Reviewerly/exHarmony huggingface dataset:
Masked by annotation: Minor declarative complementizers in parsed corpora of historical English

This article discusses some of the potential problems derived from the syntactic annotation of historical corpora, especially in connection with low-frequency phenomena. By way of illustration, we examine the parsing scheme used in the Penn Parsed Corpora of Historical English (PPCHE) for clauses introduced by so-called ‘minor declarative complementizers’, originally adverbial links which come to be occasionally used in complementizer function. We show that the functional similarities between canonical declarative complement clauses introduced by the major declarative links that and zero and those headed by minor declarative complementizers are not captured by the PPCHE parsing, where the latter constructions are not tagged as complement clauses, but rather as adverbial clauses. The examples discussed reveal that, despite the obvious advantages of parsed corpora, annotation may sometimes mask interesting linguistic facts.
The C-calculus: A model for confidential name passing

Sharing confidential information in distributed systems is often a necessity in the context of many applications, however, it opens the problem of controlling information sharing even among trusted parties. In this paper, we present a formal model in which dissemination of information, in particular information forwarding, is not allowed. Namely, we introduce a fragment of the π-calculus where forwarding of channels is disabled directly at the level of the syntax. This is the only difference with respect to the π-calculus, i.e., that channels that are received cannot be forwarded later on. Apart from the presentation of the language, we also address a preliminary investigation in the behavioral theory of the model. Furthermore, by means of examples, we give an idea of how some privacy notions already studied in the past, such as group creation and name hiding, can be represented in our language, in contrast with previous approaches that required additional language constructs. Finally, we present an encoding of the (sum-free) π-calculus in our calculus and prove operational correspondence. Our encoding allows to put focus on a notion of name ownership that arises in the process model, by confining the name sending capability to well-determined processes which may be of use for security purposes but also for other resource control properties.
Aplikasi Untuk Memprediksi Kemiskinan Berbasis Data E-commerce Menggunakan Algoritma Logistic Regression Dan Sparse Learning Based Feature Selection

AbstrakKemiskinan adalah keadaan dimana terdapat ketidakmampuan untuk memenuhi suatu kebutuhan serta keperluan mulai dari kebutuhanmakan, pakaian, tempat tinggal, pendidikan, kesehatan serta yang lainnya, suatu tingkat kemiskinan dapat diukur oleh BPS. Secara konsep,untuk mengukur tingkat kemiskinan yaitu dengan cara perhitungan kemampuan seseorang untuk memenuhi kebutuhan dasar atau basicneeds approach yang diukur dari sisi pengeluaran oleh BPS. Metode lain untuk melengkapi hasil survei dan sensus yang diusulkan penelitiuntuk memprediksi kemiskinan adalah dengan mengunakan machine learning logistic regression dengan metode sparse learning basedfeature selection baerbasis data e-commerce. Dari hasil percobaan ini, menghasilkan nilai yang cukup relevan antara nilai prediksi jumlahfitur dengan nilai asli, tetapi sejumlah kecil fitur tidak selalu menunjukkan hasil yang buruk dan sebaliknya, di mana penggunaan yangbesar jumlah fitur tidak selalu mendapatkan hasil yang baik.Kata Kunci: Kemiskinan, BPS, machine learning logistic regression , sparse learning based feature selection, data e-commerce.AbstractPoverty is a condition where there is an inability to meet a need and needs ranging from food, clothing, shelter, education, health andother needs, a poverty level can be measured by BPS. Conceptually, to measure poverty, that is by calculating a person's ability to meetbasic needs or basic needs approach, which is measured in terms of expenditure by BPS. Another method to supplement survey and censusresults proposed by researchers to predict poverty is to use machine learning logistic regression with sparse learning based featuresselection methods based on e- commerce data. From the results of these experiments, produce a value that is quite relevant between thepredicted value of the number of features with the original value, but the small number of features does not always show poor resultsand vice versa, where the use of a large number of features does not always get good results.Keywords: Poverty, BPS, machine learning logistic regression, sparse learning based on feature selection, e-commerce data.
Micro‐anatomy of the ear of the southern white rhinoceros (<i>Ceratotherium simum simum</i>)

