Reviewerly/exHarmony huggingface dataset:
Neural Algorithmic Reasoners are Implicit Planners

Implicit planning has emerged as an elegant technique for combining learned models of the world with end-to-end model-free reinforcement learning. We study the class of implicit planners inspired by value iteration, an algorithm that is guaranteed to yield perfect policies in fully-specified tabular environments. We find that prior approaches either assume that the environment is provided in such a tabular form -- which is highly restrictive -- or infer local neighbourhoods of states to run value iteration over -- for which we discover an algorithmic bottleneck effect. This effect is caused by explicitly running the planning algorithm based on scalar predictions in every state, which can be harmful to data efficiency if such scalars are improperly predicted. We propose eXecuted Latent Value Iteration Networks (XLVINs), which alleviate the above limitations. Our method performs all planning computations in a high-dimensional latent space, breaking the algorithmic bottleneck. It maintains alignment with value iteration by carefully leveraging neural graph-algorithmic reasoning and contrastive self-supervised learning. Across eight low-data settings -- including classical control, navigation and Atari -- XLVINs provide significant improvements to data efficiency against value iteration-based implicit planners, as well as relevant model-free baselines. Lastly, we empirically verify that XLVINs can closely align with value iteration.
Crossmodal Matching Transformer based X-ray and CT image registration for TEVAR

Since the mapping relationship between definitized intra-interventional 2D X-ray and undefined pre-interventional 3D Computed Tomography(CT) is uncertain, auxiliary positioning devices or body markers, such as medical implants, are commonly used to determine this relationship. However, such approaches can not be widely used in clinical due to the complex realities. To determine the mapping relationship, and achieve a initializtion post estimation of human body without auxiliary equipment or markers, a cross-modal matching transformer network is proposed to matching 2D X-ray and 3D CT images directly. The proposed approach first learns skeletal features from 2D X-ray and 3D CT images. The features are then converted into 1D X-ray and CT representation vectors, which are combined using a transformer module. As a result, the well-trained network can directly predict the spatial correspondence between arbitrary 2D X-ray and 3D CT. The experimental results show that when combining our approach with the conventional approach, the achieved accuracy and speed can meet the basic clinical intervention needs, and it provides a new direction for intra-interventional registration.
Compliance of the Directions and Programs of Training in Postgraduate Studies: The Transition Period

The new classification of scientific majors for which academic degrees are awarded and the amendments to the Federal Law “On Education in the Russian Federation”, which came into force on September 1, 2021, have significantly changed the list of scientific majors and the status of postgraduate studies. There is a need to establish the correspondence between scientific and pedagogical personnel training directions in postgraduate studies and scientific majors of the Classification 2021, in which academic degrees are awarded. The article presents an analysis of the adapting document “Fields of training in the postgraduate studies of OKSO 2016 – Scientific majors/branches of science of the Classification 2021” and considers measures on the way to reorganize the system of training and certification of academic degree holders.
LibGuides: A LibGuide for ECE's: Home

Are you in the Early Childhood Education program at Algonquin College? Check out this guide for information to help you with your program!
Sometimes We Want Ungrammatical Translations

Rapid progress in Neural Machine Translation (NMT) systems over the last few years has focused primarily on improving translation quality, and as a secondary focus, improving robustness to perturbations (e.g. spelling). While performance and robustness are important objectives, by over-focusing on these, we risk overlooking other important properties. In this paper, we draw attention to the fact that for some applications, faithfulness to the original (input) text is important to preserve, even if it means introducing unusual language patterns in the (output) translation. We propose a simple, novel way to quantify whether an NMT system exhibits robustness or faithfulness, by focusing on the case of word-order perturbations. We explore a suite of functions to perturb the word order of source sentences without deleting or injecting tokens, and measure their effects on the target side. Across several experimental conditions, we observe a strong tendency towards robustness rather than faithfulness. These results allow us to better understand the trade-off between faithfulness and robustness in NMT, and opens up the possibility of developing systems where users have more autonomy and control in selecting which property is best suited for their use case.
Amazigh part-of-speech tagging with machine learning and deep learning

NLP is a part of artificial intelligence that dissects, comprehends, and changes common dialects with computers in composed and spoken settings. At that point in scripts. Grammatical features (POS) allow marking the word as per its statement. We find in the literature that POS is used in a few dialects, in particular: French, English… etc. This paper investigates the attention-based LSTM networks and simple Recurrent Neural Network in TIFINAGH part-of-speech (POS) tagging when it is compared to Conditional Random Fields  (CRF) and Decision Tree. The attractiveness of LSTM networks is their strength in modeling long-distance dependencies. The experiment results show that long short-term memory (LSTM) networks perform better than RNN, CRF and Decision Tree that has a near performance.
Introduction of mobile education in the educational process in the university

