Reviewerly/exHarmony huggingface dataset:
Debiasing Methods in Natural Language Understanding Make Bias More Accessible

Model robustness to bias is often determined by the generalization on carefully designed out-of-distribution datasets.Recent debiasing methods in natural language understanding (NLU) improve performance on such datasets by pressuring models into making unbiased predictions.An underlying assumption behind such methods is that this also leads to the discovery of more robust features in the model's inner representations.We propose a general probing-based framework that allows for posthoc interpretation of biases in language models, and use an information-theoretic approach to measure the extractability of certain biases from the model's representations.We experiment with several NLU datasets and known biases, and show that, counter-intuitively, the more a language model is pushed towards a debiased regime, the more bias is actually encoded in its inner representations. 1
TopicBank: Collection of coherent topics using multiple model training with their further use for topic model validation

Probabilistic topic modeling of a text collection is a tool for unsupervised learning of the inherent thematic structure of the collection. Given only the text of documents as input, the topic model aims to reveal latent topics as probability distributions over words. The shortcomings of topic models are that they are unstable in the sense that topics may depend on the random initialization, and incomplete in the sense that each new run of the model on the same collection may discover some new topics. This means that data exploration using topic modeling usually requires too many experiments for looking over many topic models and tuning their parameters in search of a model that describes the data best. To deal with the instability and incompleteness of topic models, we propose to gradually accumulate interpretable topics in a “topic bank” using multiple model training. To add topics into the bank, we learn a child level in a hierarchical topic model, then we analyze the coherence of child subtopics and their relationships with parent bank topics in order to exclude irrelevant and duplicate subtopics instead of adding them to the bank. Then we introduce a new way to topic model evaluation by comparing the topics found by the model with the ones that were collected beforehand in a bank. Our experiments with several datasets and topic models show that the proposed method does help in finding a model with more interpretable topics.
Feature Extraction aligned Email Classification based on Imperative Sentence Selection through Deep Learning

Most commonly used channel for communication among peoples is emails. In this era where everyone is so busy in their routine and work, it is very difficult to check all email when one receives huge amount of emails. Previous research has done work on email categorization in which they have mostly done spam filtration. The problem with spam filtration is that sometimes person mistakenly mark an important email received from high authority as spam and according to previous research, this email will be filtered as spam that can cause a great threat for job of an employee. In this research, we are introducing a methodology which classifies email text into three categories i.e. order, request and general on basis of imperative sentences. This research use Word2Wec for words conversion into vector and use two approaches of deep learning i.e. Convolutional neural network and Recurrent neural network for email classification. We conduct experiment on Dataset collected from Personal Gmail account and Enron which consists of 1000 emails. The experiment result show that RNN gives better accuracy than CNN. We also compare our methods with previously used method Fuzzy ANN results and Our proposed methods CNN and RNN gives better results than Fuzzy ANN. This research has also included different experimental result in which CNN and RNN applied on different ratios of training and testing dataset. These experiment show that increasing in the ratio of training dataset results in increasing accuracy of algorithm.
Mining Closed High Utility Itemsets based on Propositional Satisfiability

A high utility itemset mining problem is the question of recognizing a set of items that have utility values greater than a given user utility threshold. This generalization of the classical problem of frequent itemset mining is a useful and well-known task in data analysis and data mining, since it is used in a wide range of real applications. In this paper, we first propose to use symbolic Artificial Intelligence for computing the set of all closed high utility itemsets from transaction databases. Our approach is based on reduction to enumeration problems of propositional satisfiability. Then, we enhance the efficiency of our SAT-based approach using the weighted clique cover problem. After that, in order to improve scalability, a decomposition technique is applied to derive smaller and independent sub-problems in order to capture all the closed high utility itemsets. Clearly, our SAT-based encoding can be constantly enhanced by integrating the last improvements in powerful SAT solvers and models enumeration algorithms. Finally, through empirical evaluations on different real-world datasets, we demonstrate that the proposed approach is very competitive with state-of-the-art specialized algorithms for high utility itemsets mining, while being sufficiently flexible to take into account additional constraints to finding closed high utility itemsets.
Seasonal Variability of Waterlogging in Rangpur City Corporation Using GIS and Remote Sensing Techniques

