Reviewerly/exHarmony huggingface dataset:
The Effects of Learning in Morphologically Evolving Robot Systems

Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring. To mitigate this problem, the addition of an infant learning period has been proposed relatively long ago by the so-called Triangle of Life approach. However, an empirical assessment is still lacking to-date. In this paper, we investigate the effects of such a learning mechanism from different perspectives. Using extensive simulations we show that learning can greatly increase task performance and reduce the number of generations required to reach a certain fitness level compared to the purely evolutionary approach. Furthermore, we demonstrate that the evolved morphologies will be also different, even though learning only directly affects the controllers. This provides a quantitative demonstration that changes in the brain can induce changes in the body. Finally, we examine the learning delta defined as the performance difference between the inherited and the learned brain, and find that it is growing throughout the evolutionary process. This shows that evolution produces robots with an increasing plasticity, that is, consecutive generations become better learners and, consequently, they perform better at the given task. Moreover, our results demonstrate that the Triangle of Life is not only a concept of theoretical interest, but a system methodology with practical benefits.
DYPLODOC: Dynamic Plots for Document Classification

Narrative generation and analysis are still on the fringe of modern natural language processing yet are crucial in a variety of applications. This paper proposes a feature extraction method for plot dynamics. We present a dataset that consists of the plot descriptions for thirteen thousand TV shows alongside meta-information on their genres and dynamic plots extracted from them. We validate the proposed tool for plot dynamics extraction and discuss possible applications of this method to the tasks of narrative analysis and generation.
Towards Equal Gender Representation in the Annotations of Toxic Language Detection

Classifiers tend to propagate biases present in the data on which they are trained.Hence, it is important to understand how the demographic identities of the annotators of comments affect the fairness of the resulting model.In this paper, we focus on the differences in the ways men and women annotate comments for toxicity, investigating how these differences result in models that amplify the opinions of male annotators.We find that the BERT model associates toxic comments containing offensive words with male annotators, causing the model to predict 67.7% of toxic comments as having been annotated by men.We show that this disparity between gender predictions can be mitigated by removing offensive words and highly toxic comments from the training data.We then apply the learned associations between gender and language to toxic language classifiers, finding that models trained exclusively on female-annotated data perform 1.8% better than those trained solely on male-annotated data, and that training models on data after removing all offensive words reduces bias in the model by 55.5% while increasing the sensitivity by 0.4%.
Examining Saudi Students’ Perceptions on the Use of the Blackboard Platform during the COVID-19 Pandemic

The present study aims to examine students’ perceptions on using the Blackboard learning system during the Covid-19 pandemic. The study investigates the effectiveness of the Blackboard on the students’ learning during Coronavirus lockdown. It aims at finding out the difficulties and the challenges students face while using the Blackboard platform as a learning tool. It is expected that this study will contribute to the growing literature on the use of the Blackboard, mainly on its purposes to replace face-to-face learning/teaching process. Therefore, this study provides information about the learning experiences of Saudi students while the highly active use of the Blackboard and presents the challenges faced by the students during the online learning/teaching process. The study employs the quantitative research method as a questionnaire is used for collecting data. It is designed in Google format and emailed to the students. A sample of 270 respondents was selected from the Community College Abqaiq, King Faisal University. The method of analysis is descriptive statistical analysis, where the SPSS is employed for data analysis. The result reveals that students’ perceptions are positive because students are aware of the benefit of the Blackboard platform during the COVID-19 pandemic. And when comparing choice preference between blended courses and online courses, students prefer blended courses. This is due to the challenges and difficulties that students face while using the Blackboard platform.
The Quality Control System of Green Composite Wind Turbine Blade Supply Chain Based on Blockchain Technology

Nowadays, blockchain technology is expected to promote the quality control of traditional industry due to its traceability, transparency and non-tampering characteristics. Although blockchain could offer the traditional industry new energy, there are still some predictable difficulties in the early stage of its application, such as the structure of the blockchain-based system, the role of regulators in the system and high transaction fee by block packing. In this paper, we establish a pioneering quality control system for the green composite wind turbine blade supply chain based on blockchain technology. Firstly, the framework of this system is proposed to ensure that the quality of the product could not only be examined and verified by regulator, but also be monitored by other related nodes. Next, we develop a new way to store the data by hash fingerprint and the cost of transaction fees is significantly reduced in the case of a large amount of data. Then, the information on-chain method is developed to realize the data traceability of each node. At last, the tests of this system are carried out to prove its validity, the satisfactory results are obtained and information supervision and sharing role of the regulators are discussed.
Generating Gender Augmented Data for NLP

