Reviewerly/exHarmony huggingface dataset:
Does Finance Lead to Economic Growth Convergence in Africa?

The literature on the finance-growth nexus predominantly suggests that finance affects growth through capital accumulation in developing countries. The existing literature has also produced conflicting findings on the impact of finance on economic growth. This paper suggests that the infusion of financial technology into Africa can propel the continent to distribute credit into productive sectors that can induce per capita income convergence between the continent and the United State of America, the world's technological leader. The paper also examines whether shocks in the growth of financial resources are responsible for economic growth in Africa. In the first instance, the paper employed contemporaneous cross-sectional analysis using the robust least square tool to ascertain the long-run per capita income convergence between Africa and the United States if financial resources are channeled into productivity growth. In the second instance, a dynamic generalized method of moment tool was employed to implement both symmetry and asymmetry impact of finance on the growth of Africa. The paper's objectives are analyzed based on country-level data obtained from the World Bank, PennWorld Table 9.0, and the Heritage Foundation. The paper also explores the impact of sub-regional economic integration by analyzing the role of the various economic groupings in Africa in channeling financial resources into growth. The contemporaneous analyses suggest that the level of financial development can propel African countries to converge to the per capita income level of the United States of America in the long run provided other growth fundamentals such as human capital, trade openness, and telecommunication infrastructure are present. 13 African countries, given their level of financial development and other policy measures are likely to converge to the per capita income of the USA. In the dynamic analysis, we find that lower growth in financial systems development is detrimental to economic growth. However, the growth in financial development in the various economic groupings in the continent produces divergent responses on economic growth. Whereas financial innovation in COMESA and ECCAS induces economic growth, that of ECOWAS and ARABMAG is detrimental to economic growth. The evidence makes a strong case for measures to promote productivity-oriented financial intermediation in Africa. The African Union should liaise with the financial institutions to encourage them to channel more financial resources to support innovation in the private sector to boost industrialization and therefore aid in the realization of sustainable development. The partnership with the financial sector should be tailored-made to suit the special needs of the various sub-regions of the continent.
Quantum Entropy Model of an Integrated Quantum-Random-Number-Generator Chip

We present the physical model for the entropy source of a quantum-random-number-generator chip based on the quantum fluctuations of the photon number emitted by light-emitting diodes. This model, combined with a characterization of the chip, estimates a quantum min-entropy of over 0.98 per bit without postprocessing. Finally, we show with our model that the performances in terms of security are robust against fluctuations over time.
Sentiment Analysis Method of Network Text Based on Improved AT-BiGRU Model

In order to solve the problems existing in the current method of emotional analysis of network text, such as long training time, complex calculation, and large space cost, this paper proposes an Internet text sentiment analysis method based on the improved AT-BiGRU model. Firstly, the textblob package is imported to correct spelling errors before text preprocessing. Secondly, pad_sequences are used to fill in the input layer with a fixed length, the two-way gated recurrent network is used to extract information, and the attention mechanism is used to highlight the key information of the word vector. Finally, the GNU memory unit is transformed, and an improved BiGRU that can adapt to the recursive network structure is constructed. The proposed model is experimentally demonstrated on the SemEval-2014 Task 4 and SemEval-2017 Task 4 datasets. Experimental results show that the proposed model can effectively avoid the text sentiment analysis bias caused by spelling errors and prove the effectiveness of the improved AT-BiGRU model in terms of accuracy, loss rate, and iteration time.
Versatile multilinked aerial robot with tilted propellers: Design, modeling, control, and state estimation for autonomous flight and manipulation

Abstract A multilinked structure can benefit aerial robots in terms of both maneuvering and manipulation owing to its ability of aerial transformation. A coplanar multilinked model was developed in our previous study. However, the maneuvering and manipulation performances of that model were limited owing to the weak controllability. Therefore, we adopt tilted propellers in this study to enhance controllability. The related design, modeling, and control method are developed to achieve stable hovering and transformation with tilted propellers. Further, state estimation which involves time synchronization between sensors and multilinked kinematics is also presented in this study to enable fully autonomous flight in outdoor environments. The experimental evaluation of the design, modeling, and control method is performed to verify stability during aerial transformation. While, various autonomous outdoor experiments including trajectory following, fast maneuvering for intercepting a target, object grasping for delivery, and blanket manipulation for firefighting are also performed to verify the versatility of the proposed robot platform. To the best of our knowledge, this is the first study of a multilinked aerial robot that can achieve fully autonomous flight and manipulation tasks in an outdoor environment. We also applied our platform in all challenges of the 2020 Mohammed Bin Zayed International Robotics Competition, and we ranked in the third place in Challenge 1 and in the sixth place in Challenge 3 internationally, thereby demonstrating the reliable flight performance in the fields.
Decision Confidence Assessment in Multi-Class Classification

