Reviewerly/exHarmony huggingface dataset:
A Binary-Search-Based Locality-Sensitive Hashing Method for Cross-Site User Identification

In recent years, we have witnessed increasing attention paid to the problem of cross-site user identification (CSUI) with the bloom of social media. Despite noticeable progress in this field, the problem of enormous computation posed by the full-scale pairwise comparison still remains unsolved, which constrains the real application of the state-of-the-art approaches, especially when the user number reaches tens of millions. To address this issue, we propose a novel trajectory-oriented method, which aims at incorporating the locality-sensitive hashing (LSH) technology into the strategy of approximate nearest neighbors searching. Specifically, we design an LSH function based on binary search (BLSH) for processing user trajectory easily, and by searching the BLSH buckets, we cluster similar users to avoid the onerous full-scale pairwise comparison. Moreover, a hierarchy of discrete attention is devised to further improve the effectiveness, which develops our method into the mature one, hierarchically attentioned binary-search-based LSH (HA-BLSH). Extensive experiments on ground-truth datasets demonstrate that our HA-BLSH outperforms the cutting-edge approaches on efficiency and effectiveness. The further discussion indicates that our method is also quite flexible and able to accommodate more scenarios.
Medical image segmentation using boundary-enhanced guided packet rotation dual attention decoder network

The automatic segmentation of medical images is an important task in clinical applications. However, due to the complexity of the background of the organs, the unclear boundary, and the variable size of different organs, some of the features are lost during network learning, and the segmentation accuracy is low.To address these issues, this prompted us to study whether it is possible to better preserve the deep feature information of the image and solve the problem of low segmentation caused by unclear image boundaries.In this study, we (1) build a reliable deep learning network framework, named BGRANet,to improve the segmentation performance for medical images; (2) propose a packet rotation convolutional fusion encoder network to extract features; (3) build a boundary enhanced guided packet rotation dual attention decoder network, which is used to enhance the boundary of the segmentation map and effectively fuse more prior information; and (4) propose a multi-resolution fusion module to generate high-resolution feature maps. We demonstrate the effffectiveness of the proposed method on two publicly available datasets.BGRANet has been trained and tested on the prepared dataset and the experimental results show that our proposed model has better segmentation performance. For 4 class classifification (CHAOS dataset), the average dice similarity coeffiffifficient reached 91.73%. For 2 class classifification (Herlev dataset), the prediction, sensitivity, specifificity, accuracy, and Dice reached 93.75%, 94.30%, 98.19%, 97.43%, and 98.08% respectively. The experimental results show that BGRANet can improve the segmentation effffect for medical images.We propose a boundary-enhanced guided packet rotation dual attention decoder network. It achieved high segmentation accuracy with a reduced parameter number.
Transfer Learning and Recurrent Neural Networks for Automatic Arabic Sign Language Recognition

Arabic Sign Language (ArSL) is the most utilized for hearing and speech impairments in Arab countries. The recognition system of ArSL could be an innovation to empower communication between the deaf and others. Recent advances in gesture recognition using deep learning and computer vision-based techniques have proved promising. Due to a lack of ArSL datasets, the ArSL dataset was created. The dataset was then expanded using augmentation methods. This paper aims to create an architecture based on both Transfer Learning (TL) models and Recurrent Neural network (RNN) models for recognizing ArSL. The extraction of spatial and temporal data was accomplished by combining TL and RNN models. Furthermore, the hybrid models outperformed current architectures when tested on both the original and augmented datasets. More overall, the highest recognition accuracy of 93.4% was attained.
A Conceptual UX Model for the Design and Evaluation of Interactive Digital Artifacts over Time

To elicit and assess prior studies related to the dimensioning and modeling of user experience, this paper used a survey of literature technique. Despite the fact that there are various UX models, the review found that none of them is sufficiently complete for holistic user experience design and evaluation. In addition, the research discovered 10 major facets that are associated to or that contribute to the knowledge of user experience. The facets' attributes were also taken into account. This study built a user experience framework for the design and evaluation of digital artifacts based on this discovery, which consists of ten separate aspects or dimensions, each with its own set of quality characteristics.
Efficient Query Obfuscation with Keyqueries

Search engine users who do not want a sensitive query to actually appear in a search engine's query log can use query obfuscation or scrambling techniques to keep their information need private. However, the practical applicability of the state-of-the-art obfuscation technique is rather limited since it compares hundreds of thousands of candidate queries on a local corpus to select the final obfuscated queries. We propose a new approach to query obfuscation combining an efficient enumeration algorithm with so-called keyqueries. Generating only hundreds of candidate queries, our approach is orders of magnitude faster and makes close to real-time obfuscation of sensitive information needs feasible.
Cloud education – the first AWS Academy in Slovakia