Waterlogging hazard is a significant environmental issue closely linked to land use for sustainable urbanization. NDWI is widely and effectively used in identifying and visualizing surface water distribution based on satellite imagery. Landsat 7 ETM+ and Landsat 8 OLI TIRS images of pre and post-monsoon (2002, 2019) have been used. The main objective of this study is to detect the seasonal variation of waterlogging in Rangpur City Corporation (RPCC) in 2002 and 2019. In the present study, we used an integrated procedure by using ArcGIS raster analysis. For pre and post-monsoon, almost 93% accuracy was obtained from image analysis. Results show that in 2002 during the pre and post-monsoon period, waterlogged areas were about 159.58 km2 and 32.32 km2, respectively, wherein in 2019, the changes in waterlogged areas are reversed than 2002. In 2019, during pre-monsoon, waterlogged area areas were 122.79 km2, and during post-monsoon, it increased to 127.05 km2. The research also depicts that the trend of the waterlogging situation largely depends on seasonal rainfall and a flawed drainage system.&#x0D; Keywords : Seasonal variation; Waterlogging; Remote sensing; GIS; Rangpur City Corporation&#x0D; &#x0D; Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember&#x0D; This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
Expanding and Improving Our Library’s Virtual Chat Service

With the onset of the COVID-19 pandemic and the ensuing shutdown of the library building for several months, there was a sudden need to adjust how the Hilton C. Buley Library at Southern Connecticut State University (SCSU) delivered its services. Overnight, the library’s virtual chat service went from a convenient way to reach a librarian to the primary method by which library patrons contacted the library for help. In this article, the authors will discuss what was learned during this time and how the service has been adjusted to meet user needs. Best practices and future improvements will be discussed.
Risk-Perception-Aware Control Design Under Dynamic Spatial Risks

This letter proposes a novel Risk-Perception-Aware (RPA) control design using non-rational perception of risks associated with uncertain dynamic spatial costs. We use Cumulative Prospect Theory (CPT) to model the risk perception of a Decision Maker (DM) and use it to construct perceived risk functions that transform the uncertain dynamic spatial cost to deterministic perceived risks of a DM. These risks are then used to build safety sets which can represent risk-averse to risk-insensitive perception. Using these sets, we define novel notions of “inclusiveness” and “versatility” which can be employed to compare and evaluate any risk models in the context of RPA safety-critical controls. We then prove that CPT is the most “inclusive” and “versatile” model w.r.t. Conditional Value at Risk (CVaR) and Expected Risk (ER). Given a RPM, we construct a class of Control Barrier Functions (CBFs) and generate perceived-safety-critical controls using a Quadratic Program (QP) to guide an agent safely to a goal. For a class of truncated-Gaussian costs, we provide sufficient geometric conditions for the above QP to be feasible. We also prove that CPT-equipped RPA controller has both a larger feasible control set and more accurate stabilization w.r.t CVaR and ER models. We present simulations in a 2D environment to illustrate the proposed controller.
Allocating Large Vocabulary Capacity for Cross-Lingual Language Model Pre-Training

Compared to monolingual models, crosslingual models usually require a more expressive vocabulary to represent all languages adequately.We find that many languages are under-represented in recent cross-lingual language models due to the limited vocabulary capacity.To this end, we propose an algorithm VOCAP to determine the desired vocabulary capacity of each language.However, increasing the vocabulary size significantly slows down the pre-training speed.In order to address the issues, we propose k-NN-based target sampling to accelerate the expensive softmax.Our experiments show that the multilingual vocabulary learned with VOCAP benefits cross-lingual language model pre-training.Moreover, k-NN-based target sampling mitigates the side-effects of increasing the vocabulary size while achieving comparable performance and faster pre-training speed.
Sequence Length is a Domain: Length-based Overfitting in Transformer Models

Transformer-based sequence-to-sequence architectures, while achieving state-of-the-art results on a large number of NLP tasks, can still suffer from overfitting during training.In practice, this is usually countered either by applying regularization methods (e.g.dropout, L2regularization) or by providing huge amounts of training data.Additionally, Transformer and other architectures are known to struggle when generating very long sequences.For example, in machine translation, the neuralbased systems perform worse on very long sequences when compared to the preceding phrase-based translation approaches (Koehn and Knowles, 2017).We present results which suggest that the issue might also be in the mismatch between the length distributions of the training and validation data combined with the aforementioned tendency of the neural networks to overfit to the training data.We demonstrate on a simple string editing task and a machine translation task that the Transformer model performance drops significantly when facing sequences of length diverging from the length distribution in the training data.Additionally, we show that the observed drop in performance is due to the hypothesis length corresponding to the lengths seen by the model during training rather than the length of the input sequence.
Flash Flood Susceptibility Mapping at Andungbiru Watershed, East Java Using AHP-Information Weighted Method

