Reviewerly/exHarmony huggingface dataset:
READING INTERESTS OF PRIMARY SCHOOL CHILDREN

This article presents the results of a study that examined the interest and influence of gender of students studying in International schools in Sri Lanka on reading. The objectives of this study was to find the nature of reading interest of the primary school children and to examine whether it differ by gender. The sample was selected from three International schools in Colombo district. Data was collected through a questionnaire and analyzed with Chi-square test. Findings indicated that, boys preferred comic books and the girls opted to picture books. Reading Newspapers and web pages are not popular among students, despite computer was much preferred in the past. This could indicate that children prefer to read a book. Where genres are concerned, Mystery &amp; Adventure and Fantasy are the selection of the majority. Both these fall into the fiction category which was confirmed by previous researches. Since a vast majority of the sample showed preference for series books as well as comic books, it can be concluded that books from the Wimpy Kid and the Geronimo Stilton series can be used in order to encourage and motivate young readers by making reading an enjoyable activity.
Type Inference in Java: Characteristics and Limitations

With the introduction of parametric types in Java, the type system becomes more complex to handle. The Java generics requires more programming efforts to instantiate appropriate types. In such a situation, a sound type inference algorithm may reduce programming load and ensure safety. Java type inference algorithm has eased programming efforts by reducing explicit instantiation of types. The Java type inference algorithm is being expanded. Still, the impact of inference in mainstream programming is limited. Moreover, the unsound approach for inference causes unpredictable behavior in program code. The limitations and unsoundness of Java type inference need to be discussed to avoid the failure of codes. In this paper, we discuss limitations and issues of type inference algorithm, such as the incapability to infer wildcards as return type in generic methods, and limitations of local variable type inference. Further, we discuss how the machine learning approaches can help to develop a safe type inference algorithm.
Using Mobile Devices as Effective Learning Tools in a Library Setting

Mobile devices are prolific and young people have embraced this technology in all facets of their lives. Using a small number of iTouch and iPad devices in a school library has provided students rich learning opportunities which have enhanced cognitive, personal and interpersonal skills. By harnessing their power and motivation, mobile devices can become powerful learning tools. Integrating QR codes, Student response apps, Thinking and Presentation tools have been used to develop cognitive skills. This article will explain, justify and provide illustrations of practice of how these devices have been used and demonstrate their value in making connections in the school library.
Detecting over/under-translation errors for determining adequacy in human translations

We present a novel approach to detecting over and under translations (OT/UT) as part of adequacy error checks in translation evaluation. We do not restrict ourselves to machine translation (MT) outputs and specifically target applications with human generated translation pipeline. The goal of our system is to identify OT/UT errors from human translated video subtitles with high error recall. We achieve this without reference translations by learning a model on synthesized training data. We compare various classification networks that we trained on embeddings from pre-trained language model with our best hybrid network of GRU + CNN achieving 89.3% accuracy on high-quality human-annotated evaluation data in 8 languages.
Big Data and Sketching

The term big databig datarefers to the easy collection and storage of large amounts of data that by virtue of its scale causes rethinking linearly CPU-bound computational approaches and analysis methodologies. These challenges, and the development of approaches to handle them, were in many ways a necessary precursor to the data science revolution. For complex phenomena, with small data, the central limit theorem may not reach convergence, and there may not be enough data points to successfully hold out data to faithfully cross-validate. With small data, abstract analysis models are less essential: low-dimensional data does not need its dimension reduced, and data may be clustered or classified by hand (perhaps with the help of plotting).
Invertible Image Signal Processing

Unprocessed RAW data is a highly valuable image format for image editing and computer vision. However, since the file size of RAW data is huge, most users can only get access to processed and compressed sRGB images. To bridge this gap, we design an Invertible Image Signal Processing (InvISP) pipeline, which not only enables rendering visually appealing sRGB images but also allows recovering nearly perfect RAW data. Due to our framework's inherent reversibility, we can reconstruct realistic RAW data instead of synthesizing RAW data from sRGB images without any memory overhead. We also integrate a differentiable JPEG compression simulator that empowers our framework to reconstruct RAW data from JPEG images. Extensive quantitative and qualitative experiments on two DSLR demonstrate that our method obtains much higher quality in both rendered sRGB images and reconstructed RAW data than alternative methods.
Students are the Best Teacher: Exit-Ensemble Distillation with Multi-Exits

