Reviewerly/exHarmony huggingface dataset:
Four-party deterministic quantum operation sharing with a generalized seven-qubit Brown state

Abstract A four-party deterministic single-qubit operation sharing scheme is proposed by using a generalized seven-qubit Brown state in a given qubit distribution as the quantum channel. Its security is confirmed via analyses. The sharing determinacy and the sharer symmetry of the scheme are identified. The experimental feasibility of the scheme is confirmed through discussions. Moreover, the scheme is compared with a competing one which utilizes the same quantum channel. It is found that the present scheme reduces the necessary operations and two-qubit unitary operation types as well as the classical communication cost. The decreases of both the necessary operation complexity and the classical communication cost eventually mean both the increase of the scheme efficiency and the decrease of the resource consumption. Consequently, the present scheme is superior to the competing one.
From partners to populations: A hierarchical Bayesian account of coordination and convention.

Languages are powerful solutions to coordination problems: They provide stable, shared expectations about how the words we say correspond to the beliefs and intentions in our heads. Yet, language use in a variable and nonstationary social environment requires linguistic representations to be flexible: Old words acquire new ad hoc or partner-specific meanings on the fly. In this article, we introduce continual hierarchical adaptation through inference (CHAI), a hierarchical Bayesian theory of coordination and convention formation that aims to reconcile the long-standing tension between these two basic observations. We argue that the central computational problem of communication is not simply transmission, as in classical formulations, but continual learning and adaptation over multiple timescales. Partner-specific common ground quickly emerges from social inferences within dyadic interactions, while community-wide social conventions are stable priors that have been abstracted away from interactions with multiple partners. We present new empirical data alongside simulations showing how our model provides a computational foundation for several phenomena that have posed a challenge for previous accounts: (a) the convergence to more efficient referring expressions across repeated interaction with the same partner, (b) the gradual transfer of partner-specific common ground to strangers, and (c) the influence of communicative context on which conventions eventually form. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
ASAD: Arabic Social media Analytics and unDerstanding

This system demonstration paper describes the Arabic Social media Analysis and unDerstanding (ASAD) toolkit, which is a suite of seven individual modules that allows users to determine dialects, sentiment, news category, offensiveness, hate speech, adult content, and spam in Arabic tweets 1 .The suite is made available through a web API and a web interface where users can enter text or upload files.
A NOVEL METHOD FOR PROTECTIVE FACE MASK DETECTION USING CONVOLUTIONAL NEURAL NETWORKS AND IMAGE HISTOGRAMS

Abstract. This paper proposes a new hybrid method for automatic detection and recognition of the presence/absence of a protective mask on human's face. It combines visual features extracted using Convolutional Neural Network (CNN) with image histograms that convey information about pixel intensity. Several pre-trained models for building feature extraction systems using a CNN and several types of image histograms are considered in this paper. We test our approach on the Medical Mask Dataset and perform cross-corpus analysis on two other databases named Masked Faces (MAFA) and Real-World Masked Face Dataset (RMFD). We demonstrate that the proposed hybrid method increases the Unweighted Average Recalls (UARs) of recognition of the presence/absence of a protective mask on human's face in comparison with traditional CNNs on the MAFA and RMFD databases by 0.96% and 1.32%, respectively. The proposed method can be generalized and used for other tasks of biometry, computer vision, machine learning and automatic face recognition.
Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages

Unsupervised translation has reached impressive performance on resource-rich language pairs such as English-French and English-German.However, early studies have shown that in more realistic settings involving lowresource, rare languages, unsupervised translation performs poorly, achieving less than 3.0 BLEU.In this work, we show that multilinguality is critical to making unsupervised systems practical for low-resource settings.In particular, we present a single model for 5 lowresource languages (Gujarati, Kazakh, Nepali, Sinhala, and Turkish) to and from English directions, which leverages monolingual and auxiliary parallel data from other high-resource language pairs via a three-stage training scheme.We outperform all current state-of-the-art unsupervised baselines for these languages, achieving gains of up to 14.4 BLEU.Additionally, we outperform strong supervised baselines for various language pairs as well as match the performance of the current state-of-the-art supervised model for NeÑEn.We conduct a series of ablation studies to establish the robustness of our model under different degrees of data quality, as well as to analyze the factors which led to the superior performance of the proposed approach over traditional unsupervised models.
Federated Data: Toward New Generation of Credible and Trustable Artificial Intelligence

