Reviewerly/exHarmony huggingface dataset:
Explainable AI for Classification Using Probabilistic Logic Inference

The overarching goal of Explainable AI is to develop systems that not only exhibit intelligent behaviours, but also are able to explain their rationale and reveal insights. In explainable machine learning, methods that produce a high level of prediction accuracy as well as transparent explanations are valuable. In this work, we present an explainable classification method, which works by first constructing a symbolic Knowledge Base from the training data, and then performing probabilistic inferences on such Knowledge Base with linear programming. Our approach achieves a level of learning performance comparable to that of traditional classifiers such as random forests, support vector machines and neural networks. It identifies decisive features that are responsible for a classification as explanations and produces results similar to the ones found by SHAP, a state-of-the-art Shapley Value based explainable AI method.
Adapting and Evaluating the Story-Card-Method

Domain modelling languages (DMLs) grow and change over time. These languages are artefacts that are developed within communities via multiple participants. Methods, associated with the emerging DMLs, also need to be supported and need adaptation, informed by practice. This study refers to a DML called DEMO (Design and Engineering Methodology for Organizations), of which the language specification evolved from the DEMO Specification Language (DEMOSL) version 3 to version 4. We adapt a method, called the story-card-method (SCM), to accommodate DEMOSL 4. Also, the previous DEMOSL 3-based SCM implied physical interaction between participants, using sticky notes to create a shared understanding, whereas the adapted SCM has to facilitate a digital way-of-collaboration due to COVID-19 restrictions. We re-visit participant feedback from the initial version of the SCM and demonstrate how we applied design science research to design an adapted SCM as the main contribution of this article. In addition, we evaluate whether the adapted SCM is useful in providing ample guidance in compiling a Coordination Structure Diagram (CSD) in a collaborative way, and we evaluate the quality of the CSDs. Finally, we demonstrate how the CSD can be used within a low-code-development ecosystem to structure user stories.
A Scholarly Knowledge Graph-Powered Dashboard: Implementation and User Evaluation

Scholarly knowledge graphs provide researchers with a novel modality of information retrieval, and their wider use in academia is beneficial for the digitalization of published works and the development of scholarly communication. To increase the acceptance of scholarly knowledge graphs, we present a dashboard, which visualizes the research contributions on an educational science topic in the frame of the Open Research Knowledge Graph (ORKG). As dashboards are created at the intersection of computer science, graphic design, and human-technology interaction, we used these three perspectives to develop a multi-relational visualization tool aimed at improving the user experience. According to preliminary results of the user evaluation survey, the dashboard was perceived as more appealing than the baseline ORKG-powered interface. Our findings can be used for the development of scholarly knowledge graph-powered dashboards in different domains, thus facilitating acceptance of these novel instruments by research communities and increasing versatility in scholarly communication.
Anonymization Level and Compliance for Differential Privacy: A Systematic Literature Review

Currently, personal data protection and additional methods providing the required level of confidentiality do not meet the current European Union legal norms. It still has numerous research gaps. Thus, securing the correct application of privacy-enhancing technology (PET) methods concerning General Data Protection Regulation (GDPR) compliance is the most crucial goal demanded by many stakeholders. To the best of our knowledge, there is no systematic literature review of the overlap between the PET as differential privacy (DP) and compliance domain focusing on assessing the anonymization level regarding legal and technical requirements. Consequently, this review focuses on examining studies on improving the safety of personal data in the framework of evaluating compliance with the anonymization norms and DP concept used by considering the results of existing studies. Additionally, we will investigate the articles that associate anonymity and degree of personal data protection with the DP method. Finally, we will assess the impact of GDPR norms on the anonymization level and the choice of the DP model. To answer these questions, we conduct a systematic literature review. We collect the latest studies published at the leading privacy conferences and classify them according to the GDPR requirement categories. We also provide qualitative analysis of articles representing the category or a specific area in each category and explore and visualize links between categories. Thus, this review aims to provide a snapshot of the current state of affairs in these domains and studies with an additional focus on DP anonymization measures and identify directions for future research.
Research of the 51% attack based on blockchain

