Reviewerly/exHarmony huggingface dataset:
CSECU-DSG at WNUT-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID-19 English Tweets

COVID-19 pandemic has become the trending topic on twitter and people are interested in sharing diverse information ranging from new cases, healthcare guidelines, medicine, and vaccine news.Such information assists the people to be updated about the situation as well as beneficial for public safety personnel for decision making.However, the informal nature of twitter makes it challenging to refine the informative tweets from the huge tweet streams.To address these challenges WNUT-2020 introduced a shared task focusing on COVID-19 related informative tweet identification.In this paper, we describe our participation in this task.We propose a neural model that adopts the strength of transfer learning and hand-crafted features in a unified architecture.To extract the transfer learning features, we utilize the state-of-the-art pre-trained sentence embedding model BERT, RoBERTa, and In-ferSent, whereas various twitter characteristics are exploited to extract the hand-crafted features.Next, various feature combinations are utilized to train a set of multilayer perceptron (MLP) as the base-classifier.Finally, a majority voting based fusion approach is employed to determine the informative tweets.Our approach achieved competitive performance and outperformed the baseline by 7% (approx.).
Cutting the Cord: Key Performance Indicators for the Future of Wireless Virtual Reality Applications

The new generation of telecommunication networks' (5G) improved throughput and low latency capabilities open the door for novel distributed VR architectures that will accelerate the development of wireless immersive VR. While the downlink requirements for wireless VR have been previously studied, the uplink transmissions have been completely neglegted. In this paper, we study both the uplink and downlink requirements for a successful implementation of a fully immersive wireless VR. We thoroughly analyze VR technologies state of art to extract an initial set of data flow and processing times requirements for a sub-group of identified key algorithms. Besides, we propose the peak throughput and latency requirements Key Performance Indicators (KPIs) estimation as a simple optimization problem, given the data gathered in the state of the art analysis. Using this optimization problem, we determine a set of valid ranges of peak throughput and latency requirements for a selected group of 3 different offloading scenarios. Finally, we present different radio access configurations that fulfill such requirements.
COMBINING FACTS, SEMANTIC ROLES AND SENTIMENT LEXICON IN A GENERATIVE MODEL FOR OPINION MINING

Opinion mining is a popular task, that is applied, for example, to determine news polarisation and identify product review classes. Our task is unsupervised clusterization of opinionated texts, in particular news on political events. Many papers that tackle this issue use generative models based on lexical features. Our goal is to determine the entities defying an opinion amongst lexical, syntactic and semantic features as well as their compositions. More specifically, we test the hypothesis that an opinion is determined by the composition of the mentioned facts (SPO triples), the semantic roles of the words and the sentiment lexicon used in it. In this paper we formalise this task and prove that using a composition of the above features provides the best quality when clusterising opinionated texts. To test this hypothesis we have gathered and labelled two corpuses of news on political events and proposed a set of unsupervised algorithms for extracting the features.
Customized Smart Glasses for Needy Blind People

&lt;p class="normal" align="center"&gt;Visually challenged individuals are one of the most concerned people in the society in terms of independence and confidence. There is a class of needy blind people who are not able to afford the technical devices designed for them. A personalized smart device designed using deep learning technologies will be of greater help to them. A brief discussion was made with them to make the design of this device more user specific. Requirements gathered from this meeting are processed using deep learning methods and are composed into a smart glass using Raspberry pi. This glass is customized for every user such that it detects presence of known people around them and also does priority specific object detection. The compact, stand alone and economically efficient design of this product is welcomed by the needy blind people with great enthusiasm. This product will thus effectively increase their morale and ability to roam outdoors notably.&lt;/p&gt;
Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning

Computer-aided classification serves as the basis of virtual cultural relic management and display. The majority of the existing cultural relic classification methods require labelling of the samples of the dataset; however, in practical applications, there is often a lack of category labels of samples or an uneven distribution of samples of different categories. To solve this problem, we propose a 3D cultural relic classification method based on a low dimensional descriptor and unsupervised learning. First, the scale-invariant heat kernel signature (Si-HKS) was computed. The heat kernel signature denotes the heat flow of any two vertices across a 3D shape and the heat diffusion propagation is governed by the heat equation. Secondly, the Bag-of-Words (BoW) mechanism was utilized to transform the Si-HKS descriptor into a low-dimensional feature tensor, named a SiHKS-BoW descriptor that is related to entropy. Finally, we applied an unsupervised learning algorithm, called MKDSIF-FCM, to conduct the classification task. A dataset consisting of 3D models from 41 Tang tri-color Hu terracotta Eures was utilized to validate the effectiveness of the proposed method. A series of experiments demonstrated that the SiHKS-BoW descriptor along with the MKDSIF-FCM algorithm showed the best classification accuracy, up to 99.41%, which is a solution for an actual case with the absence of category labels and an uneven distribution of different categories of data. The present work promotes the application of virtual reality in digital projects and enriches the content of digital archaeology.
SciWING– A Software Toolkit for Scientific Document Processing

