Reviewerly/exHarmony huggingface dataset:
The AI Settlement Generation Challenge in Minecraft

This article outlines what we learned from the first year of the AI Settlement Generation Competition in Minecraft, a competition about producing AI programs that can generate interesting settlements in Minecraft for an unseen map. This challenge seeks to focus research into adaptive and holistic procedural content generation. Generating Minecraft towns and villages given existing maps is a suitable task for this, as it requires the generated content to be adaptive, functional, evocative and aesthetic at the same time. Here, we present the results from the first iteration of the competition. We discuss the evaluation methodology, present the different technical approaches by the competitors, and outline the open problems.
A hybrid modified step Whale Optimization Algorithm with Tabu Search for data clustering

Clustering is a popular data analysis tool that groups similar data objects into disjoint clusters. Clustering algorithms have major drawbacks, such as: sensitivity to initialization and getting trapped in local optima. Swarm Intelligence (SI) have been combined with clustering techniques to enhance their performance. Despite their improvements, some SI methods, such as Particle Swarm Optimization (PSO) and its rivals, had their drawbacks, such as low convergence rates and generating low quality solutions. This is due to storing a single best solution about solution space, which can lead swarm members to local optima. Whale Optimization Algorithm (WOA) is a recent SI method, which has been proved as a global optimizer over multiple optimization problems. Unlikely, WOA uses the same concept as PSO, it uses the best solution to reposition swarm members. To overcome this problem, WOA needs to store multiple best solutions about solution space. Tabu Search (TS) is a meta-heuristic method, which uses memory components to explore and exploit search space. In this paper, we propose combining WOA with TS (WOATS) for data clustering. To assess the efficiency of WOATS, it has been applied in data clustering. WOATS uses an objective function inspired by partitional clustering to maintain the quality of clustering solutions. WOATS was tested over multiple real life datasets, which their sizes vary from small to large-sized datasets. WOATS was able to find centers with high quality in a small number of iterations regardless of the size of the dataset, which clarifies its ability to cover the solution space efficiently. The generated clusters were evaluated according to different outlier criteria: Davies-Bouldin Index and Dunn Index. According to these criteria, WOATS presented its superiority over multiple recent original and hybrid SI methods for different datasets. Results obtained by WOATS were better than the results of the other methods with big differences regarding to the different outlier criteria, which ensured the efficiency of WOATS.
Batch Normalization Biases Deep Residual Networks Towards Shallow Paths

Batch normalization dramatically increases the largest trainable depth of residual networks, and this benefit has been crucial to the empirical success of deep residual networks on a wide range of benchmarks. We show that this key benefit arises because, at initialization, batch normalization downscales the residual branch relative to the skip connection, by a normalizing factor on the order of the square root of the network depth. This ensures that, early in training, the function computed by normalized residual blocks in deep networks is close to the identity function (on average). We use this insight to develop a simple initialization scheme that can train deep residual networks without normalization. We also provide a detailed empirical study of residual networks, which clarifies that, although batch normalized networks can be trained with larger learning rates, this effect is only beneficial in specific compute regimes, and has minimal benefits when the batch size is small.
Blockchain, GDPR, and fantasies of data sovereignty

Like the European Union’s General Data Protection Regulation (GDPR), the broader, mainstream emergence of blockchain technology in the present moment of, what I call, data dysphoria is no accident. It is in part reaction to data dysphoria, and in part exploitation of it, a duality underpinned by the tantalising promise of the prosumer ‘taking control’ of their data and establishing sovereignty over it. Blockchain and GDPR alike aim to resolve ‘problem’/’solution’ matrices with deep roots in a wide variety of global economic, political, social, legal and cultural contexts. This article explores the problem of achieving resolution based on innovation and technology by offering an account of the rise of blockchain and implementation of GDPR within a psycho-political framework, one in which fantasies of taking control are predominant yet highly contestable actualities in the lives of technology users.
TextAttack: A Framework for Adversarial Attacks in Natural Language Processing

