Reviewerly/exHarmony huggingface dataset:
Understanding the Impact of Introducing Lambda Expressions in Java Programs

Background: The Java programming language version eight introduced several features that encourage the functional style of programming, including the support for lambda expressions and the Stream API. Currently, there is common wisdom that refactoring legacy code to introduce lambda expressions, besides other potential benefits, simplifies the code and improves program comprehension. Aims: The purpose of this work is to investigate this belief, conducting an in-depth study to evaluate the effect of introducing lambda expressions on program comprehension. Method: We conduct this research using a mixed-method study. For the quantitative method, we quantitatively analyze 166 pairs of code snippets extracted directly either from GitHub or from recommendations from three tools (RJTL, NetBeans, and IntelliJ). We also surveyed practitioners to collect their perceptions about the benefits on program comprehension when introducing lambda expressions. We asked practitioners to evaluate and rate sets of pairs of code snippets. Results: We found contradictory results in our research. Based on the quantitative assessment, we could not find evidence that the introduction of lambda expressions improves software readability—one of the components of program comprehension. Our results suggest that the transformations recommended by the aforementioned tools decrease program comprehension when assessed by two state-of-the-art models to estimate readability. Differently, our findings of the qualitative assessment suggest that the introduction of lambda expression improves program comprehension in three scenarios: when we convert anonymous inner classes to a lambda expression, structural loops with inner conditional to a anyMatch operator, and structural loops to filter operator combined with a collect method. Implications: We argue in this paper that one can improve program comprehension when she applies particular transformations to introduce lambda expressions (e.g., replacing anonymous inner classes with lambda expressions). Also, the opinion of the participants shines the opportunities in which a transformation for introducing lambda might be advantageous. This might support the implementation of effective tools for automatic program transformations.
Combating the Covid-19 Hate and Racism Speech on Social Media

The spread of Covid-19 worldwide has been associated with hate and racism speech on social media which sometimes encourages violence and bullying in the different communities. Some officials, public figures and even common people, including students, have been expressing hate, racism, negative, hostile, and intolerant attitudes towards certain groups of people based on their color, origin, race, religion or social/political stance. This study surveys students and instructors’ views of Covid-19 five months after the outbreak of the pandemic, and whether they consider it a punishment from God to certain countries or not. Based on the findings, it proposes the creation of an anti-hate Twitter page to teach students tolerance rather than negative sentiment associated with Covid-19.
TDJEE: A Document-Level Joint Model for Financial Event Extraction

Extracting financial events from numerous financial announcements is very important for investors to make right decisions. However, it is still challenging that event arguments always scatter in multiple sentences in a financial announcement, while most existing event extraction models only work in sentence-level scenarios. To address this problem, this paper proposes a relation-aware Transformer-based Document-level Joint Event Extraction model (TDJEE), which encodes relations between words into the context and leverages modified Transformer to capture document-level information to fill event arguments. Meanwhile, the absence of labeled data in financial domain could lead models be unstable in extraction results, which is known as the cold start problem. Furthermore, a Fonduer-based knowledge base combined with the distant supervision method is proposed to simplify the event labeling and provide high quality labeled training corpus for model training and evaluating. Experimental results on real-world Chinese financial announcement show that, compared with other models, TDJEE achieves competitive results and can effectively extract event arguments across multiple sentences.
Streaming Models for Joint Speech Recognition and Translation

Using end-to-end models for speech translation (ST) has increasingly been the focus of the ST community.These models condense the previously cascaded systems by directly converting sound waves into translated text.However, cascaded models have the advantage of including automatic speech recognition output, useful for a variety of practical ST systems that often display transcripts to the user alongside the translations.To bridge this gap, recent work has shown initial progress into the feasibility for end-to-end models to produce both of these outputs.However, all previous work has only looked at this problem from the consecutive perspective, leaving uncertainty on whether these approaches are effective in the more challenging streaming setting.We develop an end-to-end streaming ST model based on a re-translation approach and compare against standard cascading approaches.We also introduce a novel inference method for the joint case, interleaving both transcript and translation in generation and removing the need to use separate decoders.Our evaluation across a range of metrics capturing accuracy, latency, and consistency shows that our end-to-end models are statistically similar to cascading models, while having half the number of parameters.We also find that both systems provide strong translation quality at low latency, keeping 99% of consecutive quality at a lag of just under a second.
Multisensory integration of visual cues from first- to third-person perspective avatars in the perception of self-motion

