Reviewerly/exHarmony huggingface dataset:
Gen2Vec: Deep Learning based Distributed Representation Framework of Words and Documents for Diagnostic Services of Power Generation Facility

Since the advent of deep learning technology, research and development have been conducted in various fields. In particular, deep learning technology related to embedding that vectorizes the similarity between words has been attracting attention in the natural language processing sector. However, this technology has not been applied to the electric power industry, and no corresponding service frameworks have been developed. Moreover, thousands of knowledge documents produced by electric generator operation experts have been collected for about 20 years by Korea Electric Power Corporation, but they have rarely been applied to electric generator operation. Therefore, this report proposes Gen2Vec, a search engine-based framework for operating power plant using deep learning technology. Gen2Vec can be the core engine of the knowledge system for power plant operators and can be used in search and chatbot services for power plant operation programs and new employee education.
The Changing Face of Academic Music Media Collections in Response to the Rise of Online Music Delivery

The evolution of online media delivery methods and the ubiquity of mobile devices has led to a shift in user preferences away from physical formats to freely accessible streaming content via sites like YouTube and Spotify and downloads from sites like iTunes. This progression has also resulted in content that libraries are precluded from owning due to prohibitive end-user license agreements. As a result, academic libraries must reexamine the role and objectives of their media collections as well as barriers to their use. This article presents the findings of a survey of North American music media selectors and provides a baseline context for the current and changing state of their collections and collecting habits. It finds that librarians are still actively collecting physical formats even though collection use is shrinking. In addition, they feel local pressures related to budgets, space, and preservation. Librarians have reservations about the costs and stability of commercial streaming products and what that means for creating unique collections. Respondents articulated numerous questions that the library profession should be asking itself and library patrons about the future of academic music media collections. Several possible approaches to these issues are put forth.
Communications: Making Quantum Computing Real for JADC2 With Qiskit: Quantum Enabling Technologies for Applications to Support Communication and Networking: White Paper Submitted for the Global Competition: AFRL Innovare Million Dollar International Quantum U Tech Accelerator

Our research objective is making quantum computing real with Qiskit for USAF-Pentagon JADC2 (Joint All Domain Command & Control) thus contributing to USAF-DoD core missions. JADC2 is the US Air Force led and Joint Staff coordinated key effort central to Joint Services C4I-ISR and C2 (Command & Control) integration enabling the new Joint Warfighting Concept for All-Domain Operations. The resulting ‘New American Way of War’ is anticipated to yield “significant advantage over everybody in the world for a long time” according to the Vice-Chairman of the Joint Chiefs General John Hyten.
Local Facial Makeup Transfer via Disentangled Representation

Facial makeup transfer aims to render a non-makeup face image in an arbitrary given makeup one while preserving face identity. The most advanced method separates makeup style information from face images to realize makeup transfer. However, makeup style includes several semantic clear local styles which are still entangled together. In this paper, we propose a novel unified adversarial disentangling network to further decompose face images into four independent components, i.e., personal identity, lips makeup style, eyes makeup style and face makeup style. Owing to the disentangled makeup representation, our method can not only flexible control the degree of local makeup styles, but also can transfer local makeup styles from different images into the final result, which any other approaches fail to handle. For makeup removal, different from other methods which regard makeup removal as the reverse process of makeup transfer, we integrate the makeup transfer with the makeup removal into one uniform framework and obtain multiple makeup removal results. Extensive experiments have demonstrated that our approach can produce visually pleasant and accurate makeup transfer results compared to the state-of-the-art methods.
From Digital Archaeology to Data-Centric Archaeological Research

Since the end of the 20th century the widespread use of digital applications in archaeology has legitimized their inclusion in the archaeological toolbox. Together with archaeological sciences, databases, GIS and other computer-based methods are nowadays present in every respectable archaeological investigation. This makes archaeology a peculiar discipline, where the scientific method combines with the historical one to produce new knowledge. However, the large availability of archaeological data creates the risk of a data deluge and may suggest using online information just to collect previous interpretations rather than to re-use the data supporting them. A ‘Grand Challenges’ list compiled some years ago includes important research questions that undergird contemporary issues and require an appropriate digital methodology to be addressed. The present paper discusses the benefits, or better the absolute need, of a data-centric methodology to address large-scale research. It argues that an acritical use of the so-called ‘Big Data’ approach may be questionable. It suggests how the combination of artificial intelligence with human intelligence is the key to progress into the understanding of phenomena of paramount societal importance for researchers and for the public at large.
Educator Training For Ecological Education Of Students In A Multicultural Environment