With the explosion of Nakamoto’s paper, blockchain technology has developed rapidly, but at the same time, security problems are emerging one after another. As a potential security hazard in the payment field, 51% attack brings huge risks to the normal operation of the blockchain system. Miners with great computing power have the ability to monopolize the generation of blocks and modify the generated blocks. Therefore, it is necessary to do research of this kind of attacks. This article cites both Nakamoto model and Rosenfeld model to illustrate relationship between computing power and attack success rate. Through a series of mining experiments, this paper preliminarily introduces the operation principles of blockchain based on Ethereum, including proof of stake, smart contracts etc. Models show that for rational attackers who pursue interests, they lack the motivation to launch 51% attacks. For attackers who only destroy the bitcoin system, they need to master huge financial resources to launch 51% of attacks, and even need financial support at the national level, which is very difficult. It can be said that 51% attacks against bitcoin are only theoretically possible, but users still need to pay enough attention. As an emerging technology, blockchain technology is currently in the research and exploration stage. While it is applied in the financial field, it is also expanding to other fields. In the future, blockchain technology will not only be used to solve the trust and security problems in the centralized service architecture, but also appear in more decentralized service scenarios. So, the research on blockchain security is particularly important.
An Efficient Contesting Procedure for AutoML Optimization

Automated Machine Learning (AutoML) frameworks are designed to select the optimal combination of operators and hyperparameters. Classical AutoML-based Bayesian Optimization (BO) approaches often integrate all operator search spaces into a single search space. However, a disadvantage of this history-based strategy is that it can be less robust when initialized randomly than optimizing each operator algorithm combination independently. To overcome this issue, a novel contesting procedure algorithm, Divide And Conquer Optimization (DACOpt), is proposed to make AutoML more robust. DACOpt partitions the AutoML search space into a reasonable number of sub-spaces based on algorithm similarity and budget constraints. Furthermore, throughout the optimization process, DACOpt allocates resources to each sub-space to ensure that (1) all areas of the search space are covered and (2) more resources are assigned to the most promising sub-space. Two extensive sets of experiments on 117 benchmark datasets demonstrate that DACOpt achieves significantly better results in 36% of AutoML benchmark datasets: 5% when to compared to TPOT, 8% - to AutoSklearn, 15% - to H20 and 18% - to ATM.
Efficient Knowledge Graph Embedding Training Framework with Multiple GPUs

When training a large-scale knowledge graph embedding (KGE) model with multiple graphics processing units (GPUs), the partition-based method is necessary for parallel training. However, existing partition-based training methods suffer from low GPU utilization and high input/output (10) overhead between the memory and disk. For a high 10 overhead between the disk and memory problem, we optimized the twice partitioning with fine-grained GPU scheduling to reduce the 10 overhead between the CPU memory and disk. For low GPU utilization caused by the GPU load imbalance problem, we proposed balanced partitioning and dynamic scheduling methods to accelerate the training speed in different cases. With the above methods, we proposed fine-grained partitioning KGE, an efficient KGE training framework with multiple GPUs. We conducted experiments on some benchmarks of the knowledge graph, and the results show that our method achieves speedup compared to existing framework on the training of KGE.
A Crisis of Data? Transparency Practices and Infrastructures of Value in Data Broker Platforms

Despite the prevalence of transparency discourses in economic life (e.g., postcrisis socioeconomic reforms), scholarship is just beginning to analyze how these discourses produce new relations between market actors in platform economies. In this article, we argue that in the context of financial markets and the political economy of data, transparency functions as a discursive construction that creates suitable conditions for the manufacture and extraction of data as an asset. First, we examine the role of transparency and opacity in various understandings of the 2007–2008 global financial crisis (GFC). In doing so we link the emergence of FinTech firms to a careful ex post facto reconstruction of the GFC as what we term a “crisis of data.” Then, through problematizing the idea that transparent economic relations necessarily lead to greater accountability, equity, or public good, we argue that transparency is better understood as a relational practice that is continuously and contingently renegotiated. Taking up the example of debt data, we provide case studies of the data brokerage companies BlackRock and dv01 to analyze how transparency constitutes the material infrastructure of debt markets, which facilitates the construction of data assets for profitable circulation in a financialized political economy. We analyze our case studies with a focus on four transparency practices used to infuse data with value—building relationality, increasing granularity, managing directionality, and creating legibility.
Physician Experience Design (PXD): More Usable Machine Learning Prediction for Clinical Decision Making.

