Reviewerly/exHarmony huggingface dataset:
Development of Picture Stories Electronic Book (E-Book) for German Learning at Class X Madrasah Aliyah Negeri 1 Malang City

The purpose of this research and development is to develop learning media in the form of e-books with pictorial stories. This research and development uses a research model developed by Borg and Gall and has been modified by researchers. The stages of developing Borg and Gall that have been modified by researchers are: 1) research and data collection, 2) planning and development of product drafts, 3) expert validation, 4) revision of expert validation results, 5) field trials and 6) product revisions end. The data obtained in this study are quantitative data and qualitative data. Both data were obtained from filling out expert validation questionnaires and filling out observation sheets by two observers in field trial activities. Subjects in this study, namely students of class X MAN 1 Malang City who are taking odd semesters. From the results of the validation of the media experts that have been carried out, the product eligibility results are 80%, while the results of the material expert validation get 80% of the product eligibility results. Based on the validity level criteria, it can be concluded that the picture story e-book product is included in the level of "very valid" or suitable for use in learning. Meanwhile, the results of filling out observation sheets and student response questionnaires by observers in field trial activities showed that the media developed was interesting and could help students practice reading skills in German.
Research on the Autonomous Learning Mode of College Students Based on Mobile Terminals under the Internet Environment

the advent of the Internet era, while liberating social productivity, has brought great convenience to human social life. At present, Internet technology has been fully applied to our study, work and life. So far, the mobile terminal under the influence of the Internet environment has developed more mature, and mobile terminal and other devices have become the necessities of people's life. In the process of daily travel, people basically have a mobile device. The nationwide coverage of WiFi signals accelerates the spread of the network. Only a small mobile device can complete travel payment, online shopping, signal transmission, etc. At the same time, the advent of the information age has also brought certain factors to the reform of higher education. Taking educational informatization as the research background and mobile Internet as the media, autonomous mutual learning has become an important development direction of the next generation of digital learning. At present, colleges and universities have realized the importance of information-based teaching reform, and have designed and shared the self-learning resource system in the actual teaching reform. They have actively constructed a set of harmonious self-help learning system in the campus, constantly improved the evaluation system of College Students' autonomous learning effect, and created a good learning atmosphere of self-help in university campus, in order to cultivate students' autonomous learning ability.
Liability for Artificial Intelligence: A Proposal of the European Parliament

Digital autonomous systems are characterized by their ability to make their “own” decisions, i.e. decisions that are not fully determined by the software that animates them. As such, they pose a challenge to existing liability systems and to the general law of delict or torts. At the European level, the European Parliament took the initiative and drafted a Regulation on Liability for the Operation of Artificial-Intelligence-Systems it recommended for adoption by the Commission. The European Parliament distinguishes between high-risk-AI-systems, that shall be governed by a regime of strict liability, and “other” AI-systems, that create only normal risks and are left to fault-based liability, as defined in the legal systems of the Member States. With a view to the addresses of the new liability scheme, the draft regulation distinguishes between frontend- and backend-operators. The following chapter discusses the fundamental choices which the framers of the draft made. It concludes that the focus of the proposal on user liability is misguided, as the manufacturers are the central actors who determine the safety features of AI-systems. Moreover, introducing the concept of a backend-operator creates needless friction with the Products Liability Directive. While the commitment to strict liability for high-risk-AI-systems draws a wedge into existing regimes of strict liability under national law, the imposition of fault-based liability on users of ordinary AI-systems forces some Member States to roll back more generous rules of tort law.
Work and Play During Covid-19

The global pandemic and resultant lockdowns are challenging our traditional assumptions about the times and spaces of labour and leisure - but how were these norms established and why have they had such an enduring appeal? In this paper, we take a long view to investigate the philosophical and historical roots of the binary distinction between work and play and outline ways in which these long-held ideas are being increasingly challenged. As lockdown measures are relaxed, we urgently need to develop more nuanced conceptions of the spatial and temporal conditions of work and leisure to ensure a more equitable and compassionate post-lockdown world.
Elements of Empathy in VR - The Cases from Sundance Film Festival