Gender bias is a frequent occurrence in NLPbased applications, especially pronounced in gender-inflected languages.Bias can appear through associations of certain adjectives and animate nouns with the natural gender of referents, but also due to unbalanced grammatical gender frequencies of inflected words.This type of bias becomes more evident in generating conversational utterances where gender is not specified within the sentence, because most current NLP applications still work on a sentence-level context.As a step towards more inclusive NLP, this paper proposes an automatic and generalisable re-writing approach for short conversational sentences.The rewriting method can be applied to sentences that, without extra-sentential context, have multiple equivalent alternatives in terms of gender.The method can be applied both for creating gender balanced outputs as well as for creating gender balanced training data.The proposed approach is based on a neural machine translation (NMT) system trained to 'translate' from one gender alternative to another.Both the automatic and manual analysis of the approach show promising results for automatic generation of gender alternatives for conversational sentences in Spanish.
A New Approach to the Histogram-Based Segmentation of Synthetic Aperture Radar Images

Radar machine vision is an emerging research field in the mobile robotics. Because Synthetic ApertureRadars (SAR) are robust against weather and light condition, they provide more useful and reliable in formation than optical images. On the other hand, the data processing is more complicated and less researched than visible light images processing. The main goal of our reasarch is to build simple and efficient method of SAR image analysis. In this article we describe our research related to SAR image segmenta tion and attempts to detect elements such as the build ings, roads and forest areas. Tests were carried out for the images made available by Leonardo Airborne &amp; Space System Company.
Modified Vigenere cryptosystem: An integrated data encryption module for learning management system

Security is a practice of defending our computers, servers, networks, mobile devices, electronic systems, and digital information against accidental and malicious attacks in the field of Information Technology. One way to protect such valuable information is to use cryptography. Cryptography or encryption algorithm is the process of transforming sensitive data into confounding data in such a way that the person or the machine with a key can decode the hidden information. In this article, a hybrid approach of the Vigenere cryptosystem was used in encrypting and decrypting the data. This approach will be integrated with the learning management system using matrix manipulation and the Base94 encoding scheme. The experimental result of the study shows a significant high avalanche effect compared to the original Vigenere cryptosystem.
Methodological foundations of soft skills of musical art teachers in pedagogical institutions of higher education

Discussions about the exact sciences dominate education these days, with a lot of emphasis on teaching STEM disciplines. Because of this, teachers of other subjects, such as art or history, felt a little left out when talking about how to properly prepare students for future careers. However, there is even less attention is paid to the soft skills that students need to succeed after they graduate and work. The classes designed specifically for soft skills are the rare occurrence, but that does not mean these skills are not important. This determines the relevance of this paper. The purpose of the paper was to highlight the necessary soft skills for future specialists in musical or artistic disciplines, as well as to trace their differences with hard skills. The key research method was the analysis of theoretical materials and the development of general soft skills, which are necessary for the training of teachers of musical art, and the development of which is important for teachers to pay attention to. Teachers will not always have time to plan classes that perfectly combine hard and interpersonal skills.
A Delay-Based Machine Learning Model for DMA Attack Mitigation

Direct Memory Access (DMA) is a state-of-the-art technique to optimize the speed of memory access and to efficiently use processing power during data transfers between the main system and a peripheral device. However, this advanced feature opens security vulnerabilities of access compromise and to manipulate the main memory of the victim host machine. The paper outlines a lightweight process that creates resilience against DMA attacks minimal modification to the configuration of the DMA protocol. The proposed scheme performs device identification of the trusted PCIe devices that have DMA capabilities and constructs a database of profiling time to authenticate the trusted devices before they can access the system. The results show that the proposed scheme generates a unique identifier for trusted devices and authenticates the devices. Furthermore, a machine learning–based real-time authentication scheme is proposed that enables runtime authentication and share the results of the time required for training and respective accuracy.
NUS-IDS at CASE 2021 Task 1: Improving Multilingual Event Sentence Coreference Identification With Linguistic Information

