Reviewerly/exHarmony huggingface dataset:
IceNet for Interactive Contrast Enhancement

A CNN-based interactive contrast enhancement algorithm, called IceNet, is proposed in this paper, which enables a user to adjust image contrast easily according to his or her preference. Specifically, a user provides a parameter for controlling the global brightness and two types of scribbles to darken or brighten local regions in an image. Then, given these annotations, IceNet estimates a gamma map for the pixel-wise gamma correction. Finally, through color restoration, an enhanced image is obtained. The user may provide annotations iteratively to obtain a satisfactory image. IceNet is also capable of producing a personalized enhanced image automatically, which can serve as a basis for further adjustment if so desired. Moreover, to train IceNet effectively and reliably, we propose three differentiable losses. Extensive experiments demonstrate that IceNet can provide users with satisfactorily enhanced images.
Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach

Edge bundling techniques cluster edges with similar attributes (i.e. similarity in direction and proximity) together to reduce the visual clutter. All edge bundling techniques to date implicitly or explicitly cluster groups of individual edges, or parts of them, together based on these attributes. These clusters can result in ambiguous connections that do not exist in the data. Confluent drawings of networks do not have these ambiguities, but require the layout to be computed as part of the bundling process. We devise a new bundling method, Edge-Path bundling, to simplify edge clutter while greatly reducing ambiguities compared to previous bundling techniques. Edge-Path bundling takes a layout as input and clusters each edge along a weighted, shortest path to limit its deviation from a straight line. Edge-Path bundling does not incur independent edge ambiguities typically seen in all edge bundling methods, and the level of bundling can be tuned through shortest path distances, Euclidean distances, and combinations of the two. Also, directed edge bundling naturally emerges from the model. Through metric evaluations, we demonstrate the advantages of Edge-Path bundling over other techniques.
Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and detailed data using questionnaires and interviews in traditional Chinese culture, because of face-saving and the victim's shame factors. However, online comments about specific IPV events on social media provide rich data in understanding the public's attitudes and emotions towards IPV. By applying text mining and sentiment analysis to the field of IPV, this study involved construction of a Chinese IPV sentiment dictionary and a complete research framework. We analyzed the trends of the Chinese public's emotional evolution concerning IPV events from the perspectives of a time series as well as geographic space and social media. The results show that the anonymity of social networks and the guiding role of opinion leaders result in traditional cultural factors such as face-saving and family shame for IPV events being no longer applicable, leading to the spiral of an anti-silence effect. Meanwhile, in the process of public emotional communication, anger often overwhelms reason, and the spiral of silence remains in effect in social media. In addition, there are offensive words used in the IPV event texts that indicate misogyny in emotional, sexual, economic and psychological abuse. Fortunately, mainstream media, as crucial opinion leaders in the social network, can have a positive role in guiding public opinion, improving people's ability to judge the validity of network information, and formulating people's rational behaviour.
Sequential Cross-Document Coreference Resolution

Relating entities and events in text is a key component of natural language understanding.Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks.In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings.Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model.In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.
SPRING Goes Online: End-to-End AMR Parsing and Generation

In this paper we present SPRING Online Services, a Web interface and RESTful APIs for our state-of-the-art AMR parsing and generation system, SPRING (Symmetric PaRs-Ing aNd Generation).The Web interface has been developed to be easily used by the Natural Language Processing community, as well as by the general public.It provides, among other things, a highly interactive visualization platform and a feedback mechanism to obtain user suggestions for further improvements of the system's output.Moreover, our RESTful APIs enable easy integration of SPRING in downstream applications where AMR structures are needed.Finally, we make SPRING Online Services freely available at http://nlp.uniroma1.it/springand, in addition, we release extra model checkpoints to be used with the original SPRING Python code. 1
Using Objectives and Key Results (OKRs) and Slack: A Case Study of Coordination in Large-Scale Distributed Agile

