Reviewerly/exHarmony huggingface dataset:
Asymmetric impact of financial market development on entrepreneurship in Nigeria

Entrepreneurship is increasingly used as the framework for promotion of innovation, economic growth and development. Using the finance-growth model, this chapter explores the asymmetric link between financial market development (FMD), regulatory quality and entrepreneurship in Nigeria using time series data from 1996 to 2018 and a non-linear auto-regressive distributed lag (NARDL) approach. The findings show that regulatory quality positively moderates the relationship between institution depth index, financial institution access index and financial institution efficiency index as the dimensions of FMD and self-employment in the long run, while the short-run effect was inconsistent. The theoretical and practical implications are then discussed and policy recommendations provided, especially relating to developing, emerging and transition economies (DETEs).
Educational Environment of Pedagogical University: Integration of Communication Formats

From the standpoint of pedagogical bioethics, approaches to organise the educational environment of a pedagogical university are proposed for the effective acquisition of teaching experience by future educators. The design of the educational environment should provide an understanding of the ethical boundaries of various communication formats and an experience of performing communicative roles. The variety of these formats and roles corresponds to the variety of loci in the modern educational space.
A new stochastic gradient descent possibilistic clustering algorithm

Several well known clustering algorithms have their own online counterparts, in order to deal effectively with the big data issue, as well as with the case where the data become available in a streaming fashion. However, very few of them follow the stochastic gradient descent philosophy, despite the fact that the latter enjoys certain practical advantages (such as the possibility of (a) running faster than their batch processing counterparts and (b) escaping from local minima of the associated cost function), while, in addition, strong theoretical convergence results have been established for it. In this paper a novel stochastic gradient descent possibilistic clustering algorithm, called O- PCM 2 is introduced. The algorithm is presented in detail and it is rigorously proved that the gradient of the associated cost function tends to zero in the L 2 sense, based on general convergence results established for the family of the stochastic gradient descent algorithms. Furthermore, an additional discussion is provided on the nature of the points where the algorithm may converge. Finally, the performance of the proposed algorithm is tested against other related algorithms, on the basis of both synthetic and real data sets.
Dynamic white-light interferometry via multiwavelength tilt iteration and sliding local least squares

The growing interest in microprofile measurements for advanced semiconductor manufacturing and electronic glass screens has stimulated the demand for in situ dynamic white-light interferometry (DWLI). However, it is challenging to perform vibration-insensitive measurements because of the broad-spectrum interference. In this Letter, we report a vertical scanning DWLI using multiwavelength tilt iteration and sliding local least squares. Numerical simulations and comparative experiments were conducted to verify the principle and performance of the proposed method. To the best of our knowledge, this is the first time such a DWLI has been proposed to achieve excellent vibration-resistant performance without using complex photoelectric compensation systems.
Handling within-word and cross-word pronunciation variation for Arabic speech recognition (knowledge-based approach)

Arabic is one of the phonetically complex languages, and the creation of accurate speech recognition system is a challengeable task. Phonetic dictionary is essential component in automatic speech recognition system (ASR). The pronunciation variations in Arabic are tangible and are investigated widely using data driven approach or knowledge based approach. The phonological rules are used to get the pronunciation of each word accurately to reduce the mismatch between the actual phoneme representation of the spoken words and ASR dictionary. Several studies in Arabic ASR system are conducted using different number of phonological rules. In this paper we focus on those rule that handle within-word pronunciation variation and cross-word pronunciation variation. The experimental results indicate that handling within-word pronunciation variation using phonological rule doesn’t enhance the recognition performance, but using these rules to handle cross-word variation provide a good performance.
Topic modelling on archive documents from the 1970s: global policies on refugees

Abstract This study conducts a historical analysis of global policies on refugees within typewritten and digitally born documents (c. 55,000 pages) from international and national archives. The data originate from the 1970s and are stored in archives from the UK and US governments, plus the United Nations High Commissioner for Refugees (UNHCR). The overarching theme is to analyse the involvement of the UK, the USA, and the UNHCR in different refugee cases that occurred during the 1970s. To do so, we (1) identify the main topics in each document; (2) investigate the transmission of topics horizontally (between organizations) and vertically (through time); and (3) suggest targeted areas of the document set for further close reading by historians. Standard Optical Character Recognition and object detection are used to extract information from documents and categorize them. Then, natural language processing (NLP) methods like topic modelling and clustering are used to identify topics and the relationships between them across time. The results identify several main themes covered by different organizations and how the focus of each organization changes diachronically. Besides its academic contribution, this study also demonstrates how, through the use of existing techniques with limited customization, digital technologies in the hands of the historian can augment and complement qualitative methods in bringing to light the themes and trends demonstrated in large bodies of historical documents.
Transfer of correlations from photons to electron excitations and currents induced in semiconductor quantum wells by non-classical twisted light