Federated ecology can provide an effective solution forthe serious isolated data island issues caused by data privacy protection and information security requirements in the era of artificial intelligence (AI). As the data foundation of federated ecology, federated data include the data of all the nodes in the federation, as wellas their storage, computation, and communication resources. For privacy-preserving, federated data are divided into private data and non-private data, and through the federated control of these data, data federalization can be realized. In data-driven AI technologies, federated data play an important role, and it can help realize effective data retrieval, pre-processing, processing, mining, and visualization for AI-based applications. It can also provide effective solutionsfor the dilemmas faced by AI technologies, such as training AI models without sufficient data, increasing the generality of AI models for different application scenarios and establishing a unified processing workflow for data security and privacy control in AI-based applications.
Non-uniform illumination correction based on multi-scale Retinex in digital image correlation

Digital image correlation (DIC) is an effective optical measurement method. It aims to obtain the displacement field and strain field of the measured object by correlating two digital speckle images before and after deformation. In the actual acquisition of speckle images, due to the large volume of the measured object, the light source cannot cover all areas evenly or has some random change. These issues may easily lead to a non-uniform distribution of light intensity speckle images and reduce the quality of speckle images, which affects the accuracy of DIC measurement to a certain extent. To solve this problem, a non-uniform illumination correction algorithm based on multi-scale Retinex is introduced. First, to analyze the influence of non-uniform illumination on DIC measurement accuracy, the displacement comparison experiment of the numerical simulation speckle images with different non-uniform illumination is conducted. Then, a non-uniform illumination correction algorithm based on multi-scale Retinex is applied to reduce or eliminate the effects of non-uniform illumination by the simulation experiment. Finally, the quantitative measurement of rigid body rotation and uniaxial tensile experiment in plane is studied to verify the feasibility of the correction method for the speckle images. The experimental results show that the measurement accuracy of DIC is improved significantly with the aid of non-uniform illumination variation correction.
RETRACTED: Research on the innovation of college music teaching mode based on Artificial Intelligence

Abstract Nowadays, many college students like all kinds of music. Students even form their own bands. Unfortunately, music education is not valued in many schools. Some famous music schools are trying to change the current situation of music education mode. The integration of artificial intelligence technology and education has led to the change of modern music teaching concept. However, the probability of challenge and opportunity brought by AI technology is the same. This paper analyzes the current situation of traditional music education. On this basis, this paper explains the innovation strategy of college music teaching mode led by artificial intelligence. I hope this article can give you some inspiration.
Improving Neural RST Parsing Model with Silver Agreement Subtrees

Most of the previous Rhetorical Structure Theory (RST) parsing methods are based on supervised learning such as neural networks, that require an annotated corpus of sufficient size and quality.However, the RST Discourse Treebank (RST-DT), the benchmark corpus for RST parsing in English, is small due to the costly annotation of RST trees.The lack of large annotated training data causes poor performance especially in relation labeling.Therefore, we propose a method for improving neural RST parsing models by exploiting silver data, i.e., automatically annotated data.We create large-scale silver data from an unlabeled corpus by using a state-of-the-art RST parser.To obtain high-quality silver data, we extract agreement subtrees from RST trees for documents built using the RST parsers.We then pre-train a neural RST parser with the obtained silver data and fine-tune it on the RST-DT.Experimental results show that our method achieved the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2, respectively.Furthermore, we obtained a remarkable gain in the Relation score, 3.0 points, against the previous state-of-the-art parser.
Detection of Harassment Type of Cyberbullying: A Dictionary of Approach Words and Its Impact

The purpose of this paper is to analyse the effects of predatory approach words in the detection of cyberbullying and to propose a mechanism of generating a dictionary of such approach words. The research incorporates analysis of chat logs from convicted felons, to generate a dictionary of sexual approach words. By analysing data across multiple social networks, the study demonstrates the usefulness of such a dictionary of approach words in detection of online predatory behaviour through machine learning algorithms. It also shows the difference between the nature of contents across specific social network platforms. The proposed solution to detect cyberbullying and the domain of approach words are scalable to fit real-life social media, which can have a positive impact on the overall health of online social networks. Different types of cyberbullying have different characteristics. However, existing cyberbullying detection works are not targeted towards any of these specific types. This research is tailored to focus on sexual harassment type of cyberbullying and proposes a novel dictionary of approach words. Since cyberbullying is a growing threat to the mental health and intellectual development of adolescents in the society, models targeted towards the detection of specific type of online bullying or predation should be encouraged among social network researchers.
Exposing Manipulated Photos and Videos in Digital Forensics Analysis

