Reviewerly/exHarmony huggingface dataset:
Country Risk, Corporate Strategy and Profitability: A System GMM Estimation for Multinational Banks in Sub Saharan Africa

Over the past 2 decades, foreign investment flows in Africa have steadily increased due to; high growth in GDP, economic liberalization, improved business climate (political and socioeconomic), the fast growth and continuous shifts in customer needs, improved general infrastructure development, technological advancement and the formation of free trade zones. However, political instability, government interference through deregulation, corruption, sharp practices, stiff competition, inadequate demand, cultural barriers and failing public infrastructures are factors to worry about. The dynamic environment in the region provides both opportunities and threats for businesses. This research examines the effect of country risk as a whole, and corporate strategy (diversification, debt and equity financing) on performance of multinational banks within sub Saharan Africa. To accomplish this, a panel dataset (2007-2017) on best performing multinational banks in the region is used. Accounting for the dynamic nature and persistent country risk, corporate strategy and bank profitability, a two-step system GMM estimator is used in the analysis.Results indicate country risk negatively and significantly affecting multinational bank performance. Corporate strategies: diversification and equity financing indicate a positive and significant effect on multinational banks’ profitability in terms of return on assets. The effect however, is higher for diversification than equity financing. Debt financing indicates a negative and significant effect on profitability of these banks. Also, the study finds no performance difference between the local indigenous, and overseas multinational banks. The resultant Arellano-Bond AR(1&2) test for zero autocorrelation, the Hansen test of overidentifying restrictions and the difference-in-Hansen test for the validity of the additional moment restriction necessary for system GMM; all, suggest that the underlying assumptions under the two-step system GMM estimator, are not violated since their p-values are very high. The implication of these tests is that the results obtained are robust, efficient and reliable.
Pedestrian localization based on image plane constraint

Human-robot safe operation requires pedestrian localization and tracking capability from a mobile robot, so a novel method for tracking walking humans on a mobile robot which installs a 2D LiDAR (Light Detection and Ranging) and a camera is proposed. Firstly, the 2D LiDAR point-to-image plane constraint is established after the camera and 2D LiDAR are calibrated with external parameters, and the horizontal 2D LiDAR beams taken from approximately the knee level are visible from the camera image. Secondly, pedestrians are tracked by MobileNet-SSD network and SORT algorithm, the 2D LiDAR data are obtained from the tracking bounding box through the point-to-image constraint plane. Thirdly, the adaptive parametric DBSCAN clustering algorithm is designed for classifying the point cloud as clusters. If two clusters satisfy predefined conditions defined as two legs, a human target is generated and tracked. Finally, hazard assessment is obtained by pedestrian velocity calculated through the tracking result. Results comparison among different methods demonstrates the proposed method competitive performance for the pedestrian localization task.
Action Spotting and Temporal Attention Analysis in Soccer Videos

Action spotting is the task of finding a specific action in a video. In this paper, we consider the task of spotting actions in soccer videos, e.g., goals, player substitutions, and card scenes, which are temporally sparse within a complete game. We spot actions using a Transformer model, which allows capturing important features before and after action scenes. Moreover, we analyze which time instances the model focuses on when predicting an action by observing the internal weights of the transformer. Quantitative results on the public SoccerNet dataset show that the proposed method achieves an mAP of 81.6%, a significant improvement over previous methods. In addition, by analyzing the attention weights, we discover that the model focuses on different temporal neighborhoods for different actions.
Research and Practice of MOOC Based Innovative Education in Universities under the Background of “Internet Plus”

Abstract Under the background of “Internet plus”, the emerging open education resource MOOC (Mogao) has increasingly become the focus of attention worldwide. The advantages of MOOC, the maturity of Internet and information technology, the strong attack of famous universities and the huge market demand have become the reasons for the rapid development of MOOC. Through the analysis, we can see that MOOC has certain influence on the improvement of teachers’ teaching methods, the change of students’ learning methods, the expansion of education objects and the improvement of higher education quality.
Features of the numerical solution of bi-characteristic equations by methods of computer mathematics

Abstract The features of applying the methods of computer mathematics to the numerical solution of the bi-characteristic system of equations describing the propagation of radio waves in the ionospheric plasma are considered. Based on the comparison of the numerical and exact solutions obtained for the parabolic plasma layer, the accuracy of the numerical solution was investigated.
Investigating syntactic priming cumulative effects in MT-human interaction