This paper proposes a novel knowledge distillation-based learning method to improve the classification performance of convolutional neural networks (CNNs) without a pre-trained teacher network, called exit-ensemble distillation. Our method exploits the multi-exit architecture that adds auxiliary classifiers (called exits) in the middle of a conventional CNN, through which early inference results can be obtained. The idea of our method is to train the network using the ensemble of the exits as the distillation target, which greatly improves the classification performance of the overall network. Our method suggests a new paradigm of knowledge distillation; unlike the conventional notion of distillation where teachers only teach students, we show that students can also help other students and even the teacher to learn better. Experimental results demonstrate that our method achieves significant improvement of classification performance on various popular CNN architectures (VGG, ResNet, ResNeXt, WideResNet, etc.). Furthermore, the proposed method can expedite the convergence of learning with improved stability. Our code will be available on Github.
ACEHNESE STUDENTS’ SKILLS IN PARAPHRASING ENGLISH TEXTS: A CASE STUDY AT TWO UNIVERSITIES IN BANDA ACEH

This qualitative study was conducted to investigate university students ’ abilities to paraphrase English texts and to investigate the strategies used by students to help them to paraphrase. The population surveyed for this study consisted of 300 students from a private and a public university in Banda Aceh, while the sampling was drawn randomly, extracting 30 students as the respondents. The data were gathered using a written test, analyzed qualitatively and explained descriptively. The results of this study showed that the students still failed to properly paraphrase the English texts as we observed that the students ’ works nearly copied most of the original sources. However, the use of some paraphrasing strategies was visible. The strategy mostly used was varying the structure and the least used were direct copying and combining sources. It is suggested that English writing instruction at any university provide more training on paraphrasing for the students .
A Pragmatic Study on Hybrid-Crypto-Coding Schemes for Secure Data Access

Cloud technology is a fast developing computing technology through which resources are shared via the internet. Though it is growing fast, the hurdle to its growth is security. In general, sophisticated encryption algorithms are using the locking key, to prevent unauthorized access of data placed in the cloud. If the unlocking key is placed along with the data in the cloud then attack is very easy. To increase the toughness in accessing the data some additional security mechanism will help to save the data in the cloud. In this paper, a hybrid encryption algorithm with channel coding scheme and additional interleaver is proposed. Computational complexity is analyzed based on time complexity for the proposed algorithm. The hybrid cryptography schemes chosen for analysis are AES-ElGamal, DES-RSA, and TDES-Rabin. These hybrid cryptography schemes along with Turbo coding ensure joint-defense on security and reliability. This work concentrates on applying the Hybrid-Crypto-Coding scheme (HCC) for enhancing security at distributed data storage centers. Simulated outputs show that the proposed system provides better security and reliability when compared to conventional schemes. Among the three hybrid cryptography schemes, the AES-ElGamal pair outperforms the rest.
APPLICATION OF INFORMATION TECHNOLOGIES AT CUSTOMER-ORIENTED SERVICES IN THE MINSK INNOVATIVE UNIVERSITY LIBRARY

The article considers a theoretical aspect of the concepts ‘customer focus and a client’, as well as the use of information technologies which operate on the basis of the library of the Minsk Innovation University as one of the areas of customer-oriented services.
Scheduling Applications on Containers Based on Dependency of The Applications

Cloud computing technology has been one of the most critical developments in provisioning both hardware and software infrastructure in recent years. Container technology is a new cloud technology that boosts the booting of applications, increases the ability to deploy applications on containers and improves the host machine resource sharing. Thus, enhancing a cloud container system needs a robust algorithm that deploys the applications efficiently. Most of the schedulers associated with container technology are focused on load balancing for increasing container performance. The traffic over networks plays a significant role in the performance of containers. Container deployment considering only load balancing may not be the best scheduling strategy due to the dependency between the applications that might be deployed in different pods (zones) in the container's cloud. This project aims to develop an algorithm that deploys applications into containers by considering the dependencies between applications as well as load balancing. The proposed algorithm performs better in terms of improving the throughput and reducing the network traffic as compared to state-of-the-art container scheduling algorithms.
Computational framework for discontinuity network characterization