Cloud computing is a technology that drives the innovations today. One of the sectors on which it has a major impact is the education. Cloud technology shifts the emphases from physical resources to virtual ones. On the one hand, it supports today’s learners to be always connected and helps to overcome the challenges of providing daytime teaching in today’s turbulent pandemic era. On the other hand, knowledge of cloud technologies provides students with new skills from the real world and prepares them for a career that is in great demand today. Education in the field of cloud technologies providing their knowledge is therefore one of the very current requirements for the content innovation in education. This article introduces one of the cloud educational programs ant how we plan to implement it into our study programs. The Amazon Web Services Academy, which can become a building block for the preparation of cloud-oriented courses that provide students with a great advantage in the job market.
View Composition Algebra for Ad Hoc Comparison

Comparison is a core task in visual analysis. Although there are numerous guidelines to help users design effective visualizations to aid known comparison tasks, there are few techniques available when users want to make ad hoc comparisons between marks, trends, or charts during data exploration and visual analysis. For instance, to compare voting count maps from different years, two stock trends in a line chart, or a scatterplot of country GDPs with a textual summary of the average GDP. Ideally, users can directly select the comparison targets and compare them, however what elements of a visualization should be candidate targets, which combinations of targets are safe to compare, and what comparison operations make sense? This article proposes a conceptual model that lets users compose combinations of values, marks, legend elements, and charts using a set of composition operators that summarize, compute differences, merge, and model their operands. We further define a View Composition Algebra (VCA) that is compatible with datacube-based visualizations, derive an interaction design based on this algebra that supports ad hoc visual comparisons, and illustrate its utility through several use cases.
Impact of COVID-19 on the adoption of digital pathology

Digital pathology was developed decades ago to allow pathologists to remotely collaborate on cases and improve the accuracy of diagnostic techniques by sharing digital images across laboratories. Before the COVID-19 pandemic, the United States lagged behind most other developed countries in terms of digital pathology adoption due to strict federal regulations of the US Food and Drug Administration (FDA) and the Clinical Laboratory Improvement Amendments (CLIA) from the Centers for Medicare & Medicaid Services (CMS). The FDA relaxed some of its requirements to approve and validate digital pathology-related technology to meet the pandemic response's needs. Similarly, the CMS waived restrictive CLIA rules that now allowed pathologists to use digital pathology platforms from home. Thereby, the COVID-19 pandemic accelerated the broad adoption of digital pathology for remotely rendering pathology diagnoses. This imaging technology has provided pathologists and pathologists-in-training with a variety of resources to help them continue to remotely care for patients, collaborate, and support virtual education. This paradigm shift will impact not only how we routinely work in the postpandemic era but also how we will virtually teach pathology in the future and possibly even modify regulations that govern how digital pathology systems can be used and get approved for diagnostic use. The focus of this chapter is on digital pathology concerning the COVID-19 pandemic.
AI Based Automated Image Caption Tool Implementation for Visually Impaired

Image captioning is a rapidly emerging area in the Artificial Intelligence applications for natural language definitions. It works at the confluence of image data obtained through datasets, and the sentence definitions towards capturing meaningful interpretations of the interaction that exists between them. It uses CNN&#x0027;s (Convolutional Neural Networks) reading techniques in image and LSTM (Long Short Term Memory) type RNN (Recurrent Neural Network) over sentences together so that the computer can see the context of the image and display it in a natural language like English. This paper combines the application of computer vision and natural language processing towards building assistive technology that supplements visual data like images by providing braille readable captions for the visually impaired to get a better sense of what is happening around them and understand their surroundings.
Remember the Context! ASR Slot Error Correction Through Memorization

Accurate recognition of slot values such as domain specific words or named entities by automatic speech recognition (ASR) systems forms the core of the Goal-oriented Dialogue Systems. Although it is a critical step with direct impact on downstream tasks such as language understanding, many domain agnostic ASR systems tend to perform poorly on domain specific or long tail words. They are often supplemented with slot error correcting systems but it is often hard for any neural model to directly output such rare entity words. To address this problem, we propose <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -nearest neighbor ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -NN) search that outputs domain-specific entities from an explicit datastore. We improve error correction rate by conveniently augmenting a pretrained joint phoneme and text based transformer sequence to sequence model with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -NN search during inference. We evaluate our proposed approach on five different domains containing long tail slot entities such as full names, airports, street names, cities, states. Our best performing error correction model shows a relative improvement of 7.4% in word error rate (WER) on rare word entities over the baseline and also achieves a relative WER improvement of 9.8% on an out of vocabulary (OOV) test set.
Leveraging Transformers for StarCraft Macromanagement Prediction