Delirium is an acute neurocognitive disorder, which is difficult to identify and predict. Using GEMINI, Canada's largest hospital data and analytics study, we had a labeled sample of around 4,000 cases with approximately 25% of cases being labeled as having delirium. Based on this labeled data, we developed machine learning (ML) models and interacted with physicians to interpret the ML models and their predictions. We developed a preliminary Explainable Artificial Intelligence (XAI) framework for physician experience design (PXD) to improve the uptake of ML models by improving the transparency of model results, thereby increasing physician trust in models as well as the uptake of model results for clinical decision making. We developed our PXD approach first with Conceptual Investigation to collect and extract physicians' feedback on ML models and their evaluation requirements. We carried out a case study, working closely with the physicians in a participatory design process to develop a dashboard that presents ML delirium identification results interactively based on physician selections and inputs. In this approach a physician-preferred ML model for clinical decision making is selected through PXD evaluation.
An earth observation potential evaluation model and its application to SDG indicators

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Artificial Humans: an Overview of Photorealistic Synthetic Datasets and Possible Applications

In this scientific paper, an overview of different photorealistic synthetic human datasets is presented. The creation of more and more artificial data is leading to rapid progress in various fields. Synthetic faces and whole bodies are needed during the processes of training and exploitation of applications in the field. The state of the art synthetic human representations are listed, including their applications.
Depth Map Super Resolution Using Structure-Preserving Guided Filtering

Due to the limited resolution of depth maps captured by RGB-D sensors, depth map Super Resolution (SR) techniques have received a lot of attention. Intensity guided depth map SR methods based on bilateral filter or guided image filter are commonly used for depth upsampling. Although promising edge-preserving results have been reported in these methods, texture-copying artifacts caused by structure discrepancy between depth map and associated intensity image cannot be addressed, easily. In this paper we aim to balance the trade-off between preserving structure and suppressing texture defects. Based on this, a structure-preserving guided filter is presented that not only keeps the advantages of aforementioned methods, but also overcomes texture-copying artifacts. Unlike conventional guided filtering-based methods which rely on only one guidance, we emphasize on the use of both intensity and depth information as guidance to alleviate the deficiencies of the existing works. We replace the mean filtering scheme in guided filters with a weighted average strategy, where the weights are described by the local depth kernel depended on the input depth map. This enables our method to considerably reduce texture-copying artifacts while preserving 3D structural details. Visual evaluation of results shows that the algorithm can also avoid halo artifacts near the edges whereas traditional guided filters suffer from it. Quantitative results of comprehensive experiments demonstrate the effectiveness of our approach over prior depth map SR works.
CLUZH at SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection Generation

This paper describes the submissions of the team of the Department of Computational Linguistics, University of Zurich, to the SIGMOR-PHON 2022 Shared Tasks on Morpheme Segmentation and Inflection Generation.Our submissions use a character-level neural transducer that operates over traditional edit actions.While this model has been found particularly well-suited for low-resource settings, using it with large data quantities has been difficult.Existing implementations could not fully profit from GPU acceleration and did not efficiently implement mini-batch training, which could be tricky for a transition-based system.For this year's submission, we have ported the neural transducer to PyTorch and implemented true mini-batch training.This has allowed us to successfully scale the approach to large data quantities and conduct extensive experimentation.We report competitive results for morpheme segmentation (including sharing first place in part 2 of the challenge).We also demonstrate that reducing sentence-level morpheme segmentation to a word-level problem is a simple yet effective strategy.Additionally, we report strong results in inflection generation (the overall best result for large training sets in part 1, the best results in low-resource learning trajectories in part 2).Our code is publicly available.
Word-order Typology in Multilingual BERT: A Case Study in Subordinate-Clause Detection

The capabilities and limitations of BERT and similar models are still unclear when it comes to learning syntactic abstractions, in particular across languages.In this paper, we use the task of subordinate-clause detection within and across languages to probe these properties.We show that this task is deceptively simple, with easy gains offset by a long tail of harder cases, and that BERT's zero-shot performance is dominated by word-order effects, mirroring the SVO/VSO/SOV typology.
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts

We present a novel approach incorporating transformer-based language models into infectious disease modelling.Text-derived features are quantified by tracking high-density clusters of sentence-level representations of Reddit posts within specific US states' COVID-19 subreddits.We benchmark these clustered embedding features against features extracted from other high-quality datasets.In a thresholdclassification task, we show that they outperform all other feature types at predicting upward trend signals, a significant result for infectious disease modelling in areas where epidemiological data is unreliable.Subsequently, in a time-series forecasting task we fully utilise the predictive power of the caseload and compare the relative strengths of using different supplementary datasets as covariate feature sets in a transformer-based time-series model.
Revisiting the Role of Classical Readability Formulae Parameters in Complex Word Identification (Part 2)