While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop NLP attacks and utilize them to improve model performance. This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack's modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets, including BERT and other transformers, and all GLUE tasks. TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness. TextAttack is democratizing NLP: anyone can try data augmentation and adversarial training on any model or dataset, with just a few lines of code. Code and tutorials are available at this https URL.
Incorporating Physical Knowledge Into Machine Learning for Planetary Space Physics

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. In comparison, the previous mission to Saturn, Voyager over 20 years earlier, had onboard a ~70 kB 8-track storage ability. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of "black box" or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. The present work uses Cassini data as a case study as these data are similar to space physics and planetary missions at Earth and other solar system objects. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetic environment, or magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine leaning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.
Forecasting the number of firefighter interventions per region with local-differential-privacy-based data

Statistical studies on the number and types of firefighter interventions by region are essential to improve service to the population. It is also a preliminary step if we want to predict these interventions in order to optimize the placement of human and material resources of fire departments, for example. However, this type of data is sensitive and must be treated with the utmost care. In order to avoid any leakage of information, one can think of anonymizing them using Differential Privacy (DP), a safe method by construction. This work focuses on predicting the number of firefighter interventions in certain localities while respecting the strong concept of DP. A local Differential Privacy approach was first used to anonymize location data. Statistical estimators were then applied to reconstruct a synthetic data set that is uncorrelated from the users. Finally, a supervised learning approach using extreme gradient boosting was used to make the predictions. Experiments have shown that the anonymization-prediction method is very accurate: the introduction of noise to sanitize the data does not affect the quality of the predictions, and the predictions faithfully reflect what happened in reality.
OpenAPI Bot: A Chatbot to Help You Understand REST APIs

REST APIs are an essential building block in many Web applications. The lack of a standard machine-readable format to describe these REST APIs triggered the creation of several specification languages to formally define REST APIs, with the OpenAPI specification currently taking the lead. OpenAPI definitions are consumed by a growing ecosystem of tools aimed at automating tasks such as generating server/client SDKs and API documentations. However, current OpenAPI documentation tools mostly provide simple descriptive Web pages enumerating all the API operations and corresponding parameters, but do not offer interactive capabilities to help navigate the API and ask relevant information. Therefore, learning how to use an API and how its different parts are interrelated still requires a considerable time investment. To overcome this situation we present our OpenAPI Bot, a chatbot able to read an OpenAPI definition for you and answer the questions you may have about it.
The Nested QR Code

The QR code is the most widely used two-dimensional barcode in the world, and it has been successfully applied in many applications in manufacturing and marketing fields. This letter presents a new variant of the QR code named the nested QR code. It comprises two QR codes with individual messages on a shared square image. The two QR codes can be read separately by slightly adjusting the distance and angle at which images are obtained using standard QR code readers. Construction methods for generating the proposed nested QR code with high decoding robustness are presented, and experimental results verify the feasibility of the proposed method.
Enchantment - Disenchantment-Re-Enchantment: Postdigital Relationships between Science, Philosophy, and Religion

This collectively written article explores postdigital relationships between science, philosophy, and religion within the continuum of enchantment, disenchantment, and re-enchantment. Contributions are broadly classified within four sections related to academic fields of philosophy, theology, critical theory, and postdigital studies. The article reveals complex and nuanced relationships between various disciplinary perspectives, religions, and political positions, and points towards lot of commonalities between their views to the enchantment, disenchantment, re-enchantment continuum. Some commonly discussed questions include: Where do the mythical, mystical and spiritual end and the rational, objective and empirical begin? How do we find our bearings in the midst of this complexity and where do we search for resources that are trustworthy and reliable? While the article inevitably offers more questions than answers, a common thread between all contributions is the need for an open postdigital dialogue conducted in the spirit of mutual understanding and respect. It is with this conclusion that the article offers a possible route for further development of such dialogue in the future.
Multi-Label Feature Selection Based on High-Order Label Correlation Assumption

Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning. Feature selection plays an important role in multi-label learning to address multi-label data. Exploring label correlations is crucial for multi-label feature selection. Previous information-theoretical-based methods employ the strategy of cumulative summation approximation to evaluate candidate features, which merely considers low-order label correlations. In fact, there exist high-order label correlations in label set, labels naturally cluster into several groups, similar labels intend to cluster into the same group, different labels belong to different groups. However, the strategy of cumulative summation approximation tends to select the features related to the groups containing more labels while ignoring the classification information of groups containing less labels. Therefore, many features related to similar labels are selected, which leads to poor classification performance. To this end, Max-Correlation term considering high-order label correlations is proposed. Additionally, we combine the Max-Correlation term with feature redundancy term to ensure that selected features are relevant to different label groups. Finally, a new method named Multi-label Feature Selection considering Max-Correlation (MCMFS) is proposed. Experimental results demonstrate the classification superiority of MCMFS in comparison to eight state-of-the-art multi-label feature selection methods.
A Cryo-CMOS Digital Cell Library for Quantum Computing Applications

We present a digital cell library optimized for 4.2 K to create controllers that keep quantum processors coherent and entangled.The library, implemented on a standard 40-nm CMOS technology, was employed in the creation of the first 4.2 K RISC-V processor.It has achieved a minimum supply voltage of 590 mV, energy-delay product of 37 fJ/MHz, and maximum operating frequency of 740 MHz, all at 4.2 K in continuous operation.These results have been obtained from stand-alone characterization, successfully executing small C programs/benchmarks at 4.2 K.The overall performance of the library compares well against the state-of-the-art libraries designed for room temperature.In particular, we compared the performance of the proposed library against a foundry supplied library for the same process in several combinational benchmark circuits, showing significant improvements in power dissipation and frequency of operation.
Deep learning for semantic segmentation of organs and tissues in laparoscopic surgery

Abstract Semantic segmentation of organs and tissue types is an important sub-problem in image based scene understanding for laparoscopic surgery and is a prerequisite for context-aware assistance and cognitive robotics. Deep Learning (DL) approaches are prominently applied to segmentation and tracking of laparoscopic instruments. This work compares different combinations of neural networks, loss functions, and training strategies in their application to semantic segmentation of different organs and tissue types in human laparoscopic images in order to investigate their applicability as components in cognitive systems. TernausNet-11 trained on Soft-Jaccard loss with a pretrained, trainable encoder performs best in regard to segmentation quality (78.31% mean Intersection over Union [IoU]) and inference time (28.07 ms) on a single GTX 1070 GPU.
Introduction to the Special Issue on HCI and the Body

introduction Free Access Share on Introduction to the Special Issue on HCI and the Body: Reimagining Women’s Health Authors: Teresa Almeida IT University of Copenhagen, Denmark IT University of Copenhagen, DenmarkView Profile , Madeline Balaam KTH Royal Institute of Technology, Stockholm, Sweden KTH Royal Institute of Technology, Stockholm, SwedenView Profile , Shaowen Bardzell Indiana University, Bloomington Indiana University, BloomingtonView Profile , Lone Koefoed Hansen Aarhus University, Denmark Aarhus University, DenmarkView Profile Authors Info & Claims ACM Transactions on Computer-Human InteractionVolume 27Issue 4August 2020 Article No.: 20pp 1–32https://doi.org/10.1145/3406091Published:26 August 2020Publication History 11citation1,324DownloadsMetricsTotal Citations11Total Downloads1,324Last 12 Months546Last 6 weeks51 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection

Recent years, human-object interaction (HOI) detection has achieved impressive advances. However, conventional two-stage methods are usually slow in inference. On the other hand, existing one-stage methods mainly focus on the union regions of interactions, which introduce unnecessary visual information as disturbances to HOI detection. To tackle the problems above, we propose a novel one-stage HOI detection approach DIRV in this paper, based on a new concept called interaction region for the HOI problem. Unlike previous methods, our approach concentrates on the densely sampled interaction regions across different scales for each human-object pair, so as to capture the subtle visual features that is most essential to the interaction. Moreover, in order to compensate for the detection flaws of a single interaction region, we introduce a novel voting strategy that makes full use of those overlapped interaction regions in place of conventional Non-Maximal Suppression (NMS). Extensive experiments on two popular benchmarks: V-COCO and HICO-DET show that our approach outperforms existing state-of-the-arts by a large margin with the highest inference speed and lightest network architecture. Our code is publicly available at www.github.com/MVIG-SJTU/DIRV.
Vyper: A Security Comparison with Solidity Based on Common Vulnerabilities