Tampered multimedia content is being increasingly used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are amongst the most recurrent crimes in which manipulated digital photos and videos are the perpetrating and disseminating medium. Criminal investigation has been challenged in applying machine learning techniques to automatically distinguish between fake and genuine seized photos and videos. Despite the pertinent need for manual validation, easy-to-use platforms for digital forensics are essential to automate and facilitate the detection of tampered content and to help criminal investigators with their work. This paper presents a machine learning Support Vector Machines (SVM) based method to distinguish between genuine and fake multimedia files, namely digital photos and videos, which may indicate the presence of deepfake content. The method was implemented in Python and integrated as new modules in the widely used digital forensics application Autopsy. The implemented approach extracts a set of simple features resulting from the application of a Discrete Fourier Transform (DFT) to digital photos and video frames. The model was evaluated with a large dataset of classified multimedia files containing both legitimate and fake photos and frames extracted from videos. Regarding deepfake detection in videos, the Celeb-DFv1 dataset was used, featuring 590 original videos collected from YouTube, and covering different subjects. The results obtained with the 5-fold cross-validation outperformed those SVM-based methods documented in the literature, by achieving an average F1-score of 99.53%, 79.55%, and 89.10%, respectively for photos, videos, and a mixture of both types of content. A benchmark with state-of-the-art methods was also done, by comparing the proposed SVM method with deep learning approaches, namely Convolutional Neural Networks (CNN). Despite CNN having outperformed the proposed DFT-SVM compound method, the competitiveness of the results attained by DFT-SVM and the substantially reduced processing time make it appropriate to be implemented and embedded into Autopsy modules, by predicting the level of fakeness calculated for each analyzed multimedia file.
Formalising a Turing-Complete Choreographic Language in Coq

The theory of choreographic languages typically includes a number of complex results that are proved by structural induction. The high number of cases and the subtle details in some of them lead to long reviewing processes, and occasionally to errors being found in published proofs. In this work, we take a published proof of Turing completeness of a choreographic language and formalise it in Coq. Our development includes formalising the choreographic language, its basic properties, Kleene’s theory of partial recursive functions, the encoding of these functions as choreographies, and a proof that this encoding is correct.With this effort, we show that theorem proving can be a very useful tool in the field of choreographic languages: besides the added degree of confidence that we get from a mechanised proof, the formalisation process led us to a significant simplification of the underlying theory. Our results offer a foundation for the future formal development of choreographic languages.
Structural Guidance for Transformer Language Models

Transformer-based language models pretrained on large amounts of text data have proven remarkably successful in learning generic transferable linguistic representations.Here we study whether structural guidance leads to more human-like systematic linguistic generalization in Transformer language models without resorting to pre-training on very large amounts of data.We explore two general ideas.The "Generative Parsing" idea jointly models the incremental parse and word sequence as part of the same sequence modeling task.The "Structural Scaffold" idea guides the language model's representation via additional structure loss that separately predicts the incremental constituency parse.We train the proposed models along with a vanilla Transformer language model baseline on a 14 million-token and a 46 million-token subset of the BLLIP dataset, and evaluate models' syntactic generalization performances on SG Test Suites and sized BLiMP.Experiment results across two benchmarks suggest converging evidence that generative structural supervisions can induce more robust and humanlike linguistic generalization in Transformer language models without the need for data intensive pre-training.
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models

Pre-trained language models (PLMs) have achieved great success in natural language processing.Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate dimension in feed-forward sub-networks) in BERT (Devlin et al., 2019).Few studies have been conducted to explore the design of architecture hyper-parameters in BERT, especially for the more efficient PLMs with tiny sizes, which are essential for practical deployment on resource-constrained devices.In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters.Specifically, we carefully design the techniques of one-shot learning and the search space to provide an adaptive and efficient development way of tiny PLMs for various latency constraints.We name our method AutoTinyBERT 1 and evaluate its effectiveness on the GLUE and SQuAD benchmarks.The extensive experiments show that our method outperforms both the SOTA searchbased baseline (NAS-BERT) and the SOTA distillation-based methods (such as DistilBERT, TinyBERT, MiniLM and MobileBERT).In addition, based on the obtained architectures, we propose a more efficient development method that is even faster than the development of a single PLM.
Distributed ledger technology: State-of-the-art and current challenges

