Reviewerly/exHarmony huggingface dataset:
A model for representing the semantics of MWEs: From lexical semantics to the semantic annotation of complex predicates

Multiword expressions (MWEs) are sequences of words that pose a challenge to the computational processing of human languages due to their idiosyncrasies and the mismatch between their phrasal structure and their semantics. These idiosyncrasies are of lexical, morphosyntactic and semantic 11 nature, namely: non-compositionality, i.e., the meaning of the expression cannot be computed from the meanings of its constituents; discontinuity, i.e., alien elements may intervene; non-13 substitutability, i.e., at least one of the expression constituents is lexicalized and therefore, does not enter in alternations at the paradigmatic axis; and non-modifiability, in that they enter in syntactically 15 rigid structures, posing further constraints over modification, transformations, etc. The paper presents a model for representing MWEs at the level of semantics by taking into account all these inherent idiosyncrasies. The model assumes the form of a linguistic ontology and is applied to Greek verbal multi-word expressions (VMWEs); moreover, the semantics of the lexical entries under scrutiny is also represented via the semantics of their arguments based on corpus evidence. In this regard, modeling the semantics of VMWEs is placed in the lexicon-corpus interface.
Improvement of the Educational Process Management System

The article is devoted to the search for the optimal formula of the educational process management system. It is determined that the main condition for providing high-quality education is to increase human resources, providing them with advanced innovative technologies.
Differential Neural Networks Prediction Using Slow and Fast Hybrid Learning: Application to Prognosis of Infectionsand Deaths of COVID-19 Dynamics

This essay discusses a potential method for predicting the behavior of various physical processes and uses the COVID-19 outbreak to demonstrate its applicability. This study assumes that the current data set reflects the output of a dynamic system that is governed by a nonlinear ordinary differential equation. This dynamic system may be described by a Differential Neural Network (DNN) with time-varying weights matrix parameters. A new hybrid learning scheme based on the decomposition of the signal to be predicted. The decomposition considers the slow and fast components of the signal which is more natural to signals such as the ones corresponding to the number of infected and deceased patients who suffered of COVID 2019 sickness. The paper results demonstrate the recommended method offers competitive performance (70 days of COVID prediction) in comparison to similar studies.
A Distributed-GPU Deep Reinforcement Learning System for Solving Large Graph Optimization Problems

Graph optimization problems (such as minimum vertex cover, maximum cut, traveling salesman problems) appear in many fields including social sciences, power systems, chemistry, and bioinformatics. Recently, deep reinforcement learning (DRL) has shown success in automatically learning good heuristics to solve graph optimization problems. However, the existing RL systems either do not support graph RL environments or do not support multiple or many GPUs in a distributed setting. This has compromised the ability of reinforcement learning in solving large-scale graph optimization problems due to lack of parallelization and high scalability. To address the challenges of parallelization and scalability, we develop RL4GO , a high-performance distributed-GPU DRL framework for solving graph optimization problems. RL4GO focuses on a class of computationally demanding RL problems, where both the RL environment and policy model are highly computation intensive. Traditional reinforcement learning systems often assume either the RL environment is of low time complexity or the policy model is small. In this work, we distribute large-scale graphs across distributed GPUs and use the spatial parallelism and data parallelism to achieve scalable performance. We compare and analyze the performance of the spatial parallelism and data parallelism and show their differences. To support graph neural network (GNN) layers that take as input data samples partitioned across distributed GPUs, we design parallel mathematical kernels to perform operations on distributed 3D sparse and 3D dense tensors. To handle costly RL environments, we design a parallel graph environment to scale up all RL-environment-related operations. By combining the scalable GNN layers with the scalable RL environment, we are able to develop high-performance RL4GO training and inference algorithms in parallel. Furthermore, we propose two optimization techniques—replay buffer on-the-fly graph generation and adaptive multiple-node selection—to minimize the spatial cost and accelerate reinforcement learning. This work also conducts in-depth analyses of parallel efficiency and memory cost and shows that the designed RL4GO algorithms are scalable on numerous distributed GPUs. Evaluations on large-scale graphs show that (1) RL4GO training and inference can achieve good parallel efficiency on 192 GPUs, (2) its training time can be 18 times faster than the state-of-the-art Gorila distributed RL framework [ 34 ], and (3) its inference performance achieves a 26 times improvement over Gorila.
CrossFix: Resolution of GitHub issues via similar bugs recommendation