The white rhinoceros is the largest of the five extant rhinoceros species. The population is declining rapidly because of intense poaching. However, normal anatomical descriptions in this species are lacking. The purpose of this study is to describe the osseous anatomy of the middle and inner ear of the southern white rhinoceros using micro-focus X-ray computed tomography imaging. Four temporal bones obtained from two 1-day old southern white rhinoceros preserved in 10% formalin were scanned. Tri-dimensional reconstructions were obtained and volumes of the middle ear ossicles and inner ear structures were calculated. Excellent high spatial resolution 3D images were obtained for all samples and virtual models of the auditory ossicles and bony labyrinth were generated. Visualization of the tympanic membrane, middle ear and inner ear structures was possible in all samples. Whereas the stapes and incus had a shape similar to their human or equine counterparts, the malleus showed a unique appearance with a long rostral branch projecting latero-distally to the manubrium. The cochlea described 2 turns rostro-laterally around its axis, with a medial direction of rotation. However, identification of the soft tissue structures of the middle ear was sometimes difficult and visualization of the small structures of the membranous labyrinth was not possible using this formalin fixation and alternative techniques should be investigated. Further investigations are needed in order to provide a complete virtual model including both soft and bone tissues of this difficultly accessible region.
Access and Use of Networked Electronic Devices for Educational Information Management by Undergraduates in South Africa

Electronic networked devices have broken down fences of communication and information access from anywhere in the world, and its capacity to store, manage, as well as transmit vast amounts of information to anybody anywhere in the world often makes it to be referred to as the “Information Highway.” But digital divide has constraint access and use of these devices especially in developing countries like South Africa where economic-digital divide is very common. Four electronic devices (desktop, laptop computer, mobile phones, and tablets) that are mainly used for schoolwork and educational information management were sampled. The objectives of the study were to find out the electronic devices owned/accessible to undergraduate students for educational information management, identify the location where undergraduate students mainly access the internet, appraise the means and cost of accessing the internet by undergraduate student, and ascertain how undergraduate students utilise networked devices to manage educational information. Survey research design was adopted for the study and quantitative data was collected with the use of a questionnaire from 390 respondents. Results from the study indicated that mobile phone (362 or 92.8%) and laptop computers (305 or 78.2%) are the most owned electronic devices. Only 3 or 0.8% do not own any of the electronics listed in the survey while 2 (0.5%) do not have access to any electronic device listed for the survey. Most of the respondents (307 or 78.7%) access the internet from their mobile phones with access to the internet being free wi-fi on campus 349 (89.9%). Ninety percent of the respondents make use of their mobile phone in managing educational information while only 19% manage educational information on their laptops when outside the university campuses. Digital divide still plays a major role in the hindering undergraduate students in the management of educational information using networked devices.
Difficulty Within Deep Learning Object-Recognition Due to Object Variance

It is one of the areas where deep learning models demonstrate possible performance bottleneck to learn objects with variations rapidly and precisely. We research on the variances of visual objects in terms of the difficulty levels of learning performed by deep learning models. Multiple dimensions and levels of variances are defined and formalized in the form of categories. We design "variance categories" and "quantitative difficulty levels" and translate variance categories into difficulty levels. We experiment how multiple learning models are affected by the categorization of variance separately and in combination (of several categories). Our experimental analysis on learning of the dataset demonstrates not only the expected way of utilization of variance of the data differs from models, the amount of learning or information gained for each data fed into models also varies significantly. Our results suggest it matters to search for a possible key explicit representation of the "invariance" part of objects (or of the respective cognitive mechanism) and for the pertinent elements and capabilities in the deep learning architectures. It can be used to make the learning models a match to humans on complex object recognition tasks.
Deep Cardiovascular Disease Prediction with Risk Factors Powered Bi-attention

Cardiovascular disease (CVD) is one of the serious diseases endangering human life and health. Therefore, using the electronic medical record information to automatically predict CVD has important application value in intelligent auxiliary diagnosis and treatment, and is a hot issue in intelligent medical research. In recent years, attention mechanism utilized in natural language processing has focused heeds on a small part of the context and congregated it using fixed-size vectors, coupling attention in time, and/or often forming a unidirectional attention. In this paper, we propose a CVD risk factor powered bi-directional attention network named as RFPBiA, which is a multi-stage hierarchical architecture, fusing the information at different granularity levels, and employs the bi-directional attention to obtain the text representation of risk factors without early aggregation. The experimental results show that the proposed method can obviously improve the performance of CVD prediction, and the F-score reaches 0.9424, which is better than the existing related methods.
A Game-Theoretic Approach to Secure Control of 3D Printers