Distributed Ledger Technology (DLT) is making the first steps toward becoming a solution for the growing number of various decentralized systems worldwide. Unlike pure Blockchain, DLT finds many uses across different industries, including eHealth, finance, supply chain monitoring, and the Internet of Things (IoT). One of the vital DLT features is the ability to provide an immutable and commonly verifiable ledger for larger-scale and highly complex systems. Today?s centralized systems can no longer guarantee the required level of availability and reliability due to the growing number of the involved nodes, complicated heterogeneous architectures, and task load, while the publicly available distributed systems are still in their infancy. This paper aims to provide an exhaustive topical review of the state-of-theart of Distributed Ledger Technology applicability in various sectors. It outlines the importance of the practical integration of technology-related challenges, as well as potential solutions.
Robust PDF Document Conversion using Recurrent Neural Networks

The number of published PDF documents in both the academic and commercial world has increased exponentially in recent decades. There is a growing need to make their rich content discoverable to information retrieval tools. Achieving high-quality semantic searches demands that a document's structural components such as title, section headers, paragraphs, (nested) lists, tables and figures (including their captions) are properly identified. Unfortunately, the PDF format is known to not conserve such structural information because it simply represents a document as a stream of low-level printing commands, in which one or more characters are placed in a bounding box with a particular styling. In this paper, we present a novel approach to document structure recovery in PDF using recurrent neural networks to process the low-level PDF data representation directly, instead of relying on a visual re-interpretation of the rendered PDF page, as has been proposed in previous literature. We demonstrate how a sequence of PDF printing commands can be used as input into a neural network and how the network can learn to classify each printing command according to its structural function in the page. This approach has three advantages: First, it can distinguish among more fine-grained labels (typically 10-20 labels as opposed to 1-5 with visual methods), which results in a more accurate and detailed document structure resolution. Second, it can take into account the text flow across pages more naturally compared to visual methods because it can concatenate the printing commands of sequential pages. Last, our proposed method needs less memory and it is computationally less expensive than visual methods. This allows us to deploy such models in production environments at a much lower cost. Through extensive architectural search in combination with advanced feature engineering, we were able to implement a model that yields a weighted average F1 score of 97% across 17 distinct structural labels. The best model we achieved is currently served in production environments on our Corpus Conversion Service (CCS), which was presented at KDD18. This model enhances the capabilities of CCS significantly, as it eliminates the need for human annotated label ground-truth for every unseen document layout. This proved particularly useful when applied to a huge corpus of PDF articles related to COVID-19.
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World

We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language.We factorize PIGLeT into a physical dynamics model, and a separate language model.Our dynamics model learns not just what objects are but also what they do: glass cups break when thrown, plastic ones don't.We then use it as the interface to our language model, giving us a unified model of linguistic form and grounded meaning.PIGLeT can read a sentence, simulate neurally what might happen next, and then communicate that result through a literal symbolic representation, or natural language.Experimental results show that our model effectively learns world dynamics, along with how to communicate them.It is able to correctly forecast "what happens next" given an English sentence over 80% of the time, outperforming a 100x larger, text-to-text approach by over 10%.Likewise, its natural language summaries of physical interactions are also judged by humans as more accurate than LM alternatives.We present comprehensive analysis showing room for future work.
Computing Optimal Repairs of Quantified ABoxes w.r.t. Static $$\mathcal {EL}$$ TBoxes

Abstract The application of automated reasoning approaches to Description Logic (DL) ontologies may produce certain consequences that either are deemed to be wrong or should be hidden for privacy reasons. The question is then how to repair the ontology such that the unwanted consequences can no longer be deduced. An optimal repair is one where the least amount of other consequences is removed. Most of the previous approaches to ontology repair are of a syntactic nature in that they remove or weaken the axioms explicitly present in the ontology, and thus cannot achieve semantic optimality. In previous work, we have addressed the problem of computing optimal repairs of (quantified) ABoxes, where the unwanted consequences are described by concept assertions of the lightweight DL $$\mathcal {EL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>EL</mml:mi></mml:math> . In the present paper, we improve on the results achieved so far in two ways. First, we allow for the presence of terminological knowledge in the form of an $$\mathcal {EL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>EL</mml:mi></mml:math> TBox. This TBox is assumed to be static in the sense that it cannot be changed in the repair process. Second, the construction of optimal repairs described in our previous work is best case exponential. We introduce an optimized construction that is exponential only in the worst case. First experimental results indicate that this reduces the size of the computed optimal repairs considerably.