In the perception of self-motion, visual cues originating from an embodied humanoid avatar seen from a first-person perspective (1st-PP) are processed in the same way as those originating from a person's own body. Here, we sought to determine whether the user's and avatar's bodies in virtual reality have to be colocalized for this visual integration. In Experiment 1, participants saw a whole-body avatar in a virtual mirror facing them. The mirror perspective could be supplemented with a fully visible 1st-PP avatar or a suggested one (with the arms hidden by a virtual board). In Experiment 2, the avatar was viewed from the mirror perspective or a third-person perspective (3rd-PP) rotated 90° left or right. During an initial embodiment phase in both experiments, the avatar's forearms faithfully reproduced the participant's real movements. Next, kinaesthetic illusions were induced on the static right arm from the vision of passive displacements of the avatar's arms enhanced by passive displacement of the participant's left arm. Results showed that this manipulation elicited kinaesthetic illusions regardless of the avatar's perspective in Experiments 1 and 2. However, illusions were more likely to occur when the mirror perspective was supplemented with the view of the 1st-PP avatar's body than with the mirror perspective only (Experiment 1), just as they are more likely to occur in the latter condition than with the 3rd-PP (Experiment 2). Our results show that colocalization of the user's and avatar's bodies is an important, but not essential, factor in visual integration for self-motion perception.
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary

Current models for Word Sense Disambiguation (WSD) struggle to disambiguate rare senses, despite reaching human performance on global WSD metrics.This stems from a lack of data for both modeling and evaluating rare senses in existing WSD datasets.In this paper, we introduce FEWS (Few-shot Examples of Word Senses), a new low-shot WSD dataset automatically extracted from example sentences in Wiktionary.FEWS has high sense coverage across different natural language domains and provides: (1) a large training set that covers many more senses than previous datasets and (2) a comprehensive evaluation set containing few-and zero-shot examples of a wide variety of senses.We establish baselines on FEWS with knowledgebased and neural WSD approaches and present transfer learning experiments demonstrating that models additionally trained with FEWS better capture rare senses in existing WSD datasets.Finally, we find humans outperform the best baseline models on FEWS, indicating that FEWS will support significant future work on low-shot WSD.
Attention Can Reflect Syntactic Structure (If You Let It)

Since the popularization of the Transformer as a general-purpose feature encoder for NLP, many studies have attempted to decode linguistic structure from its novel multi-head attention mechanism.However, much of such work focused almost exclusively on Englisha language with rigid word order and a lack of inflectional morphology.In this study, we present decoding experiments for multilingual BERT across 18 languages in order to test the generalizability of the claim that dependency syntax is reflected in attention patterns.We show that full trees can be decoded above baseline accuracy from single attention heads, and that individual relations are often tracked by the same heads across languages.Furthermore, in an attempt to address recent debates about the status of attention as an explanatory mechanism, we experiment with fine-tuning mBERT on a supervised parsing objective while freezing different series of parameters.Interestingly, in steering the objective to learn explicit linguistic structure, we find much of the same structure represented in the resulting attention patterns, with interesting differences with respect to which parameters are frozen.
Microshape Measurement Method Using Speckle Interferometry Based on Phase Analysis

A method for the measurement of the shape of a fine structure beyond the diffraction limit based on speckle interferometry has been reported. In this paper, the mechanism for measuring the shape of the fine structure in speckle interferometry using scattered light as the illumination light is discussed. Furthermore, by analyzing the phase distribution of the scattered light from the surface of the measured object, this method can be used to measure the shapes of periodic structures and single silica microspheres beyond the diffraction limit.
Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale

Assessing the quality of arguments and of the claims the arguments are composed of has become a key task in computational argumentation.However, even if different claims share the same stance on the same topic, their assessment depends on the prior perception and weighting of the different aspects of the topic being discussed.This renders it difficult to learn topic-independent quality indicators.In this paper, we study claim quality assessment irrespective of discussed aspects by comparing different revisions of the same claim.We compile a large-scale corpus with over 377k claim revision pairs of various types from kialo.com, covering diverse topics from politics, ethics, entertainment, and others.We then propose two tasks: (a) assessing which claim of a revision pair is better, and (b) ranking all versions of a claim by quality.Our first experiments with embedding-based logistic regression and transformer-based neural networks show promising results, suggesting that learned indicators generalize well across topics.In a detailed error analysis, we give insights into what quality dimensions of claims can be assessed reliably.We provide the data and scripts needed to reproduce all results. 1
Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.
A New Symmetric Digital Video Encryption Model

Abstract In this paper a new symmetric cryptographic method for digital video file is presented. The proposed algorithm is based on combining two chaotic maps. Extended cryptographic is provided for evaluation and proving the efficiency and the level of security of the encrypted files. The empirical tests are explained and the obtained results are presented in the manuscript.
A Novel Compressive Image Encryption with an Improved 2D Coupled Map Lattice Model

The digital image, as the critical component of information transmission and storage, has been widely used in the fields of big data, cloud and frog computing, Internet of things, and so on. Due to large amounts of private information in the digital image, the image protection is fairly essential, and the designing of the encryption image scheme has become a hot issue in recent years. In this paper, to resolve the shortcoming that the probability density distribution (PDD) of the chaotic sequences generated in the original two-dimensional coupled map lattice (2D CML) model is uneven, we firstly proposed an improved 2D CML model according to adding the offsets for each node after every iteration of the original model, which possesses much better chaotic performance than the original one, and also its chaotic sequences become uniform. Based on the improved 2D CML model, we designed a compressive image encryption scheme. Under the condition of different keys, the uniform chaotic sequences generated by the improved 2D CML model are utilized for compressing, confusing, and diffusing, respectively. Meanwhile, the message authentication code (MAC) is employed for guaranteeing that the encryption image be integration. Finally, theoretical analysis and simulation tests both demonstrate that the proposed image encryption scheme owns outstanding statistical, well encryption performance, and high security. It has great potential for ensuring the digital image security in application.
An Improved Image Encryption using 2D Logistic Adjusted Sine Chaotic Map with Shuffled Index Matrix

Image encryption is used to enhance the protection of images when they are transferred over the network. Due to the complex properties of chaotic systems, chaos-based image encryption has been extensively investigated and used for real-time, secure transmission of images over networks. The proposed method uses the Two-Dimensional Logistic Adjusted Sine Chaotic Map (2D-LASCM) image encryption scheme derived from the Sine and Logistic maps. Shuffled index matrices created from chaotic matrices and plain-image added with random values are used in the encryption process to improve the security level of cipher-image. The proposed approach has excellent image encryption efficiency and a high ability to withstand various attacks, based on simulation results and security analysis.
An ensemble approach of improved quantum inspired gravitational search algorithm and hybrid deep neural networks for computational optimization

In this paper, an autonomous ensemble approach of improved quantum inspired gravitational search algorithm (IQI-GSA) and hybrid deep neural networks (HDNN) is proposed for the optimization of computational problems. The IQI-GSA is a combinational variant of gravitational search algorithm (GSA) and quantum computing (QC). The improved variant enhances the diversity of mass collection for retaining the stochastic attributes and handling the local trapping of mass agents. Further, the hybrid deep neural network encompasses the convolutional and recurrent neural networks (HDCR-NN) which analyze the relational &amp; temporal dependencies among the different computational components for optimization. The proposed ensemble approach is evaluated for the application of facial expression recognition by experimentation on Karolinska Directed Emotional Faces (KDEF) and Japanese Female Facial Expression (JAFFE) datasets. The experimentation evaluations evidently exhibit the outperformed recognition rate of the proposed ensemble approach in comparison with state-of-the-art techniques.
Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis

We examine the task of detecting implicitly abusive comparisons (e.g.Your hair looks like you have been electrocuted).Implicitly abusive comparisons are abusive comparisons in which abusive words (e.g.dumbass or scum) are absent.We detail the process of creating a novel dataset for this task via crowdsourcing that includes several measures to obtain a sufficiently representative and unbiased set of comparisons.We also present classification experiments that include a range of linguistic features that help us better understand the mechanisms underlying abusive comparisons.
High-end equipment data desensitization method based on improved Stackelberg GAN