We introduce SciWING, an open-source software toolkit which provides access to state-ofthe-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification.Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python interface for SDP.When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules.Transfer learning from general and scientific documents specific pretrained transformers (i.e., BERT, SciBERT, etc.) can be performed.SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development.The toolkit is available from http://sciwing.
Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm

We address the problem of unsupervised extractive document summarization, especially for long documents.We model the unsupervised problem as a sparse auto-regression one and approximate the resulting combinatorial problem via a convex, norm-constrained problem.We solve it using a dedicated Frank-Wolfe algorithm.To generate a summary with k sentences, the algorithm only needs to execute ≈ k iterations, making it very efficient.We explain how to avoid explicit calculation of the full gradient and how to include sentence embedding information.We evaluate our approach against two other unsupervised methods using both lexical (standard) ROUGE scores, as well as semantic (embedding-based) ones.Our method achieves better results with both datasets and works especially well when combined with embeddings for highly paraphrased summaries.
Recognizing and Tracking Outdoor Objects by Using Artoolkit Markers

We created an augmented reality platform for spatial exploration that recognizes buildings facades and displays various multimedia for different time points.In order to provide the user with the best user experience fast recognition and stable tracking are the key elements of any augmented reality app.In an outdoor environment, lighting, reflective surfaces and occlusion can drastically affect the user experience.In a setup where these conditions are similar, marker creation methodology and the app parameters are key.In this paper we focus on resizing the photo prior marker creating and the importance of camera calibration and resolution and their effect on the recognition speed and quality of tracking outdoor objects.
The Role of Effective Leadership in Application of E-Human Resources Management System Applied research at Baghdad International Airport

The research acquires its importance by motivating the behavioural side of the employees to apply modern technology in the work, because of its great importance in increasing the efficiency of employees’ performance and excellence. The research was based on two main hypotheses to show the relationship and impact between the variables through the adoption of a questionnaire to collect data and information related to the research, which consisted of (50) people from administrators working at different levels, based on personal interviews and field visits to collect research data. The data collection process was subjected to statistical analysis using the statistical program (SPSS) (Statistical package for social science) to reach a set of results which showed a positive correlation between the variable of effective leadership and the variable of (E-HRM). The research was conducted at (Baghdad International Airport). It’s included questionnaire distribution on the research sample, collecting the questionnaire, field visits and analysis of the results. The research also concluded that effective leaders should have a role in change the behaviour of employees and improving their level of performance, and train them to use technology at work. The research also presented several suggestions, including the importance of training employees with the adoption of material and moral incentives for those who are distinguished in the application and use of modern technology, as well as providing all the tools physical requirements for the application and success of the (E-HRM) and processed.&#x0D; Paper type Field research
Controlling the Imprint of Passivization and Negation in Contextualized Representations

Contextualized word representations encode rich information about syntax and semantics, alongside specificities of each context of use.While contextual variation does not always reflect actual meaning shifts, it can still reduce the similarity of embeddings for word instances having the same meaning.We explore the imprint of two specific linguistic alternations, namely passivization and negation, on the representations generated by neural models trained with two different objectives: masked language modeling and translation.Our exploration methodology is inspired by an approach previously proposed for removing societal biases from word vectors.We show that passivization and negation leave their traces on the representations, and that neutralizing this information leads to more similar embeddings for words that should preserve their meaning in the transformation.We also find clear differences in how the respective features generalize across datasets.
Efficient generation of random derangements with the expected distribution of cycle lengths

