Reviewerly/exHarmony huggingface dataset:
New Parallel Corpora of Baltic and Slavic Languages — Assumptions of Corpus Construction

In this article, we describe the design principles of the ten newly published CLARIN-PL corpora of Slavic and Baltic languages. In relation to other non-commercial online corpora, we highlight the distinctive features of these CLARIN-PL corpora: resource selection, preprocessing, manual segmentation at the sentence level, lemmatisation, annotation and metadata. We also present current and planned work on the development of the CLARIN-PL Balto–Slavic corpora.
Explainable Data Analytics for Disease and Healthcare Informatics

With advancements in technology, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. Examples of these valuable data include healthcare and disease data such as privacy-preserving statistics on patients who suffered from diseases like the coronavirus disease 2019 (COVID-19). Analyzing these data can be for social good. For instance, data analytics on the healthcare and disease data often leads to the discovery of useful information and knowledge about the disease. Explainable artificial intelligence (XAI) further enhances the interpretability of the discovered knowledge. Consequently, the explainable data analytics helps people to get a better understanding of the disease, which may inspire them to take part in preventing, detecting, controlling and combating the disease. In this paper, we present an explainable data analytics system for disease and healthcare informatics. Our system consists of two key components. The predictor component analyzes and mines historical disease and healthcare data for making predictions on future data. Although huge volumes of disease and healthcare data have been generated, volumes of available data may vary partially due to privacy concerns. So, the predictor makes predictions with different methods. It uses random forest With sufficient data and neural network-based few-shot learning (FSL) with limited data. The explainer component provides the general model reasoning and a meaningful explanation for specific predictions. As a database engineering application, we evaluate our system by applying it to real-life COVID-19 data. Evaluation results show the practicality of our system in explainable data analytics for disease and healthcare informatics.
CURIE: a cellular automaton for concept drift detection

Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose $$\textit{CURIE}$$ , a drift detector relying on cellular automata. Specifically, in $$\textit{CURIE}$$ the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that $$\textit{CURIE}$$ , when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy. $$\textit{CURIE}$$ is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.
Hybrid Classical-Quantum Software Services Systems: Exploration of the Rough Edges

The development that quantum computing technologies are achieving is beginning to attract the interest of companies that could potentially be users of quantum software. Thus, it is perfectly feasible that during the next few years hybrid systems will start to appear integrating both the classical software systems of companies and new quantum ones providing solutions to problems that still remain unmanageable today. A natural way to support such integration is Service-Oriented Computing. While conceptually the invocation of a quantum software service is similar to that of a classical one, technically there are many differences. To highlight these differences and the difficulties to develop quality quantum services, this paper takes a well-known problem to which a quantum solution can be provided, integer factorization, and the Amazon Braket quantum service platform. The exercise of trying to provide the factorization as a quantum service is carried out. This case study is used to show the rough edges that arise in the integration of classical-quantum hybrid systems using Service-Oriented Computing. The conclusion of the study allows us to point out directions in which to focus research efforts in order to achieve effective Quantum Service-Oriented Computing.
Nearmi: A Framework for Designing Point of Interest Techniques for VR Users with Limited Mobility

We propose Nearmi, a framework that enables designers to create customizable and accessible point-of-interest (POI) techniques in virtual reality (VR) for people with limited mobility. Designers can use Nearmi by creating and combining instances of its four components—representation, display, selection, and transition. These components enable users to gain awareness of POIs in virtual environments, and automatically re-orient the virtual camera toward a selected POI. We conducted a video elicitation study where 17 participants with limited mobility provided feedback on different Nearmi implementations. Although participants generally weighed the same design considerations when discussing their preferences, their choices reflected tradeoffs in accessibility, realism, spatial awareness, comfort, and familiarity with the interaction. Our findings highlight the need for accessible and customizable VR interaction techniques, as well as design considerations for building and evaluating these techniques.
Editorial Energy Efficiency of Machine-Learning-Based Designs for Future Wireless Systems and Networks

While 5G standards are being developed, research is moving toward designing the next generation of communications (e.g., 5.5G and 6G) which are expected to provide data rates of the order of 1 Tb/s using frequency bands in the range of 100 GHz to 3 THz. In addition to providing massive capacity and connectivity by exploiting new network architectures (e.g., cell-free massive MIMO, integrated terrestrial-aerial-underwater networks), radio transmission technologies (e.g., THz communications) and resource management techniques (e.g., end-to-end network resource slicing), future networks will support new context-aware applications and services (e.g., those based on joint communications and sensing) and provide connected intelligence in the era of Internet-of-Everything.
Securing Node-RED Applications