Summary With the increasing popularity of Open‐Source Software (OSS), the number of GitHub issues reported daily in these OSS projects has been growing rapidly. To resolve these issues, developers need to spend time and effort in debugging and fixing these issues. Meanwhile, a recent approach shows that similar bugs exist across different projects, and one could use the GitHub issues from a different project for finding new bugs for a related project. To locate similar bugs for our approach, we first conduct a study of similar bugs in GitHub. Our study redefines similar bugs as bugs that share the (1) same libraries, (2) same functionalities, (3) same reproduction steps, (4) same configurations, (5) same outcomes, or (6) same errors. Moreover, our study revealed the usefulness of similar bugs in helping developers to find more contexts about the bug and fixing. Based on our study, we design CrossFix, a tool that automatically suggests relevant GitHub issues based on an open GitHub issue. The suggested GitHub issues may contain solutions written in natural language or pull requests that help developers in resolving the given issue. Our evaluation on 249 open issues from Java and Android projects shows that CrossFix could suggest similar bugs to help developers in debugging and fixing.
Intelligent Tunicate Swarm for Regression Neuron Modelling: Novel State Estimation Learning

Intelligent information systems are essentially needed to identify netwok state during a operation, especially abnormal state. Gravitational force of tuncate swarm optimization (gTSO) intelligent system is proposed to obtain the optimal intelligent system control weight matrix (CWM) in a general regression neural net/network (GRNN) training. Optimized CWM computed based on the variation of gTSO intelligent control $$\Delta g(t)$$ and GRNN learning parameter u(t). Now gTSO based GRNN control is computed and used for state identification either in normal operation or abnormal operation. Hereafter proposed gTSO based GRNN learning optimization applied for identification of system state by considering the different operational events like normal operational event (NoE) and abnormal operational event (ANoE). Especially ANoEs are tested with all possible test conditions to proper identification of system state.
Gesture Vocabularies for Hand Gestures for Controlling Air Conditioners in Home and Vehicle Environments

With the growing prevalence of modern technologies as part of everyday life, mid-air gestures have become a promising input method in the field of human–computer interaction. This paper analyses the gestures of actual users to define a preliminary gesture vocabulary for home air conditioning (AC) systems and suggests a gesture vocabulary for controlling the AC that applies to both home and vehicle environments. In this study, a user elicitation experiment was conducted. A total of 36 participants were filmed while employing their preferred hand gestures to manipulate a home air conditioning system. Comparisons were drawn between our proposed gesture vocabulary (HomeG) and a previously proposed gesture vocabulary which was designed to identify the preferred hand gestures for in-vehicle air conditioners. The findings indicate that HomeG successfully identifies and describes the employed gestures in detail. To gain a gesture taxonomy that is suitable for manipulating the AC at home and in a vehicle, some modifications were applied to HomeG based on suggestions from other studies. The modified gesture vocabulary (CrossG) can identify the gestures of our study, although CrossG has a less detailed gesture pattern. Our results will help designers to understand user preferences and behaviour prior to designing and implementing a gesture-based user interface.
Potential Applications of Hyperspectral Imaging on Weak Rock Degradation Studies in Engineering Geology

Rock material characteristics such as mineral composition, fractures, and rock fabric can be closely related to potential stability issues of underground constructions. The composition, durability, and potential swelling are often diagnosed by use of X-ray diffraction (XRD) analysis; however, this method does not recognize non-crystalline rock components and textural properties of weathered and altered rocks. Hyperspectral imaging (HSI) is an emerging technique in lithological and mineral mapping, which utilizes the spectral reflectance signature of the target materials and can be applied both at laboratory and field settings. A pilot study has been conducted to explore the potential of the HSI-based approach using rock chip samples from swelling rocks down to microscopic scale, using the shortwave infrared (SWIR, 1,000–2,500 nm). By comparing the efficiency between HSI and traditional laboratory methods including XRD analysis and petrographic studies, this study provides insights on using new approaches to study weak rock degradation relevant for site characterization and risk management in civil engineering projects.
Measuring Empathy in Technical Teams to Improve the UX Quality of Innovative Technological Solutions

Developing the empathy of a technical team for target users has been a practice to innovate solutions centered on the experience they can provide to their users. However, there are few studies focused on the impact of empathy practices on solution design. In order to contribute to the study of the art regarding the construction of empathy in technical teams to improve the user experience quality in developing technical solutions, this research analyzes the effectiveness of building empathy in technical teams in a case study of the electricity sector, which was based on a process concerning validations with users. The results showed that, in the perception of a group of technicians, there was an improvement in the quality of empathy for their target users and had an impact on the team’s engagement to carry out human-computer interaction practices and, consequently, on the quality of use of the developed solution.
Visualising data science workflows to support third-party notebook comprehension: an empirical study

