Reviewerly/exHarmony huggingface dataset:
EvoCraft: A New Challenge for Open-Endedness

This paper introduces EvoCraft, a framework for Minecraft designed to study open-ended algorithms. We introduce an API that provides an open-source Python interface for communicating with Minecraft to place and track blocks. In contrast to previous work in Minecraft that focused on learning to play the game, the grand challenge we pose here is to automatically search for increasingly complex artifacts in an open-ended fashion. Compared to other environments used to study open-endedness, Minecraft allows the construction of almost any kind of structure, including actuated machines with circuits and mechanical components. We present initial baseline results in evolving simple Minecraft creations through both interactive and automated evolution. While evolution succeeds when tasked to grow a structure towards a specific target, it is unable to find a solution when rewarded for creating a simple machine that moves. Thus, EvoCraft offers a challenging new environment for automated search methods (such as evolution) to find complex artifacts that we hope will spur the development of more open-ended algorithms. A Python implementation of the EvoCraft framework is available at: github.com/real-itu/Evocraft-py .
SoK: How (not) to Design and Implement Post-quantum Cryptography

Post-quantum cryptography has known a Cambrian explosion in the last decade. What started as a very theoretical and mathematical area has now evolved into a sprawling research field, complete with side-channel resistant embedded implementations, large scale deployment tests and standardization efforts. This study systematizes the current state of knowledge on post-quantum cryptography. Compared to existing studies, we adopt a transversal point of view and center our study around three areas: (i) paradigms, (ii) implementation, (iii) deployment. Our point of view allows to cast almost all classical and post-quantum schemes into just a few paradigms. We highlight trends, common methodologies, and pitfalls to look for and recurrent challenges.
Large Scale Distributed Collaborative Unlabeled Motion Planning With Graph Policy Gradients

In this letter, we present a learning method to solve the unlabelled motion problem with motion constraints and space constraints in 2D space for a large number of robots. To solve the problem of arbitrary dynamics and constraints we propose formulating the problem as a multi-agent problem. We are able to demonstrate the scalability of our methods for a large number of robots by employing a graph neural network (GNN) to parameterize policies for the robots. The GNN reduces the dimensionality of the problem by learning filters that aggregate information among robots locally, similar to how a convolutional neural network is able to learn local features in an image. Additionally, by employing a GNN we are also able to overcome the computational overhead of training policies for a large number of robots by first training graph filters for a small number of robots followed by zero-shot policy transfer to a larger number of robots. We demonstrate the effectiveness of our framework through various simulations.
MORAL GRANDSTANDING AS A THREAT TO FREE EXPRESSION

Abstract Moral grandstanding, or the use of moral talk for self-promotion, is a threat to free expression. When grandstanding is introduced in a public forum, several ideals of free expression are less likely to be realized. Popular views are less likely to be challenged, people are less free to entertain heterodox ideas, and the cost of changing one’s mind goes up.
Exploring the Profiles of Software Testing Jobs in the United States

There is an indisputable industrial need for highly skilled individuals in the role of software testers.However, little is known about what activities under testers' responsibilities, competencies, and experiences sought after from employers' perspectives.For the purpose of this research, a data set of 1000 job ads related to software testing role in the United States was collected and analyzed.Specifically, a thorough analysis was conducted to find the industrial demand for competencies for the software testing role in terms of (i) Level of education, training, and experience; (ii) Testing skills; (iii) Technical skills; and (iv) Soft-skills.Relevant correlated skills that may influence shaping the profile of the software tester were also analyzed.Also, the essential duties that a software tester is expected to perform were investigated.The results from the subsequent quantitative and qualitative analysis are reported.
A Simple Mask Detection Model Based On A Multi-Layer Perception Neural Network

A global health crisis is appeared due to the rapid transmission of the COVID-19 pandemic. According to the World Health Organization (WHO), one of the effective ways to decrease this transmission is wearing masks in crowded places. However, monitoring people by police is a weary and difficult process. Thanks to the improvement in technology and artificial intelligence that make task became easier. In this paper, a simple mask recognition model based on texture and color moments features is proposed. This model is deployed in two stages: first, texture and color moments features from the face image (31 features) are extracted using a hybridization between texture features and color moments features techniques. In order to extract the texture features, the image transformed into Gray Level Co-Occurrence Matrix (GLCM) then 22 statistical metrics were calculated. So as to extract the color moments features, the first, second and third moments have been calculated from each layer of the RGB image. Second, based on the extracted features, the images are classified using a Multi-Layer Perceptron model (MLP). The dataset used in this research consists of 1787 real images with masks and 1918 without masks. The obtained results showed that the accuracy achieved by the proposed model is 90.58% and the time complexity is 6.7379 seconds for training and 0.0023 seconds for prediction.
An Embedding-Based Topic Model for Document Classification

