Reviewerly/exHarmony huggingface dataset:
MilaNLP at SemEval-2022 Task 5: Using Perceiver IO for Detecting Misogynous Memes with Text and Image Modalities

Warning: This paper contains examples of language that some people may find offensive or upsetting.In this paper, we describe the system proposed by the MilaNLP team for the Multimedia Automatic Misogyny Identification (MAMI) challenge.We use Perceiver IO as a multimodal late fusion over unimodal streams to address both sub-tasks A and B. We build unimodal embeddings using Vision Transformer (image) and RoBERTa (text transcript).We enrich the input representation using face and demographic recognition, image captioning, and detection of adult content and web entities.To the best of our knowledge, this work is the first to use Perceiver IO combining text and image modalities.The proposed approach outperforms unimodal and multimodal baselines.
Features or Spurious Artifacts? Data-centric Baselines for Fair and Robust Hate Speech Detection

Warning: this paper contains content that may be offensive or upsetting.Avoiding to rely on dataset artifacts to predict hate speech is at the cornerstone of robust and fair hate speech detection.In this paper we critically analyze lexical biases in hate speech detection via a cross-platform study, disentangling various types of spurious and authentic artifacts and analyzing their impact on out-of-distribution fairness and robustness.We experiment with existing approaches and propose simple yet surprisingly effective datacentric baselines.Our results on English data across four platforms show that distinct spurious artifacts require different treatments to ultimately attain both robustness and fairness in hate speech detection.To encourage research in this direction, we release all baseline models and the code to compute artifacts, pointing it out as a complementary and necessary addition to the data statements practice. 1
Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity

In this paper we focus on patterns of colexification (co-expressions of form-meaning mapping in the lexicon) as an aspect of lexicalsemantic organization, and use them to build large scale synset graphs across BabelNet's typologically diverse set of 499 world languages.We introduce and compare several approaches: monolingual and cross-lingual colexification graphs, popular distributional models, and fusion approaches.The models are evaluated against human judgments on a semantic similarity task for nine languages.Our strong empirical findings also point to the importance of universality of our graph synset embedding representations with no need for any language-specific adaptation when evaluated on the lexical similarity task.The insights of our exploratory investigation of largescale colexification graphs could inspire significant advances in NLP across languages, especially for tasks involving languages which lack dedicated lexical resources, and can benefit from language transfer from large shared cross-lingual semantic spaces.
Assessing sentence readability for German language learners with broad linguistic modeling or readability formulas: When do linguistic insights make a difference?

The paper presents a new state-of-the-art sentence-wise readability assessment model for German L2 readers.We build a linguistically broadly informed machine learning model and compare its performance against four commonly used readability formulas.To understand when the linguistic insights used to inform our model make a difference for readability assessment and when simple readability formulas suffice, we compare their performance based on two common automatic readability assessment tasks: predictive regression and sentence pair ranking.We find that leveraging linguistic insights yields top performances across tasks, but that for the identification of simplified sentences also readability formulas -which are easier to compute and more accessible -can be sufficiently precise.Linguistically informed modeling, however, is the only viable option for high quality outcomes in finegrained prediction tasks.We then explore the sentence-wise readability profile of leveled texts written for language learners at a beginning, intermediate, and advanced level of German.Our findings highlight that a texts' readability is driven by the maximum rather than the overall readability of sentences.This has direct implications for the adaptation of learning materials and showcases the importance of studying readability also below the document level.
Skeleton-based similar action recognition through integrating the salient image feature into a center-connected graph convolutional network

Skeleton-based human action recognition has drawn more and more attention due to its easy implementation and stable application in intelligent human-robot interaction. However, most existing studies only used the skeleton data but completely ignored other image semantic information to build action recognition models, which would confuse the recognition of similar actions because of the ambiguity between skeleton data. Here, a center-connected graph convolutional network enhanced with salient image features (SIFE-CGCN) is proposed to address the problem of similar action recognition. First, a center-connected network (CGCN) is constructed to capture the small differences between similar actions through exploring the possible collaboration between all joints. Subsequently, a metric of movement changes is employed to optimally select the salient image from an action video, and then the EfficientNet is used to achieve the action semantic classification of the salient images. Finally, the recognition results of CGCN are strengthened with the classification results of salient images to further improve the recognition accuracy for similar actions. Additionally, a metric is proposed to measure the action similarity with the skeleton data, and then a similar action dataset is built. Extensive experiments on the datasets of similar action and NTU RGB + D 60/120 were conducted to verify the performance of the proposed methods. Experimental results validated the effectiveness of salient image feature enhancement and showed that the proposed SIFE-CGCN achieved the state-of-the-art performance on the similar action and NTU RGB + D 60/120 datasets.
A Model for Governing Information Sharing in Smart Assistants