Abstract In this paper the excitations of collective electronic modes and currents induced in nanostructured semiconductor systems by two-mode quantum light with non-zero orbital angular momenta are investigated. Transfer of photon correlations to the excitations and currents induced in the semiconductor system is demonstrated. Birth of correlated electrons arising in the conduction band of the nanostructure due to the interaction with correlated photons of quantum light is found. Azimuthal and radial spatial distributions of the entangled electrons are established. The obtained results make possible to register the correlated electrons experimentally and to implement quantum information and nanoelectronics circuits in nanosystems using the found azimuthal and radial electron entanglement.
Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable

Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the machine's reasoning process.We propose Relation Extractor-Reader and Comparator (RERC), a three-stage framework based on complex question decomposition.The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path.In the 2WikiMultiHopQA dataset, our RERC model has achieved the state-of-the-art performance, with a winning joint F1 score of 53.58 on the leaderboard.All indicators of our RERC are close to human performance, with only 1.95 behind the human level in F1 score of support fact.At the same time, the evidence path provided by our RERC framework has excellent readability and faithfulness.
Filtering for beauty

Social media "influencers" use augmented reality filtering apps to appear more beautiful, together, and cool. Results may vary.
A Systematic Literature Review on the use of Mobile-assisted Language Learning (MALL) for Enhancing Speaking Skills in Chinese EFL context

As the largest market of mobile users, China has great potential for research on mobileassisted language learning (MALL).However, the number of MALL research in Chinese EFL contexts is limited, especially focusing on a particular skill such as English speaking.The current study provides a systematic review of MALL research within the specific area of spoken English language learning during the period 2017 -2021 in terms of research designs, theories and frameworks, as well as the characteristics of the included research and their new trends.The findings show that studies of mobile technology use in the aspects of language learning support the hypothesis that mobile technology can enhance learners' speaking skill and other skills.However, most of the reviewed studies are quasi-experimental, at small-scale level, and conducted in higher education contexts.Most theories and concepts are addressed briefly in the papers.And in terms of commonly addressed topics and skills, attention is primarily on learners' perception of MALL, English-speaking progress, and correlation between speaking skill and motor skill.Affective factors such as motivation, speaking anxiety and willingness to communicate are seldom focused.
Breast Cancer Detection using Artificial Neural Networks

Abstract: Early detection of disease has emerged as a critical issue in recent years due to the fast population increase seen in medical research. The chance of dying from breast cancer increases dramatically as the world’s population continues to increase at an alarming rate. Compared to other cancers discovered thus far, breast cancer is the second most severe. In addition to assisting medical staff in disease diagnosis, an automated disease detection system also provides reliable, effective, and fast intervention, which reduces the likelihood of mortality. In this research study, the Artificial Neural Network is employed for breast cancer classification. The model is validated on well-known dataset comprised from UCI machine learning repository. The results reveal that the ANNs obtained the highest accuracy i.e. 98.24%. Keywords: Machine Learning, Neural Network, Algorithm, Artificial Intelligence.
Home is where the hijacking is

DNS interception --- when a user's DNS queries to a target resolver are intercepted en route and forwarded to a different resolver --- is a phenomenon of concern to both researchers and Internet users because of its implications for security and privacy. While the prevalence of DNS interception has received some attention, less is known about where in the network interception takes place. We introduce methods to identify where DNS interception occurs and who the interceptors may be. We identify when interception is performed before the query exits the ISP, and even when it is performed by the Customer Premises Equipment (CPE) in the user's own home. We believe that these techniques are vital in the light of the ongoing debate concerning the value of privacy-enhancing DNS transport.
Sample-efficient Cross-Entropy Method for Real-time Planning

Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse-reward environments. However, their sampling inefficiency prevents them from being used for real-time planning and control. We propose an improved version of the CEM algorithm for fast planning, with novel additions including temporally-correlated actions and memory, requiring 2.7-22x less samples and yielding a performance increase of 1.2-10x in high-dimensional control problems.
Discrete Bidirectional Matrix Factorization Hashing for Zero-Shot Cross-Media Retrieval