We show how to generate random derangements efficiently by two different techniques: random restricted transpositions and sequential importance sampling. The algorithm employing restricted transpositions can also be used to generate random fixed-point-free involutions only, a. k. a. random perfect matchings on the complete graph. Our data indicate that the algorithms generate random samples with the expected distribution of cycle lengths, which we derive, and for relatively small samples, which can actually be very large in absolute numbers, we argue that they generate samples indistinguishable from the uniform distribution. Both algorithms are simple to understand and implement and possess a performance comparable to or better than those of currently known methods. Simulations suggest that the mixing time of the algorithm based on random restricted transpositions (in the total variance distance with respect to the distribution of cycle lengths) is $$O(n^{a}\log {n}^{2})$$ with $$a \simeq \frac{1}{2}$$ and n the length of the derangement. We prove that the sequential importance sampling algorithm generates random derangements in O(n) time with probability O(1/n) of failing.
A Demonstration of Anhinga: A Mixed-Initiative EPCG Tool for Snakebird

Mixed-initiative procedural content generation (PCG) refers to systems where a human and AI cooperate in some way to produce content. While there has been increasing interest in research on these systems, there are still many domains and PCG approaches that have not yet been explored. In this demonstration we introduce a novel mixed-initiative tool that employs Exhaustive PCG for puzzle level design.
Automatic Induction of Neural Network Decision Tree Algorithms

This work presents an approach to automatically induction for non-greedy decision trees constructed from neural network architecture. This construction can be used to transfer weights when growing or pruning a decision tree, allowing non-greedy decision tree algorithms to automatically learn and adapt to the ideal architecture. In this work, we examine the underpinning ideas within ensemble modelling and Bayesian model averaging which allow our neural network to asymptotically approach the ideal architecture through weights transfer. Experimental results demonstrate that this approach improves models over fixed set of hyperparameters for decision tree models and decision forest models.
Optimal Local Explainer Aggregation for Interpretable Prediction

A key challenge for decision makers when incorporating black box machine learned models into practice is being able to understand the predictions provided by these models. One set of methods proposed to address this challenge is that of training surrogate explainer models which approximate how the more complex model is computing its predictions. Explainer methods are generally classified as either local or global explainers depending on what portion of the data space they are purported to explain. The improved coverage of global explainers usually comes at the expense of explainer fidelity (i.e., how well the explainer's predictions match that of the black box model). One way of trading off the advantages of both approaches is to aggregate several local explainers into a single explainer model with improved coverage. However, the problem of aggregating these local explainers is computationally challenging, and existing methods only use heuristics to form these aggregations. In this paper, we propose a local explainer aggregation method which selects local explainers using non-convex optimization. In contrast to other heuristic methods, we use an integer optimization framework to combine local explainers into a near-global aggregate explainer. Our framework allows a decision-maker to directly tradeoff coverage and fidelity of the resulting aggregation through the parameters of the optimization problem. We also propose a novel local explainer algorithm based on information filtering. We evaluate our algorithmic framework on two healthcare datasets: the Parkinson's Progression Marker Initiative (PPMI) data set and a geriatric mobility dataset from the UCI machine learning repository. Our choice of these healthcare-related datasets is motivated by the anticipated need for explainable precision medicine. We find that our method outperforms existing local explainer aggregation methods in terms of both fidelity and coverage of classification. It also improves on fidelity over existing global explainer methods, particularly in multi-class settings, where state-of-the-art methods achieve 70% and ours achieves 90%.
Penguatan Pembelajaran Sistem Daring

The learning process is a process control that contains a series of teacher and student implementations on the basis of reciprocal relationships that take place in educational situations to achieve certain goals. This reciprocal interaction between teachers and students is the main condition for the learning process to take place. In reality we see in schools, teachers are often too active in the learning process, while students are made passive, so that the interaction between teachers and students in the learning process is ineffective. If the learning process is dominated by teachers, the effectiveness of learning will not be achieved. To create effective learning conditions, teachers are required to be able to manage the learning process that provides stimulation to students so that they are willing and able to learn.However, learning online is not as easy as we imagine. Many students and teachers encounter obstacles due to stuttering with this online learning. Teachers who could not teach directly encountered obstacles even though the activities were carried out by means of video calls. Many school materials given to students are difficult to understand because of the many distractions when carrying out activities. The problem is generally because the subject matter is the lesson that must be exemplified face-to-face, if this is constrained because it is not direct, it is like a child is wrong and is disturbed by signals, the material also cannot be conveyed properly.
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder

Detecting public sentiment drift is a challenging task due to sentiment change over time.Existing methods first build a classification model using historical data and subsequently detect drift if the model performs much worse on new data.In this paper, we focus on distribution learning by proposing a novel Hierarchical Variational Auto-Encoder (HVAE) model to learn better distribution representation, and design a new drift measure to directly evaluate distribution changes between historical data and new data.Our experimental results demonstrate that our proposed model achieves better results than three existing state-of-theart methods.
Comparison of the performance of tools for creating a SPA application interface - React and Vue.js

The article analyses the performance of currently used tools for creating a SPA application interface. The study was conducted using two applications with the same functionality, implemented in React and Vue.js. The tools available in web browsers and appropriate implementations of own methods were used to measure SPA performance.
Representing Complex Knowledge for Exploration and Recommendation: The Case of Classical Music Information

In Digital Humanities, one of the main challenge consists in capturing the structure of complex information in data models and ontologies, in particular when connections between terms are not trivial. This is typically the case for librarian music data. In this chapter, we provide some good practices for representing complex knowledge using the DOREMUS ontology as an exemplary case. We also show various applications of a Knowledge Graph leveraging on the ontology, ranging from an exploratory search engine, a recommender system and a conversational agent enabling to answer classical music questions.
The Thieves on Sesame Street are Polyglots - Extracting Multilingual Models from Monolingual APIs

Pre-training in natural language processing makes it easier for an adversary with only query access to a victim model to reconstruct a local copy of the victim by training with gibberish input data paired with the victim's labels for that data.We discover that this extraction process extends to local copies initialized from a pre-trained, multilingual model while the victim remains monolingual.The extracted model learns the task from the monolingual victim, but it generalizes far better than the victim to several other languages.This is done without ever showing the multilingual, extracted model a well-formed input in any of the languages for the target task.We also demonstrate that a few real examples can greatly improve performance, and we analyze how these results shed light on how such extraction methods succeed.
Action Recognition in Untrimmed Videos with Composite Self-attention Two-Stream Framework

With the rapid development of deep learning algorithms, action recognition in video has achieved many important research results. One issue in action recognition, Zero-Shot Action Recognition (ZSAR), has recently attracted considerable attention, which classify new categories without any positive examples. Another difficulty in action recognition is that untrimmed data may seriously affect model performance. We propose a composite two-stream framework with a pre-trained model. Our proposed framework includes a classifier branch and a composite feature branch. The graph network model is adopted in each of the two branches, which effectively improves the feature extraction and reasoning ability of the framework. In the composite feature branch, 3-channel self-attention modules are constructed to weight each frame of the video and give more attention to the key frames. Each self-attention channel outputs a set of attention weights to focus on the particular stage of the video, and a set of attention weights corresponds to a one-dimensional vector. The 3-channel self-attention modules can inference key frames from multiple aspects. The output sets of attention weight vectors form an attention matrix, which effectively enhances the attention of key frames with strong correlation of action. This model can also implement action recognition under zero-shot conditions, and has good recognition performance for untrimmed video data. Experimental results on relevant datasets confirm the validity of our model.
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms

We study the problem of least squares linear regression where the data-points are dependent and are sampled from a Markov chain. We establish sharp information theoretic minimax lower bounds for this problem in terms of $\tau_{\mathsf{mix}}$, the mixing time of the underlying Markov chain, under different noise settings. Our results establish that in general, optimization with Markovian data is strictly harder than optimization with independent data and a trivial algorithm (SGD-DD) that works with only one in every $\tilde{\Theta}(\tau_{\mathsf{mix}})$ samples, which are approximately independent, is minimax optimal. In fact, it is strictly better than the popular Stochastic Gradient Descent (SGD) method with constant step-size which is otherwise minimax optimal in the regression with independent data setting. Beyond a worst case analysis, we investigate whether structured datasets seen in practice such as Gaussian auto-regressive dynamics can admit more efficient optimization schemes. Surprisingly, even in this specific and natural setting, Stochastic Gradient Descent (SGD) with constant step-size is still no better than SGD-DD. Instead, we propose an algorithm based on experience replay--a popular reinforcement learning technique--that achieves a significantly better error rate. Our improved rate serves as one of the first results where an algorithm outperforms SGD-DD on an interesting Markov chain and also provides one of the first theoretical analyses to support the use of experience replay in practice.