Reviewerly/exHarmony huggingface dataset:
A critical analysis of the functional parameters of the quiet eye using immersive virtual reality.

Directing ocular fixations toward a target assists the planning and control of visually guided actions. In far aiming tasks, the quiet eye, an instance of premovement gaze anchoring, has been extensively studied as a key performance variable. However, theories of quiet eye are yet to establish the exact functional role of the location and duration of the fixation. The present work used immersive virtual reality to manipulate key parameters of the quiet eye-location (Experiment 1) and duration (Experiment 2)-to test competing theoretical predictions about their importance. Across two preregistered experiments, novice participants (n = 127) completed a series of golf putts while their eye movements, putting accuracy, and putting kinematics were recorded. In Experiment 1, participants' premovement fixation was cued to locations on the ball, near the ball, and far from the ball. In Experiment 2, long and short quiet eye durations were induced using auditory tones as cues to movement phases. Linear mixed effects models indicated that manipulations of location and duration had little effect on performance or movement kinematics. The findings suggest that, for novices, the spatial and temporal parameters of the final fixation may not be critical for movement preprogramming and may instead reflect attentional control or movement inhibition functions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Image Classification Based on Automatic Neural Architecture Search Using Binary Crow Search Algorithm

Neural architectures have accelerated the advancement in various domains by enabling automatic pattern detection, image classification, audio recognition, and face recognition etc.However, they are computationally expensive to design and expert knowledge in various domains is required.In this paper, a swarm intelligence algorithm is proposed to search for novel architectures without human intervention that can achieve comparable performance to those of human-designed architectures.This work is inspired by current neural architecture search approaches based on reinforcement learning and genetic algorithm.However, not much attention is paid towards swarm intelligence metaheuristics-based neural architecture search.A framework is proposed for automatically designing neural architectures based on a swarm intelligence metaheuristic: Crow Search Algorithm.First, Crow Search Algorithm is integrated with binary network representation.To make it compatible for Neural Architecture Search, the original distance metric is replaced with hamming distance-based similarity measure.Second, the tuning parameters of Crow Search Algorithm are reduced by replacing the static flight length parameter with our dynamic flight length distribution algorithm.Third, the target selection method (random selection) is replaced by tournament select method.The proposed framework is used to search for architectures on MNIST, CIFAR10, and CIFAR100 datasets and achieved 0.18%, 3.48%, and 15.64% test error, respectively.Furthermore, small-scale transfer experiments are conducted to search architectures for Tiny ImageNet and achieved 34.43% test error.Nonparametric statistical analysis is performed to validate the impact of each modification in improving the quality of search space exploration.The proposed framework has achieved comparable performance with the state-of-the-art approaches, with a comparatively simpler approach and minimum human intervention.The proposed framework can be used to develop completely automated systems for designing architectures for various data-based classification applications.
Cybersecurity Vulnerability Behavior Scale in College During the Covid-19 Pandemic

The penetration of Indonesian internet users in first quarter of 2020 has increased by 17 percent compared to 2019. Based on Google Consumer Barometer in 2018, many 79% of Internet users in Indonesia use the internet on a daily basis. During the Covid-19 Pandemic, universities had to do Study From Home and Work From Home. This resulted, use of information technology and computers also increasing. This increase will have an impact on the level of cybercrime vulnerability. The scale of cyber vulnerability is needed to measure level of cybersecurity in universities, especially in data managers. There are five scales, Very Safe, Safe, Vulnerable, Very Vulnerable, and Dangerous. Where the scale is used in negative and positive statements. The measurement results show an average value of 3.3 or a vulnerable scale. Total average value of negative statements is 2.53 or scale close to vulnerability. So it is necessary to socialize the importance of cybersecurity to minimize occurrence of cybercrime.
Hillview

Hillview is a distributed spreadsheet for browsing very large datasets that cannot be handled by a single machine. As a spread-sheet, Hillview provides a high degree of interactivity that permits data analysts to explore information quickly along many dimensions while switching visualizations on a whim. To provide the required responsiveness, Hillview introduces visualization sketches, or vizketches , as a simple idea to produce compact data visualizations. Vizketches combine algorithmic techniques for data summarization with computer graphics principles for efficient rendering. While simple, vizketches are effective at scaling the spreadsheet by parallelizing computation, reducing communication, providing progressive visualizations, and offering precise accuracy guarantees. Using Hillview running on eight servers, we can navigate and visualize datasets of tens of billions of rows and trillions of cells, much beyond the published capabilities of competing systems.
An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks

With the rapid development of social networks, spam bots and other anomaly accounts’ malicious behavior has become a critical information security problem threatening the social network platform. In order to reduce this threat, the existing research mainly uses feature-based detection or propagation-based detection, and it applies machine learning or graph mining algorithms to identify anomaly accounts in social networks. However, with the development of technology, spam bots are becoming more advanced, and identifying bots is still an open challenge. This paper proposes a new semi-supervised graph embedding model based on a graph attention network for spam bot detection in social networks. This approach constructs a detection model by aggregating features and neighbor relationships, and learns a complex method to integrate the different neighborhood relationships between nodes to operate the directed social graph. The new model can identify spam bots by capturing user features and two different relationships among users in social networks. We compare our method with other methods on real-world social network datasets, and the experimental results show that our proposed model achieves a significant and consistent improvement.
Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes

This paper explores how intent classification can be improved by representing the class labels not as a discrete set of symbols but as a space where the word graphs associated to each class are mapped using typical graph embedding techniques.The approach, inspired by a previous algorithm used for an inverse dictionary task, allows the classification algorithm to take in account inter-class similarities provided by the repeated occurrence of some words in the training examples of the different classes.The classification is carried out by mapping text embeddings to the word graph embeddings of the classes.Focusing solely on improving the representation of the class label set, we show in experiments conducted in both private and public intent classification datasets, that better detection of out-of-scope examples (OOS) is achieved and, as a consequence, that the overall accuracy of intent classification is also improved.In particular, using the recently-released Larson dataset, an error of about 9.9% has been achieved for OOS detection, beating the previous state-of-the-art result by more than 31 percentage points.
Resource-Enhanced Neural Model for Event Argument Extraction

Event argument extraction (EAE) aims to identify the arguments of an event and classify the roles that those arguments play.Despite great efforts made in prior work, there remain many challenges: (1) Data scarcity.(2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument.(3) Integrating event trigger information into candidate argument representation.For (1), we explore using unlabeled data in different ways.For (2), we propose to use a syntax-attending Transformer that can utilize dependency parses to guide the attention mechanism.For (3), we propose a trigger-aware sequence encoder with several types of trigger-dependent sequence representations.We also support argument extraction either from text annotated with gold entities or from plain text.Experiments on the English ACE2005 benchmark show that our approach achieves a new state-of-the-art.
Antenna Optimization Design Based on Deep Gaussian Process Model

When using Gaussian process (GP) machine learning as a surrogate model combined with the global optimization method for rapid optimization design of electromagnetic problems, a large number of covariance calculations are required, resulting in a calculation volume which is cube of the number of samples and low efficiency. In order to solve this problem, this study constructs a deep GP (DGP) model by using the structural form of convolutional neural network (CNN) and combining it with GP. In this network, GP is used to replace the fully connected layer of the CNN, the convolutional layer and the pooling layer of the CNN are used to reduce the dimension of the input parameters and GP is used to predict output, while particle swarm optimization (PSO) is used algorithm to optimize network structure parameters. The modeling method proposed in this paper can compress the dimensions of the problem to reduce the demand of training samples and effectively improve the modeling efficiency while ensuring the modeling accuracy. In our study, we used the proposed modeling method to optimize the design of a multiband microstrip antenna (MSA) for mobile terminals and obtained good optimization results. The optimized antenna can work in the frequency range of 0.69–0.96 GHz and 1.7–2.76 GHz, covering the wireless LTE 700, GSM 850, GSM 900, DCS 1800, PCS1900, UMTS 2100, LTE 2300, and LTE 2500 frequency bands. It is shown that the DGP network model proposed in this paper can replace the electromagnetic simulation software in the optimization process, so as to reduce the time required for optimization while ensuring the design accuracy.
A Slimmer Network with Polymorphic and Group Attention Modules for More Efficient Object Detection in Aerial Images

Object detection is one of the core technologies in aerial image processing and analysis. Although existing aerial image object detection methods based on deep learning have made some progress, there are still some problems remained: (1) Most existing methods fail to simultaneously consider multi-scale and multi-shape object characteristics in aerial images, which may lead to some missing or false detections; (2) high precision detection generally requires a large and complex network structure, which usually makes it difficult to achieve the high detection efficiency and deploy the network on resource-constrained devices for practical applications. To solve these problems, we propose a slimmer network for more efficient object detection in aerial images. Firstly, we design a polymorphic module (PM) for simultaneously learning the multi-scale and multi-shape object features, so as to better detect the hugely different objects in aerial images. Then, we design a group attention module (GAM) for better utilizing the diversiform concatenation features in the network. By designing multiple detection headers with adaptive anchors and the above-mentioned two modules, we propose a one-stage network called PG-YOLO for realizing the higher detection accuracy. Based on the proposed network, we further propose a more efficient channel pruning method, which can slim the network parameters from 63.7 million (M) to 3.3M that decreases the parameter size by 94.8%, so it can significantly improve the detection efficiency for real-time detection. Finally, we execute the comparative experiments on three public aerial datasets, and the experimental results show that the proposed method outperforms the state-of-the-art methods.
Bootstrapping a Crosslingual Semantic Parser

