Reviewerly/exHarmony huggingface dataset:
Chaos and Logistic Map Based Key Generation Technique for AES-Driven IoT Security

Several efforts has been seen claiming the lightweight block ciphers as a necessarily suitable substitute in securing the Internet of Things. Currently, it has been able to envisage as a pervasive frame of reference almost all across the privacy preserving of smart and sensor oriented appliances. Different approaches are likely to be inefficient, bringing desired degree of security considering the easiness and surely the process of simplicity but security. Strengthening the well-known symmetric key and block dependent algorithm using either chaos motivated logistic map or elliptic curve has shown a far reaching potentials to be a discretion in secure real-time communication. The popular feature of logistic maps, such as the un-foreseeability and randomness often expected to be used in dynamic key-propagation in sync with chaos and scheduling technique towards data integrity. As a bit alternation in keys, able to come up with oversize deviation, also would have consequence to leverage data confidentiality. Henceforth it may have proximity to time consumption, which may lead to a challenge to make sure instant data exchange between participating node entities. In consideration of delay latency required to both secure encryption and decryption, the proposed approach suggests a modification on the key-origination matrix along with S-box. It has plausibly been taken us to this point that the time required proportionate to the plain-text sent while the plain-text disproportionate to the probability happening a letter on the message made. In line with that the effort so far sought how apparent chaos escalates the desired key-initiation before message transmission.
A Syntax-Aware Edit-based System for Text Simplification

Edit-based text simplification systems have attained much attention in recent years due to their ability to produce simplification solutions that are interpretable, as well as requiring less training examples compared to traditional seq2seq systems.Edit-based systems learn edit operations at a word level, but it is well known that many of the operations performed when simplifying text are of a syntactic nature.In this paper we propose to add syntactic information into a well known editbased system.We extend the system with a graph convolutional network module that mimics the dependency structure of the sentence, thus giving the model an explicit representation of syntax.We perform a series of experiments in English, Spanish and Italian, and report improvements of the state of the art in four out of five datasets.Further analysis shows that syntactic information is always beneficial, and suggest that syntax is more helpful in complex sentences.
Towards the application of calibrated Transformers to the unsupervised estimation of question difficulty from text

Being able to accurately perform Question Difficulty Estimation (QDE) can improve the accuracy of students' assessment and better their learning experience.Traditional approaches to QDE are either subjective or introduce a long delay before new questions can be used to assess students.Thus, recent work proposed machine learning-based approaches to overcome these limitations.They use questions of known difficulty to train models capable of inferring the difficulty of questions from their text.Once trained, they can be used to perform QDE of newly created questions.Existing approaches employ supervised models which are domain-dependent and require a large dataset of questions of known difficulty for training.Therefore, they cannot be used if such a dataset is not available (e.g. for new courses on an e-learning platform).In this work, we experiment with the possibility of performing QDE from text in an unsupervised manner.Specifically, we use the uncertainty of calibrated question answering models as a proxy of human-perceived difficulty.Our experiments show promising results, suggesting that model uncertainty could be successfully leveraged to perform QDE from text, reducing both costs and elapsed time.
Joint Dedusting and Enhancement of Top-Coal Caving Face via Single-Channel Retinex-Based Method with Frequency Domain Prior Information

Affected by the uneven concentration of coal dust and low illumination, most of the images captured in the top-coal caving face have low definition, high haze and serious noise. In order to improve the visual effect of underground images captured in the top-coal caving face, a novel single-channel Retinex dedusting algorithm with frequency domain prior information is proposed to solve the problem that Retinex defogging algorithm cannot effectively defog and denoise, simultaneously, while preserving image details. Our work is inspired by the simple and intuitive observation that the low frequency component of dust-free image will be amplified in the symmetrical spectrum after adding dusts. A single-channel multiscale Retinex algorithm with color restoration (MSRCR) in YIQ space is proposed to restore the foggy approximate component in wavelet domain. After that the multiscale convolution enhancement and fast non-local means (FNLM) filter are used to minimize noise of detail components while retaining sufficient details. Finally, a dust-free image is reconstructed to the spatial domain and the color is restored by white balance. By comparing with the state-of-the-art image dedusting and defogging algorithms, the experimental results have shown that the proposed algorithm has higher contrast and visibility in both subjective and objective analysis while retaining sufficient details.
Accurate Localization of Oil Tanks in Remote Sensing Images via FGMRST-Based CNN