Improving the training of educators requires taking into account the specifics of their future professional activities, which are now closely linked to multiculturalism. The actualization of a multicultural approach to the development of modern education requires special attention to the ethnic and pedagogical foundations of the development of ecological culture of the individual. The significance of the ethnic and pedagogical approach to the development of ecological culture of the personality is due to the philosophical provisions on modern civilizational restructuring, cultural foundations of environmental problems. The need for ethnic pedagogization of environmental education arises from philosophical and cultural concepts that search for the causes of environmental problems in an axiological perspective, formulate ideas about the value attitude to nature, measure the material and spiritual components of culture, comprehend the relationship of rational, utilitarian, economic, pragmatic principles and emotional and moral components of the relationship between Man and Nature. Training of future specialists in the field of education focused on solving the problems of modern society's transition to sustainable development is a priority today. This is based on the need to take into account the optimal environmental trends of cost-effective activities, as well as to optimize the mechanisms for educating students in a multicultural social environment.
CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples

This paper describes our system that participated in the SemEval-2020 task 4: Commonsense Validation and Explanation.For this task, it is obvious that external knowledge, such as knowledge graph, can help the model understand commonsense in natural language statements.But how to select the right triples for statements remains unsolved, so how to reduce the interference of irrelevant triples on model performance is a research focus.This paper adopt a modified K-BERT as the language encoder, to enhance language representation through triples from knowledge graphs.Experiments show that our method is better than models without external knowledge, and is slightly better than the original K-BERT.We got an accuracy score of 0.97 in subtaskA, ranking 1/45, and got an accuracy score of 0.948, ranking 2/35.
Formation of Students' Professional Identity at the University: Sociological Analysis

The study reveals a feature of professional identity, which influences the quality of labor capital, which in turn affects the efficient and competitive func-tioning of the economy. It is a subjective state of the individual’s sense of his/her professionalism, be-longing to the professional community and the need to develop in this direction. The acquisition of the necessary qualifications and professional skills be-gins in the process of education; therefore, profes-sional identity begins to form at the university. The results of a sociological study of professional identi-ty state of university students in Ufa demonstrate an unsatisfactory level of its development: the majority of students give an average assessment of the ac-quired knowledge, skills and abilities. They note their inadequacy for practical application; more than half of students regret choosing a specialty and/or university; about a fifth of the students do not plan to work in their specialty at all, slightly more than half of the respondents do not exclude such a pos-sibility. Based on the analysis of the data presented, it was concluded that it is necessary to introduce a high-quality practice-oriented approach, innovative teaching methods, as well as special work with stu-dents into the educational process.
An Online Readability Leveled Arabic Thesaurus

This demo paper introduces the online Readability Leveled Arabic Thesaurus interface.For a given user input word, this interface provides the word's possible lemmas, roots, English glosses, related Arabic words and phrases, and readability on a five-level readability scale.This interface builds on and connects multiple existing Arabic resources and processing tools.This one-of-akind system enables Arabic speakers and learners to benefit from advances in Arabic computational linguistics technologies.Feedback from users of the system will help the developers to identify lexical coverage gaps and errors.
TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation

In this paper, we present our submission for subtask A of the Common Sense Validation and Explanation (ComVE) shared task.We examine the ability of large-scale pre-trained language models to distinguish commonsense from non-commonsense statements.We also explore the utility of external resources that aim to supplement the world knowledge inherent in such language models, including commonsense knowledge graph embedding models, word concreteness ratings, and text-to-image generation models.We find that such resources provide insignificant gains to the performance of fine-tuned language models.We also provide a qualitative analysis of the limitations of the language model fine-tuned to this task.
Discursive Features of Nigerian Online Ponzi Schemes’ Narratives

Although Ponzi schemes have existed since the 1800s, contemporary financial challenges have rejuvenated them while the Internet has enhanced their proliferation, particularly in developing countries. The present study analyses select discursive features for digital deception in Nigerian online Ponzi schemes. We identify the use of stance and linguistic engagement, formulaic expressions and politeness strategies, narrativity, naming, and lexical range as techniques used by scheme creators. These linguistic and discursive choices are wielded as tools to attract customers and, ultimately, to deceive. The overt propagation of financial gains has underlying ideological implications, as it projects a sense of communality and encourages financial leverage which are in turn exploited to con unsuspecting – often greedy – subscribers. We conclude that language use in Ponzi schemes is intentionally crafted to appeal to diverse individual sentiments, particularly within developing economies where poverty is widespread and people seek to make money through any means in order to survive.
Camera Captured Document De-Warping and De-Skewing