Inspired by the recent success of transformers in natural language processing and computer vision applications, we introduce a transformer-based neural architecture for two key StarCraft II (SC2) macromanagement tasks: global state and build order prediction. Unlike recurrent neural networks which suffer from a recency bias, transformers are able to capture patterns across very long time horizons, making them well suited for full game analysis. Our model utilizes the MSC (Macromanagement in StarCraft II) dataset and improves on the top performing gated recurrent unit (GRU) architecture in predicting global state and build order as measured by mean accuracy over multiple time horizons. We present ablation studies on our proposed architecture that support our design decisions.One key advantage of transformers is their ability to generalize well, and we demonstrate that our model achieves an even better accuracy when used in a transfer learning setting in which models trained on games with one racial matchup (e.g., Terran vs. Protoss) are transferred to a different one. We believe that transformers’ ability to model long games, potential for parallelization, and generalization performance make them an excellent choice for StarCraft agents.
Reflections about the holographic and non-holographic acquisition of surface topography: where are the limits?

Recording and (computational) processing of complex wave fields offer a vast realm of new methods for optical 3D metrology. We discuss fundamental similarities and differences between holographic surface topography measurement and non-holographic principles, such as triangulation, classical interferometry, rough surface interferometry and slope measuring methods. Key features are the physical origin of the ultimate uncertainty limit and how the topographic information is encoded and decoded. Besides the theoretical insight, the discussion will help optical metrologists to determine if their measurement results could be improved or have already hit the ultimate limit of what physics allows.
Can Solana's high throughput be an enabler for IoT?

Bitcoin and Ethereum's low transaction per second (TPS) rate has prohibited the adoption of Blockchain in areas such as Internet of Things (IoT) and Internet of Medical Things (IoMT). Despite these constraints, there is considerable research in this area, outlining the benefits of employing Blockchain in IoT applications. Such benefits relate to ensuring that the applications and their data are secure, and integrity is maintained. However, the relatively low TPS rate is still an impediment for the widespread adoption of Blockchain in IoT. This research seeks to explore whether Solana, a new Blockchain technology designed to have a high TPS rate, can overcome this challenge. Can the Solana Blockchain achieve a TPS rate higher than that achieved in other works in the literature? Can a sufficient rate be achieved that could help Blockchain technologies become an enabler in the IoT industry? These are the questions that this research aims to answer. Our experiments conducted on an application coded in Rust on Solana Blockchain demonstrated that it can successfully achieve a 663 TPS rate. Consequently, we conclude that Solana, by overcoming the aforementioned TPS rate issue, could advance the further adoption of Blockchain in IoT context.
Securing Copyright Using 3D Objects Blind Watermarking Scheme

Recently, securing Copyright has become a hot research topic due to rapidly advancing information technology.As a host cover, watermarking methods are used to conceal or embed sensitive information messages in such a manner that it was undetectable to a human observer in contemporary times.Digital media covers may often take any form, including audio, video, photos, even DNA data sequences.In this work, we present a new methodology for watermarking to hide secret data into 3-D objects.The technique of blind extraction based on reversing the steps of the data embedding process is used.The implemented technique uses the features of the 3-D object vertex' discrete cosine transform to embed a grayscale image with high capacity.The coefficient of vertex and the encrypted picture pixels are used in the watermarking procedure.Additionally, the extraction approach is fully blind and is dependent on the backward steps of the encoding procedure to get the hidden data.Correlation distance, Euclidean distance, Manhattan distance, and the Cosine distance are used to evaluate and test the performance of the proposed approach.The visibility and imperceptibility of the proposed method are assessed to show the efficiency of our work compared to previous corresponding methods.
Segmentation-based Phishing URL Detection