&amp;lt;p&amp;gt;The topological and geometrical description of fault and fracture networks is an essential first step in any investigation of fractured or faulted media. The spatial arrangement, density, connectivity, and geometry of the discontinuities strongly impact the physical properties of the media such as resilience and permeability. Obtaining reliable metrics for characterizing fault and fracture networks is of interest for mining engineering, reservoir characterization, groundwater management, and studies on the regional fluid flow history. During large-scale studies, we mostly rely on two-dimensional lineaments obtained through structural mapping, outcrop analysis, or remote sensing. An efficient and widely applicable framework for discontinuity network characterization should therefore be based on the analysis of the frequently available two-dimensional data sets.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we present an automated framework for efficient and robust characterization of the geometric and topologic parameters of discontinuity networks. The geometry of the lineaments is characterised based on orientation, length, and sinuosity. The underlying distribution of these parameters are determined, and representative probability density functions are reported. The connection between the geometric parameters is validated, e.g. correlation between orientation and length. The spatial arrangement is determined by classical line- and window-sampling, by assessing the fractal dimension, and via graph-based topology analysis.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In addition to the statistical analysis of lineament networks, we show how the graph data structure can be utilized for further characterization by linking it to raster data such as magnetic, gravimetric, or elevation. This procedure not only yields an additional means for lineament characterization but also allows users to assess dominant pathways based, for instance, on hydraulic gradients. We demonstrate the applicability of our algorithm on synthetic data sets and real-world case studies on mapped fault and fracture networks.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We finally show how our framework can also be utilized to design detailed numerical studies on the fluid flow properties of analysed networks by conditioning mesh refinement on the type and number of intersections. In addition, due to known scaling relationships our framework can help to determine appropriate parameters for the simulations. We provide examples of statistically parametrized fluid flow simulations in natural discontinuity networks and show the impact of conceptualizing the lineaments as conduits, barriers or conduit-barrier systems.&amp;lt;/p&amp;gt;
The giant, continuous Three Forks Play, Williston Basin

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Deep NRSfM++: Towards Unsupervised 2D-3D Lifting in the Wild

The recovery of 3D shape and pose from 2D landmarks stemming from a large ensemble of images can be viewed as a non-rigid structure from motion (NRSfM) problem. Classical NRSfM approaches, however, are problematic as they rely on heuristic priors on the 3D structure (e.g. low rank) that do not scale well to large datasets. Learning-based methods are showing the potential to reconstruct a much broader set of 3D structures than classical methods -- dramatically expanding the importance of NRSfM to atemporal unsupervised 2D to 3D lifting. Hitherto, these learning approaches have not been able to effectively model perspective cameras or handle missing/occluded points -- limiting their applicability to in-the-wild datasets. In this paper, we present a generalized strategy for improving learning-based NRSfM methods to tackle the above issues. Our approach, Deep NRSfM++, achieves state-of-the-art performance across numerous large-scale benchmarks, outperforming both classical and learning-based 2D-3D lifting methods.
Elliptic and Edwards Curves Order Counting Method

We consider the algebraic affine and projective curves of Edwards over the finite field Fpn. It is well known that many modern cryptosystems can be naturally transformed into elliptic curves. In this paper, we extend our previous research into those Edwards algebraic curves over a finite field. We propose a novel effective method of point counting for both Edwards and elliptic curves. In addition to finding a specific set of coefficients with corresponding field characteristics for which these curves are supersingular, we also find a general formula by which one can determine whether or not a curve Ed[Fp] is supersingular over this field. The method proposed has complexity O ( p log2 2 p ) . This is an improvement over both Schoof’s basic algorithm and the variant which makes use of fast arithmetic (suitable for only the Elkis or Atkin primes numbers) with complexities O(log8 2 pn) and O(log4 2 pn) respectively. The embedding degree of the supersingular curve of Edwards over Fpn in a finite field is additionally investigated. Due existing the birational isomorphism between twisted Edwards curve and elliptic curve in Weierstrass normal form the result about order of curve over finite field is extended on cubic in Weierstrass normal form.
Engineering the Quantum Scientific Computing Open User Testbed (QSCOUT): Design details and user guide

The Quantum Scientific Computing Open User Testbed (QSCOUT) at Sandia National Laboratories is a trapped-ion qubit system designed to evaluate the potential of near-term quantum hardware in scientific computing applications for the US Department of Energy (DOE) and its Advanced Scientific Computing Research (ASCR) program. Similar to commercially available platforms, most of which are based on superconducting qubits, it offers quantum hardware that researchers can use to perform quantum algorithms, investigate noise properties unique to quantum systems, and test novel ideas that will be useful for larger and more powerful systems in the future. However, unlike most other quantum computing testbeds, QSCOUT uses trapped $^{171}$Yb$^{+}$ ions as the qubits, provides full connectivity between qubits, and allows both quantum circuit and low-level pulse control access to study new modes of programming and optimization. The purpose of this manuscript is to provide users and the general community with details of the QSCOUT hardware and its interface, enabling them to take maximum advantage of its capabilities.
Hasse invariant for the tame Brauer group of a higher local field