This paper presents a novel approach to the assessment of decision confidence when multi-class recognition is concerned. When many classification problems are considered, while eliminating human interaction with the system might be one goal, it is not the only possible option—lessening the workload of human experts can also bring huge improvement to the production process. The presented approach focuses on providing a tool that will significantly decrease the amount of work that the human expert needs to conduct while evaluating different samples. Instead of hard classification, which assigns a single label to each class, the described solution focuses on evaluating each case in terms of decision confidence—checking how sure the classifier is in the case of the currently processed example, and deciding if the final classification should be performed, or if the sample should instead be manually evaluated by a human expert. The method can be easily adjusted to any number of classes. It can also focus either on the classification accuracy or coverage of the used dataset, depending on user preferences. Different confidence functions are evaluated in that aspect. The results obtained during experiments meet the initial criteria, providing an acceptable quality for the final solution.
Beyond graph neural networks with lifted relational neural networks

We introduce a declarative differentiable programming framework, based on the language of Lifted Relational Neural Networks, where small parameterized logic programs are used to encode deep relational learning scenarios through the underlying symmetries. When presented with relational data, such as various forms of graphs, the logic program interpreter dynamically unfolds differentiable computation graphs to be used for the program parameter optimization by standard means. Following from the declarative, relational logic-based encoding, this results into a unified representation of a wide range of neural models in the form of compact and elegant learning programs, in contrast to the existing procedural approaches operating directly on the computational graph level. We illustrate how this idea can be used for a concise encoding of existing advanced neural architectures, with the main focus on Graph Neural Networks (GNNs). Importantly, using the framework, we also show how the contemporary GNN models can be easily extended towards higher expressiveness in various ways. In the experiments, we demonstrate correctness and computation efficiency through comparison against specialized GNN frameworks, while shedding some light on the learning performance of the existing GNN models.
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation

This paper proposes a sophisticated neural architecture to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models.By introducing three novel components: Pointer, Disambiguator, and Copier, our method PDC achieves the following merits inherently compared with previous efforts:(1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionaries can potentially be used;(2) Disambiguator synthesizes contextual information from the source view and the target view, both of which contribute to distinguishing the proper translation of a specific source word from multiple candidates in dictionaries;(3) Copier systematically connects Pointer and Disambiguator based on a hierarchical copy mechanism seamlessly integrated with Transformer, thereby building an end-to-end architecture that could avoid error propagation problems in alternative pipeline methods.The experimental results on Chinese-English and English-Japanese benchmarks demonstrate the PDC's overall superiority and effectiveness of each component.
Exploiting Position Bias for Robust Aspect Sentiment Classification

Aspect sentiment classification (ASC) aims at determining sentiments expressed towards different aspects in a sentence.While state-ofthe-art ASC models have achieved remarkable performance, they are recently shown to suffer from the issue of robustness.Particularly in two common scenarios: when domains of test and training data are different (out-of-domain scenario) or test data is adversarially perturbed (adversarial scenario), ASC models may attend to irrelevant words and neglect opinion expressions that truly describe diverse aspects.To tackle the challenge, in this paper, we hypothesize that position bias (i.e., the words closer to a concerning aspect would carry a higher degree of importance) is crucial for building more robust ASC models by reducing the probability of mis-attending.Accordingly, we propose two mechanisms for capturing position bias, namely position-biased weight and position-biased dropout, which can be flexibly injected into existing models to enhance representations for classification.Experiments conducted on out-of-domain and adversarial datasets demonstrate that our proposed approaches largely improve the robustness and effectiveness of current models. 1 * Fang Ma and Chen Zhang contribute equally to this work.The order is determined alphabetically.
A condense-then-select strategy for text summarization

