Reviewerly/exHarmony huggingface dataset:
The Experience of Using Digital Trends and Smart Technologies in Literary Studies and Teaching of World Literature

The article describes the experience of using digital trends, smart innovations and technologies in the Department of World Literature and Culture of Perm State University. Many literary studies methods where digital technologies may be used are based on dividing the corpus of literature or particular text into elements. We would like to pay attention to the opposite process bringing literary heritage together. It is necessary to study the so-called “old” knowledge which is a prolific base of studying the history of literature, literature as a form of culture. Three ways of applying of digital technologies in philology are discussed. Methodological aspect is connected with the research of intermediality. The history of literary studies cannot be torn apart from the “old” knowledge which is preserved in rare editions and must be transformed into digital format or spread with the help of the Internet. Teaching of literature now is closely connected with digital and smart innovations, systems and technologies and should answer the peculiarities of students of today and the new trends in the process of education.
A Hierarchical Sequence Labeling Model for Argument Pair Extraction

Argument pair extraction (APE) is a new task in the field of argument mining, aiming at mining interactive argument pairs from peer review and rebuttal. Previous work framed APE as a combination of a sequence labeling task and a sentence pair classification task via multi-task learning. However, this method lacks explicit modeling of the relations between arguments, and the two subtasks may not cooperate well. Towards these issues, we propose a hierarchical sequence labeling model that can efficiently represent arguments and explicitly capture the correlations between them. Our method matches argument pairs from the perspective of review and rebuttal respectively, and merges the results, enabling a more comprehensive extraction of argument pairs. Also, we propose a series of improved models and fuse them using weighted voting. Our method achieved first place in the NLPCC 2021 Shared Task on APE, proving the effectiveness of our method.
Statistical Analytics and Regional Representation Learning for COVID-19 Pandemic Understanding

The rapid spread of the novel coronavirus (COVID-19) has severely impacted almost all countries around the world. It not only has caused a tremendous burden on health-care providers to bear, but it has also brought severe impacts on the economy and social life. The presence of reliable data and the results of in-depth statistical analyses provide researchers and policymakers with invaluable information to understand this pandemic and its growth pattern more clearly. This paper combines and processes an extensive collection of publicly available datasets to provide a unified information source for representing geographical regions with regards to their pandemic-related behavior. The features are grouped into various categories to account for their impact based on the higher-level concepts associated with them. This work uses several correlation analysis techniques to observe value and order relationships between features, feature groups, and COVID-19 occurrences. Dimensionality reduction techniques and projection methodologies are used to elaborate on individual and group importance of these representative features. A specific RNN-based inference pipeline called DoubleWindowLSTM-CP is proposed in this work for predictive event modeling. It utilizes sequential patterns and enables concise record representation while using but a minimal amount of historical data. The quantitative results of our statistical analytics indicated critical patterns reflecting on many of the expected collective behavior and their associated outcomes. Predictive modeling with DoubleWindowLSTM-CP instance exhibits efficient performance in quantitative and qualitative assessments while reducing the need for extended and reliable historical information on the pandemic.
Imitative Learning for Multi-Person Action Forecasting

Multi-person action forecasting is an emerging task and a pivotal step towards video understanding. The major challenge lies in estimating a distribution characterizing the upcoming actions of all individuals in the scene. The state-of-the-art solutions attempt to solve this problem via a step-by-step prediction procedure. However, they are not adequate to address some particular limitations, such as the compounding errors, the innate uncertainty of the future and the spatio-temporal contexts. To handle the multi-person action forecasting challenges, we put forth a novel imitative learning framework upon the basis of inverse reinforcement learning. Specifically, we aim to learn a policy to model the aforementioned distribution up to a coming horizon through an objective that naturally solves the compounding errors. Such a policy is able to explore multiple plausible futures via extrapolating a series of latent variables and taking them into account to generate predictions. The impacts of these latent variables are further investigated by optimizing the directed information. Moreover, we reason the spatial context along with the temporal cue in a single pass with the usage of graph structural data. The experimental outcomes on two large-scale datasets reveal that our approach yields considerable improvements in terms of both diversity and quality with respect to recent leading studies.
Visual mapping of target tracing methods based on CiteSpace bibliometrics