<ns4:p><ns4:bold>Background:</ns4:bold> A question that deserves to be explored is whether the interaction between English language learners and the popular Google neural machine translation (GNMT) system could result in learning and increased production of a challenging syntactic structure in English that differs in word order between speakers first language and second language. <ns4:bold>Methods:</ns4:bold> In this paper, we shed light on this issue by testing 30 Brazilian Portuguese L2 English speakers in order to investigate whether they tend to describe an image in English with a relation of possession between nouns using a prepositional noun phrase (e.g. the cover of the book is red) or re-use the alternative syntactic structure seen in the output of the GNMT (e.g. the book cover is red), thus manifesting syntactic priming effects. In addition, we tested whether, after continuous exposure to the challenging L2 structure through Google Translate output, speakers would adapt to that structure in the course of the experiment, thus manifesting syntactic priming cumulative effects. <ns4:bold>Results:</ns4:bold> Our results show a robust syntactic priming effect as well as a robust cumulative effect. <ns4:bold>Conclusions:</ns4:bold> These results suggest that GNMT can influence L2 English learners linguistic behaviour and that L2 English learners unconsciously learn from the GNMT with continuous exposure to its output.</ns4:p>
Metasequents and Tetravaluations

Abstract In this paper we treat metasequents—objects which stand to sequents as sequents stand to formulas—as first class logical citizens. To this end we provide a metasequent calculus, a sequent calculus which allows us to directly manipulate metasequents. We show that the various metasequent calculi we consider are sound and complete w.r.t. appropriate classes of tetravaluations where validity is understood locally. Finally we use our metasequent calculus to give direct syntactic proofs of various collapse results, closing a problem left open in French ( Ergo , 3(5), 113–131 2016).
Evaluating Dataset Creation Heuristics for Concept Detection in Web Pages Using BERT

Dataset creation for the purpose of training natural language processing (NLP) algorithms is often accompanied by an uncertainty about how the target concept is represented in the data. Extracting such data from web pages and verifying its quality is a non-trivial task, due to the Web’s unstructured and heterogeneous nature and the cost of annotation. In that situation, annotation heuristics can be employed to create a dataset that captures the target concept, but in turn may lead to an unstable downstream performance. On the one hand, a trade-off exists between cost, quality, and magnitude for annotation heuristics in tasks such as classification, leading to fluctuations in trained models’ performance. On the other hand, general-purpose NLP tools like BERT are now commonly used to benchmark new models on a range of tasks on static datasets. We utilize this standardization as a means to assess dataset quality, as most applications are dataset specific. In this study, we investigate and evaluate the performance of three annotation heuristics for a classification task on extracted web data using BERT. We present multiple datasets, from which the classifier shall learn to identify web pages that are centered around an individual in the academic domain. In addition, we assess the relationship between the performance of the trained classifier and the training data size. The models are further tested on out-of-domain web pages, to asses the influence of the individuals’ occupation and web page domain.
Some features of linear scanning of the chemical composition of mineral aggregates using LA-ICP-MS: the modes and limits of the method

A routine linear and 2D scanning procedure using the inductively coupled plasma mass spectrometry with laser ablation (LA-ICP-MS) is often used when studying the composition of zonal and polyphase solid objects (crystals, rocks, alloys, etc.). However, the proper interpretation of the data necessitates taking into account the features inherent to the procedure. One of the most important features of the results obtained for the objects contrasting in composition is the asymmetry of the concentration profile of elements when going from high to low and, to a lesser extent, when going from low to high contents. Physical processes accompanying the analysis of rocks with the aforementioned features are considered. The duration of the time of signal distortion upon change of one crystal phase to another at large differences in the content of elements is estimated. To obtain the correct data for linear and 2D scanning using LA-ICP-MS, the scanning should be performed point by point: to avoid the influence of crater effects, points should be spaced 1.5 – 2.0 laser beam diameter apart. The goal of obtaining of reliable results requires also keeping the time intervals between measurements of at least 2 – 5 sec.
Automated authoring, onboarding developers, and extracting decision rationale [Practitioners’ Digest]

The "Practitioners’ Digest" department in this issue of IEEE Software includes papers from the 2021 International Conference on Software Engineering (ICSE). Feedback or suggestions are welcome. In addition, if you try or adopt any of the practices included in this article, please send me and the authors of the paper(s) a note about your experiences.
Integration-and-Diffusion Network for Low-Light Image Enhancement