This study attempts to investigate visually empathic elements of VR storytelling. VR is an effective medium by which users feel empathy toward a subject or situation throughout sensual experience in virtual space. Despite its significance in recent studies on VR with emerging interest in Metaverse, definitions of the precise aspects of empathy from a designer’s perspective on creation are lacking. Therefore, this study focuses on how to utilize VR to create empathy. To do this, the researcher conducted a literature review and noted visually relationships between VR and empathy, with the classifications of empathy of ‘familiarity via mimicry’, ‘empathic distress’, ‘perspective taking’, ‘online simulation’, ‘affective empathy’, ‘compassion’, and ‘altruism’ based on a meta study by Betrand et al. (2018) on empathy, after which VR works were analyzed. There were approximately 120 VR films from the Sundance Film Festival, and 15 were re-selected as subjects based on reviews each year by the press and by VR and film experts. These works were then analyzed in terms of the classification of empathy. Through this process, this study found that each work positioned the participants’ roles, situations and authorities in the different aforementioned types of empathy. With this analysis, the study notes how a precise strategy of user experience can be conducted with the aim to make viewers feel empathy. Ultimately, this study can serve as a guideline for designers to consider when they create VR work.
Concentrations of CO₂ from composting under different treatments

Received: January 28th, 2021 ; Accepted: July 7th, 2021 ; Published: July 21st, 2021 ; Correspondence: ingrid.karandusovska@uniag.sk
Anomaly Detection in Wide Area Imagery

This study is about detecting anomalies in wide area imagery collected from an aircraft. The set of anomalies have been identified as anything out of the normal course of action. For this purpose, two different data sets were used and the experiments were carried out on these data sets. For anomaly detection, a convolutional neural network model that tries to generate the next image using past images is designed. The images were pre-processed before being given to the model. Anomaly detection is performed by comparing the estimated image and the true image.
Perancangan Web Design Aplikasi E-Learning dengan Metode Prototype pada Tingkat SMA

Technology has experienced very rapid development at this time and has brought changes and has a positive and negative impact on human life. One of the fields currently contaminated by technological developments is the field of education, especially in Indonesia, where at first the learning system was still manual-based and is currently developing into an online learning system. It is clear that various levels of schools and universities have used technology to support their learning process and apply it. One of the uses of technology in the field of education is learning management systems (NGOs) such as e-learning. The use of e-learning as a learning medium has a function to improve the quality of student learning and help improve student understanding in learning. However, there are still many schools that have not implemented e-learning in their teaching and learning process, such as high school (SMA). Therefore, the design of this e-learning application web design is expected to help several schools that require an online learning system that is user friendly so that schools can be better prepared to follow current development trends.
The Vulnerability of the Financial System to a Systemic Cyberattack

The financial industry is a prime target of cybercriminal activity, mainly due to the nature of its underlying business (`that's where the money is') <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</sup> This was the reply of 1930s US bank robber Willie Sutton when asked why he robbed banks. He later coauthored a book titled Where the Money Was. See FBI History of Famous Cases & Criminals, https://www.fbi.gov/history/famous-cases [accessed 1 March 2021]., the sector's global interconnectedness, and its high level of digitalization. In response, the private sector has invested vast sums into cybersecurity, and regulators have started to worry about systemic risk. The latter comes in two forms. The first is the risk of a successful cyberattack against a specific financial institution `spilling over' into the broader financial system, hence unintentionally becoming systemic. The second is the national security concern of a systemic cyberattack launched specifically to disrupt the target's financial ecosystem and therefore the real economy. In both cases, the historic evidence is clear: neither type of event has been recorded thus far. Those who consider warnings of systemic cyberattacks to be little more than threat inflation see that as vindication. This paper takes the opposite view and argues that the probability of a systemic cyberattack is significant enough to warrant a higher degree of crossdisciplinary research and preparedness. To support its main argument, this paper proposes a conceptual framework that focuses on answering two key questions. First, are there sufficient known structural vulnerabilities in the financial ecosystem that could be exploited by a willing adversary? And second, are there plausible scenarios that could see an adversarial nation-state launch such an attack? The answer to both is positive. Given the lack of data, this analysis is largely qualitative, based on discussions with regulators, chief risk officers, academic experts, and the author's own multi-decade experience as an active participant in the financial market.
Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection

A crucial requirement for reliable deployment of deep learning models for safety-critical applications is the ability to identify out-of-distribution (OOD) data points, samples which differ from the training data and on which a model might underperform. Previous work has attempted to tackle this problem using uncertainty estimation techniques. However, there is empirical evidence that a large family of these techniques do not detect OOD reliably in classification tasks. This paper gives a theoretical explanation for said experimental findings and illustrates it on synthetic data. We prove that such techniques are not able to reliably identify OOD samples in a classification setting, since their level of confidence is generalized to unseen areas of the feature space. This result stems from the interplay between the representation of ReLU networks as piece-wise affine transformations, the saturating nature of activation functions like softmax, and the most widely-used uncertainty metrics.
ReZero is All You Need: Fast Convergence at Large Depth

