificial Intelligence

“Intelligence” is a new trend of the development of science and technology in the 21st century. With the progress of the society and the acceleration of the pace of life as well as the continuous improvement of people’s requirements for themselves, the traditional community has been unable to meet the needs and dependence of modern residents. In the current situation and background of the intelligent community to carry out a “keeper” system design and system implementation and operation. By using the advantages and technologies of big data and combining the advanced information technology means such as Internet of Things and cloud computing, it provides a good platform environment for the development of smart community, maximizes its functions, improves work efficiency, reduces the waste of manpower and material resources, and becomes an intelligent equipment developed by city and community property managers for more convenient work.
Cognition based Recognition of Partially Occluded Traffic Signs

Computer vision based traffic sign detection and recognition is an active field of research but the task becomes challenging when the sign of interest is partially occluded by nearby objects like a tree, pole or vehicle. Another difficulty posed especially in the developing countries is the lost colors problem that arises due to aging and poor maintenance. This work presents an automatic technique that focuses on visible parts only and suppresses occluded portions. Features are collected using a convolutional neural network inspired invariant feature extraction technique augmented with feature interaction based dimensionality reduction. Further, with the use of dynamic parameter estimation, an adaptive system for continuous learning is also proposed. Since the effect of partial occlusion has not been thoroughly studied, there is no benchmark database available for this purpose. We have prepared two datasets by combining originally and synthetically occluded images taken from field surveys and from famous GTSRB database. Experiments revealed that our technique outperforms state of the art recognition methods previously used for visible and occluded signs by obtaining 0.81 precision and 0.79 recall values on the average. The proposed method also shows a remarkably low error rate as the amount of occlusion is increased.
Research and Practice of “M + LIF” Information-Based Teaching Application Mode Based on Teacher-Student Dual Subject

Under the background of the rapid development of the current first-class course construction, digital teaching mode is the development trend of future education.How to grasp the essence of deep learning in online teaching and better integrate online learning with offline physical teaching is the direction of future exploration of universities.Based on the concept of "teacher-student dual subject", the teaching mode of online teaching is optimized and adjusted, build give a kind of suitable under the background of the current era is suitable for the domestic large quantities of teaching and learning based on MOOC online teaching of a new generation of information-based teaching mode, and through the actual teaching application to verify the teaching effect and the feasibility and validity of the model, Promote the improvement of online teaching efficiency and quality in colleges and universities in China.
The Partitioning Fallacy

Contraposition for conditionals is the principle that conditionals entail their contrapositives: if A then B, entails if not-B then not-A . This principle is often thought to hold for indicative but not subjunctive conditionals. Yet, purported counterexamples to Contraposition for indicative conditionals abound in the semantics literature. Typical examples, with deviant (#) contrapositives [in italics] of each conditional underneath, are these:
Privacy Protection - Emerging Issues and Technological Responses

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
The Use of Collaborative Strategic Reading (CSR) to Enhance the Students’ Reading Comprehension Achievement at Junior High School

This classroom action research aimed to enhance the eighth grade students’ reading comprehension achievement by using Collaborative Strategic Reading (CSR) and to know how CSR enhanced the students’ participation during learning process of reading comprehension. The research participants were the VIII A grade students of SMPN 1 Pakusari. The data were collected by using reading comprehension test and observation during the teaching and learning process of reading comprehension. After that, the data were analyzed using descriptive statistic to know the students’ reading comprehension achievement and their participation after being taught using CSR. The results showed that the use of CSR could enhance students’ reading comprehension achievement and participation in reading comprehension class. Hence, it is suggested for English teachers to use CSR in process of teaching and learning reading comprehension.
DSS Theorem: Decentralization, Scalability and Security Constraints in Blockchain Networks

The sum of Big Data generated from different sources is increasing significantly with each passing day to extent that it is becoming challenging for traditional storage methods to store this massive amount of data. For this reason, most organizations have resolved to use third-party cloud storage to store data. Cloud storage has advanced in recent times, but it still faces numerous challenges with regard to security and privacy. This paper discusses Big Data security and privacy challenges and the minimum requirements that must be provided by future solutions. The main objective of this paper is to propose a new technical framework to control and manage Big Data security and privacy risks. A design science research methodology is used to carry out this project. The proposed framework takes advantage of Blockchain technology to provide secure storage of Big Data by managing its metadata and policies and eliminating external parties to maintain data security and privacy. Additionally,...
E-negotiation design and engineering

Abstract This paper is an excerpt from a book by Gregory Kersten, which was not yet completed before his unexpected passing in May 2020. Following a note from Gregory’s family, the paper includes a section of a book chapter dealing with negotiation protocols. It shows how negotiation, as a purposeful process governed by explicit and implicit rules has protocols that can be employed to help negotiators orient themselves in this process. In particular, with respect to e-negotiations, the paper stresses the need to have a shared and clear understanding of the terms that are used by negotiators and software, which should be provided by taxonomy or ontology. The paper distinguishes three principal aspects of negotiations: decision and choice; language; and process. It also identifies key challenges related to the successful implementation of negotiation software agents.
Real-Time Emotional Analysis from A Live Webcam Using Deep Learning

In the Covid-19 age, we are becoming increasingly reliant on virtual interactions like as Zoom and Google meetings / Teams chat. The videos received from live webcamera in virtual interactions become great source for researchers to understand the human emotions. Due to the numerous applications in human-computer interaction, analysis of emotion from facial expressions has piqued the interest of the newest research community (HCI). The primary objective of this study is to assess various emotions using unique facial expressions captured via a live web camera. Traditional approaches (Conventional FER) rely on manual feature extraction before classifying the emotional state, whereas Deep Learning, Convolutional Neural Networks, and Transfer Learning are now widely used for emotional classification due to their advanced feature extraction mechanisms from images. In this implementation, we will use the most advanced deep learning models, MTCNN and VGG-16, to extract features and classify seven distinct emotions based on their facial landmarks in live video. Using the FER2013 standard dataset, we achieved a maximum accuracy of 97.23 percent for training and 60.2 percent for validation for emotion classification.
nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging

In recent years, deep learning models have considerably advanced the performance of segmentation tasks on Brain Magnetic Resonance Imaging (MRI). However, these models show a considerable performance drop when they are evaluated on unseen data from a different distribution. Since annotation is often a hard and costly task requiring expert supervision, it is necessary to develop ways in which existing models can be adapted to the unseen domains without any additional labelled information. In this work, we explore one such technique which extends the CycleGAN [2] architecture to generate label-preserving data in the target domain. The synthetic target domain data is used to train the nn-UNet [3] framework for the task of multi-label segmentation. The experiments are conducted and evaluated on the dataset [1] provided in the ‘Cross-Modality Domain Adaptation for Medical Image Segmentation’ challenge [23] for segmentation of vestibular schwannoma (VS) tumour and cochlea on contrast enhanced (ceT1) and high resolution (hrT2) MRI scans. In the proposed approach, our model obtains dice scores (DSC) 0.73 and 0.49 for tumour and cochlea respectively on the validation set of the dataset. This indicates the applicability of the proposed technique to real-world problems where data may be obtained by different acquisition protocols as in [1] where hrT2 images are more reliable, safer, and lower-cost alternative to ceT1.