De-warping is the elementary step in the analysis of document images which are camera based. Processing of warped image is a challenging task. Therefore, to make the document images OCR understandable de-warping has become a major task. In this paper, we have presented an effective pre-processing, de-warping and de-skewing techniques for camera captured document image processing. In pre-processing, we have divided input color document image into R, G and B-band, further convert R, G, B-band to C, M and Y-color space respectively, convert the average CMY grey scale image to binary by determining threshold value automatically. In de-warping and de-skewing, we have presented an effective techniques to remove geometrical and perspective distortion in camera captured document by combination of centroid and mid-point of bounding box height, bounding box is plotted on text blocks using connected component analysis technique. The introduced work is robust in correcting geometric distortion and skew correction for standard printed data set and also for Kannada handwritten documents.
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM

Machine learning (ML) models trained by differentially private stochastic gradient descent (DP-SGD) have much lower utility than the non-private ones. To mitigate this degradation, we propose a DP Laplacian smoothing SGD (DP-LSSGD) to train ML models with differential privacy (DP) guarantees. At the core of DP-LSSGD is the Laplacian smoothing, which smooths out the Gaussian noise used in the Gaussian mechanism. Under the same amount of noise used in the Gaussian mechanism, DP-LSSGD attains the same DP guarantee, but in practice, DP-LSSGD makes training both convex and nonconvex ML models more stable and enables the trained models to generalize better. The proposed algorithm is simple to implement and the extra computational complexity and memory overhead compared with DP-SGD are negligible. DP-LSSGD is applicable to train a large variety of ML models, including DNNs. The code is available at \url{this https URL}.
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities

Named entities pose a unique challenge to traditional methods of language modeling.While several domains are characterised with a high proportion of named entities, the occurrence of specific entities varies widely.Cooking recipes, for example, contain a lot of named entitiesviz.ingredients, cooking techniques (also called processes), and utensils.However, some ingredients occur frequently within the instructions while most occur rarely.In this paper, we build upon the previous work done on language models developed for text with named entities by introducing a Hierarchically Disentangled Model.Training is divided into multiple branches with each branch producing a model with overlapping subsets of vocabulary.We found the existing datasets insufficient to accurately judge the performance of the model.Hence, we have curated 158,473 cooking recipes from several publicly available online sources.To reliably derive the entities within this corpus, we employ a combination of Named Entity Recognition (NER) as well as an unsupervised method of interpretation using dependency parsing and POS tagging, followed by a further cleaning of the dataset.This unsupervised interpretation models instructions as action graphs and is specific to the corpus of cooking recipes, unlike NER which is a general method applicable to all corpora.To delve into the utility of our language model, we apply it to tasks such as graph-to-text generation and ingredients-to-recipe generation, comparing it to previous state-of-the-art baselines.We make our dataset (including annotations and processed action graphs) available for use, considering their potential use cases for language modeling and text generation research.
COVID-19 and Higher Education in Jordan: Insights from Middle East University

“The sudden disruption was a poignant lesson in organisational resilience. After an initial period of uncertainty, we steadily trimmed superfluous operations and shifted the focus to the contingencies of online teaching and learning. Although the crisis was totally unexpected and unplanned for, we were adapting at an impressive speed. Operations felt leaner and more supple. There was less bureaucracy and more resourcefulness. We were not doing less, but rather doing it differently.”
Time correlation techniques for subsample time delay estimation of complex signals based on polynomial interpolation.