Accessibility of text is an attribute that deserves the attention of researchers and content creators. This study is an attempt to determine the lexical features that play a key role in identifying complex words in Hindi text. As the first step, we studied the parameters used in readability metrics in different languages and tested their importance on classifiers built on datasets created with the help of a user study. In part of the study, we reported the results of two different approaches used to label a word as complex. In this part, we compare the previous results with the results obtained from a third labeling approach. We found satisfactory evidence for certain parameters and also observed a new parameter that could be used while devising readability metrics for Hindi.
A Dual-Core High-Performance Processor for Elliptic Curve Cryptography in GF(p) Over Generic Weierstrass Curves

A dual-core high-performance processor for Elliptic Curve Cryptography (ECC) in GF( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> ) over Generic Weierstrass Curves was presented in this brief. Firstly, an optimized radix-128 Montgomery modular multiplication algorithm was proposed. It takes 6 clock cycles to calculate modular multiplication, which promotes the speed of elliptic curve point-addition (PA) and elliptic curve point-doubling (PD). Secondly, a dual-core hardware architecture which includes PD core and PA core to calculate elliptic curve point-multiplication (PM) was developed. It can speed up the PM by adopting parallelism with different levels and optimizing the scheduling. Finally, a data transfer architecture between PA and PD cores was proposed which requires only a 1024-bit register. It improves the speed of PM. The proposed design consumes 996k gates and compute the PM in 0.017 ms on ASIC with a 90 nm standard cell library. Moreover, the proposed design is also implemented on FPGA (Virtex-6 platform), which consumes 182k LUTs and compute the PM in 0.056 ms.
Recipe Recommendations for Toddlers Using Integrated Nutritional and Ingredient Similarity Measures

Nutritious and healthy diets in early childhood are critical determinants of children's health, growth and development. An extensive selection of cookery books and recipe sharing websites, containing toddler's recipes, have been provided. The overload of recipe data available makes the recommendation system become indispensable in assisting individuals when making food decisions for their young children. This paper presents a framework that utilizes a combination of nutrient-based and weighted ingredient-based similarity measures to make recipe recommendations. The method is divided into three processes: nutrient analysis, ingredient extraction and integration of similarity measures. The dataset from a reliable source, containing the total of 35 recipes and 87 ingredients, is used in the study. The experimental results show that the proposed technique can effectively generate similarity-based recipe recommendations. The recipe results share both high nutritional and ingredient similarity scores. The comparison results further suggest that the presented approach offers a promising balance between nutrients and ingredients. It is shown that the existing nutrient-based similarity measure tends to overproduce the outputs, while the weighted ingredient-based similarity plays a role to mitigate the shortcomings by removing insignificant recipe pairs with tf-idf weights.
Data Analysis and Mining Using Logical Synthesis Methods

The article discusses the possibilities of the application of logic synthesis methods in data mining tasks. In particular, the method of reducing attributes and the method of inducing decision rules is considered. It is shown that by applying specialized logic synthesis methods, these issues can be effectively improved and successfully used for solving data mining tasks. In justification of the advisability of such proceedings, the patient's diagnosis with the possibility of eliminating troublesome tests is discussed.
A General Framework for Distributed Partitioned Optimization

Distributed optimization is widely used in large scale and privacy preserving machine learning and various distributed control and sensing systems. It is assumed that every agent in the network possesses a local objective function, and the nodes interact via a communication network. In the standard scenario, which is mostly studied in the literature, the local functions are dependent on a common set of variables, and, therefore, have to send the whole variable set at each communication round. In this work, we study a different problem statement, where each of the local functions held by the nodes depends only on some subset of the variables. Given a network, we build a general algorithm-independent framework for distributed partitioned optimization that allows to construct algorithms with reduced communication load using a generalization of Laplacian matrix. Moreover, our framework allows to obtain algorithms with non-asymptotic convergence rates with explicit dependence on the parameters of the network, including accelerated and optimal first-order methods. We illustrate the efficacy of our approach on a synthetic example.
Data-Free Knowledge Distillation for Privacy-Preserving Efficient UAV Networks

UAVs have been widely used in various fields such as military, rescue, and agriculture, where a significant number of data sets involve military secrecy and data privacy. Deep neural networks play a crucial role in these applications, but they require tremendous computational resources. Model compression and acceleration algorithms have been proposed to tackle this problem, which has a strong assumption that the training samples for the original network are available. However, in real-world applications, the training dataset is often inaccessible due to privacy issue and transmission constraints. In the paper, we propose a novel privacy-preserving efficient UAV framework by compressing the deep neural network paradigm in a data-free manner. Our method leverages data-free teacher-student learning for UAV recognition model compression, which preserves the source data privacy while achieving efficient model inference at the test phase. Extensive experiments demonstrate that our approach attains satisfactory results on visual object recognition using UAV networks.