Reviewerly/exHarmony huggingface dataset:
Evaluation of a Maturity Model for the Adoption of Learning Analytics in Higher Education Institutions

Learning Analytics (LA) aims to analyze the data generated by both students and teachers in online environments in order to promote actions to improve teaching and learning processes. The results of such analyzes can help teachers to know their students’ study processes, as well as being able to assist with the verification and correction of both educational activities and practices. For students, LA can help with reflection and self-regulation of learning. However, despite its benefits, institutions have difficulties in adopting it. In this sense, an instrument that can support the use of LA is the Maturity Model (MM), which has been used in different knowledge areas in order to indicate an improvement roadmap for organizations. Hence, this paper aims to present the assessment results of a MM proposed for the adoption of LA in Higher Education Institutions, called MMALA. The evaluation focused on the model composition and was carried out through a questionnaire addressed to LA researchers and professionals. After conducting analyzes, both qualitative and quantitative, suggestions for improvement for the proposed model were identified, and the model was validated, supporting its further development.
PAS-TA-U: PASsword-Based Threshold Authentication with Password Update

A single-sign-on (SSO) is an authentication system that allows a user to log in with a single identity and password to any of several related, yet independent, server applications. SSO solutions eliminate the need for users to repeatedly prove their identities to different applications and hold different credentials for each application. Token-based authentication is commonly used to enable a SSO experience on the web, and on enterprise networks. A large body of work considers distributed token generation which can protect the long term keys against a subset of breached servers. A recent work (CCS’18) introduced the notion of Password-based Threshold Authentication (PbTA) with the goal of making password-based token generation for SSO secure against server breaches that could compromise both long-term keys and user credentials. They also introduced a generic framework called PASTA that can instantiate a PbTA system. The existing SSO systems built on distributed token generation techniques, including the PASTA framework, do not admit password-update functionality. In this work, we address this issue by proposing a password-update functionality into the PASTA framework. We call the modified framework PAS-TA-U. As a concrete application, we instantiate PAS-TA-U to implement in Python a distributed SSH key manager for enterprise networks (ESKM) that also admits a password-update functionality for its clients. Our experiments show that the overhead of protecting secrets and credentials against breaches in our system compared to a traditional single server setup is low (average 119 ms in a 10-out-of-10 server setting on Internet with 80 ms round trip latency).
A New Normal of Lifelong Education According to the Artificial Intelligence and EduTech Industry Trends and the Spread of the Untact Trend

The edutech-based lifelong education system is the most pressing alternative for democratizing education and improving the quality of education by resolving polarization in education. Lifelong education transforms the educational purpose of ‘continue the individual's physical and personal maturity and social, economic and cultural growth and development throughout life’ into ‘educational infrastructure to foster democratic citizens who lead the future society’ shall. This paper presents a New Normal of lifelong education as a lifelong education classification system innovation, self-directed learning ability cultivation, future-oriented entrepreneurship and startup education, Collaborative literacy education, maker's experience, adaptive learning system, and digital literacy education platform.
Web-Based Inventory Information System in Antariksa Stores

Abstract Antariksa Store is a shop that specializes in electronics and audio. As a seller, the Antariksa Shop must manage inventory items well. Inventory system that runs on the Antariksa Shop, such as recording incoming items, stock, and items out is still running manually, thus causing difficulties in recording items and errors in making reports. Because of these problems, the Antariksa Shop wants to use the information system in the Antariksa Shop to overcome the problems that exist in the Antariksa Shop. Based on these problems, a web-based inventory information system was created to manage existing inventory at the Antariksa Shop. Web- based inventory information system created using the SSAD method and with Waterfall modeling. Data storage uses a MySQL database and the programming languages used to create information systems are html, css, js, and php. Information systems are carried out using the Black Box and User Acceptance Test (UAT) methods. The Web-Based Inventory Information System at the Antariksa Store is expected to help manage incoming items, inventory and outgoing items at the Antariksa Store and make it easier to make reports, and reduce errors in the recording of items.
Cryptanalysis and improvement of a user authentication scheme for wireless sensor networks using chaotic maps

One of the challenging issues in wireless sensor networks (WSNs) is to provide secure communication as the channel used is wireless. User authentication is a mechanism that is used to provide secure communication. The authors analyse the security of a recent authentication scheme for WSNs and show that it is not resistant to smart card loss, insider, and denial of service attacks besides the user traceability problem. Here, they design an improved user authentication scheme using the chaotic maps for WSNs. The chaotic maps-based public key cryptosystem helps a scheme to consider a large integer as the secret key rather than a large prime number and makes it fast. They show its formal security analysis using the random oracle model. They also analyse its security informally to show that it is resistant to various known attacks. They show that their scheme offers more security features than the existing schemes.
A Basic Study on Ballroom Dance Figure Classification with LSTM Using Multi-modal Sensor