Visual target tracking is a hot problem in computer vision research in recent years, and the application fields are gradually increasing, such as unmanned driving, intelligent surveillance, etc. In recent years, with the wide application of deep learning in the field of computer vision, the field of target tracking has also developed rapidly, and many scholars have improved and innovated the target tracking. Common target tracking areas include single target tracking, multi-target tracking, pedestrian re-identification, multi-target multi-camera tracking, pose tracking for many complex scenarios. Eventually the problem will also be attributed to single target tracking or multi-target tracking, which has become the focus of many scholars' research. In view of the rapid development of this field, this paper presents a review of visual target tracking research, including a review and analysis of single-target tracking methods, a review and analysis of multi-target tracking methods, and a summary of the shortcomings of these methods, including the lack of fusion based on target detection methods, the decrease of real-time accuracy, and the problem of target loss in long-term target tracking. . And according to the shortcomings, the following suggestions are made: combining traditional algorithms based on filtering and deep learning algorithms, focusing on the improvement of the ideas of deep learning frontier theories, and improving the long-term target tracking loss.
ELEMENTARY MUSIC IN THE SOVIET UNION: PREREQUISITES FOR IMPLEMENTATION IN THE TRAINING AND EDUCATIONAL SPACE

The purpose of the work is to study historical aspects of the development in the USSR of Elementary Music Education, created by a German teacher, musician and composer Carl Orff. The article substantiates the value of this pedagogical system and the prospects for its use in the training and educational process. The initial stage of the introduction of Elementary Music Method in the Soviet Union and the organization of the Carl Orff Pedagogical Society are considered. As a result of applying theoretical scientific research methods, the main factors that influence the process of introducing a new direction in music education are established. The significance of the unified system of music and movement education created by Carl Orff is defined. The system developed by Russian and Soviet enlightener, music theorist, teacher, performer and public figure Boleslav Leopoldovich Yavorsky correlates with German teacher's system. The author analyzes the reasons for which the system developed by B. L. Yavorsky is not widespread in our country. The significance of the activities of the musicologist, historian and publicist Oksana Timofeevna Leontyeva, an active promoter of the Elementary Music System and the first researcher of Orff-composer and Orff-children's music teacher, is determined. The article stresses the first contacts between Soviet musicians and their German colleagues during the visit to the Carl Orff Institute in Salzburg. Emphasis is placed on the importance of publishing literature describing the content, methods and principles of Elementary Music for Soviet teachers interested in new areas of music education and upbringing. Educational institutions are indicated, in which attempts are made to use C. Orff's methods in classrooms. The author reveals the optimization of processes of introducing domestic musicians to the methods of music and movement education, caused by the arrival in the USSR of foreign experts in the field of musical pedagogics. The role of Lev Vyacheslavovich Vinogradov, one of the first followers of new directions in music education, is priceless, as is the importance of his pedagogical activity and his contribution to the popularization and implementation of the method combining music and movement education.
Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes

Exploring the most task-friendly camera setting&#x2014;optimal camera placement (OCP) problem&#x2014;in tasks that use multiple cameras is of great importance. However, few existing OCP solutions specialize in depth observation of indoor scenes, and most versatile solutions work offline. To this problem, an OCP online solution to depth observation of indoor scenes based on reinforcement learning is proposed in this paper. The proposed solution comprises a simulation environment that implements scene observation and reward estimation using shadow maps and an agent network containing a soft actor-critic (SAC)-based reinforcement learning backbone and a feature extractor to extract features from the observed point cloud layer-by-layer. Comparative experiments with two state-of-the-art optimization-based offline methods are conducted. The experimental results indicate that the proposed system outperforms seven out of ten test scenes in obtaining lower depth observation error. The total error in all test scenes is also less than 90% of the baseline ones. Therefore, the proposed system is more competent for depth camera placement in scenarios where there is no prior knowledge of the scenes or where a lower depth observation error is the main objective.
No-frills Dynamic Planning using Static Planners

In this paper, we address the task of interacting with dynamic environments where the changes in the environment are independent of the agent. We study this through the context of trapping a moving ball with a UR5 robotic arm. Our key contribution is an approach to utilize a static planner for dynamic tasks using a Dynamic Planning add-on; that is, if we can successfully solve a task with a static target, then our approach can solve the same task when the target is moving. Our approach has three key components: an off-the-shelf static planner, a trajectory forecasting network, and a network to predict robot’s estimated time of arrival at any location. We demonstrate the generalization of our approach across environments. More information and videos at https://mlevy2525.github.io/DynamicAddOn/.
Orthogonal Range Search Data Structures

Abstract We first review existing space-efficient data structures for the orthogonal range search problem. Then, we propose two improved data structures, the first of which has better query time complexity than the existing structures and the second of which has better space complexity that matches the information-theoretic lower bound.
Selective Compression Method for High-Quality DaaS (Desktop as a Service) on Mobile Environments