The goal is to consider the possibilities of a virtual educational ecosystem using mobile devices, interactions of students and teachers, faculties and laboratories of the university, interactive activation of educational material and monitoring of the student learning process. The research methodology includes methods of system analysis, synthesis, decision-making, multi-agent systems and situational modeling. Results of the work: 1) a systematic interpretation of the category "mobile training" is proposed, its "pros" and "cons" are classified; 2) an informological scheme and a methodological scheme of mobile training are proposed; 3) practical examples of training (on the example of training managers in the field of tourism) using the organization of modeling, independent work of students are considered; 4) shows examples of approaches to making team business decisions and conducting situational modeling; 5) a systemically significant conclusion was made that simple, auxiliary tasks and simple memorization of facts students transfer to independent work, freeing up time for creative cooperation.
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents.

We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as machine reading comprehension task based on a single given document or passage. In this work, we aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. To facilitate such task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains. We also explore modeling the dialogue-based and document-based contexts in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task.
Quantum steering enhancement using correlation injection structure based on a cascaded four-wave-mixing processes

Multipartite quantum steering is an essential quantum resource in the process of secure quantum communication. The enhancement of quantum steering is important for specific quantum communication tasks. In this work, we present a quadripartite quantum steering scheme based on correlation injection structure by symmetrical cascaded four-wave-mixing processes and analyze steering properties of different mode combinations. We theoretically prove that for some mode combinations, a correlation injection scheme can enhance the quantum steering under certain conditions, while for other combinations, a correlation injection scheme can decrease the quantum steering under certain conditions. The method offers a reference for the secure quantum information network.
Probing Across Time: What Does RoBERTa Know and When?

Models of language trained on very large corpora have been demonstrated useful for natural language processing. As fixed artifacts, they have become the object of intense study, with many researchers “probing” the extent to which they acquire and readily demonstrate linguistic abstractions, factual and commonsense knowledge, and reasoning abilities. Recent work applied several probes to intermediate training stages to observe the developmental process of a large-scale model (Chiang et al., 2020). Following this effort, we systematically answer a question: for various types of knowledge a language model learns, when during (pre)training are they acquired? Using RoBERTa as a case study, we find: linguistic knowledge is acquired fast, stably, and robustly across domains. Facts and commonsense are slower and more domain-sensitive. Reasoning abilities are, in general, not stably acquired. As new datasets, pretraining protocols, and probes emerge, we believe that probing-across-time analyses can help researchers understand the complex, intermingled learning that these models undergo and guide us toward more efficient approaches that accomplish necessary learning faster.
Quick(er) Calculations

This article contains book reviews of Poems that Solve Puzzles by Chris Bleakley, Quick Calculations by Trevor Davis Lipscombe, The Error of Truth by Steven Osterling, Bernoulli's Fallacy by Aubrey Clayton, and A History of Data Visualization and Graphic Communication by Michael Friendly and Howard Wainer.
Seongdam( ) Song Hwan-gi( )’s 性潭 宋煥箕 Lecturing Educational Activities