Select-then-compress is a popular hybrid, framework for text summarization due to its high efficiency. This framework first selects salient sentences and then independently condenses each of the selected sentences into a concise version. However, compressing sentences separately ignores the context information of the document, and is therefore prone to delete salient information. To address this limitation, we propose a novel condense-then-select framework for text summarization. Our framework first concurrently condenses each document sentence. Original document sentences and their compressed versions then become the candidates for extraction. Finally, an extractor utilizes the context information of the document to select candidates and assembles them into a summary. If salient information is deleted during condensing, the extractor can select an original sentence to retain the information. Thus, our framework helps to avoid the loss of salient information, while preserving the high efficiency of sentence-level compression. Experiment results1 on the CNN/DailyMail, DUC-2002, and Pubmed datasets demonstrate that our framework outperforms the select-then-compress framework and other strong baselines.
On the normalized power prior

The power prior is a popular tool for constructing informative prior distributions based on historical data. The method consists of raising the likelihood to a discounting factor in order to control the amount of information borrowed from the historical data. However, one often wishes to assign this discounting factor a prior distribution and estimate it jointly with the parameters, which in turn necessitates the computation of a normalizing constant. In this article, we are concerned with how to approximately sample from joint posterior of the parameters and the discounting factor. We first show a few important properties of the normalizing constant and then use these results to motivate a bisection-type algorithm for computing it on a fixed budget of evaluations. We give a large array of illustrations and discuss cases where the normalizing constant is known in closed-form and where it is not. We show that the proposed method produces approximate posteriors that are very close to the exact distributions and also produces posteriors that cover the data-generating parameters with higher probability in the intractable case. Our results suggest that the proposed method is an accurate and easy to implement technique to include this normalization, being applicable to a large class of models. They also reinforce the notion that proper inclusion of the normalizing constant is crucial to the drawing of correct inferences and appropriate quantification of uncertainty.
ASimOV: A Framework for Simulation and Optimization of an Embedded AI Accelerator

Artificial intelligence algorithms need an external computing device such as a graphics processing unit (GPU) due to computational complexity. For running artificial intelligence algorithms in an embedded device, many studies proposed light-weighted artificial intelligence algorithms and artificial intelligence accelerators. In this paper, we propose the ASimOV framework, which optimizes artificial intelligence algorithms and generates Verilog hardware description language (HDL) code for executing intelligence algorithms in field programmable gate array (FPGA). To verify ASimOV, we explore the performance space of k-NN algorithms and generate Verilog HDL code to demonstrate the k-NN accelerator in FPGA. Our contribution is to provide the artificial intelligence algorithm as an end-to-end pipeline and ensure that it is optimized to a specific dataset through simulation, and an artificial intelligence accelerator is generated in the end.
Learning from experience for rapid generation of local car maneuvers

Being able to rapidly respond to the changing scenes and traffic situations by generating feasible local paths is of pivotal importance for car autonomy. We propose to train a deep neural network (DNN) to plan feasible and nearly-optimal paths for kinematically constrained vehicles in a small constant time. Our DNN model is trained using a novel weakly supervised approach and a gradient-based policy search. On real and simulated scenes and a large set of local planning problems, we demonstrate that our approach outperforms the existing planners with respect to the number of successfully completed tasks. While the path generation time is about 40 ms, the generated paths are smooth and comparable to those obtained from conventional path planners.
Experimental requirements for entangled two-photon spectroscopy

Coherently controlling the spectral properties of energy-entangled photons is a key component of future entangled two-photon spectroscopy schemes that are expected to provide advantages with respect to classical methods. We present here an experimental setup based on a grating compressor. It allows for the spectral shaping of entangled photons with a sevenfold increase in resolution, compared to previous setups with a prism compressor. We evaluate the performances of the shaper by detecting sum frequency generation in a nonlinear crystal with both classical pulses and entangled photon pairs. The efficiency of both processes is experimentally compared and is in accordance with a simple model relating the classical and entangled two-photon absorption coefficients. Finally, the entangled two-photon shaping capability is demonstrated by implementing an interferometric transfer function.
Global research productivity in cybersecurity: a scientometric study