Flash floods are among the most frequent natural disasters caused by heavy rain associated with a severe thunderstorm, which leads to social and economic losses in infrastructure and agriculture. Therefore, this research aims to map flash flood potential susceptibility (FFPS) in the Pekalen watershed, using Geographic Information System (GIS) technology and statistical analysis to reduce the risk of flooding. The opinion and experience of an expert on the weight assessment method were carried out using the Analytical Hierarchy Process (AHP). Furthermore, the probability statistical methods and GIS were used in flash flood areas in the Pekalen watershed in Andungbiru, Probolinggo village. This study was carried out using geomorphological factors, namely elevation, slope, stream power index, and topographic wetness index, with a resolution of 30 m. Thematic map scale of the land use, river density, distance to the river, rainfall, and geology is in the ratio of is in a ratio of 1:25.000. Imagery processing was carried out using Landsat 8 30 m x 30 m resolution imagery, such as the Normalized Difference Vegetation Index. The result showed that the model map of FFPS obtained low 8%, low 23%, moderate 27%, moderate to high 26%, high 13%, and very high 2% index values. The next stage of modeling analysis led to validation using statistic receiver operating Characteristic Curve (ROC) of area Under Curve (AUC) with a value of 90.15. In conclusion, the factors that significantly trigger flash floods are distance to the river, land use, and slope.&#x0D; &#x0D; Keywords: AHP-weighted; information content; FFSP; GIS; Geomorphology&#x0D; Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember&#x0D; &#x0D; This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
Analysis of social interactions and risk factors relevant to the spread of infectious diseases at hospitals and nursing homes

Ensuring the safety of healthcare workers is vital to overcome the ongoing COVID-19 pandemic. We here present an analysis of the social interactions between the healthcare workers at hospitals and nursing homes. Using data from an automated hand hygiene system, we inferred social interactions between healthcare workers to identify transmission paths of infection in hospitals and nursing homes. A majority of social interactions occurred in medication rooms and kitchens emphasising that health-care workers should be especially aware of following the infection prevention guidelines in these places. Using epidemiology simulations of disease at the locations, we found no need to quarantine all healthcare workers at work with a contagious colleague. Only 14.1% and 24.2% of the health-care workers in the hospitals and nursing homes are potentially infected when we disregard hand sanitization and assume the disease is very infectious. Based on our simulations, we observe a 41% and 26% reduction in the number of infected healthcare workers at the hospital and nursing home, when we assume that hand sanitization reduces the spread by 20% from people to people and 99% from people to objects. The analysis and results presented here forms a basis for future research to explore the potential of a fully automated contact tracing systems.
Dehumanisation of ‘Outgroups’ on Facebook and Twitter: towards a framework for assessing online hate organisations and actors

Whilst preventing dehumanization of outgroups is a widely accepted goal in the field of countering violent extremism, current algorithms by social media platforms are focused on detecting individual samples through explicit language. This study tests whether explicit dehumanising language directed at Muslims is detected by tools of Facebook and Twitter; and further, whether the presence of explicit dehumanising terms is necessary to successfully dehumanise 'the other'—in this case, Muslims. Answering both these questions in the negative, this analysis extracts universally useful analytical tools that could be used together to consistently and competently assess actors using dehumanisation as a measure, even where that dehumanisation is cumulative and grounded in discourse, rather than explicit language. The output of one prolific actor identified by researchers as an anti-Muslim hate organisation, and four (4) other anti-Muslim actors, are discursively analysed, and impacts considered through the comments they elicit. Whilst this study focuses on material gathered with respect to anti-Muslim discourses, the findings are relevant to a range of contexts where groups are dehumanised on the basis of race or other protected attribute. This study suggests it is possible to predict aggregate harm by specific actors from a range of samples of borderline content that each might be difficult to discern as harmful individually.
Kineverse: A Symbolic Articulation Model Framework for Model-Agnostic Mobile Manipulation