19세기 초반을 전후로 한 시기를 살았던 성담 송환기는 송시열의 5세손으로, 남당 한원진·운평 송능상으로 이어지는 호론계의 종장이었다. 송환기는 우암학의 기조를 계승하면서, 독서와 저술 및 강론&#65381;강학 활동에 중점을 둔 삶을 영위했던 학구형의 산림이었다. 이번의 논의에서는 송환기가 35세 때 과거업을 중단하고 삶의 전회를 이루게 된 이면을 자세하게 규명하고, 또 강학 활동을 통해서 ‘은자류(隱者流)’의 삶을 영위한 내용들을 소개하였다. 소싯적부터 뛰어난 경서 이해력과 학문에의 정진 의지를 보였던 송환기는 주변의 큰 기대를 받으면서 성장하였다. ‘천생학자’라는 평을 받았던 송환기는 진사&#65381;생원시에 합격 했으나, 결국 대과를 포기하는 결단을 내린다. 이러한 방향 전회의 이면에는 송환기가 출사 관리형보다는, 침잠과 정진&#65381;사색을 통한 학문 위주의 삶을 훨씬 더 선호했기 때문이다. 송환기의 심리적 트라우마와 그 극복 의지가 감지되는 <농포소은설>(36세)에 등장하는 동일시 모델인 주희&#65381;정몽주는 성담의 생애에 대한 방향성을 시사해 주고 있다. 실제 송환기는 30세 이후로 진지한 강론&#65381;토론 활동을 공유해 나갔고, 40세 이후부터 는 자택과 내곡&#65381;남간정사&#65381;한천서원 등지에서 왕성한 강학 활동을 전개하게 된다. 송환기가 60세 후반 이후로 세자시강원의 주요 직책으로 원자를 보도한 일도 강학 활동에 포함된다. 차후 송환기의 문인록에는 500여 명이 넘는 명단이 수록되어 있을뿐더러, 상례 시에는 400여 명의 문하생들이 운집하는 진풍경을 연출하였다. 결과적으로 송환기가 전개한 강학 활동은 전국적인 인적 네트워크를 구축한 한 사람의 재야 유학자가 선보인 유의미한 학문적 커뮤니티의 사례로 남게 되었다.Seongdam Song Hwan-gi was a master and non-government Confucian scholar representing Horon(湖論係) scholars. Succeeding to the keynote of Theory of Uam (尤菴學), Song Hwan-gi lived a life focusing on the reading, lecturing and educational activities. In this discussion, this study intends to investigate and verify the hidden side of Song Hwan-gi’s suspension of the state examination(科擧) and change in life, introducing how he led a life thinking of himself as a ‘hermit (隱者流)’ through lecturing and educational activities. Despite Song Hwan-gi passed the first examination for office(進士) and secondary grade civil service exams (生員試), he made a decision to give up the rest examinations at the age of 35. In the <Nongposoeunseol(農圃小隱說)> that shows Seongdam’s trauma with his will to overcome such trauma, it is noticeable that Song identified himself with Zhu Xi(朱熹) and Jeong Mong-ju(鄭夢周). After 30, Song Hwan-gi kept sharing lecturing&#65381;educational activities with his acquaintances, and developed educational activities at home and Confucian academies after he turned 40. Teaching the eldest son of the king (元子) at the key position of Saejasigangwon(世子侍講院) from his late 60s are also included in his educational activities.
The use of guided discovery learning method to improve participation in answering reading comprehension questions

This research was done by using Classroom Action Research method to solve the problems faced by the students. This research was conducted to the eleventh Grade Students of SMA Boedi Oetomo Pontianak in academic year of 2017/2018. Here, the researcher used purposive sampling as the sampling technique by choosing eleventh grade which consisted of 26 students. Techniques of data collecting were observation technique and measurement. To analyze the data, the researcher used qualitative and quantitative analyses. In qualitative data analysis, the researcher analyzed the displayed data, and drawing conclusion. While in quantitative data analysis, the researcher analyzed the data from the test which calculated by percentage of the students’ participation during each cycle in three cycles. In the initial cycle, the students’ participation was 21.30%. Meanwhile in the second cycle, the participation of the students was 45.62%. In the last cycle, the students’ participation was 60.60%. The researcher concluded that Discovery learning method was effective in improving the students’ participation in responding to the teacher’s questions. Therefore,discovery learning method could help the teacher in teaching and learning process because this method encouraged students to learn actively.
The Concept of Computer Crime

The object of this scientific study is represented by the analysis of the concept of computer crimealso called computer-oriented crime, which is not generally acknowledged or accepted.The result of the studyconsists in analysing the evolution of computer crime.The conclusion is that certain legal provisions shall lead,sooner or later, to an Internet Law.
Numerological symbols in Schoenberg's creation

Expressionism in music was the movement that brought the contrast to previous eras promoting a tense music, exacerbation of means of expression and destruction of tonal balance. The works that belong to this innovative artistic movement involve diverse interpretations, because they capture the general tense atmosphere, from an aestheticphilosophical perspective, highlighting some text-sonority correspondences and the expressiveness of the elements of musical language. Over time, various connections have been made between music and mathematics, proving that mathematics can give a solid, deep foundation to music, without having a major impact on the rendering of feelings. This research presents some aspects of how numerology influenced and marked the creation of the expressionist composer Arnold Schoenberg. By using the symbolism of numbers, we can better understand the way the composer thought and created his music, strongly marked by the dissolution of tonality, the twelve-tone technique, and the transition from belcanto to "Sprechgesang" and “Sprechmelodie”.
Impact of Sales Employees’ Self-leadership Behaviors on Turnover in the Digital Era