Event Sentence Coreference Identification (ESCI) aims to cluster event sentences that refer to the same event together for information extraction.We describe our ESCI solution developed for the ACL-CASE 2021 shared tasks on the detection and classification of sociopolitical and crisis event information in a multilingual setting.For a given article, our proposed pipeline comprises of an accurate sentence pair classifier that identifies coreferent sentence pairs and subsequently uses these predicted probabilities to cluster sentences into groups.Sentence pair representations are constructed from fine-tuned BERT embeddings plus POS embeddings fed through a BiLSTM model, and combined with linguistic-based lexical and semantic similarities between sentences.Our best models ranked 2 nd , 1 st and 2 nd and obtained CoNLL F 1 scores of 81.20%, 93.03%, 83.15% for the English, Portuguese and Spanish test sets respectively in the ACL-CASE 2021 competition.
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients

Adversarial attacks by generating examples which are almost indistinguishable from natural examples, pose a serious threat to learning models. Defending against adversarial attacks is a critical element for a reliable learning system. Support vector machine (SVM) is a classical yet still important learning algorithm even in the current deep learning era. Although a wide range of researches have been done in recent years to improve the adversarial robustness of learning models, but most of them are limited to deep neural networks (DNNs) and the work for kernel SVM is still vacant. In this paper, we aim at kernel SVM and propose adv-SVM to improve its adversarial robustness via adversarial training, which has been demonstrated to be the most promising defense techniques. To the best of our knowledge, this is the first work that devotes to the fast and scalable adversarial training of kernel SVM. Specifically, we first build connection of perturbations of samples between original and kernel spaces, and then give a reduced and equivalent formulation of adversarial training of kernel SVM based on the connection. Next, doubly stochastic gradients (DSG) based on two unbiased stochastic approximations (i.e., one is on training points and another is on random features) are applied to update the solution of our objective function. Finally, we prove that our algorithm optimized by DSG converges to the optimal solution at the rate of O(1/$t$) under the constant and diminishing stepsizes. Comprehensive experimental results show that our adversarial training algorithm enjoys robustness against various attacks and meanwhile has the similar efficiency and scalability with classical DSG algorithm.
DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling

We present the results of Shared Task at Workshop DialDoc 2021 that is focused on document-grounded dialogue and conversational question answering.The primary goal of this Shared Task is to build goal-oriented information-seeking conversation systems that can identify the most relevant knowledge in the associated document for generating agent responses in natural language.It includes two subtasks on predicting agent responses: the first subtask is to predict the grounding text span in the given document for next agent response; the second subtask is to generate agent response in natural language given the context.Many submissions outperform baseline significantly.For the first task, the best-performing system achieved 67.1 Exact Match and 76.3 F1.For the second subtask, the best system achieved 41.1 SacreBLEU and highest rank by human evaluation.
Measuring Throughput and Latency Distributed Ledger Technology: Hyperledger

In this paper, we report the measuring outcomes of Hyperledger, a Distributed Ledger, which is the derivation Blockchain Technology. A technique to evaluate Hyperledger in a limited infrastructure is developed. The measured infrastructure consists of 8 nodes with a load of up to 20000 transactions/second. Hyperledger constantly runs all evaluation, namely, for 20,000 transactions, the run time 74.30s, latency 73.40ms latency, and 257 tps. This initial evaluation can provide an overview for practitioners in making choices about the adoption of blockchain technology in their IT systems.
ON THE CONSTRUCTION OF PERFECT CODES FOR $n$-DIMENSIONAL INTERLEAVING

The authors propose an n-dimensional interleaving technique which spreads a cluster of errors having a quasicircular shape.Consequently simple one-dimensional random-error-correcting codes can be used to correct this kind of cluster instead of the more complex n-dimensional burst-errorcorrecting codes.Moreover let p ≥ 2 be a positive integer, whenever n = 1 mod 3, the corresponding n-dimensional interleaving technique provides a perfect code.Also, whenever n = 3p -2 = 1 mod 3, the corresponding ndimensional interleaving technique provides neither a perfect code nor a quasiperfect code.
Cross-Modality Person Re-Identification Based on Heterogeneous Center Loss and Non-Local Features