We generalize the Hasse invariant of local class field theory to the tame Brauer group of a higher dimensional local field, and use it to study the arithmetic of central simple algebras, which are given <italic>a priori</italic> as tensor products of standard cyclic algebras. We also compute the tame Brauer dimension (or <italic>period-index bound</italic>) and the cyclic length of a general henselian-valued field of finite rank and finite residue field.
A model for improving students’ technical creativity competences on the basis of elements of robotics

In this article, information will be given to secondary school students on the possibilities of using robotics in their teaching and extracurricular activities, the importance of physics in the development of technical creativity competences. Also, the model of improvement of students ‘technical creativity competences on the basis of elements of robotics, model components, model through the use of education in the educational process, the information on the opportunities for the development of students’ technical creativity competences is covered.
Domain-specific MT for Low-resource Languages: The case of Bambara-French

Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data. In this paper we address the issue of domain-specific MT for Bambara, an under-resourced Mande language spoken in Mali. We present the first domain-specific parallel dataset for MT of Bambara into and from French. We discuss challenges in working with small quantities of domain-specific data for a low-resource language and we present the results of machine learning experiments on this data.
MultiScope: Efficient Video Pre-processing for Exploratory Video Analytics

Performing analytics tasks over large-scale video datasets is increasingly common in a wide range of applications. These tasks generally involve object detection and tracking operations that require applying expensive machine learning models, and several systems have recently been proposed to optimize the execution of video queries to reduce their cost. However, prior work generally optimizes execution speed in only one dimension, focusing on one optimization technique while ignoring other potential avenues for accelerating execution, thereby delivering an unsatisfactory tradeoff between speed and accuracy. We propose MultiScope, a general-purpose video pre-processor for object detection and tracking that explores multiple avenues for optimizing video queries to extract tracks from video with a superior tradeoff between speed and accuracy over prior work. We compare MultiScope against three recent systems on seven diverse datasets, and find that it provides a 2.9x average speedup over the next best baseline at the same accuracy level.
Hyperspectral images destriping approach with weighted block sparsity regularization and non-convex low-rank penalty

针对高光谱图像（hyperspectral images, HSI）去条带易引起影像结构细节丢失问题，提出一种基于加权块稀疏（weighted block sparsity, WBS）正则化联合最小最大非凸惩罚（minimax concave penalty, MCP）约束的HSI去条带方法。本算法采用加权<i>ℓ</i><sub>2, 1</sub>范数和MCP范数对条带稀疏结构和低秩约束，<i>ℓ</i><sub>1</sub>范数对干净图像结构保持正则化约束，构建加权块稀疏和MCP约束的条带去除模型，采用交替方向乘子（alternating direction method of multipliers, ADMM）算法迭代求解对应模型，重建获得干净的HSI图像。实验结果表明，提出方法在实际HSI的平均等效视数从28.45提高到83.47，边缘保持指数较其他算法至少增加0.056，特别是对于非周期条带噪声，采用自适应权值更新稀疏水平，增强了组稀疏性，在保持影像边缘和加强区域平滑性方面性能更佳，去噪声效果更好。
Politeness and Insult in Computer Games – From a Pragmatic Point of View

Abstract In line with the principle of technological determinism, the linguistic context of computer games influences the (linguistic) behaviour of millions of active gamers. This makes it important to explore gamer communication thoroughly with respect to politeness, too. Indeed, the communication of gamers during games may also affect the users’ off-game communicative situations. The international literature suggests that the quasi-anonymity of online communication and the lack or weakness of sanction make it ruder than offline communication: it involves a higher number of insults or offensive personal remarks. The paper looks at this issue, in particular by a pragmatic – politeness-centred – investigation of a particular kind of online insults. The corpus of analysis is provided by “taunts”, i.e. inbuilt instructions triggering “mocking” remarks of League of Legends (LoL), a multiple-participant online arena game. The authors interpret in-game insults in the framework of speech act theory, the Cooperative Principle (conversational and politeness maxims), face threatening, and a matrix of aims and functions. The paper wishes to be a contribution to cyberpragmatics, a pragmatically-oriented branch of Internet linguistics.
Deep Neural Networks for Relation Extraction

Relation extraction from text is an important task for automatic knowledge base population. In this thesis, we first propose a syntax-focused multi-factor attention network model for finding the relation between two entities. Next, we propose two joint entity and relation extraction frameworks based on encoder-decoder architecture. Finally, we propose a hierarchical entity graph convolutional network for relation extraction across documents.