In the digital era, the application of new technology tools has reshaped the original work pattern, changed the way employees accomplish tasks and interpersonal communication, and provided sales employees with more autonomy. As a result, self-leadership has become increasingly important.To investigate the impact of self-leadership on employee turnover in the digital era and its mechanism, this paper uses both qualitative and quantitative methods based on the sample of sales employees. First, using structured interviews, we discover that sales employees’ self-leadership behaviors are empowered by digital platform and manifested in active information seeking and internal social network building. Second, based on the self-control theory and the job embeddedness theory, we propose a theoretical model with the sales performance as a mediator. Using the behavior records of sales employees from a digital platform, we employ the survival model and Monte Carlo methods to analyze the data and find that sales performance plays a mediating role in the relationship between self-leadership and turnover. That is, the more self-leadership behaviors of sales employees on the digital platforms, the higher their sales performance; and the higher their performance, the less likely they turnover.The theoretical contributions of this paper are as follows: Firstly, this paper provides an in-depth understanding of the specific behaviors of successful self-lead sales employees in the digital era, and contributes to the scarce results on self-leadership in the Chinese context. Secondly, this paper enriches the research on the impact of self-leadership on employee turnover in the digital era. It expands the understanding of employee turnover by explaining the causes of turnover at the individual level from the perspective of self-leadership. Finally, this paper pioneers the use of objective employee behavior records from a mobile work platform as the data source for the empirical study, which is a very useful exploration. The findings also provide practical insights for sales performance and turnover management from the self-leadership perspective in the digital era.
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification.

Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table. Symbolic reasoning with logical operations plays a crucial role in this task. Existing methods leverage programs that contain rich logical information to enhance the verification process. However, due to the lack of fully supervised signals in the program generation process, spurious programs can be derived and employed, which leads to the inability of the model to catch helpful logical operations. To address the aforementioned problems, in this work, we formulate the table-based fact verification task as an evidence retrieval and reasoning framework, proposing the Logic-level Evidence Retrieval and Graph-based Verification network (LERGV). Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table. After that, we construct a logic-level graph to capture the logical relations between entities and functions in the retrieved evidence, and design a graph-based verification network to perform logic-level graph-based reasoning based on the constructed graph to classify the final entailment relation. Experimental results on the large-scale benchmark TABFACT show the effectiveness of the proposed approach.
Lucrative Website for Business Group

The customer to-client part of web-based business is the most apparent business utilization of the internet. The principle focusses of a web-based business site is to sell items and offer different types of assistance on the web. This venture manages building up an online business-based site for GN Business Group for online item deal. Three sections including construction business, garment business and dairy products business are included in the website. The construction section includes the information of the sites that they have developed and also about the sites that are under-construction. The garments section displays the information about the garments and provides the contact. The dairy products section includes ecommerce and provides the user with a catalogue of different dairy products and frozen items available for purchase in the store along with their price. This is a task with a goal to build up a fundamental site where a customer is furnished with a shopping basket and furthermore become more acquainted with about the advancements used to build up something similar. It includes of the planning process, which starts with determining the client’s requirements, domain modelling, selecting theme and finalizing web design. The entire development process is primarily divided into two parts: the front-end development and back-end development. As such, this project aims to create a website and store construction, garment and dairy products information into a database system using MySQL to enable the comprehensive management of site information generated during the construction phase .
Library guides / Biblioteekgidse: Library Staff Training and Development Libguide: Virtual Tools for staff

In the Stellenbosch University Strategic Directions 2021-2025, goal 6 speaks of developing and sustaining competent, diverse and agile staff.
SummerTime: Text Summarization Toolkit for Non-experts.

Recent advances in summarization provide models that can generate summaries of higher quality. Such models now exist for a number of summarization tasks, including query-based summarization, dialogue summarization, and multi-document summarization. While such models and tasks are rapidly growing in the research field, it has also become challenging for non-experts to keep track of them. To make summarization methods more accessible to a wider audience, we develop SummerTime by rethinking the summarization task from the perspective of an NLP non-expert. SummerTime is a complete toolkit for text summarization, including various models, datasets, and evaluation metrics, for a full spectrum of summarization-related tasks. SummerTime integrates with libraries designed for NLP researchers, and enables users with easy-to-use APIs. With SummerTime, users can locate pipeline solutions and search for the best model with their own data, and visualize the differences, all with a few lines of code. We also provide explanations for models and evaluation metrics to help users understand the model behaviors and select models that best suit their needs. Our library, along with a notebook demo, is available at https://github.com/Yale-LILY/SummerTime.
Dissecting the Diffusion Process in Linear Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have attracted more and more attentions in recent years. A typical GCN layer consists of a linear feature propagation step and a nonlinear transformation step. Recent works show that a linear GCN can achieve comparable performance to the original non-linear GCN while being much more computationally efficient. In this paper, we dissect the feature propagation steps of linear GCNs from a perspective of continuous graph diffusion, and analyze why linear GCNs fail to benefit from more propagation steps. Following that, we propose Decoupled Graph Convolution (DGC) that decouples the terminal time and the feature propagation steps, making it more flexible and capable of exploiting a very large number of feature propagation steps. Experiments demonstrate that our proposed DGC improves linear GCNs by a large margin and makes them competitive with many modern variants of non-linear GCNs.