Object localization is an important application of remote sensing images and the basis of information extraction. The acquired accuracy is the key factor to improve the accuracy of object structure information inversion. The floating roof oil tank is a typical cylindrical artificial object, and its top cover fluctuates up and down with the change in oil storage. Taking the oil tank as an example, this study explores the localization by combining the traditional feature parameter method and convolutional neural networks (CNNs). In this study, an improved fast radial symmetry transform (FRST) algorithm called fast gradient modulus radial symmetry transform (FGMRST) is proposed and an approach based on FGMRST combined with CNN is proposed. It effectively adds the priori of circle features to the calculation process. Compared with only using CNN, it achieves higher precision localization with fewer network layers. The experimental results based on SkySat data show that the method can effectively improve the calculation accuracy and efficiency of the same order of magnitude network, and by increasing the network depth, the accuracy still has a significant improvement.
Data Augmentation of Incorporating Real Error Patterns and Linguistic Knowledge for Grammatical Error Correction

Data augmentation aims at expanding training data with clean text using noising schemes to improve the performance of grammatical error correction (GEC).In practice, there are a great number of real error patterns in the manually annotated training data.We argue that these real error patterns can be introduced into clean text to effectively generate more real and high quality synthetic data, which is not fully explored by previous studies.Moreover, we also find that linguistic knowledge can be incorporated into data augmentation for generating more representative and more diverse synthetic data.In this paper, we propose a novel data augmentation method that fully considers the real error patterns and the linguistic knowledge for the GEC task.We conduct extensive experiments on public data sets and the experimental results show that our method outperforms several strong baselines with far less external unlabeled clean text data, highlighting its extraordinary effectiveness in the GEC task that lacks large-scale labeled training data. Our MethodIn this section, we first give the task definition of data augmentation in section 2.1 and then describe the overview architecture of our method in section 2.2.Finally, we will describe our proposed noising schemes that incorporate real error patterns and linguistic knowledge in detail in section 2.3.
graphic novel as a popular genre of book publishing in the context of modern design

The relevance of the research is due both to the lack of research that would comprehend a graphic novel as an independent phenomenon and subject of art history, the youth of the genre, and its relative non-proliferation in the Ukrainian literary space. The vast majority of Russian publications touch on the analysis of specific graphic novels in the context of modern literary studies, which focuses on the narrative and ideological components of individual works without taking into account the role of the visual component for the genre as a whole. In this regard, the article is aimed at identifying the essence of the graphic novel, its differences from the classic for book design relationships of illustration and text, clarifying the nature and role of its communication and semantic saturation taking into account the functions of design as a form of communication, highlighting the unique characteristics of the genre against the background of the traditional genre means and searching for the information and aesthetic potential of the graphic novel in the practice of Ukrainian book design.
Federated Mining of Interesting Association Rules Over EHRs

Federated learning has a great potential to create solutions working over different sources without data transfer. However current federated methods are not explainable nor auditable. In this paper we propose a Federated data mining method to discover association rules. More accurately, we define what we consider as interesting itemsets and propose an algorithm to obtain them. This approach facilitates the interoperability and reusability, and it is based on the accessibility to data. These properties are quite aligned with the FAIR principles.
Voice’s Effect on Student Effort Ratings and Recall Performance While Under Cognitive Stress

Computer-generated speech is becoming more commonplace in classrooms, so it is important to examine the impact of computer voice on students’ cognition in a variety of learning situations. The current study investigated the effects of voice in a cognitively stressful, audio-only learning situation. In this experiment, 122 college students were recruited to listen to an audio lecture and respond to test questions. Our findings indicated that participants rated the classic voice engine as demanding the most effort and the human voice as demanding the least effort. Students also had the best recall performance in the human voice condition and the worst recall performance in the classic voice condition. Our results indicated that when learners are placed under cognitive stress, the human voice is the superior pedagogical agent. These results contribute to a growing body of research examining modern computer voice as a pedagogical agent.
Quantum Computing is Scalable on a Planar Array of Qubits with Fabrication Defects