Deep networks often suffer from vanishing or exploding gradients due to inefficient signal propagation, leading to long training times or convergence difficulties. Various architecture designs, sophisticated residual-style networks, and initialization schemes have been shown to improve deep signal propagation. Recently, Pennington et al. used free probability theory to show that dynamical isometry plays an integral role in efficient deep learning. We show that the simplest architecture change of gating each residual connection using a single zero-initialized parameter satisfies initial dynamical isometry and outperforms more complex approaches. Although much simpler than its predecessors, this gate enables training thousands of fully connected layers with fast convergence and better test performance for ResNets trained on an image recognition task. We apply this technique to language modeling and find that we can easily train 120-layer Transformers. When applied to 12 layer Transformers, it converges 56% faster.
A novel feature selection approach based on constrained eigenvalues optimization

It is often tricky in real-life classification applications to select model features that would ensure an adequate sample classification, given a large number of candidate features. Our main contribution is threefold: (1) Evaluate the relevance and redundancy of feature. (2) Define the feature selection problem as eigenvalue computation problem with linear constraint. (3) Select the best features in an efficient way. We considered 20 UCI benchmark datasets to validate and test our approach. The results were compared with those obtained using one of the more widely used approaches, namely mRMR, the conventional features and two moderns state-of-the-art approaches. The experimental results revealed that our approach could improve the classification task, using only 20% of the conventional features.
An Advertising Overflow Attack Against Android Exposure Notification System Impacting COVID-19 Contact Tracing Applications

The digital contact tracing applications are one of the many initiatives to fight the COVID-19 virus.Some of these Apps use the Exposure Notification (EN) system available on Google and Apple's operating systems.However, EN-based contact tracing Apps depend on the availability of Bluetooth interfaces to exchange proximity identifiers, which, if compromised, directly impact their effectiveness.This paper discloses and details the Advertising Overflow attack, a novel internal Denial of Service (DoS) attack targeting the EN system on Android devices.The attack is performed by a malicious App that occupies all the Bluetooth advertising slots in an Android device, effectively blocking any advertising attempt of EN or other Apps.The impact of the disclosed attack and other previously disclosed DoS-based attacks, namely Battery Exhaustion and Storage Drain, were tested using two target smartphones and other six smartphones as attackers.The results show that the Battery Exhaustion attack imposes a battery discharge rate 1.95 times higher than in the normal operation scenario.Regarding the Storage Drain, the storage usage increased more than 30 times when compared to the normal operation scenario results.The results of the novel attack reveal that a malicious App can prevent any other App to place their Bluetooth advertisements, for any chosen time period, thus canceling the operation of the EN system and compromising the efficiency of any COVID contact tracing App using this system.
Semantic Approach for Al-Quran Knowledge Representation in Islamic Knowledge Management Framework

Using ontology data model to accurately represent Al-Quran knowledge using its inherent structure of Division (Juz), Chapter (Surah), and Verse (Ayah) should be done within the framework of the Islamic Knowledge Management (IKM). Data modelling neglecting the IKM framework would result in inaccuracy and irrelevant result in knowledge processing activities involving the Al-Quran. Many of the current approaches utilizing conventional knowledge management methods such as taxonomy, hierarchy, or tree structure are limited in the sense that they only define the concepts of knowledge without relating it to a theme that defined from corpus of Islamic knowledge. In the case of Al-Quran, a classification of themes from background knowledge are important to provide a structural explanation of the knowledge and to ensure that the results obtained from a searching function of the corpus of the knowledge will result in an accurate and appropriate meaning intended by the Islamic knowledge. The aims of this paper is to demonstrate the construction of Al-Quran ontology and elaborate the searching method for representing and retrieving the relevance and accurate verses toward the theme of Al-Quran by using the semantic-based approach. The specification of Al-Quran ontology of which the prototype application was built on has been validated by experts. The evaluation test of the Al-Quran ontology application furthermore shows high precision result from searching functions done. With a good level of accuracy achieved from the test, it is hopeful that Al-Quran ontology application will provide a new experience of Al-Quran themes searching and browsing within the IKM framework for the Al-Quran readers. A capability to efficiently and accurately performing search of Al-Quran will certainly improve their understanding of Al-Quran contents.
Automatic Target Recognition Method of Flight Vehicle Based on Template Matching