Service robots in the future need to execute abstract instructions such as "fetch the milk from the fridge". To translate such instructions into actionable plans, robots require in-depth background knowledge. With regards to interactions with doors and drawers, robots require articulation models that they can use for state estimation and motion planning. Existing frameworks model articulated connections as abstract concepts such as prismatic, or revolute, but do not provide a parameterized model of these connections for computation. In this paper, we introduce a novel framework that uses symbolic mathematical expressions to model articulated structures -- robots and objects alike -- in a unified and extensible manner. We provide a theoretical description of this framework, and the operations that are supported by its models, and introduce an architecture to exchange our models in robotic applications, making them as flexible as any other environmental observation. To demonstrate the utility of our approach, we employ our practical implementation Kineverse for solving common robotics tasks from state estimation and mobile manipulation, and use it further in real-world mobile robot manipulation.
Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling

An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.
HyperStorylines: Interactively untangling dynamic hypergraphs

We present the design and evaluation of HyperStorylines, a technique that generalizes Storylines to visualize the evolution of relationships involving multiple types of entities such as, for example, people, locations, and companies. Datasets which describe such multi-entity relationships are often modeled as hypergraphs, that can be difficult to visualize, especially when these relationships evolve over time. HyperStorylines builds upon Storylines, enabling the aggregation and nesting of these dynamic, multi-entity relationships. We report on the design process of HyperStorylines, which was informed by discussions and workshops with data journalists; and on the results of a comparative study in which participants had to answer questions inspired by the tasks that journalists typically perform with such data. We observe that although HyperStorylines takes some practice to master, it performs better for identifying and characterizing relationships than the selected baseline visualization (PAOHVis) and was preferred overall.
Transductive Learning for Unsupervised Text Style Transfer

Unsupervised style transfer models are mainly based on an inductive learning approach, which represents the style as embeddings, decoder parameters, or discriminator parameters and directly applies these general rules to the test cases.However, the lacking of parallel corpus hinders the ability of these inductive learning methods on this task.As a result, it is likely to cause severe inconsistent style expressions, like the salad is rude.To tackle this problem, we propose a novel transductive learning approach in this paper, based on a retrieval-based context-aware style representation.Specifically, an attentional encoder-decoder with a retriever framework is utilized.It involves top-K relevant sentences in the target style in the transfer process.In this way, we can learn a context-aware style embedding to alleviate the above inconsistency problem.In this paper, both sparse (BM25) and dense retrieval functions (MIPS) are used, and two objective functions are designed to facilitate joint learning.Experimental results show that our method outperforms several strong baselines.The proposed transductive learning approach is general and effective to the task of unsupervised style transfer, and we will apply it to the other two typical methods in the future. Rules
Comparing word embedding models for Arabic aspect category detection using a deep learning-based approach

Aspect category detection (ACD) is a task of aspect-based sentiment analysis (ABSA) that aims to identify the discussed category in a given review or sentence from a predefined list of categories. ABSA tasks were widely studied in English; however, studies in other low-resource languages such as Arabic are still limited. Moreover, most of the existing Arabic ABSA work is based on rule-based or feature-based machine learning models, which require a tedious task of feature-engineering and the use of external resources like lexicons. Therefore, the aim of this paper is to overcome these shortcomings by handling the ACD task using a deep learning method based on a bidirectional gated recurrent unit model. Additionally, we examine the impact of using different vector representation models on the performance of the proposed model. The experimental results show that our model outperforms the baseline and related work models significantly by achieving an enhanced F1-score of more than 7%.
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach

Reinforcement learning is widely used in applications where one needs to perform sequential decisions while interacting with the environment. The problem becomes more challenging when the decision requirement includes satisfying some safety constraints. The problem is mathematically formulated as constrained Markov decision process (CMDP). In the literature, various algorithms are available to solve CMDP problems in a model-free manner to achieve epsilon-optimal cumulative reward with epsilon feasible policies. An epsilon-feasible policy implies that it suffers from constraint violation. An important question here is whether we can achieve epsilon-optimal cumulative reward with zero constraint violations or not. To achieve that, we advocate the use of a randomized primal-dual approach to solve the CMDP problems and propose a conservative stochastic primal-dual algorithm (CSPDA) which is shown to exhibit O(1/epsilon^2) sample complexity to achieve epsilon-optimal cumulative reward with zero constraint violations. In the prior works, the best available sample complexity for the epsilon-optimal policy with zero constraint violation is O(1/epsilon^5). Hence, the proposed algorithm provides a significant improvement compared to the state of the art.