Vyper has been proposed as a new high-level language for Ethereum smart contract development due to numerous security vulnerabilities and attacks witnessed on contracts written in Solidity since the system’s inception. Vyper aims to address these vulnerabilities by providing a language that focuses on simplicity, auditability and security. We present a survey where we study how well-known and commonly-encountered vulnerabilities in Solidity feature in Vyper’s development environment. We analyze all such vulnerabilities individually and classify them into five groups based on their status in Vyper. To the best of our knowledge, our survey is the first attempt to study security vulnerabilities in Vyper.
Optimizing quantum optimization algorithms via faster quantum gradient computation

We consider a generic framework of optimization algorithms based on gradient descent. We develop a quantum algorithm that computes the gradient of a multi-variate real-valued function f : Rd → R by evaluating it at only a logarithmic number of times in superposition. Our algorithm is an improved version of Jordan's gradient computation algorithm [28], providing an approximation of the gradient ▽f with quadratically better dependence on the evaluation accuracy of f, for an important class of smooth functions. Furthermore, we show that objective functions arising from variational quantum circuits usually satisfy the necessary smoothness conditions, hence our algorithm provides a quadratic improvement in the complexity of computing their gradient. We also show that in a continuous phase-query model, our gradient computation algorithm has optimal query complexity up to poly-logarithmic factors, for a particular class of smooth functions. Moreover, we show that for low-degree multivariate polynomials our algorithm can provide exponential speedups compared to Jordan's algorithm in terms of the dimension d.One of the technical challenges in applying our gradient computation procedure for quantum optimization problems is the need to convert between a probability oracle (which is common in quantum optimization procedures) and a phase oracle (which is common in quantum algorithms) of the objective function f. We provide efficient subroutines to perform this delicate interconversion between the two types of oracles incurring only a logarithmic overhead, which might be of independent interest. Finally, using these tools we improve the runtime of prior approaches for training quantum auto-encoders, variational quantum eigensolvers (VQE), and quantum approximate optimization algorithms (QAOA).
S2DNet: Learning Image Features for Accurate Sparse-to-Dense Matching

Establishing robust and accurate correspondences is a fundamental backbone to many computer vision algorithms. While recent learning-based feature matching methods have shown promising results in providing robust correspondences under challenging conditions, they are often limited in terms of precision. In this paper, we introduce S2DNet, a novel feature matching pipeline, designed and trained to efficiently establish both robust and accurate correspondences. By leveraging a sparse-to-dense matching paradigm, we cast the correspondence learning problem as a supervised classification task to learn to output highly peaked correspondence maps. We show that S2DNet achieves state-of-the-art results on the HPatches benchmark, as well as on several long-term visual localization datasets.
BCVehis: A Blockchain-Based Service Prototype of Vehicle History Tracking for Used-Car Trades in China

The vehicle information in used-car transactions is always asymmetric and disputes always happen in China. In order to reduce such disputes caused by the lack of transparency in a transaction, Blockchain technology is adopted to construct a trust mechanism for vehicle information storage and sharing in a transparent manner. In this work, a Blockchain-based vehicle history storing and tracking service, named BCVehis, is proposed. BCVehis allows vehicle owners, vehicle authority, mechanic workshops, insurance brokers and other related individuals/organizations to upload vehicle historical records via simple manners (i.e. mobile app or application programming interfaces). The system provides trustworthy vehicle history to used-car dealers, potential buyers, and other business-related parties. The design rationale and functional implementation of the BCVehis are introduced, and the increased deal volume in a local used-car dealer X is presented, which integrated BCVehis to its online dealing system.