Recently, cross-modal retrieval has gained much attention due to ever-increasing multimedia data. However, existing cross-modal algorithms require that the training set and the test set share the same categories, which cannot well search data of newly emerged categories. Therefore, more practical zero-shot cross-modal retrieval (ZSCMR) has become a promising direction, which aims to search unseen classes (new classes) that never present in the training set. It is very challenging that ZSCMR needs to solve not only the inconsistent semantic between seen and unseen classes but also the semantic gap of the heterogeneous multimedia data. To mitigate these problems, a novel discrete bidirectional matrix factorization hashing method is developed for zero-shot cross-modal retrieval (DMZCR). The proposed DMZCR contains three contributions: 1) A bidirectional matrix factorization scheme is proposed in our model, more discriminative low-rank representation can be learned and the redundant information can also be removed. 2) Inspired by zero-shot learning, we build a multi-layer semantic transmission scheme to model the relationships between classes, features and attributes, then the knowledge can be transferred from seen to unseen classes. 3) The hash codes can be learned by a discrete scheme, reducing the large quantization error caused by relaxation. As far as we know, this work first employs matrix factorization scheme to solve ZSCMR task. Experiments on three popular databases illustrate the efficacy of DMZCR compared with several state-of-the-art algorithms for ZSCMR task.
An Intelligent Scheduling Access Privacy Protection Model of Electric Vehicle Based on 5G-V2X

It is more and more important to optimize electric power system scheduling in the development of the Internet of Vehicles. How to improve the applicability and scientific nature of electric vehicle charging is an urgent problem to be solved. This paper proposes an intelligent scheduling access model for electric vehicles based on blockchain. Firstly, the layout simplification calculation is carried out for the layout of the traditional distributed power grid. Then, a data storage and consensus system is built using blockchain smart contracts to ensure that all historical data are not tampered with and are traceable. Finally, the model forms an electricity price guidance model in the intelligent scheduling and access of electric vehicles, optimizes the multivehicle line congestion in operation, and can dynamically schedule and correct the model. In terms of the experiment, 13 test electric vehicles were dispatched based on 12 real power station nodes and 36 test nodes of Yunnan Power Grid Co. Information Center for verification. The result analysis shows that the model can effectively and quickly solve the blocking and unblocking of the Internet of Vehicles and can develop a scheduling scheme conforming to the safety constraints of electric vehicles in a relatively short time.
Using a smartphone on the move: do visual constraints explain why we slow walking speed?

Viewing one's smartphone whilst walking commonly leads to a slowing of walking. Slowing walking speed may occur because of the visual constraints related to reading the hand-held phone whilst in motion. We determine how walking-induced phone motion affects the ability to read on-screen information. Phone-reading performance (PRP) was assessed whilst participants walked on a treadmill at various speeds (Slow, Customary, Fast). The fastest speed was repeated, wearing an elbow brace (Braced) or with the phone mounted stationary (Fixed). An audible cue ('text-alert') indicated participants had 2 s to lift/view the phone and read aloud a series of digits. PRP was the number of digits read correctly. Each condition was repeated 5 times. 3D-motion analyses determined phone motion relative to the head, from which the variability in acceleration in viewing distance, and in the point of gaze in space in the up-down and right-left directions were assessed. A main effect of condition indicated PRP decreased with walking speed; particularly so for the Braced and Fixed conditions (p = 0.022). Walking condition also affected the phone's relative motion (p < 0.001); post-hoc analysis indicated that acceleration variability for the Fast, Fixed and Braced conditions were increased compared to that for Slow and Customary speed walking (p ≤ 0.05). There was an inverse association between phone acceleration variability and PRP (p = 0.02). These findings may explain why walking speed slows when viewing a hand-held phone: at slower speeds, head motion is smoother/more regular, enabling the motion of the phone to be coupled with head motion, thus making fewer demands on the oculomotor system. Good coupling ensures that the retinal image is stable enough to allow legibility of the information presented on the screen.
Non-adiabatic quantum wavepacket dynamics simulation based on electronic structure calculations using the variational quantum eigensolver

A non-adiabatic nuclear wavepacket dynamics simulation of the H2O+ de-excitation process is performed based on electronic structure calculations using the variational quantum eigensolver. The adiabatic potential energy surfaces and non-adiabatic coupling vectors are computed with algorithms for noisy intermediate-scale quantum devices, and time propagation is simulated with conventional methods for classical computers. The results of non-adiabatic transition dynamics from the D2 state to the D1 state reproduce the trend reported in previous studies, which suggests that this quantum–classical hybrid scheme may be a useful application for noisy intermediate-scale quantum devices.
A Preliminary Study of Peer Assessment Feedback within Team Software Development Projects