Smart Personal Assistants (SPAs), such as Amazon Alexa, Google Assistant and Apple Siri, leverage different AI techniques to provide convenient help and assistance to users. However, inappropriate information sharing decisions can lead SPAs to incorrectly disclose user information to undesired parties, or mistakenly block their reasonable access in specific scenarios to desired parties. In fact, reports about privacy violations in SPAs and associated user concerns are well known and understood in the related literature. It is difficult for SPAs to automatically decide how data should be shared with respect to the privacy preferences of the users. We argue norms, which are regarded as shared standards of acceptable behaviour of groups and/or individuals, can be used to govern and reason about the best course of action of SPAs with regards to information sharing, and our work is the first to propose a practical model to address the above issues and govern SPAs based on normative systems and the contextual integrity theory of privacy. We evaluated the performance of the model using a real dataset of user preferences for privacy in SPAs and the results showed a very marked and significant improvement in understanding user preferences and making the right decisions with respect to data sharing.
Blockchain-based E-Voting Technology: Opportunities and Challenges

In a country like India, resolution of any major issue or conflict primarily lies in the hands of the government. Given that India is one of the world's largest democracies, the voting system is one of the most essential phases, considering its limitations. The voting system is mostly based on a centralized system. As a result, there are many chances of risks like vote tampering, security, transparencies, DoS attacks and many more. Due to these problems, the population of the world, is not fully trusting the voting system. The motive is to overcome the abovementioned loopholes and provide the nation a better environment for a better government. The one solution came out to overcome this problem is a Blockchain Technology. Blockchain is a method of storing information in such a way that it is difficult or impossible to edit, hack, or trick the system. The Blockchain is a distributed ledger that can efficiently and verifiably record transactions between two parties. The Blockchain Technology is used to protect the data from theft and to prevent vote tampering. This paper presents the study and analysis of various blockchain-based voting mechanisms.
Designing intelligence amplification: a design canvas for practitioners

Designing, developing, and implementing applications based on the concepts of Intelligence Amplification (IA) is a complex process. Although some design theories are present in literature, to our best knowledge, no comprehensive IA design approach exists for practitioners. Based on action design research, an IA design canvas is developed and guiding design principles are derived in two iterations. The main contribution of this research is a comprehensive IA design approach, consisting of an IA design canvas and four guiding design principles. Evaluation of the IA design canvas in three concurrent design workshops with 25 participants representing, 14 organizations, provides empirical support that the proposed IA design approach can ease the design processes, especially during the emphasize, ideate, and conceptualize stages of design thinking. Generalization is however not possible. Future research can explore the broader use of the IA canvas for explanation, analysis, prediction, and quantification, and formalize the IA design approach in a design theory.
Translating Principles into Practices of Digital Ethics: Five Risks of Being Unethical

Modern digital technologies—from web-based services to Artificial Intelligence (AI) solutions—increasingly affect the daily lives of billions of people. Such innovation brings huge opportunities, but also concerns about design, development, and deployment of digital technologies. This article identifies and discusses five clusters of risk in the international debate about digital ethics: ethics shopping; ethics bluewashing; ethics lobbying; ethics dumping; and ethics shirking.
Educational policies in response to the pandemic caused by the COVID-19 virus in Latin America: An integrative documentary review

Educational policies in the face of the pandemic caused by the COVID-19 virus took an unexpected turn in Latin America. Virtuality constituted a key opportunity for the continuity of basic fundamental services in the citizen’s right to education. The objective of this research was to analyze the educational public policies adopted by governments in Latin America in the face of the pandemic. The methodology was an integrative documentary review of the main international organizations whose documents provided relevant information on the actions to be implemented in fourteen Latin American countries. The results obtained show that the priority was to reestablish the continuity of educational services using mass communication resources, such as radio, television, digital platforms, making visible the inequity in the access to the Internet at home. It was also identified a deficiency in the competencies and digital resources of the educational community, dis-crimination and inclusion of people with some type of disability or different languages, especially in urban or rural areas because they do not have technological means. It was concluded that the educational policies in Latin America proposed during the COVID-19 period were designed with-out a real situational diagnosis in each country, to meet the demands of urban and rural areas in an equitable manner with the will of governments, providing budgets and resources that benefit the educational community, as an achievement of state policies.
Comparative analysis of estimated solar radiation with different learning methods and empirical models