High-end equipment refers to a type of technical equipment with high technical content, large capital investment, and long development cycle. Therefore, high-end equipment data has extraordinary significance and its desensitization is an urgent problem in data analysis. Traditional data desensitization principles are processing original data such as substitution and adding noise. These methods may not only damage data correlation information, but also result in data disclosure and high computing cost. Given the aforementioned reasons, the study proposes a high-end equipment data desensitization method based on improved Stackelberg Generative Adversarial Networks (GAN). When compared with the normal GAN, the structure proposed in the study includes more generators and discriminators. By inputting the original data, the trained GAN can output indistinguishable data from the original data which helps data mining and also ensures the privacy of data. We experimented on two datasets: optimal improvement was determined by Gaussian dataset experiments, i.e. Stackelberg GAN with eight discriminators. The second experiment results on real-world datasets proved that the 8-discriminator Stackelberg GAN better fits the original data and significantly aids data desensitization.
COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet

In this paper we present COCO-EX, a tool for Extracting Concepts from texts and linking them to the ConceptNet knowledge graph.COCO-EX extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.COCO-EX takes into account the challenging characteristics of ConceptNet, namely that -unlike conventional knowledge graphs -nodes are represented as non-canonicalized, free-form text.This means that i) concepts are not normalized; ii) they often consist of several different, nested phrase types; and iii) many of them are uninformative, over-specific, or misspelled.A commonly used shortcut to circumvent these problems is to apply string matching.We compare COCO-EX to this method and show that COCO-EX enables the extraction of meaningful, important rather than overspecific or uninformative concepts, and allows to assess more relational information stored in the knowledge graph. 1
Vocational Guidance in General Secondary Schools

In this article, taking into account the age and psychophysiological characteristics of students of general secondary education, it is necessary to inculcate in them the universal values and high spirituality, to educate them in the spirit of patriotism and humanity, to organize spiritual and educational work in general secondary schools. - Development of proposals and recommendations for vocational guidance.Purpose - Reinforcingof scientific proposals and practical recommendations for vocational guidance in general secondary schools.Design/methodology/approach -Problems as a methodology of research and literature review; methods of pedagogical observation, conversations, pedagogical-experimental works, expert evaluation.Originality/value - The appropriateness of the approaches and methods used in the study is determined by the fact that they are based on the analysis of other officially published practical data and the relevant conclusion is that the proposals have been put into practice by the relevant organizations.
Dissipative engineering of a tripartite Greenberger–Horne–Zeilinger state for neutral atoms

The multipartite Greenberger–Horne–Zeilinger (GHZ) states are indispensable elements for various quantum information processing tasks. Here we put forward two deterministic proposals to dissipatively prepare tripartite GHZ states in a neutral atom system. The first scheme utilizes the polychromatic driving fields and the engineered spontaneous emission of Rydberg states to generate a tripartite GHZ state with a high efficiency, which is then optimized by introducing the Gaussian soft control pulse. In the second scenario, we exploit the natural spontaneous emission of the Rydberg states as a resource, thence a steady tripartite GHZ state with fidelity around 98% can be obtained by simultaneously integrating the switching driving of unconventional Rydberg pumping and the Rydberg antiblockade effect.
Visitor-artwork network analysis using object detection with image-retrieval technique

Recent museum exhibitions are becoming a means by which to satisfy visitor demands. In order to provide visitor-centric exhibitions, artwork must be analyzed based on the behavior of visitors, and not merely according to museum professionals' points of view. This study aims to analyze the relationship between museum visitors and artwork via a network analysis based on visitor behavior using object detection techniques. Cameras installed in a museum recorded visitors, and an object detector with a content-based image-retrieval technique tracked visitors from the videos. The durations spent with different artworks were measured, and the data was converted into a bipartite graph. The relationships between different artwork types were analyzed with a visitor-centered artwork network. Based on the visitors’ behavior, significant artworks were identified and the artwork network was compared to the arrangement of the museum. The tendency of edges in the artwork network was also examined considering visitors' preferences for artworks. The method used here makes it possible to collect quantitative data, with the results possibly used as a basis and for reference when analyzing artwork in a visitor-centered approach.