Data science is an exploratory and iterative process that often leads to complex and unstructured code. This code is usually poorly documented and, consequently, hard to understand by a third party. In this paper, we first collect empirical evidence for the non-linearity of data science code from real-world Jupyter notebooks, confirming the need for new approaches that aid in data science code interaction and comprehension. Second, we propose a visualisation method that elucidates implicit workflow information in data science code and assists data scientists in navigating the so-called garden of forking paths in non-linear code. The visualisation also provides information such as the rationale and the identification of the data science pipeline step based on cell annotations. We conducted a user experiment with data scientists to evaluate the proposed method, assessing the influence of (i) different workflow visualisations and (ii) cell annotations on code comprehension. Our results show that visualising the exploration helps the users obtain an overview of the notebook, significantly improving code comprehension. Furthermore, our qualitative analysis provides more insights into the difficulties faced during data science code comprehension.
ON A STRANGE QUOTATION IN ALEXANDER BLOK’S ARTICLE “APOLLON GRIGORIEV’S LIFE”

Until now, a German quote from Grigoriev’s article “Th e Last Phase of Love” (1846) in A. Blok’s “The Fate of Apollo Grigoriev”, has remained not only uncommented, but also untranslated. A dictionary analysis showed that a major misprint was made in the magazine “Repertuar i Pantheon” (Erwürdigster instead of Ehrwürdigster). In Blok’s article, another inaccuracy was added to this typo, as a result of which the quote turned into an “obscure passage in the text”. Correction of typos showed that Grigoriev quotes a remark from a Masonic ritual, which corresponds to his passion for Freemasonry and mysticism during the translation of the “Hymns”, with which Block correlated the quoted fragment.
Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing

A report published in 2000 from the Institute of Medicine revealed that medical errors were a leading cause of patient deaths, and urged the development of error detection and reporting systems. The field of radiation oncology is particularly vulnerable to these errors due to its highly complex process workflow, the large number of interactions among various systems, devices, and medical personnel, as well as the extensive preparation and treatment delivery steps. Natural language processing (NLP)-aided statistical algorithms have the potential to significantly improve the discovery and reporting of these medical errors by relieving human reporters of the burden of event type categorization and creating an automated, streamlined system for error incidents. In this paper, we demonstrate text-classification models developed with clinical data from a full service radiation oncology center (test center) that can predict the broad level and first level category of an error given a free-text description of the error. All but one of the resulting models had an excellent performance as quantified by several metrics. The results also suggest that more development and more extensive training data would further improve future results.
ShuffleDetect: Detecting Adversarial Images against Convolutional Neural Networks

Recently, convolutional neural networks (CNNs) have become the main drivers in many image recognition applications. However, they are vulnerable to adversarial attacks, which can lead to disastrous consequences. This paper introduces ShuffleDetect as a new and efficient unsupervised method for the detection of adversarial images against trained convolutional neural networks. Its main feature is to split an input image into non-overlapping patches, then swap the patches according to permutations, and count the number of permutations for which the CNN classifies the unshuffled input image and the shuffled image into different categories. The image is declared adversarial if and only if the proportion of such permutations exceeds a certain threshold value. A series of 8 targeted or untargeted attacks was applied on 10 diverse and state-of-the-art ImageNet-trained CNNs, leading to 9500 relevant clean and adversarial images. We assessed the performance of ShuffleDetect intrinsically and compared it with another detector. Experiments show that ShuffleDetect is an easy-to-implement, very fast, and near memory-free detector that achieves high detection rates and low false positive rates.
Students’ Difficulties in Learning Listening at the Eleventh Grade Students of SMAN 3 Praya

Listening is one of the important language skills in learning English because it is a receptive skill that provides language input for students. However, in reality students still experience many difficulties when learning listening, as well as the eleventh grade students of SMAN 3 Praya. This research was aimed to find out students’ achievement in learning listening at the eleventh grade students of SMAN 3 Praya, to find out the difficulties experienced by students in learning listening and to find out the factors that cause these difficulties. This research used descriptive qualitative method. Out of 121 eleventh grade students, only 44 students were selected as a sample using a simple random technique. The data were collected through test, questionnaire and interview. The results of this study showed that students’ achievement in listening is very poor. Students’ difficulties in listening such as: unfamiliar words, complexities of grammatical structures, long spoken materials, unfamiliar topics, listening without repetition, the way speakers pronounces words, unable to quickly remember words or phrases they had just heard, the speed and accents of the speakers, focus of the students, environmental noises, and quality of recorded material. Then, the factors that cause these difficulties are lack of students practicing listening, students’ lack of vocabulary mastery, students’ lack of motivation, and noisy environment.
Looking for Needles in Haystacks

Abstract The erosion of the credibility of science is aided and abetted by data mining because data-mined results are easy to find, likely to be coincidental, and unlikely to persist. A virtually unlimited number of coincidental sequences, correlations, and clusters can be discovered in large databases. Those who use data-mining algorithms to discover something—anything—are sure to succeed. Those who do not appreciate the inevitability of patterns are sure to be impressed. Those who are seduced by these shiny patterns are likely to be disappointed. Those who act on the basis of data-mined conclusions are likely to lose faith.
Squeezing Blood from Rocks