To successfully execute large-scale algorithms, a quantum computer will need to perform its elementary operations near perfectly. This is a fundamental challenge since all physical qubits suffer a considerable level of noise. Moreover, real systems are likely to have a finite yield, i.e., some nonzero proportion of the components in a complex device may be irredeemably broken at the fabrication stage. We present a threshold theorem showing that an arbitrarily large quantum computation can be completed with a vanishing probability of failure using a two-dimensional array of noisy qubits with a finite density of fabrication defects. To complete our proof we introduce a robust protocol to measure high-weight stabilizers to compensate for large regions of inactive qubits. We obtain our result using a surface-code architecture. Our approach is therefore readily compatible with ongoing experimental efforts to build a large-scale quantum computer.
Collaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants

Intelligent personal assistants (IPAs) such as Amazon Alexa, Google Assistant and Apple Siri extend their built-in capabilities by supporting voice apps developed by third-party developers. Sometimes the smart assistant is not able to successfully respond to user voice commands (aka utterances). There are many reasons including automatic speech recognition (ASR) error, natural language understanding (NLU) error, routing utterances to an irrelevant voice app or simply that the user is asking for a capability that is not supported yet. The failure to handle a voice command leads to customer frustration. In this paper, we introduce a fallback skill recommendation system to suggest a voice app to a customer for an unhandled voice command. One of the prominent challenges of developing a skill recommender system for IPAs is partial observation. To solve the partial observation problem, we propose collaborative data relabeling (CDR) method. In addition, CDR also improves the diversity of the recommended skills. We evaluate the proposed method both offline and online. The offline evaluation results show that the proposed system outperforms the baselines. The online A/B testing results show significant gain of customer experience metrics.
Online Newspaper Clustering in Aceh using the Agglomerative Hierarchical Clustering Method

The rapid progress in the field of information technology, especially the internet, has given birth to a lot of information. The ease of publishing an article on a website causes an explosion of news pages which will certainly confuse readers. The diversity and the increasing number of news articles make it increasingly difficult for internet users to find news and large piles of news data on online newspaper sites in Aceh. The grouping of text documents is needed to classify news in online newspapers in Aceh based on the content contained in news articles. In this study, the process of grouping online news in Aceh was tried using the Agglomerative Hierarchical Clustering method. News is grouped with a Bottom-Up design strategy that starts with placing each object as a cluster then combined into a larger cluster based on the similarity of keywords in each news, then the cluster results are compared and put into each news category. The research design was carried out in a structured manner using data flow diagrams in forming the research framework. The study was conducted by taking online news text data on 10 online news websites in Aceh from July 2016 to March 2017 with 1000 randomly generated documents. The process of crawling news data is done using a php script which will only take text files from the news on the website. News grouping is done based on religion, politics, law, sports, tourism, education, culture, economy and technology. The results of the grouping performance of the Agglomerative Hierarchical Clustering method in this study have an average accuracy of 89.84%.
ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective

Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraBERT and AraELECTRA, shows an impressive result on downstream tasks.However, pre-training Transformer-based language models is computationally expensive, especially for large-scale models.Recently, Funnel Transformer has addressed the sequential redundancy inside Transformer architecture by compressing the sequence of hidden states, leading to a significant reduction in the pretraining cost.This paper empirically studies the performance and efficiency of building an Arabic language model with Funnel Transformer and ELECTRA objective.We find that our model achieves state-of-the-art results on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models.
A Study on Future Perspectives of Healthcare Data Analytics Using Internet of Things

In the fields of electronics and computer science combined result, technological advances are formed with the realization of IoT (internet of things), connected healthcare, and smart concerns. The impact related to IoT in healthcare still has significant stages of development in the early days. The networked sensors are used in it that is embedded in living environments for the possible gathering of rich information with mental and physical health. This research work tries to comprehend and review the applications related to IoT with custom-made healthcare for excellent achievement at a reasonable cost. The explanation is given out in brief with IoT over which functions are used in the combination of sensing and wireless techniques with the implementation of healthcare applications. Here, the brief clarification is also given out with the challenges and opportunities for IoT. The research also highlights the opportunities and challenges for IOT in realizing the vision of the future of health care. Telemedicine is defined clearly in the research with different terms and conditions.
Explaining Errors in Machine Translation with Absolute Gradient Ensembles