This paper deals with the problem of subsample time delay estimation of complex signal based on polynomial interpolation. Time delay estimation is performed by cross-correlation time approach. Three polynomial interpolation techniques applied to the discrete complex cross-correlation function in the neighborhood of its maximum are proposed. These methods show high processing speed and allow obtaining accurate real-valued time delay estimation when digital complex signals are processed. The comparative analysis between these methods is performed. A rigorous analytical solution for the correction of time delay estimation for one of the proposed methods is obtained for the case of the third-order polynomial interpolation. This solution is applied for an equidistant grid of discrete cross-correlation function samples. One can improve the accuracy of time delay estimates by using aforementioned correction. A numerical simulation is performed to quantify the accuracy of the time delay estimates when using the proposed methods for the case where a stationary random process described by the first-order autoregressive mode is chosen as a model of original signal. The main results were presented and discussed at XIV All-Russian conference “Radar and telecommunication”.
Active World Model Learning with Progress Curiosity

World models are self-supervised predictive models of how the world evolves. Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal horizons, and an understanding of the behavioral patterns of other agents. In this work, we study how to design such a curiosity-driven Active World Model Learning (AWML) system. To do so, we construct a curious agent building world models while visually exploring a 3D physical environment rich with distillations of representative real-world agents. We propose an AWML system driven by $\gamma$-Progress: a scalable and effective learning progress-based curiosity signal. We show that $\gamma$-Progress naturally gives rise to an exploration policy that directs attention to complex but learnable dynamics in a balanced manner, thus overcoming the white noise problem. As a result, our $\gamma$-Progress-driven controller achieves significantly higher AWML performance than baseline controllers equipped with state-of-the-art exploration strategies such as Random Network Distillation and Model Disagreement.
LAND SUBSIDENCE ANALYSIS OBSERVED BY PS-INSAR METHOD IN SOUTHERN PART OF BALI, INDONESIA (A CASE STUDY OF DENPASAR AND BADUNG AREA)

The utilization of groundwater unwise leads to problems for the life of sentient beings. The majority of environmental damage, mainly by groundwater utilization, is done or caused by human activities. The land subsidence, drought, seawater intrusion are some examples of damage caused by groundwater utilization unwise. This research aims to estimate land subsidence in southern Bali and analyze groundwater level reduction with land subsidence. The PS-InSAR technique to monitor land subsidence has been carried out in several regions in Indonesia and other countries. In this study, 38 images of Sentinel-1 taken from February 2015 to December 2018 are used to analyze the PS-InSAR technique. Land subsidence is found in Denpasar Utara district and Kerobokan district with deformation in -8 mm to -19 mm and velocity up to -5 mm/year. The utilization of groundwater causing land subsidence in Southern Bali is no significant relationship with coefficient correlation 0,16 and influenced only 2,7%. Limited groundwater data also influences the correlation between groundwater utilization and the occurrence of land subsidence.&#x0D; Keywords: Land Subsidence; Sentinel-1A; PS-InSAR; Southern Bali.
Setting Up ML Agents Toolkit

We have seen in the previous chapters how states, actions, and rewards play a crucial role in driving an agent to reach its goal in a reinforcement learning (RL) environment. Now that we are familiar with the fundamentals of generic state-based RL, we will move ahead with the installation of all the libraries, frameworks, and extensions related to the usage of Unity ML agents as well as to further progress in the later modules of deep learning. Before we dive into the installation, let us try to understand the ML agents package made by Unity. Since its inception in 2017, the Unity ML Agents Toolkit has provided researchers, developers, and game programmers with a plethora of resources in the field of deep RL. The ML Agents Toolkit was initially created with the vision to assist researchers and developers in transforming games and simulations created with the help of Unity Editor into deep learning environments where the agents can be trained using state-of-the-art (SOTA) deep RL algorithms, evolutionary and genetic strategies, and other deep learning methods (involving computer vision, synthetic data generation) through a simplified Python API. With the latest release of package version 1.0 (at the time of writing this book), there have been huge advancements in the development of the Toolkit in terms of streamlining the C# SDK, Python API linking, robust compatibility with Tensorflow library, and huge advancements in areas of curriculum learning, self- and adversarial play, generative adversarial imitation learning, and pre-existing deep learning algorithms (PPO, SAC). There have been significant modifications of the core library that use the open AI Gym environment as a wrapper. The Unity ML Agents Toolkit version 1.0 has stable communicators linking Unity (C#) and the Python API (deep learning) and has provided game developers with the flexibility of writing deep learning agents and simulating their own AI in their games. Researchers, who are interested in modifying the core algorithms for their use-cases, are also provided with a flexible Gym wrapper environment, which provides a template for writing their own deep learning algorithms. All of these can be done with the simple usage of the Unity Engine, Unity ML Toolkit, Jupyter Notebook, and Tensorflow framework. Now that we understand the scope and possibilities of Unity ML Agents, we will discuss the entire process of installing all the necessary libraries and frameworks in this chapter. The different agents in the Unity ML Agents Toolkit are presented in Figure 3-1.