Topic modeling is an unsupervised learning task that discovers the hidden topics in a collection of documents. In turn, the discovered topics can be used for summarizing, organizing, and understanding the documents in the collection. Most of the existing techniques for topic modeling are derivatives of the Latent Dirichlet Allocation which uses a bag-of-word assumption for the documents. However, bag-of-words models completely dismiss the relationships between the words. For this reason, this article presents a two-stage algorithm for topic modelling that leverages word embeddings and word co-occurrence. In the first stage, we determine the topic-word distributions by soft-clustering a random set of embedded n -grams from the documents. In the second stage, we determine the document-topic distributions by sampling the topics of each document from the topic-word distributions. This approach leverages the distributional properties of word embeddings instead of using the bag-of-words assumption. Experimental results on various data sets from an Australian compensation organization show the remarkable comparative effectiveness of the proposed algorithm in a task of document classification.
Selecting an Effective Entropy Estimator for Short Sequences of Bits and Bytes with Maximum Entropy

Entropy makes it possible to measure the uncertainty about an information source from the distribution of its output symbols. It is known that the maximum Shannon’s entropy of a discrete source of information is reached when its symbols follow a Uniform distribution. In cryptography, these sources have great applications since they allow for the highest security standards to be reached. In this work, the most effective estimator is selected to estimate entropy in short samples of bytes and bits with maximum entropy. For this, 18 estimators were compared. Results concerning the comparisons published in the literature between these estimators are discussed. The most suitable estimator is determined experimentally, based on its bias, the mean square error short samples of bytes and bits.
ASPset: An outdoor sports pose video dataset with 3D keypoint annotations

Recent advances in deep learning approaches to computer vision problems have led to renewed interest in the task of predicting 3D human joint locations from raw image data, with application areas including sports analysis, human-computer interaction, and physical rehabilitation. Although supervised learning of deep neural networks has proven to be effective for pose estimation, it requires a wealth of varied data to generalise well to previously unseen examples. Consequently, progress in 3D human pose estimation has been slowed by the fact that 3D keypoint annotations are notoriously difficult to obtain, traditionally requiring a large array of cameras and/or the use of wearable markers/sensors. In this paper we describe a methodology for obtaining 3D human pose annotations using only three video cameras and without any wearables. We apply this methodology to construct ASPset-510 (Australian Sports Pose Dataset), a large collection of natural sports-related video with 3D pose annotations. Using ASPset-510 as an additional source of training examples, we found that we could improve pose model generalisation on the established MPI-INF-3DHP benchmark. We make ASPset-510 publicly available, and provide strong baseline results for future work to compare against.
Learning non-local perfusion textures for high-quality computed tomography perfusion imaging

Background. Computed tomography perfusion (CTP) imaging plays a critical role in the acute stroke syndrome assessment due to its widespread availability, speed of image acquisition, and relatively low cost. However, due to its repeated scanning protocol, CTP imaging involves a substantial radiation dose, which might increase potential cancer risks.Methods. In this work, we present a novel deep learning model called non-local perfusion texture learning network (NPTN) for high-quality CTP imaging at low-dose cases. Specifically, considering abundant similarities in the CTP images, i.e. latent self-similarities within the non-local region in the CTP images, we firstly search the most similar pixels from the adjacent frames within a fixed search window to obtain the non-local similarities and to construct non-local textures vector. Then, both the low-dose frame and these non-local textures from adjacent frames are fed into a convolution neural network to predict high-quality CTP images, which can help better characterize the structure details and contrast variants in the targeted CTP image rather than simply utilizing the targeted frame itself. The residual learning strategy and batch normalization are utilized to boost the performance of the convolution neural network. In the experiment, the CTP images of 31 patients with suspected stroke disease are collected to demonstrate the performance of the presented NPTN method.Results. The results show the presented NPTN method obtains superior performance compared with the competing methods. From numerical value, at all dose levels, the presented NPTN method has achieved around 3.0 dB improvement of average PSNR, an increase of around 1.4% of average SSIM, and a decrease of around 4.8% of average RMSE in the low-dose CTP reconstruction task, and also has achieved an increase of around 3.4% of average SSIM and a decrease of around 61.1% of average RMSE in the cerebral blood flow (CBF) estimation task.Conclusions. The presented NPTN method can obtain high-quality CTP images and estimate high-accuracy CBF map by characterizing more structure details and contrast variants in the CTP image and outperform the competing methods at low-dose cases.
A Report from the Field: Providing Psychoanalytic Care during the Pandemic