Today, many large-scale software projects have members working from home, which has changed the way teams coordinate work.To better understand coordination in this setting, we conducted a case study through which we examined two teams in a large-scale agile project by observing meetings and conducting 17 interviews.Through the lens of Relational Coordination Theory (RCT), we analyzed the use of the goal-setting framework Objectives and Key Results (OKRs) and the collaboration tool Slack.Slack was used for frequent, timely, and problem-solving communication and, and its use decreased the number of planned meetings.However, discussions often started on Slack and continued in virtual ad-hoc meetings.The use of OKRs facilitated knowledge sharing, helped the teams align their goals, and provided inter-team insights.The main implication of our research is that projects using OKRs need to support project members, especially in formulating the key results that align and motivate the teams to work toward the same mission.
Multiple-role perspective on assessing teaching ability: reframing TVET teachers’ competency in the information age

In the information age, technology has changed people's lives as well as working and learning styles. The teaching competency of vocational teachers’ is facing new challenges. Based on the modernization of vocational education, "Internet + Vocational Education", and "reform of teachers, teaching materials and teaching methods", the study intends to enrich the research on vocational teachers’ teaching competency theoretically by establishing the framework, criteria and assessing instrument. In practice, the study aims to provide benchmarks and tools for the diagnosis and assessment of vocational teachers’ teaching competency. Furthermore, the study wishes to provide guidelines for vocational teachers' professional development and improve the quality of the ICT teaching practice.
What Makes a Scientific Paper be Accepted for Publication?

Despite peer-reviewing being an essential component of academia since the 1600s, it has repeatedly received criticisms for lack of transparency and consistency.We posit that recent work in machine learning and explainable AI provide tools that enable insights into the decisions from a given peer-review process.We start by simulating the peer-review process using an ML classifier and extracting global explanations in the form of linguistic features that affect the acceptance of a scientific paper for publication on an open peerreview dataset.Second, since such global explanations do not justify causal interpretations, we propose a methodology for detecting confounding effects in natural language and generating explanations, disentangled from textual confounders, in the form of lexicons.Our proposed linguistic explanation methodology indicates the following on a case dataset of ICLR submissions: a) the organising committee follows, for the most part, the recommendations of reviewers, and b) the paper's main characteristics that led to reviewers recommending acceptance for publication are originality, clarity and substance.
Enhanced Secure User Data on Cloud using Cloud Data Centre Computing and Decoy Technique

In recent years the storage of cloud platform was increased a lot. Large set of personal and business data can be stored and accessed at anywhere at any time. Data’s are stored in a secured manner using various secured and optimized algorithms. Over this data theft and modifications are done in a highly manner. Therefore this creates a various security risks and data theft problems in cloud. There is need of security technique to overcome this problem. A proposed technique called cloud data centre technique is a new model for securing data from different attacks and security risks. This technique incorporate the FEBER and CUSCUM algorithms, which will monitor the user activity and then identify the legitimacy and prevent from any unauthorized user access. This data center technique can be used to detect and avoid data theft attacks by malicious insiders. The anonymous and miss use of data also prevented in a very good manner while compared to other techniques.
Designing Online Professional Learning to Support Educators to Teach for Equity During COVID and Black Lives Matter

The massive racial inequities exposed by the COVID-19 pandemic and the nationwide protests in response to the killings of unarmed Black people forced a reckoning among many educators about racial injustice in the educational system. In March 2020, we launched a massive open online course designed to support teachers in adopting antiracist equity mind-sets and practices. We used a mixed-methods approach to describe how the participants experienced the online course between March and July 2020. Participants in immediate post- and follow-up surveys reported statistically significant shifts in their mind-sets and practices toward equitable teaching practices (effect size = 0.18–58 SD). In interviews, participants described how the course helped them change their practice through acquiring new language, reflecting collaboratively on practice, and engaging in calls to action. The findings provide insight to designers of online professional learning experiences focused on equity and open up new research areas on online professional learning for equity teaching mind-sets and practices.
E-Learning System Success Adoption in Indonesia Higher Education