As computing is defined as a concept that various IT devices are able to be used at any time at any place, with the ongoing growth of BYOD (bring your own device) tending to use DaaS on personal smart devices and company or official work as it is, now DaaS has to be supported smoothly in mobile environment. Hence, the study suggests high-quality transmission method to use smoothly on smart devices required in order to improve VDI environment to DaaS. To prove goal achievement, effectiveness results were analyzed through function and scenario tests on “gradual terminal protocol” technology and other virtualization common use solution protocols.
A Dynamic Selection Algorithm of Tor Relay Based on Client Bias

Tor network is one of the most widely used low latency anonymous communication systems. To balance the network load, the Tor network uses an adjusted bandwidth weighted random selection algorithm to uniformly select relays of a circuit. A client can not adjust the bias of the relays during the circuit establishment process. However, the client's different requirements for network anonymity and performance may affect the further extension of Tor. This paper proposes a relay dynamic selection algorithm that allows the client to set the relay preference when establishing a circuit. The algorithm defines a dynamic parameter that can be adjusted by the client. Defining different dynamic parameters can realize the degree of bias for high-bandwidth relay or low-bandwidth relay during circuit establishment. The proposed algorithm is implemented in the Tor source code and the homogeneous network and heterogeneous network are respectively deployed on the Shadow simulation platform for experiments. Based on the simulation results, we have observed that setting different dynamic parameters can achieve partial improvement of network performance or network anonymity.
Free Speech in Australia: Protecting Speech Without Explicit Free Speech Rights

Unlike many other liberalLiberals democratic countries, Australia does not have explicit free speech protections, such as those contained in many national constitutions, bills of rights, or other statutory protections. Instead, speech protections rely on the common lawCommon law tradition, or are found in statutory defenses, as part of legislation designed to prohibit or limit speech, such as anti-vilification laws.
In memory of Jean Van Bladel

The scientific career of Jean Van Bladel is briefly reviewed followed by an overview of recent research results obtained in the university department he founded 1964.
Towards a high robust neural network via feature matching

Abstract Image classification systems have been found vulnerable to adversarial attack, which is imperceptible to human but can easily fool deep neural networks. Recent researches indicate that regularizing the network by introducing randomness could greatly improve the model’s robustness against adversarial attack, but the randomness module would normally involve complex calculations and numerous additional parameters and seriously affect the model performance on clean data. In this paper, we propose a feature matching module to regularize the network. Specifically, our model learns a feature vector for each category and imposes additional restrictions on image features. Then, the similarity between image features and category features is used as the basis for classification. Our method does not introduce any additional network parameters than undefended model and can be easily integrated into any neural network. Experiments on the CIFAR10 and SVHN datasets highlight that our proposed module can effectively improve both clean data and perturbed data accuracy in comparison with the state-of-the-art defense methods and outperform the L2P method by 6.3 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>%</mml:mo></mml:math> , 24 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>%</mml:mo></mml:math> on clean and perturbed data, respectively, using ResNet-V2(18) architecture.
LibGuides: Information Technology Help: Email

Resources to help Central Penn College students navigate the technology required in their courses.
Conceptualizing Role Development in Agile Transformations: Deriving Design Goals and Principles for Agile Roles

Design knowledge on agile role development is still nascent. Following the call for a theoretical and empirical investigation into the formation of roles in agile transformations, we elicited three design goals, six design principles and defined the pathway to achieving these goals. Our concept provides new insights into the dynamic and cohesive character of agile roles and adds role development as a key activity to the core of agile transformation. The results are based on a qualitative research methodology and design science research. We contribute to theory by providing a grounded approach for the situational development of dynamic agile roles based on design goals and principles, while practice can profit from the approach as our concept provides greater flexibility than the strict role definitions of agile frameworks.
Depth-of-focus extension in null interferometric microscopy for ICF capsule surface-defects detection

Null interferometric microscope (NIM) is an effective method for detecting the isolated defects on the ICF capsule’s surfaces thanks to its null interference and high-resolution imaging ability. However, the limited depth-of-focus (DOF) caused by the large numerical aperture is the main drawback that prevents measuring defects in the full field of view (FOV) on the curved surface. In this work, a depth-of-focus (DOF) extension method based on numerical propagation is proposed to expand the field of view (FOV) of the NIM. The capability of the proposed DOF extension method is proved by the measurement of a 1-mm diameter ICF capsule. Experiment results indicate that the FOV of NIM is expanded from 140 &mu;m to 320 &mu;m.
Two Stage Learning for Argument Pairs Extraction

