Reviewerly/exHarmony huggingface dataset:
Practical Analysis of Sending or Not-Sending Twin-Field Quantum Key Distribution with Frequency Side Channels

The twin-field quantum key distribution (TF-QKD) and its variants can overcome the fundamental rate-distance limit of QKD. However, their physical implementations with the side channels remain the subject of further research. We test the side channel of a type of external intensity modulation that applies a Mach–Zehnder-type electro-optical intensity modulator, which shows the distinguishability of the signal and decoy states in the frequency domain. Based on this security loophole, we propose a side-channel attack, named the passive frequency-shift attack, on the imperfect implementation of the sending or not-sending (SNS) TF-QKD protocol. We analyze the performance of the SNS protocol with the actively odd-parity pairing (AOPP) method under the side-channel attack by giving the formula of the upper bound of the real secret key rate and comparing it with the lower bound of the secret key rate under Alice and Bob’s estimation. The simulation results quantitatively show the effectiveness of the attack on the imperfect devices at a long distance. Our results emphasize the importance of practical security at the light source and might provide a valuable reference for device selection in the practical implementation of the SNS protocol.
Discontinuous grammar as a foreign language

In order to achieve deep natural language understanding, syntactic constituent parsing is a vital step, highly demanded by many artificial intelligence systems to process both text and speech. One of the most recent proposals is the use of standard sequence-to-sequence models to perform constituent parsing as a machine translation task, instead of applying task-specific parsers. While they show a competitive performance, these text-to-parse transducers are still lagging behind classic techniques in terms of accuracy, coverage and speed. To close the gap, we here extend the framework of sequence-to-sequence models for constituent parsing, not only by providing a more powerful neural architecture for improving their performance, but also by enlarging their coverage to handle the most complex syntactic phenomena: discontinuous structures. To that end, we design several novel linearizations that can fully produce discontinuities and, for the first time, we test a sequence-to-sequence model on the main discontinuous benchmarks, obtaining competitive results on par with task-specific discontinuous constituent parsers and achieving state-of-the-art scores on the (discontinuous) English Penn Treebank.
Towards Finding a Research Lineage Leveraging on Identification of Significant Citations

Abstract Finding the lineage of a research topic is crucial for understanding the prior state of the art and advancing scientific displacement. The deluge of scholarly articles makes it difficult to locate the most relevant prior work and causes researchers to spend a considerable amount of time building up their literature list. Citations play a significant role in discovering relevant literature. However, not all citations are created equal. A majority of the citations that a paper receives are for providing contextual, and background information to the citing papers and are not central to the theme of those papers. However, some papers are pivotal to the citing paper and inspire or stem up the research in the citing paper. Hence the nature of citation the former receives from the latter is significant. In this work in progress paper, we discuss our preliminary idea towards establishing a lineage for a given research via identifying significant citations. We hypothesize that such an automated system can facilitate relevant literature discovery and help identify knowledge flow for at least a certain category of papers. The distal goal of this work is to identify the real impact of research work or a facility beyond direct citation counts.
Deep Learning–Based Constituency Parsing for Arabic Language

Constituency parse tree is considered the backbone of several Natural Language Processing (NLP) tasks. Deep learning techniques are adopted because they generate parse tree using a dataset without any predefined rules, making them extensible to any language. To capture the semantic meaning, dense words representation technique is necessary. This chapter combines both dense Arabic word representations and deep learning model to generate constituent parse tree. The resultant tree is used in a complete workflow. It contains a web-based application to enable linguists to choose the sentence, generate its constituent, review resultant tree, and edit needed parts. Moreover, the curated output sentence will be used to retrain the model for self-correction. The model is efficient and parallel, resulting in a quick training process.
Beyond Adaptive Sports: Challenges &amp; Opportunities to Improve Accessibility and Analytics

A recent surge in sensing platforms for sports has been accompanied by drastic improvements in the quality of data analytics. This improved quality has catalyzed notable progress in training techniques, athletic performance tracking, real-time strategy management, and even better refereeing. However, despite a sustained growth in the number of para-athletes, there has been little exploration into the accessibility and data analytics needs for adaptive sports. We interviewed 18 participants in different roles (athletes, coaches, and high-performance managers) across six adaptive sports. We probed them on their current practices, existing challenges, and analytical needs. We uncovered common themes prevalent across all six sports and further examined findings in three groups: (1) blind sports; (2) wheelchair sports; and (3) adaptive sports with high equipment. Our study highlights the challenges faced by different adaptive sports and unearths opportunities for future research to improve accessibility and address specific needs for each sport.
Low Cost Automated OS Security Audit Platform Using Robot Framework