The disaster of the COVID-19 pandemic abruptly forced nearly the entire psychoanalytic community to move treatment, supervision, and classes online, leaving many feeling uncentered, unprepared, and vulnerable. The authors, members of the APsaA COVID-19 Advisory Team and longtime researchers in screen relations-based psychoanalytic care, recount lessons learned from their pandemic experience. As both disaster victim and mental health responder, they start with the cultural context for pandemic-specific mental health practice. Going deeper into the technological context for psychoanalytic care, they describe clinically-relevant differences between screen relations and physical co-presence, highlighting risk and trust, richness, and relational embodiment as key influences on clinical process and outcome. Common adaptions and responses that helped make pandemic-specific screen relations-based treatment work emerged from their own work and discussions and consultations with numerous colleagues. Curiosity, humility, clinical creativity, and self-care are highlighted. They close with hopes for a post-pandemic world in which we can mourn loss, reestablish trust, and renew and deepen our appreciation for relationships and intimacies only possible when we are bodies together in the same place at the same time.
A supervised machine learning method to detect anomalous real-time broiler breeder body weight data recorded by a precision feeding system

A precision feeding (PF) system is an intelligent computer-controlled feeding system that can be used to feed individual broilers, breeders or layers automatically based on measuring real-time body weight (BW). Vast amounts of real-time BW data can be generated every day when birds visit a PF station. However, anomalous observations occurred in real-time BW observations, which were caused by multiple birds entering the station at the same time, upward or downward variation in scale measurement in the recorded data due to the movement of the bird, or a misread for radio frequency identification tag. Known anomalous data should be removed because they have a negative impact on the interpretation of the data. Manually cleaning the anomalies is accurate, but it is time-consuming and labor-intensive. Statistical methods and unsupervised machine learning methods are effective in detecting anomalies to some extent because they just check data distribution. The current study reported a supervised machine learning method to detect anomalies in real-time BW recorded by the PF system. Real-time BW data of 5 broiler breeders from day 15 to 306 were checked and the anomalies were manually labeled. Variables regarding the statistical distribution of data and features regarding the feeding activity recorded by the PF system in each day were extracted from the dataset. Among the 4 machine learning algorithms including k-nearest neighbor (KNN), random forest classifier (RF), support vector machine (SVM), and artificial neural network (ANN), RF produced the highest F1 score (0.9712) and area under the precision-recall curve (0.9948). Compared with 4 other common anomaly detection methods including Z-scores, interquartile range (IQR), density-based spatial clustering of applications with noise (DBSCAN), and local outlier factor (LOF), RF had a higher average F1 score (0.9448), which indicated that RF was a more effective anomaly detection algorithm for this type of data.
Automated Procedure Reconfiguration Framework for Augmented Reality-Guided Maintenance Applications

Abstract The application of augmented reality (AR) to maintenance issues has resulted in significant improvements in reducing the time operators spend finding and comprehending manual maintenance procedures. One area that requires innovation is reducing the rigidity of procedures within AR-guided maintenance applications. Current widely applicable strategies are limited in that they can only be completed off-site or they can be completed on-site but rely on operator knowledge or expert intervention in order to perform reconfiguration. In this work, a novel framework is presented to allow for automated reconfiguring of procedures within AR-guided maintenance applications. Once triggered, the presented framework is able to work autonomously. The framework relies on subassemblies of the machine being maintained and analyzes the effect a defective part has within its subassembly. This information is used to create a modified procedure using automatic procedure creation methods. An implementation of the framework is presented using a simple example. The framework is utilized in a complete AR-guided maintenance application and test.
Introducing Atomic Structure to First-Year Undergraduate Chemistry Students with an Immersive Virtual Reality Experience