Images captured under extreme low-light conditions often suffer from low Signal-to-Noise Ratio(SNR) caused by low photon count, making low-light image enhancement challenging. Deep learning-based methods have recently yielded impressive progress by reconstructing extreme low-light images from raw sensor data. Despite their promising results, they still fail at recovering detailed textures and corresponding colors. To address these issues, we propose an Information Integration-and-Diffusion (InD) module to reconstruct excellent details from extreme low-light raw images. Precisely, a pixel-intensive global information matrix is calculated by separately integrating spatial-wise and channel-wise information and then diffusing them to each other by a matrix multiplication operation. In addition to this, we propose a Bottleneck Guided Channel Attention (BGCA) module to achieve unified channel information through low-light image enhancement networks for better color recovery. Extensive experimental results show that the networks equipped with our proposed modules outperform state-of-the-art approaches both quantitatively and visually.
Accounting in the CloudVeneto private cloud

CloudVeneto is a private cloud implemented as the result of merging two existing cloud infrastructures: the INFN Cloud Area Padovana, and a private cloud owned by 10 departments of University of Padova. This infrastructure is a full production facility, in continuous growth, both in terms of users, and in terms of computing and storage resources. Even if the usage of CloudVeneto is not regulated by a strict pay-per-use model, the availability of accounting information for such infrastructure is a requirement, to detect if the resources allocated to the user communities are effciently used, and to perform an effective capacity planning. We present in this paper how the accounting system used in CloudVeneto evolved over time, focusing on the accounting framework being used now, implemented by integrating existing components.
Building a COVID-19 Literature Knowledge Graph Based on PubMed

COVID-19, the most destructive global event in 2020, poses gigantic challenges to global medical systems. Meanwhile, the useful concepts and newly-emerging technical terms in medical field generate ambiguity and bring difficulties in extraction, which would set immense obstacles to anti-epidemic actions. To solve this problem, we built a knowledge graph by extracting six kinds of medical entities from titles and abstracts related to COVID-19 in PubMed. Then, we eliminated author name ambiguity and integrated articles' publication information as well as authors' affiliation information into the graph. By this way, connections are established between authors, articles, entities and affiliations. Our model which aims at named entity recognition achieved an F1 score of 92.57% on average. This graph not only allows us to seek out hotspots, utilize specific knowledge and transfer research results quickly, but also helps us understand the research development process. It can also aid scholars to focus on specific authors and entities. This method of a knowledge graph is extensible and transplantable, which means it will not be limited to COVID-19 research in the future.
Simplification of Nested Real Radicals Revisited

The problem of simplification of nested radicals over arbitrary number fields was studied by many authors. The case of real radicals over real number fields is somewhat easier to study (at least, from theoretical point of view). In particular, an efficient (i.e., a polynomial-time) algorithm of simplification of at most doubly nested radicals is known. However, this algorithm does not guarantee complete simplification for the case of radicals with nesting depth more than two. In the paper, we give a detailed presentation of the theory that provides an algorithm which simplifies triply nested reals radicals over $$\mathbb {Q}$$ . Some examples of triply nested real radicals that cannot be simplified are also given.
Technological stress in higher level students in San Luis Potosí

During 2020, in the face of the recession caused by the COVID19 pandemic, public and private sectors suffer massive losses that force them to promote strategies for immediate reactivation. Education in México, despite estimating a considerable lag, had a favorable reaction towards "online classes” in all education levels. Through technological resources, administrative, teachers, parents and students were involved without having the opportunity to be questioned. As a result of this, a study was proposed with a quantitative approach and descriptive method, to keep record of the effects presented by college students before this modality. Natural stress is associated with social isolation through dimensions involving ICTs with the aim of diagnosing the personal, family, social and technological impact on college students in San Luis Potosí, to determine the effects that are adverse to their learning and professional life. The results will contribute to the development of strategies associated with the effective use of the ICTs that institutions offer and facilitate the teaching-learning process; the study also concluded according to the integral well-being of the student in this new modality.
Cryptocurrency, Mining Pools' Concentration, and Asset Prices

This paper introduces mining pools' concentration into a dynamic asset pricing setup. Our theory builds on the intuitive yet novel insight that modeling competition requires that mining pools clear markets separately from their competitors in equilibrium. Our model predicts that as concentration increases, the cryptocurrency price falls, and its volatility spikes---in line with our empirical analysis of Bitcoin. We further reveal that the entry and exit of mining pools affect prices only through their effect on concentration. Lastly, equilibrium shows that mining pools' revenues determine the cryptocurrency's value, and if a pricing bubble exists, it amplifies the concentration effects.
The Usage of Two Chinese Synonyms for Learners: A Corpus-Based Approach