Trigger-Action Platforms (TAPs) play a vital role in fulfilling the promise of the Internet of Things (IoT) by seamlessly connecting otherwise unconnected devices and services. While enabling novel and exciting applications across a variety of services, security and privacy issues must be taken into consideration because TAPs essentially act as persons-in-the-middle between trigger and action services. The issue is further aggravated since the triggers and actions on TAPs are mostly provided by third parties extending the trust beyond the platform providers.
On the Evaluation of Deep Learning-Based Side-Channel Analysis

Deep learning-based side-channel analysis is rapidly positioning itself as a de-facto standard for the most powerful profiling side-channel analysis.The results from the last few years show that deep learning techniques can efficiently break targets that are even protected with countermeasures. While there are constant improvements in making the deep learning-based attacks more powerful, little is done on evaluating the attacks’ performance. Indeed, how the evaluation process is done today is not different from what was done more than a decade ago from the perspective of evaluation metrics. This paper considers how to evaluate deep learning-based side-channel analysis and whether the commonly used approaches give the best results. To that end, we consider different summary statistics and the influence of algorithmic randomness on the stability of profiling models. Our results show that besides commonly used metrics like guessing entropy, one should also show the standard deviation results to assess the attack performance properly. Even more importantly, using the arithmetic mean for guessing entropy does not yield the best results, and instead, a median value should be used.
Transformer-Based Automatic Punctuation Prediction and Word Casing Reconstruction of the ASR Output

The paper proposes a module for automatic punctuation prediction and casing reconstruction based on transformers architectures (BERT/T5) that constitutes the current state-of-the-art in many similar NLP tasks. The main motivation for our work was to increase the readability of the ASR output. The ASR output is usually in the form of a continuous stream of text, without punctuation marks and with all words in lowercase. The resulting punctuation and casing reconstruction module is evaluated on both the written text and the actual ASR output in three languages (English, Czech and Slovak).
Database Matching Under Column Deletions

De-anonymizing user identities by matching various forms of user data available on the internet raises privacy concerns. A fundamental understanding of the privacy leakage in such scenarios requires a careful study of conditions under which correlated databases can be matched. Motivated by synchronization errors in time indexed databases, in this work, matching of random databases under random column deletion is investigated. Adapting tools from information theory, in particular ones developed for the deletion channel, conditions for database matching in the absence and presence of deletion location information are derived, showing that partial deletion information significantly increases the achievable database growth rate for successful matching. Furthermore, given a batch of correctly-matched rows, a deletion detection algorithm that provides partial deletion information is proposed and a lower bound on the algorithm's deletion detection probability in terms of the column size and the batch size is derived. The relationship between the database size and the batch size required to guarantee a given deletion detection probability using the proposed algorithm suggests that a batch size growing double-logarithmic with the row size is sufficient for a nonzero detection probability guarantee.
Augmented Reality as Educational Tool: Perceptions, Challenges, and Requirements from Teachers

In the recent decade the number of augmented reality educational applications (AR-EAs) has increased, but their actual uptake in real-life contexts remains low. To identify reasons for the limited uptake and resolutions for this issue, we conducted a teacher survey. Based on the analysis of 65 valid responses, we derived teacher requirements that could improve the adoption of AR-EAs.
SE4ExSum: An Integrated Semantic-aware Neural Approach with Graph Convolutional Network for Extractive Text Summarization

Recently, advanced techniques in deep learning such as recurrent neural network (GRU, LSTM and Bi-LSTM) and auto-encoding (attention-based transformer and BERT) have achieved great successes in multiple application domains including text summarization. Recent state-of-the-art encoding-based text summarization models such as BertSum, PreSum and DiscoBert have demonstrated significant improvements on extractive text summarization tasks. However, recent models still encounter common problems related to the language-specific dependency which requires the supports of the external NLP tools. Besides that, recent advanced text representation methods, such as BERT as the sentence-level textual encoder, also fail to fully capture the representation of a full-length document. To address these challenges, in this paper we proposed a novel s emantic-ware e mbedding approach for ex tractive text sum marization , called as: SE4ExSum. Our proposed SE4ExSum is an integration between the use of feature graph-of-words (FGOW) with BERT-based encoder for effectively learning the word/sentence-level representations of a given document. Then, the g raph c onvolutional n etwork (GCN) based encoder is applied to learn the global document's representation which is then used to facilitate the text summarization task. Extensive experiments on benchmark datasets show the effectiveness of our proposed model in comparing with recent state-of-the-art text summarization models.
Visual Protection Using RC5 Selective Encryption in Telemedicine