Security baseline hardening is a baseline configuration framework aims to improve the robustness of the operating system, lowering the risk and impact of breach incidents. In typical best practice, the security baseline hardening requires to have regular check and follow-up to keep the system in-check, this set of activities are called "Security Baseline Audit". The Security Baseline Audit process is responsible by the IT department. In terms of business, this process consumes a fair number of resources such as man-hour, time, and technical knowledge. In a huge production environment, the resources mentioned can be multiplied by the system's amount in the production environment. This research proposes improving the process with automation while maintaining the quality and security level at the standard. Robot Framework, a useful and flexible opensource automation framework, is being utilized in this research following with a very successful result where the configuration is aligned with CIS (Center for Internet Security) run by the automation process. A tremendous amount of time and process are decreased while the configuration is according to this tool's standard.
Encoding a Categorical Independent Variable for Input to TerrSet’s Multi-Layer Perceptron

The profession debates how to encode a categorical variable for input to machine learning algorithms, such as neural networks. A conventional approach is to convert a categorical variable into a collection of binary variables, which causes a burdensome number of correlated variables. TerrSet’s Land Change Modeler proposes encoding a categorical variable onto the continuous closed interval from 0 to 1 based on each category’s Population Evidence Likelihood (PEL) for input to the Multi-Layer Perceptron, which is a type of neural network. We designed examples to test the wisdom of these encodings. The results show that encoding a categorical variable based on each category’s Sample Empirical Probability (SEP) produces results similar to binary encoding and superior to PEL encoding. The Multi-Layer Perceptron’s sigmoidal smoothing function can cause PEL encoding to produce nonsensical results, while SEP encoding produces straightforward results. We reveal the encoding methods by illustrating how a dependent variable gains across an independent variable that has four categories. The results show that PEL can differ substantially from SEP in ways that have important implications for practical extrapolations. If users must encode a categorical variable for input to a neural network, then we recommend SEP encoding, because SEP efficiently produces outputs that make sense.
Reciprocal polynomials and curves with many points over a finite field

Let $${\mathbb {F}}_{q^2}$$ be the finite field with $$q^2$$ elements. We provide a simple and effective method, using reciprocal polynomials, for the construction of algebraic curves over $${\mathbb {F}}_{q^2}$$ with many rational points. The curves constructed are Kummer covers or fiber products of Kummer covers of the projective line. Further, we compute the exact number of rational points for some of the curves.
IMPACT OF DIGITALIZATION AND PANDEMIC ON EMPLOYMENT AND BUSINESS COMPETENCE OF YOUTH

The article examines the competencies of entrepreneurs that are necessary in modern conditions for young people to ensure their successful career, analyzes the areas in which business initiatives of young people are most often used. &#x0D; The competencies necessary for graduates, specific areas of employment in modern conditions of digital transformation are analyzed. Special emphasis is placed on the dynamics of changes in key competencies that are important for business success in modern conditions of economic development. The results of an operational survey of managers are presented, which make it possible to identify new requirements and new qualities: digital development management, data management and use, application of digital technologies, "adoption and implementation of employee initiatives". The groups of transformations that have occurred due to the introduction of digital technologies in small businesses are highlighted.&#x0D; The aim of the work is to highlight those of them that have received the greatest development in the context of digitalization, and to establish the impact on them of the COVID-19 pandemic. To conduct the study, the methods of systemic, comparative and morphological analysis and synthesis of the general set of factors that determine changes in the business competencies of youth were used. &#x0D; The basic elements of the article can be offered as practical recommendations to state and municipal authorities on the development of youth entrepreneurship in the North Caucasus Federal District and the improvement of youth policy in the regions. The formed problems in the studied area can become the basis for the formation of a system of stimulating youth business.&#x0D; The study showed that in a crisis it is not individual qualities that are important, but their totality, with complementary characteristics.
Incorporation of Improved Differential Evolution into Hunger Games Search Algorithm

Hunger games search (HGS) is a recently proposed meta-heuristic algorithm based on hunger-driven activities and animal behavior choices, and it has been proven to possess global exploration ability and can solve both constrained and unconstrained problems effectively. Differential evolution (DE) algorithm is a heuristic random search algorithm based on the evolution differences among different individuals, and it can exploit local regions within a neighborhood of an individual. To fully utilize characteristics of both algorithms, this paper for the first time proposes a hybrid algorithm based on HGS and DE. Furthermore, DE is also enhanced by a new ranking-based mechanism to generate more promising solutions. Experimental results on IEEE CEC2017 benchmark functions indicate the effectiveness of the hybrid algorithm, namely DEHGS, in comparison with some other state-of-the-art algorithms.
Designing a large recording script for open-domain English speech synthesis*