Based on ToRCH2014 corpus and HUAYU corpus, the Interlanguage Analysis is used to study the frequency, colligation and semantic prosody of two Chinese synonyms, “dăozhì”(‘resulting in’) and “yĭnqĭ”(‘causing’). It is found that learners who take Chinese as a foreign language are different from Chinese native speakers in the frequency of using “yĭnqĭ” and the colligation “yĭnqĭ+clause”, while there is no difference in semantic prosody between native speakers and Chinese learners.
Research on Computer Realization of Building Fire Safety Assessment

Abstract Fire is a kind of sporadic local disaster phenomenon. The building fire in the fire has brought very serious losses to people’s life and property. With the increase of the urban population, the increase of the number of building floors, the increase of the density of urban settlements, and the widespread use of electrical, natural gas and chemical substances in buildings, the possibility of fire and the losses caused by the building are also increasing, especially some houses of longer construction age, lack of fire fighting facilities and serious consequences in the event of fire. In recent years, computer technology has been applied to a certain extent in all fields of our country. The effective use of computer technology has further improved the technical level of building fire safety management, and then realized the automatic management of building fire prevention to some extent. In this case, the fire prevention management of buildings has higher sensitivity and safety, which brings more protection to the safety of people’s lives and property. To understand the application of computer technology in building fire safety management, and then to better apply computer technology, can promote the development of building fire safety management.
Discovering optimal strategy in tactical combat scenarios through the evolution of behaviour trees

Abstract In this paper we address the problem of automatically discovering optimal tactics in a combat scenario in which two opposing sides control a number of fighting units. Our approach is based on the evolution of behaviour trees, combined with simulation-based evaluation of solutions to drive the evolution. Our behaviour trees use a small set of possible actions that can be assigned to a combat unit, along with standard behaviour tree constructs and a novel approach for selecting which action from the tree is performed. A set of test scenarios was designed for which an optimal strategy is known from the literature. These scenarios were used to explore and evaluate our approach. The results indicate that it is possible, from the small set of possible unit actions, for a complex strategy to emerge through evolution. Combat units with different capabilities were observed exhibiting coordinated team work and exploiting aspects of the environment.
Deep Sensor Fusion Based on Frustum Point Single Shot Multibox Detector for 3D Object Detection

We present a deep sensor fusion method based on frustum point single shot multibox detector (PointSSD) for autonomous driving scenarios. The proposed method solves the problem of precision degradation in frustum PointNets (F-PointNet) caused by relying heavily on 2D detection and making insufficient use of RGB information. The method mainly consists of two subnetworks: pyramid segmentation network (PSNet) and PointSSD. The proposed PSNet uses a novel architecture capable of performing semantic segmentation on RGB information to generate high quality image semantic information. Using these image semantic information, point cloud semantic information is obtained through projection and is then fused with raw 3D spatial features by deep fusion. The fusion results are processed by PointSSD, which is proposed for classification and bounding box regression. Evaluated on the KITTI dataset, our method is superior to other methods in 3D classification and 3D localization. In addition, our method guarantees robustness to 2D false detections.
Information Retrieval in the Hidden Web

Information retrieval is finding material of unstructured nature within large collections stored on computers. Surface web consists of indexed content accessible by traditional browsers whereas deep or hidden web content cannot be found with traditional search engines and requires a password or network permissions. In deep web, dark web is also growing as new tools make it easier to navigate hidden content and accessible with special software like Tor. According to a study by Nature, Google indexes no more than 16% of the surface web and misses all of the deep web. Any given search turns up just 0.03% of information that exists online. So, the key part of the hidden web remains inaccessible to the users. This chapter deals with positing some questions about this research. Detailed definitions, analogies are explained, and the chapter discusses related work and puts forward all the advantages and limitations of the existing work proposed by researchers. The chapter identifies the need for a system that will process the surface and hidden web data and return integrated results to the users.
Analyzing the scientific content visibility of the repository posted on the website of the journal “Geodezia i Kartografia” website in the Internet

The author examines the visibility of the scientific content from the journal and outside sources in the Internet posted on the website of the journals using the webometric method and comparative analysis. One of the indirect signs of insufficient visibility of Russian scientific content for Russian authors is the number of self-citations. Author analyzed the traffic sources of the studied site, the indexing of the following sites
Adaptive-Optics Enhanced Distribution of Entangled Photons over Turbulent Free-Space Optical Channels

We experimentally demonstrate using of adaptive optics to mitigate atmospheric turbulence effects on the distribution of entangled photons over free-space optical links and provide significantly improved photon-counting rates and biphoton correlations.