The paper presents a ballroom dance figure classification method with LSTM using video and wearable sensors. Ballroom dance is a popular sport among people regardless of age or sex. However, learning ballroom dance is very difficult for less experienced dancers as it has many complex types of “dance figures”, which is a completed set of footsteps. Therefore, we aim to develop a system to assist dance exercise which gives advice proper to each dance figure characteristic by recognizing dance figures correctly. While the major approach to recognize dance performance is to utilize video, we cannot simply adopt it for ballroom dance because the images of dancers overlap each other. To solve the problem, we propose a hybrid figure recognition method combining video and wearable sensors to enhance its accuracy and robustness. We collect video and wearable sensor data of seven dancers including acceleration, angular velocity, and body parts location change by pose estimation. After that, we preprocess them and put them into an LSTM-based deep learning network. As a result, we confirmed that our approach achieved an F1-score of 0.86 for 13 figure types recognition using the multi-modal sensors with trial-based fivefold cross-validation. We also performed user-based cross-validation, and sliding window algorithms. In addition, we compared the results with our previous method using Random Forest and also evaluated the robustness with occlusions. We found the LSTM-based method worked better than Random Forest with keypoint data. On the other hand, LSTM could not perform well with a sliding window algorithm. We consider the LSTM-based method would work better with a larger dance figure data, which is our next work. In addition, we will investigate how to solve occlusion problems with pose estimation.
Negation in Cognitive Reasoning

Negation is both an operation in formal logic and in natural language by which a proposition is replaced by one stating the opposite, as by the addition of “not” or another negation cue. Treating negation in an adequate way is required for cognitive reasoning, which aims at modeling the human ability to draw meaningful conclusions despite incomplete and inconsistent knowledge. One task of cognitive reasoning is answering questions given by sentences in natural language. There are tools based on discourse representation theory to convert sentences automatically into a formal logic representation, and additional knowledge can be added using the predicate names in the formula and knowledge databases. However, the knowledge in logic databases in practice always is incomplete. Hence, forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived, while negative knowledge is used only during the reasoning process. In consequence, we aim at eliminating syntactic negation, strictly speaking, the negated event or property. In this paper, we describe an effective procedure to determine the negated event or property in order to replace it by its inverse. This lays the basis of cognitive reasoning, employing both logic and machine learning for general question answering. We evaluate our procedure by several benchmarks and demonstrate its practical usefulness in our cognitive reasoning system.
Ontology-based Automatic Semantic Annotation Method for IoT Data Resources

The following topics are dealt with: learning (artificial intelligence); Internet of Things; feature extraction; cloud computing; neural nets; data mining; Internet; optimisation; object detection; pattern classification.
A Novel Framework for Biomedical Text Mining

Text mining has emerged as an effective method of handling and extracting useful information from the exponentially growing biomedical literature and biomedical databases. We developed a novel biomedical text mining model implemented by a multi-agent system and distributed computing mechanism. Our distributed system, TextMed, comprises of several software agents, where each agent uses a reinforcement learning method to update the sentiment of relevant text from a particular set of research articles related to specific keywords. TextMed can also operate on different physical machines to expedite its knowledge extraction by utilizing a clustering technique. We collected the biomedical textual data from PubMed and then assigned to a multiagent biomedical text mining system, where each agent directly communicates with each other collaboratively to determine the relevant information inside the textual data. Our experimental results indicate that TexMed parallels and distributes the learning process into individual agents and appropriately learn the sentiment score of specific keywords, and efficiently find connections in biomedical information through text mining paradigm.
Learning to encode user-generated short videos with lower bitrate and the same perceptual quality

On a platform of user-generated content (UGC), the uploaded videos need to be encoded again before distribution. For this specific encoding scenario, we propose a novel dataset and a corresponding learning-based scheme that is able to achieve significant bitrate saving without decreasing perceptual quality. In the dataset, each video's label indicates whether it can be encoded with a much lower bitrate while still keeps the same perceptual quality. Models trained on this dataset can then be used to classify the input video and adjust its final encoding parameters accordingly. With enough classification accuracy, more than 20% average bitrate saving can be obtained through the proposed scheme. The dataset will be further expanded to facilitate the study on this problem.
A Study of Security Challenges From a Federeated Cloud Perspective