Cross-modality person re-identification is the study of images of people matching under different modalities (RGB modality, IR modality). Given one RGB image of a pedestrian collected under visible light in the daytime, cross-modality person re-identification aims to determine whether the same pedestrian appears in infrared images (IR images) collected by infrared cameras at night, and vice versa. Cross-modality person re-identification can solve the task of pedestrian recognition in low light or at night. This paper aims to improve the degree of similarity for the same pedestrian in two modalities by improving the feature expression ability of the network and designing appropriate loss functions. To implement our approach, we introduce a deep neural network structure combining heterogeneous center loss (HC loss) and a non-local mechanism. On the one hand, this can heighten the performance of feature representation of the feature learning module, and, on the other hand, it can improve the similarity of cross-modality within the class. Experimental data show that the network achieves excellent performance on SYSU-MM01 datasets.
TemporalRI: subgraph isomorphism in temporal networks with multiple contacts

Abstract Temporal networks are graphs where each edge is associated with a timestamp denoting when two nodes interact. Temporal Subgraph Isomorphism (TSI) aims at retrieving all the subgraphs of a temporal network (called target) matching a smaller temporal network (called query), such that matched target edges appear in the same chronological order of corresponding query edges. Few algorithms have been proposed to solve the TSI problem (or variants of it) and most of them are applicable only to small or specific queries. In this paper we present TemporalRI , a new subgraph isomorphism algorithm for temporal networks with multiple contacts between nodes, which is inspired by RI algorithm. TemporalRI introduces the notion of temporal flows and uses them to filter the search space of candidate nodes for the matching. Our algorithm can handle queries of any size and any topology. Experiments on real networks of different sizes show that TemporalRI is very efficient compared to the state-of-the-art, especially for large queries and targets.
Location Independence in Machine Learning Classification of Sitting-Down and Standing-Up Actions using Wi-Fi Sensors

The recognition of human activities in real-time remains an important challenge to enable supportive residential well-being assessments. In this paper, we measure and assess patterns associated with dynamic human activities, such as the actions of sitting-down and standing-up, by analyzing Wi-Fi channel state information. Two Wi-Fi sensors are used to capture information about a person's activities within a limited space. The effect of sensor location and activity location within the space are assessed to study the independence of the proposed solution with respect to these factors. From the acquired data, feature vectors of 168 variables of kurtosis, maximum, maximum peak, mean, minimum, skew, standard deviation, and variance values are calculated. Traditional classifiers are evaluated for the prediction of dynamic sitting and standing activities. Results obtained demonstrate a classification accuracy of 98.5% using a medium Gaussian support vector machine. Deep learning using a bidirectional long short-term memory network is also tested for sequence-to-label and sequence-to-sequence classification from time series of statistical measures. These models achieved accuracies of 90.7% and 85.1%, respectively. The proposed feature extraction and applied classification demonstrate the ability of our solution to not only differentiate between static and motion activities, but also distinguish between the similar motions of standing-up and sitting-down. Therefore, this work goes beyond the state-of-the-art that generally focuses on detecting motion, not distinguishing between similar movements by subjects.
Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks

Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC).With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC.This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals.General signal process tools and pre-processing methods were applied firstly, such as Kalman filter, windowing with hamming, short-time Fourier transform (SIFT), and fast Fourier transform (FTT).Secondly, the eye-moving and tracking signals were converted into images.A convolutional neural networks-based training structure was subsequently applied; the experimental dataset was acquired by an eye-tracking device by assigning four affective stimuli (nervous, calm, happy, and sad) of 16 participants.Finally, the performance of DGCNN was compared with a decision tree (DT), Bayesian Gaussian model (BGM), and k-nearest neighbor (KNN) by using indices of true positive rate (TPR) and false negative rate (FPR).Customizing mini-batch, loss, learning rate, and gradients definition for the training structure of the deep neural network was also deployed finally.The predictive classification matrix showed the effectiveness of the proposed method for eye moving and tracking signals, which performs more than 87.2% in accuracy.This research provided a feasible way to find more natural human-computer interaction through eye moving and tracking signals and has potential application on the affective production design process.
COMBO: A New Module for EUD Parsing

We introduce the COMBO-based approach for EUD parsing and its implementation, which took part in the IWPT 2021 EUD shared task.The goal of this task is to parse raw texts in 17 languages into Enhanced Universal Dependencies (EUD).The proposed approach uses COMBO to predict UD trees and EUD graphs.These structures are then merged into the final EUD graphs.Some EUD edge labels are extended with case information using a single language-independent expansion rule.In the official evaluation, the solution ranked fourth, achieving an average ELAS of 83.79%.The source code is available at https://gitlab.clarin-pl.eu/syntactic-tools/combo.