The dissemination of the COVID-19 viruses now extends the usage of video consultations to share perspectives and virtual medical consultations, save expenses and health procedures, track the success of care proposals with detail, consistency, and ease from moment to time.The research aims to study the security of video consultations.We will also present the advantages and disadvantages of video consultations and the complications of their implementation.This paper mainly proposes a practical, high-efficiency video encoding technique for the new video encoding technique (HEVC) used in video consultations.The technology offered uses the RC5 block encryption algorithm for encrypting the sensitive bits of HEVC with low complexity, short encoding times in real-time implementations, format-compliant HEVC, and constant bitrate.In addition, this paper presents experimental results comparing the proposed HEVC RC5-based selective encryption (SE) algorithm with the other algorithms used in various operating modes using the Advanced Encryption Standard (AES).Also, this paper provides the security analysis of the proposed HEVC RC5-based SE algorithm for video consultation, including; the correlation analysis between the ciphered video and the original one, the histogram of the ciphered video, and the cipher cycle analysis.The security analysis results ensured that the proposed HEVC RC5based SE algorithm for video consultation is safe and stable.
Development and Pilot-Test of Blockchain-Based MyHealthData Platform

The concept of MyData emerged as a paradigm shift in personal data management and the process of seeking to transform the current organization-centered system. MyData enables the utilization of one’s own personal information that is scattered among various institutions as a system for data subjects to exercise rights of self-determination. We aimed to develop and demonstrate a MyData platform (MyHealthData) that allows data subjects to download and manage health-related personal data stored in various medical institutions. The platform consists of a mobile app for users, API (application–program interface) for data conversion and exchange installed in the hospital information system (HIS), and a relay server connected to the blockchain to ensure data integrity. User surveys were conducted to explore perceived usefulness, perceived ease of use, and satisfaction. We provided four services to users through the platform developed in this study: inquiring about medical and health checkup records, health coaching, checking conditions of participation in clinical research, and claims, all by using an app. A total of 1228 participants signed up for the service and the overall user satisfaction was high, especially with ‘inquire about medical and health checkup records’. MyData brings a user-centered paradigm in which data subjects can directly participate in the use of their own data. MyData will improve healthcare data interoperability, allowing it to be used not only in research areas but also in other areas by sharing and integrating various healthcare data.
Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing

The development of information technology has been increasingly changing the means of information exchange leading to the need of digitizing print documents. In the present era, there is a lot of fraud that often occurs. For example, is account fraud, to avoid account fraud there was verification using ID card extraction using OCR and NLP. Optical Character Recognition (OCR) is a technology that used to generate text from images. With OCR we can extract Indonesian ID card or kartu tanda penduduk (KTP) into text using 3 different OCR libraries, PyOCR, Pytesseract, and TesseOCR. To improve the accuracy we made text corrections using Natural Language Processing (NLP) basic tools to fixing the text. With 50 Indonesian ID card image, we compared the performance with three different OCR libraries. The result of our experiment shows that Pytesseract had the best performance with 0.78 F-score and 4510 milliseconds to extract per ID card.
3R model: A post-purchase context-aware reputation model to mitigate unfair ratings in e-commerce

In e-commerce, retailers or sellers are often assessed by customers or buyers based on reputation information to make wise purchasing decisions. Seller reputation becomes an important credential to shadow seller future behaviour. Most existing reputation models directly aggregate the ratings provided by past buyers. However, it is well documented in practical e-commerce systems that buyers' ratings can be distorted due to collusion, which negatively affects the applicability of these reputation models. To address this challenging problem, we propose the repurchase-and-return reputation (3R) model, which puts buyers' ratings into context before aggregating them to compute seller reputation. It considers buyer repurchase and product return behaviour after the point in time when the particular rating was provided. Intuitively, repurchases indicate that the buyers are satisfied with the previously purchased products. Thus, their positive ratings should be given more weight. Similarly, product return behaviours indicate that buyers are dissatisfied with their previous purchasing decisions. Thus, their negative ratings should be given more weight. Based on the proposed 3R reputation model, we design a price premium for a transaction considering the post-purchase behaviour of both the buyer and seller in their transactions. The proposed model is proven capable of achieving a pure strategy Nash equilibrium, in which sellers honestly provide products and buyers prefer to return bad products and repurchase good quality products. Experimental evaluation based on extensive simulation demonstrates that our model can accurately evaluate sellers' honesty and perform well against prevailing unfair rating attacks.
A novel Two-Factor HoneyToken Authentication Mechanism