This paper proposes a method for designing a large recording script for open domain English speech synthesis.For read-aloud style text, 12 domains and 294 sub-domains were designed using text contained in five different news media publications.For conversational style text, 4 domains and 36 sub-domains were designed using movie subtitles.The final script consists of 43,013 sentences, 27,085 read-aloud style sentences, and 15,928 conversational style sentences, consisting of 549,683 tokens and 38,356 types.The completed script is analyzed using four criteria: word coverage (type coverage and token coverage), high-frequency vocabulary coverage, phonetic coverage (diphone coverage and triphone coverage), and readability.The type coverage of our script reaches 36.86%despite its low token coverage of 2.97%.The high-frequency vocabulary coverage of the script is 73.82%, and the diphone coverage and triphone coverage of the whole script is 86.70% and 38.92%, respectively.The average readability of whole sentences is 9.03.The results of analysis show that the proposed method is effective in producing a large recording script for English speech synthesis, demonstrating good coverage in terms of unique words, high-frequency vocabulary, phonetic units, and readability.
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer’s Disease

Sensory input from multiple sources is crucial for robust and coherent human perception. Different sources contribute complementary explanatory factors. Similarly, research studies often collect multimodal imaging data, each of which can provide shared and unique information. This observation motivated the design of powerful multimodal self-supervised representation-learning algorithms. In this paper, we unify recent work on multimodal self-supervised learning under a single framework. Observing that most self-supervised methods optimize similarity metrics between a set of model components, we propose a taxonomy of all reasonable ways to organize this process. We first evaluate models on toy multimodal MNIST datasets and then apply them to a multimodal neuroimaging dataset with Alzheimer’s disease patients. We find that (1) multimodal contrastive learning has significant benefits over its unimodal counterpart, (2) the specific composition of multiple contrastive objectives is critical to performance on a downstream task, (3) maximization of the similarity between representations has a regularizing effect on a neural network, which can sometimes lead to reduced downstream performance but still reveal multimodal relations. Results show that the proposed approach outperforms previous self-supervised encoder-decoder methods based on canonical correlation analysis (CCA) or the mixture-of-experts multimodal variational autoEncoder (MMVAE) on various datasets with a linear evaluation protocol. Importantly, we find a promising solution to uncover connections between modalities through a jointly shared subspace that can help advance work in our search for neuroimaging biomarkers.
SENSE: A Flow-Down Semantics-Based Requirements Engineering Framework

The processes involved in requirements engineering are some of the most, if not the most, important steps in systems development. The need for well-defined requirements remains a critical issue for the development of any system. Describing the structure and behavior of a system could be proven vague, leading to uncertainties, restrictions, or improper functioning of the system that would be hard to fix later. In this context, this article proposes SENSE, a framework based on standardized expressions of natural language with well-defined semantics, called boilerplates, that support a flow-down procedure for requirement management. This framework integrates sets of boilerplates and proposes the most appropriate of them, depending, among other considerations, on the type of requirement and the developing system, while providing validity and completeness verification checks using the minimum consistent set of formalities and languages. SENSE is a consistent and easily understood framework that allows engineers to use formal languages and semantics rather than the traditional natural languages and machine learning techniques, optimizing the requirement development. The main aim of SENSE is to provide a complete process of the production and standardization of the requirements by using semantics, ontologies, and appropriate NLP techniques. Furthermore, SENSE performs the necessary verifications by using SPARQL (SPIN) queries to support requirement management.
Distress tolerance and adaptive capabilities in naval specialists with signs of professional burnout