Current research on quality estimation of machine translation focuses on the sentence-level quality of the translations.By using explainability methods, we can use these quality estimations for word-level error identification.In this work, we compare different explainability techniques and investigate gradient-based and perturbation-based methods by measuring their performance and required computational efforts 1 .Throughout our experiments, we observed that using absolute word scores boosts the performance of gradient-based explainers significantly.Further, we combine explainability methods to ensembles to exploit the strengths of individual explainers to get better explanations.We propose the usage of absolute gradient-based methods.These work comparably well to popular perturbation-based ones while being more time-efficient.
What BERT Based Language Model Learns in Spoken Transcripts: An Empirical Study

Language Models (LMs) have been ubiquitously leveraged in various tasks including spoken language understanding (SLU).Spoken language requires careful understanding of speaker interactions, dialog states and speech induced multimodal behaviors to generate a meaningful representation of the conversation.In this work, we propose to dissect SLU into three representative properties: conversational (disfluency, pause, overtalk), channel (speakertype, turn-tasks) and ASR (insertion, deletion, substitution).We probe BERT based language models (BERT, RoBERTa) trained on spoken transcripts to investigate its ability to understand multifarious properties in absence of any speech cues.Empirical results indicate that LM is surprisingly good at capturing conversational properties such as pause prediction and overtalk detection from lexical tokens.On the downsides, the LM scores low on turntasks and ASR errors predictions.Additionally, pre-training the LM on spoken transcripts restrain its linguistic understanding.Finally, we establish the efficacy and transferability of the mentioned properties on two benchmark datasets: Switchboard Dialog Act and Disfluency datasets.
Consumer Control Supportive Visualization

It is becoming increasingly clear to professionals and leaders, in the economic sphere, that logistics and logistical approach are now not only a service area, but also clearly, part of a competitive winning strategy.We can see the benefits of system, both in the supply chain and in the flow actions of value-creating processes.Lean, is one of the most comprehensive and respected philosophies of value-creating process development, which now weaves the organization of resupply through its wide range of tools, including external relations on more and more levels.We have good reason to ask, what can be used to make the internal and external development of needs even more transparent and plastic?It is extremely important, that the consistency of demand and production is established in a customer-centered approach, which must be experienced by all participants, even those who are not directly related to the customers or their needs.In the meantime, in the growing competition between companies, the product itself is no longer the most important, but the services associated with it, for which we will need additional production information with the highest efficiency and economies of scale.At the same time, customer expectations are changing at a faster pace than ever, which requires not only extreme flexibility and preparedness, but also immediate (up-to-date) information.Considering of all this, this study looks for the technology to help display and monitor consumption, in a controlled way.This helps targeting the goals and identify workers, within the current situation, thus demonstrating the significance of visualization, through live examples.Our main question is to look at the "whats" and "whys" of visualization and once knowing that, we will also be better equipped to perform the visualization.
Masking and Transformer-based Models for Hyperpartisanship Detection in News

Hyperpartisan news show an extreme manipulation of reality based on an underlying and extreme ideological orientation.Because of its harmful effects at reinforcing one's bias and the posterior behavior of people, hyperpartisan news detection has become an important task for computational linguists.In this paper, we evaluate two different approaches to detect hyperpartisan news.First, a text masking technique that allows us to compare style vs. topicrelated features in a different perspective from previous work.Second, the transformer-based models BERT, XLM-RoBERTa, and M-BERT, known for their ability to capture semantic and syntactic patterns in the same representation.Our results corroborate previous research on this task in that topic-related features yield better results than style-based ones, although they also highlight the relevance of using higherlength n-grams.Furthermore, they show that transformer-based models are more effective than traditional methods, but this at the cost of greater computational complexity and lack of transparency.Based on our experiments, we conclude that the beginning of the news show relevant information for the transformers at distinguishing effectively between left-wing, mainstream, and right-wing orientations.