The key factor in obtaining flight vehicle guidance information is the automatic recognition of strapdown images. When using strap-down image guidance, it is more difficult to obtain guidance information due to the influence of environment and other factors on the information collection. Aiming at the fact that deep learning algorithms cannot be directly applied to the target recognition of strapdown images, a new target recognition framework is proposed. The framework extracts features from target template images and strapdown images, and uses the extracted feature maps as the input of the deep neural network, so that the improved target recognition framework can be directly applied to the strapdown environment. Experimental results show that the proposed framework can effectively recognize strapdown images, improve recognition accuracy, and achieve precise guidance.
Enhancement of Iot Security Solutions Using Blockchain Technology

A blockchain is a growing list of records, called blocks, which are linked together using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. It is a decentralized, distributed and public digital ledger that is used to record transactions across many computers called nodes. Blockchain, in collaboration with the technologies like Artificial Intelligence, the Internet of Things, Big data, etc., provides more effective outcomes by minimizing manpower with an increase in its feasibility. IoT is all about expanding the power of the internet beyond computers and smartphones to a whole range of other things, processes, and environments. Our day-to-day life can be made interesting with the applications of IoT-enabled devices in the home, agriculture, logistics, industries, healthcare sectors, and so on. They can be combined with the disciplines like data collection, management of huge data, storage of data, data analytics, etc. These IoT devices are embedded with sensors that are vulnerable to hackers and malware. At present, we cannot give complete assurance of the security of the access and data associated with the devices as it is possible for an intruder to misuse or mishandle the scheduled process. But, in association with blockchain technology, we can acquire solutions for problems related to security.
A computer-aided diagnosis system for breast cancer molecular subtype prediction in mammographic images

Several computer-aided diagnosis (CAD) systems have been developed to assist the radiologists for early breast cancer detection and treatment. These CAD systems provide statistical features of a mammogram using computer vision and image processing techniques for characterizing the morphological structure and evolution of the tumors. In this chapter a CAD system is introduced that includes three stages: tumor detection, segmentation, and tumor-shape and molecular subtypes classification based on deep learning models. The first stage is to detect the region of interest (ROI) that contains a tumor from mammographic images by using a modified Faster R-CNN (convolutional neural network) detector, which incorporates an Inception-ResNet-v2 feature extractor with a squeeze and excitation network. While the second stage employs a conditional generative adversarial network (cGAN) to segment the breast tumor from the detected ROI. For shape classification, a CNN is then developed in the third stage of the CAD system to classify the binary masks of the cGAN network into four tumor-shape classes: irregular, lobular, oval, and round. Finally, this chapter presents a study of the correlation between the tumor shapes and molecular subtypes of breast cancer. The findings of this chapter indicate that the tumor shape can be analyzed for understanding the molecular subtype of the tumor.
USING DIGITAL TECHNOLOGIES FOR INFORMAL LEARNING: A CASE STUDY AMONG FUTURE ENGLISH TEACHERS

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 8595-8602ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1736Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Named Entity Recognition with Gating Mechanism and Parallel BiLSTM

In this paper, we propose a novel neural network for named entity recognition, which is improved in two aspects. On the one hand, our model uses a parallel BiLSTM structure to generate character-level word representations. By inputting character sequences of words into several independent and parallel BiLSTMs, we can obtain word representations from different representation subspaces, because the parameters of these BiLSTMs are randomly initialized. This method can enhance the expression abilities of character-level word representations. On the other hand, we use a two-layer BiLSTM with gating mechanism to model sentences. Since the features extracted by each layer in a multi-layer LSTM from texts contain different types of information, we use the gating mechanism to assign appropriate weights to the outputs of each layer, and take the weighted sum of these outputs as the final output for named entity recognition. Our model only changes the structure, does not need any feature engineering or external knowledge source, which is a complete end-to-end NER model. We used the CoNLL-2003 English and German datasets to evaluate our model and got better results compared with baseline models.
Full simulation of an entangled based quantum secured direct communication system using MATLAB

A full quantum secure direct communication system is implemented in MATLAB environment using linear optical devices and single-photon detectors. MATLAB has the advantage of integrating the simulation of the whole system, which has optical and electronic blocks, all together into one script. Each block of the system (transmitter, receiver, and security check) is physically and mathematically explained, and the idea behind the code is presented. Results from running the whole simulation are in accordance with those obtained either from the published practical systems or from using state-of-the-art commercially available optical simulators. Our approach opens the way to simulate with confidence hyperentangled systems where experimental data are not yet widely available.