Due to the high costs of 3D-printing infrastructure, outsourcing the production to third parties specializing in the 3D-printing process becomes necessary. The integration of a 3D-printing system with networked communications constitutes a cyber-physical system, bringing new security challenges. Adversaries can explore the vulnerabilities of networks to damage the physical parts of the system. In this chapter, we explore the vulnerabilities of 3D-printing systems and design a cross-layer approach for the system. At the physical layer, we use a Markov jump system to model the system and develop a robust control policy to deal with uncertainties. At the cyber-layer, we apply FlipIt game to model the contention between the defender and attacker for the control of the 3D-printing system. To connect these two layers, we develop a Stackelberg framework to capture the interactions between the cyber-layer attacker and defender game and the physical-layer controller and disturbance game, and define a new equilibrium concept that captures interdependence of the zero-sum and FlipIt games.
Better Queries for Aspect-Category Sentiment Classification

Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, finding aspect category-related information from the sentence is the key challenge to accurately recognize the sentiment polarity. Most previous models take both sentence and aspect category as input and query aspect category-related information based on the aspect category. However, these models represent the aspect category as a context-independent vector called aspect embedding, which may not be effective enough as a query. In this paper, we propose two contextualized aspect category representations, Contextualized Aspect Vector (CAV) and Contextualized Aspect Matrix (CAM). Specifically, we use the coarse aspect category-related information found by the aspect category detection task to generate CAV or CAM. Then the CAV or CAM as queries are used to search for fine-grained aspect category-related information like aspect embedding by aspect-category sentiment classification models. In experiments, we integrate the proposed CAV and CAM into several representative aspect embedding-based aspect-category sentiment classification models. Experimental results on the SemEval-2014 Restaurant Review dataset and the Multi-Aspect Multi-Sentiment dataset demonstrate the effectiveness of CAV and CAM.
Graph Database Management Systems

Nowadays, the increased amount and complexity of connected data stimulated by the appearance of social networks has shed a new light on the importance of managing such data, especially handling information about the connections. The most natural way of representing connected data is to represent them as nodes connected with relationships forming a graph. The idea of storing data as a set of nodes and edges comprising a graph was implemented in various forms in data models used in the past. The network data model, developed in late 1960s, can be considered as the first data model, which most accurately incorporated this idea. However, it was not long before the relational data model appeared, and took over the entire database market for years, which it dominates even nowadays. Therefore, the objective of this article is to give a timeline overview of developed graph data storage solutions in order to gain insight into past, present and future trends of GDBMSs. Additionally, the most influential factors and reasons for changes in trends in GDBMSs' usage will be analyzed.
Geochemical assessment of groundwater in the Kuiseb River basin in Namibia

Groundwater is one of the most valuable natural resources in the world, and, a limited resource, particularly for Namibia. For this reason, this resource should be protected and monitored on a regular basis for sustainability. One of Sustainable Development Goals (SDGs) aims to ensure availability and sustainable management of water and sanitation for all with specific emphasis on data and good governance. Therefore, this study was conducted in the Kuiseb Basin, Namibia to assess the physical and chemical quality of groundwater, chemical water type and chemical characteristics of sediment to determine its suitability for human consumption. Water and sediments samples from 20 sampling sites (boreholes) were analysed for physical and chemical characteristics and the following mean values were obtained for temperature (26.66 °C), electrical conductivity (396.85 mS/m), pH (7.26), total dissolved solid (494.45 mg/L) and dissolved oxygen (7.46 mg/L). Physico-chemical results suggest that the Kuiseb Basin is predominantly a calcium bicarbonate water type (Ca (HCO3)2) and most of the physico-chemical parameters tested fell within the permissible limits for drinking water set by the World Health Organisation (WHO). Analysis of the major ion chemistry (Mg2+ > + Ca2+ > + Na+ > + K+ and HCO3− > CO3− > SO4 2− > Cl−) indicated the dominance of rock-water interaction and the influence of anthropogenic activities as the main processes controlling groundwater chemistry in the area. It is recommended that the Basin, as a heavily used and sensitive environment, requires basin-wide river management.
Silicon Photonic Neural Networks for Chaos-based Secure Communication