This study aims to analyze the success of the e-learning system, which is influenced by learner computer anxiety, social influence, perceived usefulness, and usage satisfaction. This study uses a survey, with the respondents being students at universities in the Special Region of Yogyakarta, Indonesia. The number of respondents is 250 people. The sampling technique is purposive sampling, and the data analysis method uses a structural model, namely PLS-SEM. The results showed that the e-learning adoption success model was accepted. However, this study's results showed that the effect of perceived usefulness and learner computer anxiety on adoption is not significant. This research has a novelty related to adopting innovations, namely e-learning in forced conditions, and there is no choice but online learning, which must be done during the Covid-19 pandemic. This research contributes to the support of the technology adoption theory by users in situations of involuntary adoption.&#x0D; &#x0D; Received: 14 October 2021 / Accepted: 30 November 2021 / Published: 3 January 2022
Closed-System Solution of the 1D Atom from Collision Model

Obtaining the total wavefunction evolution of interacting quantum systems provides access to important properties, such as entanglement, shedding light on fundamental aspects, e.g., quantum energetics and thermodynamics, and guiding towards possible application in the fields of quantum computation and communication. We consider a two-level atom (qubit) coupled to the continuum of travelling modes of a field confined in a one-dimensional chiral waveguide. Originally, we treated the light-matter ensemble as a closed, isolated system. We solve its dynamics using a collision model where individual temporal modes of the field locally interact with the qubit in a sequential fashion. This approach allows us to obtain the total wavefunction of the qubit-field system, at any time, when the field starts in a coherent or a single-photon state. Our method is general and can be applied to other initial field states.
Analyzing BERT’s Knowledge of Hypernymy via Prompting

The high performance of large pretrained language models (LLMs) such as BERT (Devlin et al., 2019) on NLP tasks has prompted questions about BERT's linguistic capabilities, and how they differ from humans'.In this paper, we approach this question by examining BERT's knowledge of lexical semantic relations.We focus on hypernymy, the "is-a" relation that relates a word to a superordinate category.We use a prompting methodology to simply ask BERT what the hypernym of a given word is.We find that, in a setting where all hypernyms are guessable via prompting, BERT knows hypernyms with up to 57% accuracy.Moreover, BERT with prompting outperforms other unsupervised models for hypernym discovery even in an unconstrained scenario.However, BERT's predictions and performance on a dataset containing uncommon hyponyms and hypernyms indicate that its knowledge of hypernymy is still limited.
Cusp Pixel Labelling Model for Objects Outline Using R-CNN

In order to accomplish machine visualization, object detection algorithms strive to recognise all target items in the image and derive the categories and object location information. Numerous ways to solve this problem have been presented where most of them are inspired by computer vision and deep learning methodologies. Existing techniques, on the other hand, consistently fail to recognise small, dense objects and even fail to detect objects with random geometric modifications. Object skeletons can help in object representation and detection. Skeleton is a natural object’s inherent visual description that contains comprehensive shape semantics. They supplement the object outline by providing additional information, such as how the object scale in thickness that varies between object components. However, extracting object skeletons from natural photos is difficult because the extractor must be able to capture both local and non-local visual contexts in order to calculate the scale of each skeleton pixel. To solve this issue, a Cusp Pixel Labelled Model with Precise Tuned Outline using Machine Learning (CPLM-PTOML) is proposed in this paper that accurately detects the cusp points of the object in the image by extracting the skeleton of the object to recognize the exact object resided in the image. A multi scale associated outputs to each stage of the image by monitoring the relationship between the receptive field sizes of the different layers in the image and the skeleton scales they can capture. The model is trained via multi-task learning, with one job being skeleton localization, which determines whether or not a pixel is a skeleton pixel, and the other being skeleton scale prediction, which predicts the scale of each skeleton pixel. At various phases, supervision is imposed by directing the scale-associated side outputs toward the ground-truth skeletons at the appropriate scales. The proposed model is compared with the traditional model in terms Data Training Samples, Object Feature Extraction Time Levels, Feature Extraction Accuracy, Cusp Pixel Labelling Time Levels, Cusp Pixel Identification Accuracy, Cusp Point Linking Accuracy, Image Considered and Cusp Point Recognition levels and the results exhibit that the proposed model exhibits better outcome.
LightTag: Text Annotation Platform