Purpose The purpose of the study is to conduct a scientometric analysis of cybersecurity literature indexed in the core collection of the Web of Science for a period of ten years (2011–2020). Design/methodology/approach Cybersecurity is a focused topic of research across the globe. To identify the global research productivity in the field, the terms “cybersecurity, cyber-security, web security, information security, computer security, etc.” were used for retrieving the publications in the advanced search mode of the database “Web of Science”, limiting the time frame for 2011– 2020. The results retrieved were downloaded in the Excel file for further analysis and interpretation. The harvested data was analysed by using scientometric techniques to measure the progress such as growth rate, doubling time and author collaborations. Besides, the Biblioshiny and VOSviewer software were used for mapping networks. Findings The research output in the field of cybersecurity has shown an increasing trend during 2011–2020, and the maximum number of scholarly publications was published in 2020 (1,581), i.e. more than 715% of 2011 (221). A good number of countries (93) have contributed globally in cybersecurity research, and the highest share in research publications was reported by the USA (23.55%), followed by China (23.24%), South Korea (5.31%), UK (5.28%) and India (4.25%). The authorship patterns in cybersecurity publications show a collaborative trend, as most articles have been published by multiple authors. Total 5,532 (90.14%) articles have been published in co-authorship, whereas only 605 (9.86%) articles have been published by single authors. Keyword analysis shows that the most common keyword research by the authors is cybersecurity and its variants such as “cyber security” and “cyber-security” (1,698) followed by security (782), computer security (680) and information security (329). Research limitations/implications The database studied for the work does not represent the total literary output available on the theme. There are plenty of other databases, such as Scopus, Compendex, INSPEC, IEEE Xplore, arXiv, contributing to the same theme as well. Practical implications The findings of the study may help researchers, information technologists, library professionals and information specialists to identify the research progress, authorship patterns, collaborative networks and hot topics of research in the field of cybersecurity. Besides, it will assess the global response to the cybersecurity issue. Originality/value The study is the scientometric analysis of the cybersecurity based on current literature and will highlight the progress and development of global research in the said field.
System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data

The most important goal of customer service is to keep the customer satisfied. However, service resources are always limited and must prioritize specific customers. Therefore, it is essential to identify customers who potentially become unsatisfied and might lead to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">escalations</i> . Data science on IoT data (especially log data) for machine health monitoring and analytics on enterprise data for customer relationship management (CRM) have mainly been researched and applied independently. This paper presents a data-driven decision support system framework that combines IoT and enterprise data to model customer sentiment and predicts escalations. The proposed framework includes a fully automated and interpretable machine learning pipeline using state-of-the-art methods. The framework is applied in a real-world case study with a major medical device manufacturer providing data from a fleet of thousands of high-end medical devices. An anonymized version of this industrial benchmark is released for the research community based on the presented case study, which has interesting and challenging properties. In our extensive experiments, we achieve a Recall@50 of 50.0 % for the task of predicting customer escalations. In addition, we show that combining IoT and enterprise data can improve prediction results and ease troubleshooting. Additionally, we propose a practical workflow for end-users when applying the proposed framework.
Speech Representation Learning Combining Conformer CPC with Deep Cluster for the ZeroSpeech Challenge 2021

We present a system for the Zero Resource Speech Challenge 2021, which combines a Contrastive Predictive Coding (CPC) with deep cluster. In deep cluster, we first prepare pseudo-labels obtained by clustering the outputs of a CPC network with k-means. Then, we train an additional autoregressive model to classify the previously obtained pseudo-labels in a supervised manner. Phoneme discriminative representation is achieved by executing the second-round clustering with the outputs of the final layer of the autoregressive model. We show that replacing a Transformer layer with a Conformer layer leads to a further gain in a lexical metric. Experimental results show that a relative improvement of 35% in a phonetic metric, 1.5% in the lexical metric, and 2.3% in a syntactic metric are achieved compared to a baseline method of CPC-small which is trained on LibriSpeech 460h data. We achieve top results in this challenge with the syntactic metric.
Strengthening practical continuous-variable quantum key distribution against measurement angular error

The optical phase shifter that constantly rotates the local oscillator phase is a necessity in continuous-variable quantum key distribution systems with heterodyne detection. In previous experimental implementations, the optical phase shifter is generally regarded as an ideal passive optical device that perfectly rotates the phase of the electromagnetic wave of $90^\circ$. However, the optical phase shifter in practice introduces imperfections, mainly the measurement angular error, which inevitably deteriorates the security of the practical systems. Here, we will give a concrete interpretation of measurement angular error in practical systems and the corresponding entanglement-based description. Subsequently, from the parameter estimation, we deduce the overestimated excess noise and the underestimated transmittance, which lead to a reduction in the final secret key rate. Simultaneously, we propose an estimation method of the measurement angular error. Next, the practical security analysis is provided in detail, and the effect of the measurement angular error and its corresponding compensation scheme are demonstrated. We conclude that measurement angular error severely degrades the security, but the proposed calibration and compensation method can significantly help improve the performance of the practical CV-QKD systems.