Argument pair extraction (APE) is an important research area for real-world applications such as online debates and persuasive essays. To facilitate the research of APE, NLPCC 2021 shared Task1 releases the RR-dataset, which aims to extract argument pairs from the peer reviews and its rebuttals. We propose a two-stage learning strategy for this task which divides it into two sub-tasks: arguments mining and arguments pairing. In arguments pairing task, instead of modeling arguments pairing task in sentence level, we cast it as a paragraph-level pairing task, which can alleviate the mismatch between the definition of task and training. And we apply transfer learning and fine-tuning strategy on all sub-tasks, which exploits large scale pre-trained semantic knowledge to benefit downstream APE task. Experiment results show that our method achieves significant improvements compared with strong baseline BERT-based multi-task learning framework, and finally ranks the \(3^{rd}\) in NLPCC2021 shared task 1 track 3 evaluation phrase.
Document Encryption Through Asymmetric RSA Cryptography

With advances in technology like today, documents can be sent digitally via the internet media. An important problem faced in sending digital documents is that often documents sent can be accessed by parties who do not have the authority over these documents. The solution to this problem is to secure digital documents before transmission. One of the methods to secure data is cryptography. Cryptography with asymmetric keys is the strongest data security technique to use. One of the most widely used asymmetric cryptography is the RSA (Rivest-Shamir-Adleman) algorithm. The type of document that is encrypted is the most commonly attached document when sent e-mails. The document types are .docx, .pptx, .xlsx, .pdf, .jpg and .mp4. In the encryption process, a public key and a private key will be generated which can be sent separately by sending encrypted digital documents. The decryption process for digital documents is carried out from the receiving end of the document using a private key generated in the encryption process. The encryption result has a size larger than the original file size because it has been encoded in another form according to the RSA algorithm. The longer and bigger the input size, the longer it will take required for encryption.
Recasted nonlinear complex diffusion method for removal of Rician noise from breast MRI images

The evolution of magnetic resonance imaging (MRI) leads to the study of the internal anatomy of the breast. It maps the physical features along with functional characteristics of selected regions. However, its mapping accuracy is affected by the presence of Rician noise. This noise limits the qualitative and quantitative measures of breast image. This paper proposes recasted nonlinear complex diffusion filter for sharpening the details and removal of Rician noise. It follows maximum likelihood estimation along with optimal parameter selection of complex diffusion where the overall functionality is balanced by regularization parameters. To make recasted nonlinear complex diffusion, the edge threshold constraint “k” of diffusion coefficient is reformed. It is replaced by the standard deviation of the image. It offers a wide range of threshold as variability present in the image with respect to edge. It also provides an automatic selection of “k” instead of user-based value. A series of evaluation has been conducted with respect to different noise ratios further quality improvement of MRI. The qualitative and quantitative assessments of evaluations are tested for the Reference Image Database to Evaluate Therapy Response (RIDER) Breast database. The proposed method is also compared with other existing methods. The quantitative assessment includes the parameters of the full-reference image, human visual system, and no-reference image. It is observed that the proposed method is capable of preserving edges, sharpening the details, and removal of Rician noise.
Towards a Theory of Valid Inferential Models with Partial Prior Information

Inferential models (IMs) are used to quantify uncertainty in statistical inference problems, and validity is a crucial property that ensures the IM's reliability. Previous work has focused on validity in the special case where no prior information is available. Here I allow for prior information in the form of a non-trivial credal set, define a notion of validity and investigate its implications.
Augmenting Imitation Experience via Equivariant Representations

The robustness of visual navigation policies trained through imitation often hinges on the augmentation of the training image-action pairs. Traditionally, this has been done by collecting data from multiple cameras, by using standard data augmentations from computer vision, such as adding random noise to each image, or by synthesizing training images. In this paper we show that there is another practical alternative for data augmentation for visual navigation based on extrapolating viewpoint embeddings and actions nearby the ones observed in the training data. Our method makes use of the geometry of the visual navigation problem in 2D and 3D and relies on policies that are functions of equivariant embeddings, as opposed to images. Given an image-action pair from a training navigation dataset, our neural network model predicts the latent representations of images at nearby viewpoints, using the equivariance property, and augments the dataset. We then train a policy on the augmented dataset. Our simulation results indicate that policies trained in this way exhibit reduced cross-track error, and require fewer interventions compared to policies trained using standard augmentation methods. We also show similar results in autonomous visual navigation by a real ground robot along a path of over 500m.