Reviewerly/exHarmony huggingface dataset:
Quantum Key Distribution with Nonideal Heterodyne Detection: Composable Security of Discrete-Modulation Continuous-Variable Protocols

Continuous-variable quantum key distribution exploits coherent measurements of the electromagnetic field, i.e., homodyne or heterodyne detection. The most advanced security proofs developed so far relied on idealised mathematical models for such measurements, which assume that the measurement outcomes are continuous and unbounded variables. As physical measurement devices have finite range and precision, these mathematical models only serve as an approximation. It is expected that, under suitable conditions, the predictions obtained using these simplified models are in good agreement with the actual experimental implementations. However, a quantitative analysis of the error introduced by this approximation, and of its impact on composable security, have been lacking so far. Here we present a theory to rigorously account for the experimental limitations of realistic heterodyne detection. We focus on collective attacks, and present security proofs for the asymptotic and finite-size regimes, the latter within the framework of composable security. In doing this, we establish for the first time the composable security of discrete-modulation continuous-variable quantum key distribution in the finite-size regime. Tight bounds on the key rates are obtained through semi-definite programming and do not rely on a truncation of the Hilbert space.
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks

Constituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans.In this work, we cast nested NER to constituency parsing and propose a novel pointing mechanism for bottomup parsing to tackle both tasks.The key idea is based on the observation that if we traverse a constituency tree in post-order, i.e., visiting a parent after its children, then two consecutively visited spans would share a boundary.Our model tracks the shared boundaries and predicts the next boundary at each step by leveraging a pointer network.As a result, it needs only linear steps to parse and thus is efficient.It also maintains a parsing configuration for structural consistency, i.e., always outputting valid trees.Experimentally, our model achieves the state-of-the-art performance on PTB among all BERT-based models (96.01 F1 score) and competitive performance on CTB7 in constituency parsing; and it also achieves strong performance on three benchmark datasets of nested NER: ACE2004, ACE2005, and GENIA 1 .
Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects.
GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems

Over the last few years, there has been a move towards data curation for multilingual taskoriented dialogue (ToD) systems that can serve people speaking different languages.However, existing multilingual ToD datasets either have a limited coverage of languages due to the high cost of data curation, or ignore the fact that dialogue entities barely exist in countries speaking these languages.To tackle these limitations, we introduce a novel data curation method that generates GlobalWoZ -a largescale multilingual ToD dataset globalized from an English ToD dataset for three unexplored use cases of multilingual ToD systems.Our method is based on translating dialogue templates and filling them with local entities in the target-language countries.Besides, we extend the coverage of target languages to 20 languages.We will release our dataset and a set of strong baselines to encourage research on multilingual ToD systems for real use cases. 1
Measuring Hidden Bias within Face Recognition via Racial Phenotypes

Recent work reports disparate performance for intersectional racial groups across face recognition tasks: face verification and identification. However, the definition of those racial groups has a significant impact on the underlying findings of such racial bias analysis. Previous studies define these groups based on either demographic information (e.g. African, Asian etc.) or skin tone (e.g. lighter or darker skins). The use of such sensitive or broad group definitions has disadvantages for bias investigation and subsequent counter-bias solutions design. By contrast, this study introduces an alternative racial bias analysis methodology via facial phenotype attributes for face recognition. We use the set of observable characteristics of an individual face where a race-related facial phenotype is hence specific to the human face and correlated to the racial profile of the subject. We propose categorical test cases to investigate the individual influence of those attributes on bias within face recognition tasks. We compare our phenotype-based grouping methodology with previous grouping strategies and show that phenotype-based groupings uncover hidden bias without reliance upon any potentially protected attributes or ill-defined grouping strategies. Furthermore, we contribute corresponding phenotype attribute category labels for two face recognition tasks: RFW for face verification and VGGFace2 (test set) for face identification.
A Self-Trading and Authenticated Roaming Scheme Based on Blockchain for Smart Grids

The increasing volume of user and household data and number of smart meters compound the challenge of ensuring efficiency and privacy protection of electricity trading in existing smart grids. Therefore, to minimize the cost and latency in electricity trading, in this article, we design a new architecture for smart meters that can support transactions (using a blockchain-based wallet) and initiate transmission switch instructions (using smart contacts). Specifically, our approach comprises a decentralized peer-to-peer electricity trading scheme to enable automated electricity transmission (using smart contract instead of some centralized entity), and our blockchain-based anonymous authentication scheme to facilitate fast and privacy-aware roaming, in order to achieve privacy protection. We demonstrate that our proposed scheme is secure under the universally composable framework.
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing

Predicting linearized
Document-Level Text Simplification: Dataset, Criteria and Baseline

Text simplification is a valuable technique.However, current research is limited to sentence simplification.In this paper, we define and investigate a new task of document-level text simplification, which aims to simplify a document consisting of multiple sentences.Based on Wikipedia dumps, we first construct a large-scale dataset named D-Wikipedia and perform analysis and human evaluation on it to show that the dataset is reliable.Then, we propose a new automatic evaluation metric called D-SARI that is more suitable for the document-level simplification task.Finally, we select several representative models as baseline models for this task and perform automatic evaluation and human evaluation.We analyze the results and point out the shortcomings of the baseline models.
Learning to predict test effectiveness

The high cost of the test can be dramatically reduced, provided that the coverability as an inherent feature of the code under test is predictable. This article offers a machine learning model to predict the extent to which the test could cover a class in terms of a new metric called Coverageability. The prediction model consists of an ensemble of four regression models. The learning samples consist of feature vectors, where features are source code metrics computed for a class. The samples are labeled by the Coverageability values computed for their corresponding classes. We offer a mathematical model to evaluate test effectiveness in terms of size and coverage of the test suite generated automatically for each class. We extend the size of the feature space by introducing a new approach to defining sub-metrics in terms of existing source code metrics. Using feature importance analysis on the learned prediction models, we sort source code metrics in the order of their impact on the test effectiveness. As a result of which, we found the class strict cyclomatic complexity as the most influential source code metric. Our experiments with the prediction models on a large corpus of Java projects containing about 23,000 classes demonstrate the Mean Absolute Error (MAE) of 0.032, Mean Squared Error (MSE) of 0.004, and an R2-score of 0.855. Compared with the state-of-the-art coverage prediction models, our models improve MAE, MSE, and an R2-score by 5.78%, 2.84%, and 20.71%, respectively.
A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology

Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization

Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence methods attempt to understand how such black-boxes make their decisions. In this work, linear model trees (LMTs) are used to approximate the DNN controlling an autonomous surface vessel (ASV) in a simulated environment and then run in parallel with the DNN to give explanations in the form of feature attributions in real-time. How well a model can be understood depends not only on the explanation itself, but also on how well it is presented and adapted to the receiver of said explanation. Different end-users may need both different types of explanations, as well as different representations of these. The main contributions of this work are (1) significantly improving both the accuracy and the build time of a greedy approach for building LMTs by introducing ordering of features in the splitting of the tree, (2) giving an overview of the characteristics of the seafarer/operator and the developer as two different end-users of the agent and receiver of the explanations, and (3) suggesting a visualization of the docking agent, the environment, and the feature attributions given by the LMT for when the developer is the end-user of the system, and another visualization for when the seafarer or operator is the end-user, based on their different characteristics.
A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each object to a corresponding class label. These techniques proceed differently in network architecture, training strategy and optimization function. In this paper, we focus on animal species detection as an initial step to mitigate the negative impacts of wildlife–human and wildlife–vehicle encounters in remote wilderness regions and on highways. Our goal is to provide a summary of object detection techniques based on R-CNN models, and to enhance the performance of detecting animal species in accuracy and speed, by using four different R-CNN models and a deformable convolutional neural network. Each model is applied on three wildlife datasets, results are compared and analyzed by using four evaluation metrics. Based on the evaluation, an animal species detection system is proposed.
Early reading learning for special needs students: challenges on inclusive primary school during COVID-19 pandemic

This study is meant to describe the types of disability faced by students in inclusive schools and the challenges faced by teachers as well as students in early reading learning for students with special needs in inclusive primary schools during the COVID-19 pandemic. The research was conducted in 12 inclusive primary schools in Magetan Regency, Indonesia. This research is a descriptive qualitative research in which psychological assessment consisting of intelligence tests CPM, and WISC, documentation, passive observation, and in-depth interviews were used to collect the data. The data were collected from informants, events or activities of student interaction in school, and documents of students’ backgrounds and other related documents. The subjects of this study were 72 special needs students with special needs in 12 inclusive primary schools. The data were then analyzed using an interactive analysis model. The results showed that slow learning is the most common type of disability faced by students with special needs in inclusive elementary schools around Magetan Regency, Indonesia. During the COVID-19 pandemic, early reading learning was conducted through distance learning which encountered many obstacles, such as lack of mobile phone due to the low socioeconomic status of parents.
Research on super-resolution reconstruction of remote sensing images: a comprehensive review