Solar radiation, which is used in hydrological modeling, agricultural, solar energy systems, and climatological studies, is the most important element of the energy reaching the earth. The present study compared, the performance of two empirical equations -Angstrom and Hargreaves-Samani equations- and, three machine learning models -Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)-. Various learning models were developed for the variables used in each empirical equation. In the present study, monthly data of six stations in Turkey, three stations receiving the most solar radiation and three stations receiving the least solar radiation, were used. In terms of the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and determination coefficient () values of each model, LSTM was the most successful model, followed by ANN and SVM. The MAE value was 2.65 with the Hargreaves-Samani equation and, decreased to 0.987 with the LSTM model while MAE was 1.24 in the Angstrom equation and decreased to 0.747 with the LSTM model. The study revealed that the deep learning model is more appropriate to use compared to the empirical equations even in cases where there is limited data.
Course on virtual nature as a digital twin

The student of this course should already know how to use 3D modeling software to create FBX files. This Course expands on a short overview presented in the Educators' Forum. It is different in several ways. First presented is the overview of the context and justification of why botanically accurate plants and landscapes are important for educational applications, such as for use in museums, arboretums, and field trip experiences to botanical gardens. Connected to that goal is the importance of accuracy in visualization of not only the plant, but the entire virtual model of the landscape using plant inventory data and plant population density geographical information system (GIS) data. This then touches on the important issues for digital twins, as models and simulations of reality. Educational applications are different than entertainment application in the dimensions of information fidelity, or the trustworthiness of the presentation, and also the graphical fidelity, or the photorealistic capacity of the rendering systems. These are not always the same. High graphical fidelity is a byproduct of high information fidelity; the reverse is not always true. High graphical fidelity enhances information fidelity, if used for that purpose. Two immersive informal learning applications use cases are presented, one for augmented reality (AR), and the other as a virtual reality (VR) example. Both models used the same design and development process, integrating domain expertise, the botanist and the ecologist, with the art and software team to enhance the accuracy. Co-design, highly iterative review process, removes errors in educational content, and representation, as well as usability and may be generalized to any domain when learning is a goal of a digital twin. In this work it is referred to as the Expert-Learner-User-Experience (ELUX) design process. Game engines, as general purpose visualization tools, make multimodal and interaction possible to enhance user experiences and to make semantic material accessible to the learner. The technical constrains on the application design demanded two production pipelines. The AR and VR pipeline required low-polygon models for performance, and the newly released Unreal Engine 5 and Reality Capture created an opportunity to increase the graphical fidelity and the information fidelity of the plants and models. Virtual nature construction methods are covered in two processes, first with low-polygon 3D plant models ideal for AR and VR, and the second with high-polygon 3D plant models using Unreal Engine 5 and Reality Capture. As highly accurate 3D plant models, combined with stat of the art rendering for photorealistic models, when combined with GIS geospatial datasets, and visualized in immersive devices, digital twins of the natural world become possible. Once these models are connected to mathematical models of the natural world, dynamics driven by real time data feeds, and forecasts, both back in time and forward into the future will enhance our understanding of the natural world, and how it interacts with that of the artificial man-made world.
A uncertainty visual analytics approach for bus travel time

Bus travel time is uncertain due to the dynamic change in the environment. Passenger analyzing bus travel time uncertainty has significant implications for understanding bus running errors and reducing travel risks. To quantify the uncertainty of the bus travel time prediction model, a visual analysis method about the bus travel time uncertainty is proposed in this paper, which can intuitively obtain uncertain information of bus travel time through visual graphs. Firstly, a Bayesian encoder–decoder deep neural network (BEDDNN) model is proposed to predict the bus travel time. The BEDDNN model outputs results with distributional properties to calculate the prediction model uncertainty degree and provide the estimation of the bus travel time uncertainty. Second, an interactive uncertainty visualization system is developed to analyze the time uncertainty associated with bus stations and lines. The prediction model and the visualization model are organically combined to better demonstrate the prediction results and uncertainties. Finally, the model evaluation results based on actual bus data illustrate the effectiveness of the model. The results of the case study and user evaluation show that the visualization system in this paper has a positive impact on the effectiveness of conveying uncertain information and on user perception and decision making.
The ACM/AIS IS2020 Competency Model for Undergraduate Programs in Information Systems: A Joint ACM/AIS Task Force Report