In the information technology sector, cloud computing plays an important role. Information was externalized in the cloud in the IT sector and part of data as a software. These offer utilities such as storage, software as a service, and application as a service and some web models, such as the deployment models, in four forms. The third-party wanted the data to be outsourced. In this chapter, the authors research and analyze technologies and frameworks for cloud computing. In cloud services, security problems are important. Therefore, the authors discuss safety problems and the concerns found in this chapter.
DIGITAL LITERACY: ONLINE CLASS MANAGERIAL FOR EDUCATORS

&lt;p&gt;&lt;em&gt;The COVID-19 pandemic emerging in early 2020 has significantly impacted various sectors, including education. The policy of home-based learning (defined as online learning), that is implemented by the Indonesian Ministry of Education and Culture becomes a challenge for students, teachers, and educational institutions. Online-based learning is still an unfamiliar concept to the world of education in Indonesia. Lack of preparation and planning during the switch to online-based learning leads to bad learning experiences for both students and teachers alike. This event was aimed towards Indonesian educators to discuss essential elements regarding digital literacy competence, namely basic principles of distinguishing face-to-face classes and online classes, deciding on a format, design, and interaction in online classrooms, as well as the cycle of teaching and learning. As many as 454 participants from the five major islands in Indonesia virtually attended this event on May 13th, 2020. This event utilizes the ADDIE training developmental model elaborated in five stages: 1) Analyze, (2) Design, (3) Development, (4) Implementation, and (5) Evaluation. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;ABSTRACT (INDONESIAN):&lt;/strong&gt; Pandemi COVID-19 yang muncul di awal tahun 2020 memberi dampak signifikan dalam berbagai sektor, termasuk Pendidikan. Kebijakan home-based-learning atau pembelajaran jarak jauh yang ditetapkan oleh Kementrian pendidikan dan Kebudayaan Indonesia menjadi sebuah tantangan bagi peserta didik, tenaga pendidik dan institusi pendidikan. Pembelajaran berbasis daring masih asing bagi dunia pendidikan di Indonesia. Kurangnya persiapan dan perencanaan dalam kegiatan belajar mengajar daring berakibat pada pengalaman belajar-mengajar yang buruk bagi peserta didik maupun tenaga pendidik. Kegiatan ini ditujukan bagi tenaga pendidik di Indonesia untuk membahas beberapa elemen penting dalam kompetensi literasi digital seperti prinsip dasar yang membedakan kelas tatap muka dengan kelas daring, menentukan format, desain dan interaksi kelas daring, serta siklus belajar mengajar berbasis daring. Kegiatan ini diikuti oleh 454 partisipan yang tersebar pada lima pulau terbesar di Indonesia berlangsung secara virtual pada tanggal 13 Mei 2020. Kegiatan ini menggunakan model pengembangan training ADDIE dengan 5 tahapan sebagai berikut: 1) &lt;em&gt;Analyze&lt;/em&gt;, (2) &lt;em&gt;Design&lt;/em&gt;, (3) &lt;em&gt;Development&lt;/em&gt;, (4) &lt;em&gt;Implementation&lt;/em&gt; dan (5) &lt;em&gt;Evaluation&lt;/em&gt;.&lt;/p&gt;
Affine transformation of virtual 3D object using 2D localization of fingertips

Interactions with virtual 3D objects in the virtual reality (VR) environment using the gesture of fingers captured in a wearable 2D camera have emerging applications in real-life. This paper presents an approach of a two-stage convolutional neural network, one for the detection of hand and another for the fingertips. One purpose of VR environments is to transform a virtual 3D object with affine parameters by using the gesture of thumb and index fingers. To evaluate the performance of the proposed system, one existing, and another developed egocentric fingertip databases are employed so that learning involves large variations that are common in real-life. Experimental results show that the proposed fingertip detection system outperforms the existing systems in terms of the precision of detection. The interaction performance of the proposed system in the VR environment is higher than that of the existing systems in terms of estimation error and correlation between the ground truth and estimated affine parameters.
A Zero-DCE-Based Low-Light Image Enhancement Algorithm for Urban Roads

The quality of monitoring data sources has an important impact on road recognition and human vehicle detection performance in the field of smart transportation. Aiming at the problem of large light disturbance and low contrast in urban road monitoring images in practical applications, this paper proposed a method for low-light image enhancement of urban roads based on Zero-DCE. First, for the constructed urban road low-light image data set, an improved low-light enhancement network model was designed based on the idea of no-reference depth curve estimation; then, the model hyperparameters are ablated, and end-to-end training can be achieved without relying on paired training data, which overcomes the shortcomings of existing image enhancement algorithms that rely too much on high and low quality paired images in the same scene. The experimental results show that the method proposed in this paper can significantly enhance the brightness and contrast of the image, improve the visibility of details, and improve its subjective visual quality. At the same time, in terms of objective evaluation indicators and running time, this method also has certain advantages compared with traditional methods and other learning-based methods.
Fine-grained Foreground Retrieval via Teacher-Student Learning