We propose a continuous time recurrent neural network of four neurons modeled on a silicon photonic platform to generate chaos. Two such chaotic systems can be synchronized with low-latency using master-slave synchronization for high-speed secure data communication.
Website Development for Trading Between Farmers and Government

Considering that 70% of the rural household depends primarily on agriculture in India, the need of digitization in the field of agriculture becomes important. Maintaining all the records of the farmers produce can ease out the process. Websites can be used by the consumers or the government for buying the produce from farmers in an efficient way. With this website, farmers and government (or consumers) can create their accounts as seller and buyer, respectively. Third-party buyers can also use the database to buy crops of interest. Authentication is done through username and password to get the separate ID. Users can use this ID to access the database relevant to them. This project will help in creating a database that will in turn help in better supply of crops from farmers to government (or consumers) without the need of any intermediate party.
Research Hotspots and Trends of "Artificial Intelligence + E ducation" Abroad in the Last Decade——Knowledge graph analysis based on 876 articles in the WOS database from 2011 to 2020

In order to more clearly grasp the development of artificial intelligence in education abroad in the past decade, using document measurement analysis method, CiteSpace software was used to visually analyze 876 sample documents of the core collection of web of science from 2011 to 2020. Studies have shown that the research and research of artificial intelligence abroad in the field of education in the past decade have attracted a lot of attention and become more and more extensive. Based on the keyword co-occurrence map, the research content is sorted out, and the LLR clustering method forms four hot research topics of "artificial intelligence+education".
Binary Linear Codes with Near-Extremal Maximum Distance

Let $C$ denote a binary linear code with length $n$ all of whose coordinates are essential; i.e., for each coordinate there is a codeword that is not zero in that position. Then the maximum distance $D$ is strictly bigger than $n/2$, and the extremum $D=(n+1)/2$ is attained exactly by punctured Hadamard codes. In this paper, we classify binary linear codes with $D=n/2+1$. All of these codes can be produced from punctured Hadamard codes in one of essentially three different ways, each having a transparent description.
Gradient as a Foundation for Building a Loss Function

Deep neural networks have achieved a tremendous success in a variety of applications across many disciplines. Yet, training a neural network comes with several challenges that have to be solved. The performance of a deep learning models relies not only on the network architecture but also on the choice of a loss function. Cross-entropy loss found to be the most common choice for the classification problem. But its main downside is that it cannot handle data with severe class imbalance. In order to tackle this problem, the focal loss has been proposed. In this paper we investigate the reasons behind its good performance. We find several properties of the focal loss' gradient that can be applied for building new loss functions and propose a few of them. We also show an experimental evidence of the validity of the proposed functions.
Attitude of Trans-Baikal State University Students to Conditions of Education in Electronic Environment during Coronavirus Period

This article covers a number of problems and the effectiveness of the implementation of distance education at Trans-Baikal State University.The advantages and disadvantages of distance education at the university, indicated by the respondents who took part in the anonymous survey, are revealed.The article concludes that distance education in the context of the coronavirus was the only way to continue the educational process at the university.But the existing problems of the technical and economic plan were not a priority for students living in remote areas of Trans-Baikal Territory, since the technical connection for the implementation of the educational process was extremely low.When designing further optimal technical and software solutions for each educational program, it is necessary for the university administration to take into account the proposals of student youth to ensure the conditions for the implementation of the pedagogical principles of work and the technical capabilities of the available online technologies and equipment.The solution of these problems will make it possible to solve a number of problems related to ensuring the advantages of telecommunication forms of education in higher educational institutions.
Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

Abstract Multispectral sensors, along with common and advanced algorithms, have become efficient tools for routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this paper sought to evaluate and discuss the detection and mapping of magnetite on the Eastern Limb of the Bushveld Complex, using high spectral resolution multispectral remote sensing imagery and GIS techniques. Despite the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes, not many studies had looked at the detection and exploration of magnetite using remote sensing in this region. The Maximum Likelihood and Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope Analytic data. A K-fold cross-validation analysis was used to measure the performance of the training as well as the test data. For each classification algorithm, a thematic landcover map was created and an error matrix, depicting the user’s and producer’s accuracies as well as kappa statistics, was derived. A pairwise comparison test of the image classification algorithms was conducted to determine whether the two classification algorithms were significantly different from each other. The Maximum Likelihood Classifier significantly outperformed the Support Vector Machine algorithm, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially iron oxide mineralization in the Eastern Limb of the Bushveld Complex.