Abstract The concept of statistical significance was developed by scientists to assess whether empirical results can be explained away by coincidence. However, a myopic focus on statistical significance fuels tenacious p-hacking quests to find models with low p-values, and models judged by p-values alone are likely to fare poorly with fresh data. When we are told that a medication, business practice, or government policy has statistically significant effects and then find that the effects vanish or are trivial when the recommendations are followed, this erodes the credibility of scientific research.
The Effect of the "Marbel Reading" Educational Game Application on the Beginning Reading Ability of Students in Islamic Elementary School

Marbel Reading Educational Game Application is an application that aims to help its users learn while playing. This study aims to determine how the influence of the "Marbel Reading" Game Application on the Beginning Reading Ability of Grade 1 Students. This type of research is a quantitative research with a pre-experimental research method One Group Pretest-Postest using all first grade students of SD Islam Nurul Hikam Banjarbendo. consisting of 20 students. Sampling with non-probability techniques. The research instrument used was in the form of multiple choice test questions regarding early reading skills. Data analysis using normality test, t-test, and eta square test. Based on the results of the pretest-posttest get an average value of 31.80. The minimum score is 55.7. The maximum score is 87.5, the statistical standard deviation is 12,547, and the standard deviation error is 2.806. The results of the paired sample t-test concluded that the value was 0.000, meaning the value of sig &lt;0.05, then there was an effect of variable X on variable Y. Then the eta-square test concluded that the value was 0.244, which means the value of sig t 0.14 means that there is a big influence. It is hoped that the results of this study can have a positive impact on its users. The use of the Marbel Reading application is suitable for low grade students to help maximize their early reading skills.
Graph convolutional network with STC attention and adaptive normalization for skeleton-based action recognition

Graph Convolutional Network (GCN) have been widely used in the field of skeleton-based action recognition and have achieved exciting results. Introducing attention mechanism in the process of extracting skeleton features has always been a hot spot in GCN-related research. In this article, we design a new graph convolutional network, which combines the advanced decoupling graph convolutional network (DC-GCN) with spatial, temporal, channel (STC) series attention module and adaptive normalization (AN). The STC attention module helps the network tend to extract important information from skeleton features. In addition, in order to improve the adaptability of the normalization method to GCN, we design the AN module instead of the BN module, which can train the weights of different normalization methods, so that each normalization layer in the network adopts the most suitable normalization operation. The experimental results show that the accuracy of our method is competitive with the state-of-the-art action recognition methods.
RUSSIAN UZHE AND ITALIAN GIÀ: A CONTRASTIVE ANALYSIS BASED ON A PARALLEL CORPUS

This paper proposes a corpus study of Italian già and Russian uzhe on the basis of the bidirectional Italian-Russian parallel corpus within the Russian National Corpus (RNC). Our aim is to create a contrastive description and to highlight language-specific functions of these linguistic TAM markers, in which temporality, aspectuality and modality are present. In addition to temporality, we highlighted modal meanings of presupposition, shared knowledge and varying degrees of expectation; we also outlined the processes of semantic expansion, intensification, focusing and metatextual functions.The findings include a reduction in the modal component in the translations from Russian into Italian.
Perspectives on Usability Testing with IoT Devices in Technical Communication Courses

This article offers perspectives on adopting smart home technology into usability testing for technical and professional communication (TPC) courses. Usability is a valued skill for technical communicators. However, usability testing methods have their problems as pedagogical tools. Internet-of-Things (IoT) devices and Smart Home Technology (SHT) may offer instructors tools to overcome some of those problems. This article details advantages and concerns associated with using SHT for curricular usability testing.
An argumentation semantics for rational human evaluation of arguments

In abstract argumentation theory, many argumentation semantics have been proposed for evaluating argumentation frameworks. This article is based on the following research question: Which semantics corresponds well to what humans consider a rational judgment on the acceptability of arguments? There are two systematic ways to approach this research question: A normative perspective is provided by the principle-based approach, in which semantics are evaluated based on their satisfaction of various normatively desirable principles. A descriptive perspective is provided by the empirical approach, in which cognitive studies are conducted to determine which semantics best predicts human judgments about arguments. In this article, we combine both approaches to motivate a new argumentation semantics called SCF2. For this purpose, we introduce and motivate two new principles and show that no semantics from the literature satisfies both of them. We define SCF2 and prove that it satisfies both new principles. Furthermore, we discuss findings of a recent empirical cognitive study that provide additional support to SCF2.