Foreground image retrieval is a challenging computer vision task. Given a background scene image with a bounding box indicating a target location, the goal is to retrieve a set of images of foreground objects from a given category, which are semantically compatible with the background. We formulate foreground retrieval as a self-supervised domain adaptation task, where the source domain consists of foreground images and the target domain of background images. Specifically, given pretrained object feature extraction networks that serve as teachers, we train a student network to infer compatible foreground features from background images. Thus, foregrounds and backgrounds are effectively mapped into a common feature space, enabling retrieval of the foregrounds that are closest to the target background in that space. A notable feature of our approach is that our training strategy does not require instance segmentation, unlike current state-of-the-art methods. Thus, our method may be applied to diverse foreground categories and background scene types and enables us to retrieve the foreground in a fine-grained manner, which is closer to the requirements of real world applications.
Block convolutional layer for position dependent features calculation

Image recognition includes problems where special features can be found only in a specific area of an image. This fact suggests us to apply different filters to different areas of input images. Convolutional networks have only fully-connected and locally-connected layers to make it. A Fully-connected layer erases the position factor for every output and a locally connected layer storage an enormous number of parameters. We need a layer that can apply different convolution kernels for different areas of an input image and not carry so many parameters as a locally-connected layer for high scale resolution images. This is why in this paper, we introduce a new type of convolutional layer - a block layer, and a way to construct a neural network using block convolutional layers to achieve better performance in the image classification problem. The influence of block layers on the quality of the neural network classifier is shown in this paper. We also provide a comparison with neural network architecture LeNet-5 as a baseline. The research was conducted on open datasets: MNIST, CIFAR-10, Fashion MNIST. The results of our research prove that this layer can increase the accuracy of neural network classifiers without increasing the number of operations for the neural network.
Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data

We consider a binary classification problem in the case where some observations in the training data are incorrectly labeled. In the presence of such label noise, conventional classification fails to obtain a classifier to be generalized to a population. In this work, we investigate label noise logistic regression and explain how it works with noisy training data. We demonstrate that, when label transition probabilities are correctly provided, label noise logistic regression satisfies the Fisher consistency and enjoys the property of robustness. To accommodate various label noise mechanisms that occur in practice, we propose a flexible label noise model in a nonparametric way. We propose an efficient algorithm under the thresholding rule for individual parameter estimation. We demonstrate its performance under synthetic and real examples. We discuss the proposed flexible transition model is also useful for robust classification.
Superbizarre Is Not Superb: Improving BERT's Interpretations of Complex Words with Derivational Morphology.

How does the input segmentation of pretrained language models (PLMs) affect their generalization capabilities? We present the first study investigating this question, taking BERT as the example PLM and focusing on the semantic representations of derivationally complex words. We show that PLMs can be interpreted as serial dual-route models, i.e., the meanings of complex words are either stored or else need to be computed from the subwords, which implies that maximally meaningful input tokens should allow for the best generalization on new words. This hypothesis is confirmed by a series of semantic probing tasks on which derivational segmentation consistently outperforms BERT's WordPiece segmentation by a large margin. Our results suggest that the generalization capabilities of PLMs could be further improved if a morphologically-informed vocabulary of input tokens were used.
Computer-Assisted Education

The purpose of this review paper is to explore and learn boundlessly about computer-assisted education, and its impact on students and their academic success. Computer-assisted learning provides academics with various teaching information and multimedia platforms for easy learning of students. It interprets the innovation of computer-assisted education and its success in today’s era. Computer-aided education signifies the integrated approach of the computer and its devices. Nevertheless, it does not mean online learning will eventually replace traditional education and teachers, rather teachers using computer-based platforms, and their applications to teaching, like smart classes, videos, and web tutoring with improved resources and reduced teacher workloads. By which, students can arrive, grasp, and engage themselves at their own pace in a co-relational environment. Moreover, in the situation of a pandemic like this, we all are choosing computer-assisted education. Therefore, without computer-facilitated education today, it would have been merely impossible for us to study, and attain education. The world has widely opened and accepted the new technology of computer-assisted education. This paper is done to distinguish the success of computer-assisted education