Uniform resource locators (URLs), used for referencing web pages, play a vital role in cyber fraud because of their complicated structure; phishers, or in other words, attackers, employ tricky by-passing techniques to deceive users. Thus, information extracted from URLs might indicate significant and meaningful patterns essential for phishing detection. To enhance the accuracy of URL-based phishing detection, we need an accurate word segmentation technique to split URLs correctly. However, in contrast to traditional word segmentation techniques used in natural language processing (NLP), URL segmentation requires meticulous attention, as tokenization, the process of turning meaningless data into meaningful data, is not as easy to apply as in NLP. In our work, we concentrate on URL segmentation to propose a novel tokenization method, named URL-Tokenizer, by combining the Bert tokenizer and WordSegment tokenizer, in addition to adopting character-level and word-level segmentations simultaneously. Our experimental evaluations in detecting the phishing URLs show that our proposed method achieves a high accuracy of 95.7% with a balanced dataset, and 97.7% with an imbalanced dataset, whereas baseline models achieved 85.4% with a balanced dataset and 85.1% with an imbalanced dataset.
Modeling Multi-Layer Access Control Policies of a Hyperledger-Fabric-Based Agriculture Supply Chain

Transparency and immutability properties of blockchains enhance traceability and trust in supply chains. However, supply chain participants resist storing most data on blockchains due to business confidentiality concerns. Permissioned blockchains can partly address such concerns. However, even on a permissioned blockchain, it is nontrivial to determine who can see what data given multiple access control models spanning multiple layers and various complex business relation-ships among the partners within a blockchain-based consortium. Consequently, most data are kept off-chain, vastly limiting the on-chain business logic execution that could otherwise enhance au-tomation, efficiency, and real-time compliance enforcement. This paper presents a multi-layered and multi-model access control analysis of an agriculture supply chain based on Hyperledger Fabric. First, we model five layers of access control in Fabric-based applications where different layers adopt different access control models. The smart contract layer may also adopt multiple and hierarchical access control models. Second, using NIST's access control rule logic circuit simulation technique, we modeled and validated the safeness of policies governing both Fabric configuration management and data access within the supply chain. Finally, we conducted a reflective privacy assessment to enhance supply chain participants' confidence in storing data on-chain by answering queries like “which other participants can see my data?”.
Reliability Analysis of Tripping Solenoid Valve Power System Based on Dynamic Fault Tree and Sequential Monte Carlo

Trip solenoid valve is an important device in the steam turbine protection system. Aiming at the problem of tripping solenoid valve malfunction caused by the action of the dual power switching device in the trip solenoid valve power distribution system, from the perspective of reliability analysis, combined with the automatic protection system of a turbine, the composition of the trip solenoid valve power distribution system was analyzed. Based on the principle of solenoid valve malfunction and interruption system failure, two power supply improvement schemes are proposed, and the minimum fault set of dynamic fault tree and sequential Monte Carlo simulation are used to analyze the reliability of tripping solenoid valve power distribution system. The analysis results show that the proposed improvement scheme has higher availability and is of reference value for improving the trip solenoid valve power distribution system.
Canonical and Surface Morphological Segmentation for Nguni Languages

Morphological Segmentation involves decomposing words into morphemes, the smallest meaning-bearing units of language. This is an important NLP task for morphologically-rich agglutinative languages such as the Southern African Nguni language group. In this paper, we investigate supervised and unsupervised models for two variants of morphological segmentation: canonical and surface segmentation. We train sequence-to-sequence models for canonical segmentation, where the underlying morphemes may not be equal to the surface form of the word, and Conditional Random Fields (CRF) for surface segmentation. Transformers outperform LSTMs with attention on canonical segmentation, obtaining an average F1 score of 72.5% across 4 languages. Feature-based CRFs outperform bidirectional LSTM-CRFs to obtain an average of 97.1% F1 on surface segmentation. In the unsupervised setting, an entropy-based approach using a character-level LSTM language model fails to outperform a Morfessor baseline, while on some of the languages neither approach performs much better than a random baseline. We hope that the high accuracy of the supervised segmentation models will help to facilitate the development of better NLP tools for Nguni languages.
Destinations, virtual reality and COVID-19. How isolation has shaped the behaviours and attitudes towards VR

Virtual Reality (VR) has been a research subject since the 1990s. Subsequently, the possibilities of applying this technology to various industries have been a subject of debates. While the tourism industry has not been an exception, a marked increase in interest is a recent phenomenon. One of the reasons for this is the unexpected pandemic that, in a way, has forced the tourism industry to search for alternative ways of communicating destinations and reducing the distance between destinations and tourists. The uncertainty of the situation and the change in tourist behaviour as an effect of COVID-19 have prompted the search for new tools to interact with tourists and influence their decisions. The new circumstances might also influence how people evaluate VR tools. The main aim of this paper is thus to diagnose the attitudes and behaviours towards VR during isolation, as well as to present their possible implications for destinations. The research results show that VR has the potential to extend the boundaries of tourism and destinations’ activities, and COVID-19 has played a role in shaping people’s attitudes and behaviours towards VR.