Recent progress in semantic parsing scarcely considers languages other than English but professional translation can be prohibitively expensive.We adapt a semantic parser trained on a single language, such as English, to new languages and multiple domains with minimal annotation.We query if machine translation is an adequate substitute for training data, and extend this to investigate bootstrapping using joint training with English, paraphrasing, and multilingual pre-trained models.We develop a Transformer-based parser combining paraphrases by ensembling attention over multiple encoders and present new versions of ATIS and Overnight in German and Chinese for evaluation.Experimental results indicate that MT can approximate training data in a new language for accurate parsing when augmented with paraphrasing through multiple MT engines.Considering when MT is inadequate, we also find that using our approach achieves parsing accuracy within 2% of complete translation using only 50% of training data. 1
Out-of-Sample Representation Learning for Knowledge Graphs

Many important problems can be formulated as reasoning in knowledge graphs.Representation learning has proved extremely effective for transductive reasoning, in which one needs to make new predictions for already observed entities.This is true for both attributed graphs (where each entity has an initial feature vector) and non-attributed graphs (where the only initial information derives from known relations with other entities).For out-of-sample reasoning, where one needs to make predictions for entities that were unseen at training time, much prior work considers attributed graph.However, this problem is surprisingly under-explored for non-attributed graphs.In this paper, we study the out-of-sample representation learning problem for non-attributed knowledge graphs, create benchmark datasets for this task, develop several models and baselines, and provide empirical analyses and comparisons of the proposed models and baselines.
Quantum theory of polarimetry: From quantum operations to Mueller matrices

This article provides a description of the quantum mechanical processes underlying polarimetry; for example, classical depolarization is represented by a quantum circuit diagram corresponding to SU(3) operations in an enlarged Hilbert space. The quantum theory of polarimetry can be used to place constraints on classical polarization transformations, find quantum-enhanced polarization measurements, and investigate the quantum structure underlying classical polarization.
Multi-modal Multi-label Emotion Detection with Modality and Label Dependence

As an important research issue in the natural language processing community, multi-label emotion detection has been drawing more and more attention in the last few years. However, almost all existing studies focus on one modality (e.g., textual modality). In this paper, we focus on multi-label emotion detection in a multi-modal scenario. In this scenario, we need to consider both the dependence among different labels (label dependence) and the dependence between each predicting label and different modalities (modality dependence). Particularly, we propose a multi-modal sequence-to-set approach to effectively model both kinds of dependence in multi-modal multi-label emotion detection. The detailed evaluation demonstrates the effectiveness of our approach.
Design of nonlinearly spaced phase-shifting algorithms using their frequency transfer function

Here we show how to design phase-shifting algorithms (PSAs) for nonuniform/nonlinear (NL) phase-shifted fringe patterns using their frequency transfer function (FTF). Assuming that the NL phase steps are known, we introduce the desired zeroes in the FTF to obtain the specific NL-PSA formula. The advantage of designing NL-PSAs based on their FTF is that one can reject many distorting harmonics of the fringes. We can also estimate the signal-to-noise ratio for interferograms corrupted by additive white Gaussian noise. Finally, for non-distorted noiseless fringes, the proposed NL-PSA retrieves the modulating phase error free, just as standard/linear PSAs do.
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension

In this work, we analyze how human gaze during reading comprehension is conditioned on the given reading comprehension question, and whether this signal can be beneficial for machine reading comprehension.To this end, we collect a new eye-tracking dataset with a large number of participants engaging in a multiple choice reading comprehension task.Our analysis of this data reveals increased fixation times over parts of the text that are most relevant for answering the question.Motivated by this finding, we propose making automated reading comprehension more human-like by mimicking human information-seeking reading behavior during reading comprehension.We demonstrate that this approach leads to performance gains on multiple choice question answering in English for a state-of-the-art reading comprehension model.
Continuous experimentation and the cyber–physical systems challenge: An overview of the literature and the industrial perspective