The majority of systems rely on user authentication on passwords, but passwords have so many weaknesses and widespread use that easily raise significant security concerns, regardless of their encrypted form. Users hold the same password for different accounts, administrators never check password files for flaws that might lead to a successful cracking, and the lack of a tight security policy regarding regular password replacement are a few problems that need to be addressed. The proposed research work aims at enhancing this security mechanism, prevent penetrations, password theft, and attempted break-ins towards securing computing systems. The selected solution approach is two-folded; it implements a two-factor authentication scheme to prevent unauthorized access, accompanied by Honeyword principles to detect corrupted or stolen tokens. Both can be integrated into any platform or web application with the use of QR codes and a mobile phone.
Context extraction module for deep convolutional neural networks

Convolutional layers convolve the input feature maps to generate valuable output features, and they help deep learning methods significantly in solving complex problems. In order to tackle problems efficiently, deep learning solutions should ensure that the parameters of the model do not increase significantly with the complexity of the problem. Pointwise convolutions are primarily used for parameter reduction in many deep learning architectures. They are convolutional filters of kernel size 1×1. The pointwise convolution, however, ignores the spatial information around the points it is processing. This design is by choice, in order to reduce the overall parameters and computations. However, we hypothesize that this shortcoming of pointwise convolution has a significant impact on network performance. We propose a novel alternative design for pointwise convolution, which uses spatial information from the input efficiently. Our approach extracts spatial context information from the input at two scales and further refines the extracted context based on the channel importance. Finally, we add the refined context to the output of the pointwise convolution. This is the first work that improves pointwise convolution by incorporating context information. Our design significantly improves the performance of the networks without substantially increasing the number of parameters and computations. We perform experiments on coarse/fine-grained image classification, few-shot fine-grained classification, and on object detection. We further perform various ablation experiments to validate the significance of the different components used in our design. Lastly, we show experimentally that our proposed technique can be combined with existing state-of-the-art network performance improvement approaches to further improve the network performance.
Geometric Constellation Shaping Using Initialized Autoencoders

Geometric constellation shaping is a promising technique to boost the transmission capacity of communication systems. Earlier, traditional optimization methods in constellation design lead to several advanced quadrature amplitude modulation (QAM) formats, such as star QAM, cross QAM, and hexagonal QAM. The difficulty in determining decision boundaries limited their use in real systems. To overcome this, machine learning based geometric constellation shaping has recently been proposed, where the detection is done via neural networks. Unfortunately, the resulting constellation shape is often unstable and highly dependent on initialization. In this paper, we use an autoencoder for constellation shaping and detection, with strategic initialization. We contrast initialization with hexagonal QAM and square QAM. We present numerical results showing the hexagonal QAM initialization achieves the best symbol error rate performance, while the square QAM initialization has better bit error rate performance.
Task Allocation and Path Planning of Many Robots with Motion Uncertainty in a Warehouse Environment

Currently, robots have been widely used in the warehouse environment to improve the logistics operation efficiency. In general, the more robots are deployed, the higher efficiency logistics operation will be. However, more robots also bring more challenges to their task allocation and path planning. Towards the two problems, this paper presents an efficient task allocation and path planning solution that can scale up the robots greatly in a warehouse environment. In particular, the decentralized auction-bid scheme is used to allocate tasks, i.e., each robot provides its estimated task completion time as a bid, and the task is allocated to the robot with the lowest bid. In order to estimate the bid accurately, we take the robot&#x0027;s motion uncertainty into account and predict the robot density in the map at runtime, and then use the Floyd algorithm to the plan the robot path. We also design an effective scheme to sufficiently avoid the robot collision. The experimental results of a hundred robots demonstrated that our solution can quickly allocate the task and plan the path. The average task completion time is also minimized compared to some other state-of-the-art approaches. The simulation code has been open sourced<sup>1</sup><sup>1</sup>https://github.com/nucleusbiao/Robot-simulation-in-a-warehouse-environment.