The super-resolution (SR) reconstruction of remote sensing images is a low-cost and efficient method to improve their resolution, and it is often used for further image analysis. To understand the development of SR reconstruction of remote sensing images and research hotspots and trends, we examined its history and reviewed existing methods categorized into traditional, learning-based, and deep-learning-based methods. To evaluate the reconstruction performance, we conducted experiments comparing various algorithms for the single- and multi-frame SR reconstruction of remote sensing images considering three datasets. The experimental results indicate the advantages and limitations of single- and multi-frame reconstruction, with the latter showing a higher performance. Finally, we provide directions for future development of this SR reconstruction.
Blockchain Enabled Reparations in Smart Buildings Cyber Physical System

Blockchain technology is evolving across the globe and is being looked upon as a definite part of the future. Blockchain is often associated with bitcoin and finance’s domain. But over the last decade, this backend technology to bitcoin has spread its association in almost all domains that we can think of. Further to this, smart contracts are making the blockchain ecosystem better. Other evolving technologies like Internet-of-things, Industrial Internet-of things, Cyber physical systems are also making their onset on the global platform. Smart buildings link Internet of-things connectivity, sensors and the cloud to remotely supervise and assure efficient heating- air conditioning, lighting and security systems etc to improve efficiency and overall sustainability. The global buildings sector over the next 40 years is expected to add 230 billion square meters of fresh construction, i.e., adding the equivalent of Paris every week. Thus integrating these technologies right at the onset, before they grow in isolation, is a coveted need today. This paper proposes a prototype to simulate architecture and discusses how blockchain-enabled smart buildings can further expedite automation, security and transparency. For an apprehension purpose, the paper focuses on smart contracts enabled repairs and service in smart buildings.
Leveraging BIM and Blockchain for Digital Twins

The digitalization of the construction industry has been supported recently with various building information modelling (BIM) methods applied to lifecycle stages. This has enabled the transition towards an improved management of information required in the development of different construction project tasks. In a digital construction context, traceability of models and collaboration across disciplines is required especially when dealing with sensitive data and critical infrastructures. Blockchain technology has the potential to advance the automation of processes in the construction industry by offering a higher level of data traceability, incentivising participation, and ensuring transparency with projects. This paper presents how BIM and Blockchain can be utilised to facilitate the establishment and implementation of Digital Twins for construction projects. A highway construction case study is used for demonstrating the implementation of smart contracts for managing tasks and processes across different disciplines involved in a construction project.
Improved Word Sense Disambiguation with Enhanced Sense Representations

Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield surprisingly good results by purely leveraging pretrained language models and short dictionary definitions (or glosses) of the different word senses.While concise and intuitive, the sense gloss is just one of many ways to provide information about word senses.In this paper, we focus on enhancing the sense representations via incorporating synonyms, example phrases or sentences showing usage of word senses, and sense gloss of hypernyms.We show that incorporating such additional information boosts the performance on WSD.With the proposed enhancements, our system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task, surpassing previous published scores on this benchmark dataset.
Insights into Organizational Security Readiness: Lessons Learned from Cyber-Attack Case Studies

This paper focuses on understanding the characteristics of multiple types of cyber-attacks through a comprehensive evaluation of case studies of real-world cyber-attacks. For each type of attack, we identify and link the attack type to the characteristics of that attack and the factors leading up to the attack, as observed from the review of case studies for that type of attack. We explored both the quantitative and qualitative characteristics for the types of attacks, including the type of industry, the financial intensity of the attack, non-financial intensity impacts, the number of impacted customers, and the impact on users’ trust and loyalty. In addition, we investigated the key factors leading up to an attack, including the human behavioral aspects; the organizational–cultural factors at play; the security policies adapted; the technology adoption and investment by the business; the training and awareness of all stakeholders, including users, customers and employees; and the investments in cybersecurity. In our study, we also analyzed how these factors are related to each other by evaluating the co-occurrence and linkage of factors to form graphs of connected frequent rules seen across the case studies. This study aims to help organizations take a proactive approach to the study of relevant cyber threats and aims to educate organizations to become more knowledgeable through lessons learned from other organizations experiencing cyber-attacks. Our findings indicate that the human behavioral aspects leading up to attacks are the weakest link in the successful prevention of cyber threats. We focus on human factors and discuss mitigation strategies.