New software development patterns are emerging aiming at accelerating the process of delivering value. One is Continuous Experimentation, which allows to systematically deploy and run instrumented software variants during development phase in order to collect data from the field of application. While currently this practice is used on a daily basis on web-based systems, technical difficulties challenge its adoption in fields where computational resources are constrained, e.g., cyber–physical systems and the automotive industry. This paper aims at providing an overview of the engagement on the Continuous Experimentation practice in the context of cyber–physical systems. A systematic literature review has been conducted to investigate the link between the practice and the field of application. Additionally, an industrial multiple case study is reported. The study presents the current state-of-the-art regarding Continuous Experimentation in the field of cyber–physical systems. The current perspective of Continuous Experimentation in industry is also reported. The field has not reached maturity yet. More conceptual analyses are found than solution proposals and the state-of-practice is yet to be achieved. However it is expected that in time an increasing number of solutions will be proposed and validated.
Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model

Solving algebraic word problems has recently emerged as an important natural language processing task.To solve algebraic word problems, recent studies suggested neural models that generate solution equations by using 'Op (operator/operand)' tokens as a unit of input/output.However, such a neural model suffered two issues: expression fragmentation and operand-context separation.To address each of these two issues, we propose a pure neural model, Expression-Pointer Transformer (EPT), which uses (1) 'Expression' token and (2) operand-context pointers when generating solution equations.The performance of the EPT model is tested on three datasets: ALG514, DRAW-1K, and MAWPS.Compared to the state-of-the-art (SoTA) models, the EPT model achieved a comparable performance accuracy in each of the three datasets; 81.3% on ALG514, 59.5% on DRAW-1K, and 84.5% on MAWPS.The contribution of this paper is two-fold; (1) We propose a pure neural model, EPT, which can address the expression fragmentation and the operandcontext separation.(2) The fully automatic EPT model, which does not use hand-crafted features, yields comparable performance to existing models using hand-crafted features, and achieves better performance than existing pure neural models by at most 40%.
CancerEmo: A Dataset for Fine-Grained Emotion Detection

Emotions are an important element of human nature, often affecting the overall wellbeing of a person.Therefore, it is no surprise that the health domain is a valuable area of interest for emotion detection, as it can provide medical staff or caregivers with essential information about patients.However, progress on this task has been hampered by the absence of large labeled datasets.To this end, we introduce CANCEREMO , an emotion dataset created from an online health community and annotated with eight fine-grained emotions.We perform a comprehensive analysis of these emotions and develop deep learning models on the newly created dataset.Our best BERT model achieves an average F1 of 71%, which we improve further using domain-specific pretraining.
LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space

Most of the successful and predominant methods for Bilingual Lexicon Induction (BLI) are mapping-based, where a linear mapping function is learned with the assumption that the word embedding spaces of different languages exhibit similar geometric structures (i.e., approximately isomorphic).However, several recent studies have criticized this simplified assumption showing that it does not hold in general even for closely related languages.In this work, we propose a novel semi-supervised method to learn cross-lingual word embeddings for BLI.Our model is independent of the isomorphic assumption and uses non-linear mapping in the latent space of two independently pre-trained autoencoders.Through extensive experiments on fifteen (15) different language pairs (in both directions) comprising resource-rich and low-resource languages from two different datasets, we demonstrate that our method outperforms existing models by a good margin.Ablation studies show the importance of different model components and the necessity of non-linear mapping.
DGST: a Dual-Generator Network for Text Style Transfer

We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer.Our model employs two generators only, and does not rely on any discriminators or parallel corpus for training.Both quantitative and qualitative experiments on the Yelp and IMDb datasets show that our model gives competitive performance compared to several strong baselines with more complicated architecture designs.
Incentivized and Secure Blockchain-based Firmware Update and Dissemination for Autonomous Vehicles

This chapter explores an incentivized blockchain-based firmware update scheme tailored for Autonomous Vehicles (AVs). As the number of autonomous vehicles increases, the security and reliability of AVs require complex server infrastructure, thus making it very expensive for the manufacturers. Blockchain was first introduced in 2008 as the underline technology behind the cryptocurrency known as Bitcoin to help make the peer-to-peer exchange of value without a centralized third party. A blockchain is a distributed, immutable, and append-only data structure formed by a sequence of blocks that are chronologically and cryptographically linked together. Attribute-based encryption (ABE) is an encryption scheme that allows access control over encrypted data. The manufacturer deploys a smart-contract by broadcasting a transaction to the blockchain network. A smart contract is used to ensure the authenticity and integrity of firmware updates, and more importantly to manage the reputation scores of AVs that transfer the new updates to other AVs.