The present technology report is aimed at demonstrating how the implementation of VR chemistry simulations of atomic structure enhances learning among students. MEL Chemistry VR lessons were incorporated into the classroom for 43 first-year undergraduate ITMO University students. The visualization techniques provided the learners with two important opportunities: (1) connecting the macroscopic and tangible component of chemistry with the submicroscopic component (the real world and the world of molecules) and (2) connecting the submicroscopic component of chemistry with the representational one (atomic structure and orbital diagrams). The results show that implementation of the VR experience into the atomic structure laboratory enhances the learning experience of the students.
KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding

A Lite BERT (ALBERT) has been introduced to scale up deep bidirectional representation learning for natural languages. Due to the lack of pretrained ALBERT models for Korean language, the best available practice is the multilingual model or resorting back to the any other BERT-based model. In this paper, we develop and pretrain KoreALBERT, a monolingual ALBERT model specifically for Korean language understanding. We introduce a new training objective, namely Word Order Prediction (WOP), and use alongside the existing MLM and SOP criteria to the same architecture and model parameters. Despite having significantly fewer model parameters (thus, quicker to train), our pretrained KoreALBERT outperforms its BERT counterpart on 6 different NLU tasks. Consistent with the empirical results in English by Lan et al., KoreALBERT seems to improve downstream task performance involving multi-sentence encoding for Korean language. The pretrained KoreALBERT is publicly available to encourage research and application development for Korean NLP.
Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning

We propose a novel online multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches like MHT and point-process-based approaches like PHD filter, and it has linear complexity while maintaining track identities. We apply this filter for tracking multiple targets in video sequences acquired under varying environmental conditions and targets density using a tracking-by-detection approach. We also adopt deep CNN appearance representation by training a verification-identification network (VerIdNet) on large-scale person re-identification data sets. We construct an augmented likelihood in a principled manner using this deep CNN appearance features and spatio-temporal information. Furthermore, we solve the problem of two or more targets having identical label considering the weight propagated with each confirmed hypothesis. Extensive experiments on MOT16 and MOT17 benchmark data sets show that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy.
Color texture image retrieval based on Copula multivariate modeling in the Shearlet domain

Due to the increasing number of images on the Internet, image retrieval framework is needed for image-based search. Shearlet transform is a sparse, multiscale, and multidimensional representation used to extract anisotropic features in image processing applications such as image fusion and image denoising. In this paper, we proposed a color texture image retrieval framework based on Shearlet domain modeling using Copula multivariate model. In the proposed framework, Gaussian Copula is used to model the dependencies between different sub-bands of the Non-Subsample Shearlet Transform (NSST) and non-Gaussian models are used for marginal modeling of the coefficients. Six different schemes are proposed for modeling NSST coefficients based on the four types of neighboring defined; moreover, Kullback–Leibler Divergence(KLD) close form is calculated in different situations for the two Gaussian Copula and non-Gaussian functions in order to investigate the similarities in the proposed retrieval framework. The Jeffery divergence (JD) criterion, which is a symmetrical version of KLD, is used for investigating similarities in the proposed framework. We have implemented our experiments on four texture image retrieval benchmark datasets, the results of which show the superiority of the proposed framework over the existing state-of-the-art methods. In addition, the retrieval time of the proposed framework is also analyzed in the two steps of feature extraction and similarity matching, which also shows that the proposed framework enjoys an appropriate retrieval time.
Ethanos

Ethereum is a popular account-based blockchain whose number of accounts and transactions has skyrocketed, causing its data explosion. As a result, ordinary clients using PCs or smartphones cannot easily bootstrap as a full node, but rely on other full nodes to verify transactions, thus being exposed to security risks. The most serious overhead is caused by synchronizing the state of all accounts in the block's state trie, which takes several tens of gigabytes. Observing that more than 95% of the accounts are dormant, we propose a novel state optimization technique, named Ethanos. Ethanos downsizes the state trie by periodically emptying it, and then re-build it only with the active accounts used in the period's transactions. Ethanos runs transactions using the accounts available in the current period's state trie as well as those available at the end of the previous period's state trie. For an account in neither of the tries, the account first restores itself by transmitting a restore transaction. One important result of this state management is that a node can now bootstrap only with the latest period's state trie, yet can fully verify all transactions thereafter. We evaluated Ethanos with real Ethereum transactions for 300,000 blocks from the 7.0 million block, with a one-week period of emptying the state trie. Our result shows that Ethanos can sharply reduce the state trie, with only a tiny fraction of the restore transactions. More importantly, unlike the Ethereum state trie which continues to grow as time goes on, the Ethanos state trie size at the end of each period is bounded by a few hundred MB, when there are more than one million, one-week-active accounts.