The Association of Computing Machinery (ACM) and the Association of Information Systems (AIS) along with the ISCAP EDSIG, recently released a joint taskforce report IS2020: A Competency Model for Undergraduate Programs in Information Systems. In this paper, the co-chairs of IS2020, the latest Information Systems curriculum guidelines, provide their insight on the problems presented with existing guidelines, illustrate the issue, and share their opinions that led to the release of these latest guidelines.
Locality-aware Qubit Routing for the Grid Architecture

Due to the short decohorence time of qubits available in the NISQ-era, it is essential to pack (minimize the size and or the depth of) a logical quantum circuit as efficiently as possible given a sparsely coupled physical architecture. In this work we introduce a locality-aware qubit routing algorithm based on a graph theoretic framework. Our algorithm is designed for the grid and certain “grid-like” architectures. We experimentally show the competitiveness of algorithm by comparing it against the approximate token swapping algorithm, which is used as a primitive in many state-of-the-art quantum transpilers. Our algorithm produces circuits of comparable depth (better on random permutations) while being an order of magnitude faster than a typical implementation of the approximate token swapping algorithm.
Where are Brazil's marine litter scientific data?

The environmental sciences work with datasets every day. Recently, data sharing has become a more familiar activity for academic researchers. Records of marine litter are scarce and generally difficult to find worldwide, especially in databases. This work reviews and analyzes data repositories to identify the existence of datasets related to marine litter in Brazil. Only one global repository specializing in marine litter was found, and it is in the early stages of operation. Only two datasets about marine litter in Brazil were found in the generalist repository Figshare that do not follow all the FAIR principles (Findable, Accessible, Interoperable, and Reusable) for data sharing. A few initiatives are being developed aiming to collect and share marine litter data, but only one of them (Our Blue Hands) is already in place and uses a standardized, replicable method, and aims to share the data by design. Our work identified interoperability as the main point to be tackled within our context. In the UN Decade of Ocean Science for Sustainable Development (2021–2030), it is essential that repositories are created, improved, and encouraged to address the specific needs of marine litter data-sharing and researchers' behavioral shift to start sharing the data already collected. Data sharing not only allows for the integrated vision of the academic community but can also contribute to public policies, helping decision-makers and encouraging a more sustainable science regarding financial and natural resource use.
Quantile hidden semi-Markov models for multivariate time series

This paper develops a quantile hidden semi-Markov regression to jointly estimate multiple quantiles for the analysis of multivariate time series. The approach is based upon the Multivariate Asymmetric Laplace (MAL) distribution, which allows to model the quantiles of all univariate conditional distributions of a multivariate response simultaneously, incorporating the correlation structure among the outcomes. Unobserved serial heterogeneity across observations is modeled by introducing regime-dependent parameters that evolve according to a latent finite-state semi-Markov chain. Exploiting the hierarchical representation of the MAL, inference is carried out using an efficient Expectation-Maximization algorithm based on closed form updates for all model parameters, without parametric assumptions about the states' sojourn distributions. The validity of the proposed methodology is analyzed both by a simulation study and through the empirical analysis of air pollutant concentrations in a small Italian city.
Practical Counterfactual Policy Learning for Top-K Recommendations

For building recommender systems, a critical task is to learn a policy with collected feedback (e.g., ratings, clicks) to decide which items to be recommended to users. However, it has been shown that the selection bias in the collected feedback leads to biased learning and thus a sub-optimal policy. To deal with this issue, counterfactual learning has received much attention, where existing approaches can be categorized as either value learning or policy learning approaches. This work studies policy learning approaches for top-K recommendations with a large item space and points out several difficulties related to importance weight explosion, observation insufficiency, and training efficiency. A practical framework for policy learning is then proposed to overcome these difficulties. Our experiments confirm the effectiveness and efficiency of the proposed framework.
A Blockchain and ABAC Based Data Access Control Scheme in Smart Grid

In the smart grid, the sharing of power data among various energy entities can make the data play a higher value. However, there may be unauthorized access while sharing data, which makes many entities unwilling to share their data to prevent data leakage. Based on blockchain and ABAC (Attribute-based Access Control) technology, this paper proposes an access control scheme, so that users can achieve fine-grained access control of their data when sharing them. The solution uses smart contract to achieve automated and reliable policy evaluation. IPFS (Interplanetary File System) is used for off-chain distributed storage to share the storage pressure of blockchain and guarantee the reliable storage of data. At the same time, all processes in the system are stored in the blockchain, ensuring the accountability of the system. Finally, the experiment proves the feasibility of the proposed scheme.