Team-based software development projects where teams of learners design and develop software artefacts are common in computing-related degree programmes in the UK and other jurisdictions. Peer assessment is a commonly used approach to ensure learners are fairly recognised and rewarded for their contributions to such projects. This poster presents a preliminary study analysing the relationship between peer marking using the Team-Q rubric and peer feedback from one cohort using open coding and sentiment analysis. The preliminary results from a UK institutional study (N=124) illustrate how learner behaviours within teams appear to impact upon peer scores and the sentiment and intensity of emotion expressed in peer feedback. Additionally, the results provide valuable insights into common behaviours within teams. Given the prominence of team projects in computing curricula, the insights offered from this UK institutional study can shape and inform future learning, teaching and assessment practice.
Knowledge Organization Systems: A Network for AI with Helping Interdisciplinary Vocabulary Engineering

Knowledge Organization Systems (KOS) as networks of knowledge have the potential to inform AI operations. This paper explores natural language processing and machine learning in the context of KOS and Helping Interdisciplinary Vocabulary Engineering (HIVE) technology. The paper presents three use cases: HIVE and Historical Knowledge Networks, HIVE for Materials Science (HIVE-4-MAT), and Using HIVE to Enhance and Explore Medical Ontologies. The background section reviews AI foundations, while the use cases provide a frame of reference for discussing current progress and implications of connecting KOS to AI in digital resource collections.
Exploring the Influence of Focal Loss on Transformer Models for Imbalanced Maintenance Data in Industry 4.0

Harnessing data from historical maintenance databases may be challenging, as they tend to rely on text data provided by operators. Thus, they often include acronyms, jargon, typos, and other irregularities that complicate the automated analysis of such reports. Furthermore, maintenance datasets may present highly imbalanced distributions: some situations happen more often than others, which hinders the effective application of classic Machine Learning (ML) models. Hence, this paper explores the use of a recent Deep Learning (DL) architecture called Transformer, which has provided cutting-edge results in Natural Language Processing (NLP). To tackle the class imbalance, a loss function called Focal Loss (FL) is explored. Results suggests that when all the classes are equally important, the FL does not improve the classification performance. However, if the objective is to detect the minority class, the FL achieves the best performance, although by degrading the detection capacity for the majority class.
BengSentiLex and BengSwearLex: creating lexicons for sentiment analysis and profanity detection in low-resource Bengali language

Bengali is a low-resource language that lacks tools and resources for various natural language processing (NLP) tasks, such as sentiment analysis or profanity identification. In Bengali, only the translated versions of English sentiment lexicons are available. Moreover, no dictionary exists for detecting profanity in Bengali social media text. This study introduces a Bengali sentiment lexicon, BengSentiLex, and a Bengali swear lexicon, BengSwearLex. For creating BengSentiLex, a cross-lingual methodology is proposed that utilizes a machine translation system, a review corpus, two English sentiment lexicons, pointwise mutual information (PMI), and supervised machine learning (ML) classifiers in various stages. A semi-automatic methodology is presented to develop BengSwearLex that leverages an obscene corpus, word embedding, and part-of-speech (POS) taggers. The performance of BengSentiLex compared with the translated English lexicons in three evaluation datasets. BengSentiLex achieves 5%-50% improvement over the translated lexicons. For identifying profanity, BengSwearLex achieves documentlevel coverage of around 85% in an document-level in the evaluation dataset. The experimental results imply that BengSentiLex and BengSwearLex are effective resources for classifying sentiment and identifying profanity in Bengali social media content, respectively.
Exploring Usable Security to Improve the Impact of Formal Verification: A Research Agenda

As software becomes more complex and assumes an even greater role in our lives, formal verification is set to become the gold standard in securing software systems into the future, since it can guarantee the absence of errors and entire classes of attack.Recent advances in formal verification are being used to secure everything from unmanned drones to the internet.At the same time, the usable security research community has made huge progress in improving the usability of security products and end-users comprehension of security issues.However, there have been no human-centered studies focused on the impact of formal verification on the use and adoption of formally verified software products.We propose a research agenda to fill this gap and to contribute with the first collection of studies on people's mental models on formal verification and associated security and privacy guarantees and threats.The proposed research has the potential to increase the adoption of more secure products and it can be directly used by the security and formal methods communities to create more effective and secure software tools.