Introduction . Modern research shows that distress tolerance and adaptive capabilities directly determine the structure, development, and the rate of formation of professional burnout, which predetermines the relevance of their study. Mission : to assess the distress tolerance and the structure of socio-psychological adaptation in naval specialists who have (or do not have) signs of professional burnout. Materials and methods . The study involved 250 naval specialists aged 25 to 45 years, divided into 2 groups — with absence (group 1, 91 people) and presence (group 2, 159 people) of the signs of professional burnout, in which the distress tolerance and the structure of social and psychological adaptation was determined. Results and their discussion . It was found that naval specialists with signs of professional burnout, in comparison with those who do not have it, reviled significantly higher indicators of adaptability, self-acceptance, acceptance of others, emotional comfort, internal control, adaptive abilities, neuropsychic stability, communicative potential, and personal adaptive potential, and significantly lower indicators of external control, statements, and escapism (avoiding problems). Conclusion . It is advisable to take into account the indicators of distress tolerance and the structure of socio-psychological adaptation, which play an important role in the rapid adaptation to various situations and prevent nervous breakdowns and prevent the occurrence of professional burnout.
EML-GAN: Generative Adversarial Network-Based End-to-End Multi-Task Learning Architecture for Super-Resolution Reconstruction and Scene Classification of Low-Resolution Remote Sensing Imagery

High spatial resolution remote sensing images (HSR-RSIs) are critical to providing fine land cover/land use information for scene classification. The global low spatial resolution remote sensing images (LSR-RSIs) can be easily obtained at present, whereas it is still a challenge to acquire large-scale HSR-RSIs. In this paper, an algorithmic-based architecture is proposed to improve the spatial resolution of RSIs beyond the limits of imaging sensors. The generative adversarial network-based end-to-end multi-task learning architecture (EML-GAN) is proposed for LSR-RSIs super-resolution reconstruction and scene classification simultaneously. In EML-GAN, the generator network is used to recover the fine geometric structures of LSR-RSIs by fusing the deep contextual, structure, and edge information. In addition, the discriminator network is designed to predict the scene label and distinguish the real/fake of the input data. The proposed architecture is evaluated on a public dataset and two self-made dataset. The experimental results show that the proposed architecture improves the visual effect and classification performance of LSR-RSIs.
Improving Non-autoregressive Machine Translation with Soft-Masking

In recent years, non-autoregressive machine translation has achieved great success due to its promising inference speedup. Non-autoregressive machine translation reduces the decoding latency by generating the target words in single-pass. However, there is a considerable gap in the accuracy between non-autoregressive machine translation and autoregressive machine translation. Because it removes the dependencies between the target words, non-autoregressive machine translation tends to generate repetitive words or wrong words, and these repetitive or wrong words lead to low performance. In this paper, we introduce a soft-masking method to alleviate this issue. Specifically, we introduce an autoregressive discriminator, which will output the probabilities hinting which embeddings are correct. Then according to the probabilities, we add mask on the copied representations, which enables the model to consider which words are easy to be predicted. We evaluated our method on three benchmarks, including WMT14 EN \(\rightarrow \) DE, WMT16 EN \(\rightarrow \) RO, and IWSLT14 DE \(\rightarrow \) EN. The experimental results demonstrate that our method can outperform the baseline by a large margin with a bit of speed sacrifice.
Modeling and generating marketplace activities with PROV-DM

A movement to support sustainability in fashion has emerged as a result of the damaging environment. We address this problem by providing sustainability-related information of the fashion product as transparent as possible, for individuals to perform better judgemental decision about the product. We propose the use of the PROV family in the modelling processes of users in purchasing a product and generating provenance activities of such behaviour. Ultimately, this will generate the lineage of a product and how it is treated.In this paper, we discuss the use of PROV family in modeling and generating the provenance records of purchasing activities. We demonstrate our work by presenting a real use case of an online fashion marketplace situated in Exeter, UK. The marketplace is set to allow its users exchanging (through buying and selling activities) their personal fashion products, with a goal to reduce the overall products waste. Hence, our contributions are an introduction of sustainable use case with provenance and investigation of the potential provenance related issues through modeling and generating the dynamic processes in the online marketplace.
Simultaneous temporal and spatial deep attention for imaged skeleton-based action recognition

The use of skeletons as a modality to represent and recognize human actions has gained interest thanks to the compactness of the data, their reliable representativeness in addition to their strong robustness. The deep learning based recognition approaches which are based on it often propose to improve the recognition pipeline by integrating the concept of attention in their modeling. The idea is to allow the model to focus on the relevant information of the action instead of attempting some kind of blind modeling. In this article, we propose an action recognition approach integrating simultaneously both spatial and temporal attentions. We first perform a transformation of the input sequence data into a color matrix, called imaged skeleton, comprising Cartesian and rotational information. Then, this new representation is given as input to an architecture composed of a main trunk, that allows features extraction and classification, and several attention branches. Different experimental evaluations on two popular benchmark databases, namely UT-Kinect [1] and SBU Kinect Interaction [2], are conducted to verify the interest of our proposed approach, where better performances are reported.