Text annotation tools assume that their user's goal is to create a labeled corpus. However,users view annotation as a necessary evil on the way to deliver business value through NLP.Thus an annotation tool should optimize for the throughput of the global NLP process, not only the productivity of individual annotators. LightTag is a text annotation tool designed and built on that principle. This paper shares our design rationale, data modeling choices, and user interface decisions then illustrates how those choices serve the full NLP lifecycle.
Semantic Segmentation of Aerial Images Using U-Net Architecture

Arial images are very high resolution. The automation for map generation and semantic segmentation of aerial images are challenging problems in semantic segmentation. The semantic segmentation process does not give us precise details of the remote sensing images due to the low resolution of the aerial images. Hence, we propose an algorithm U-Net Architecture to solve this problem. It is classified into two paths. The compression path (also called: the encoder) is the first path and is used to capture the image’s context. The encoder is just a convolutional and maximal pooling layer stack. The symmetric expanding path (also called: the decoder) is the second path, which is used to enable exact localization by transposed convolutions. This task is commonly referred to as dense prediction, which is completely connected to each other and also with the former neurons which gives rise to dense layers. Thus it is an end-to-end fully convolutional network (FCN), i.e. it only contains convolutional layers and does not contain any dense layer because of which it can accept images of any size. The performance of the model will be evaluated by improving the image using the proposed method U-NET and obtaining an improved image by measuring the accuracy compared with the value of accuracy with previous methods.
COVID-19 Face Mask Detection

The present scenario of COVID-19 demands an efficient face mask detection application.The main goal of the project is to implement this system at entrances of colleges, airports, hospitals, and offices where chances of spread of COVID-19 through contagion are relatively higher.Reports indicate that wearing face masks while at work clearly reduces the risk of transmission.It is an object detection and classification problem with two different classes (Mask and Without Mask).A hybrid model using deep and classical machine learning for detecting face mask will be presented.A dataset is used to build this face mask detector using Python, OpenCV, and TensorFlow and Keras.While entering the place everyone should scan their face and then enter ensuring they have a mask with them.If anyone is found to be without a face mask, beep alert will be generated.As all the workplaces are opening.The number of cases of COVID-19 are still getting registered throughout the country.If everyone follows the safety measures, then it can come to an end.Hence to ensure that people wear masks while coming to work we hope this module will help in detecting it.
Quantum Attacks on Sum of Even–Mansour Construction with Linear Key Schedules

Shinagawa and Iwata are considered quantum security for the sum of Even-Mansour (SoEM) construction and provided quantum key recovery attacks by Simon's algorithm and Grover's algorithm. Furthermore, quantum key recovery attacks are also presented for natural generalizations of SoEM. For some variants of SoEM, they found that their quantum attacks are not obvious and left it as an open problem to discuss the security of such constructions. This paper focuses on this open problem and presents a positive response. We provide quantum key recovery attacks against such constructions by quantum algorithms. For natural generalizations of SoEM with linear key schedules, we also present similar quantum key recovery attacks by quantum algorithms (Simon's algorithm, Grover's algorithm, and Grover-meet-Simon algorithm).
Deep Gaussian Processes for Calibration of Computer Models (with Discussion)

Bayesian calibration of black-box computer models offers an established framework for quantification of uncertainty of model parameters and predictions. Traditional Bayesian calibration involves the emulation of the computer model and an additive model discrepancy term using Gaussian processes; inference is then carried out using Markov chain Monte Carlo. This calibration approach is limited by the poor scalability of Gaussian processes and by the need to specify a sensible covariance function to deal with the complexity of the computer model and the discrepancy. In this work, we propose a novel calibration framework, where these challenges are addressed by means of compositions of Gaussian processes into Deep Gaussian processes and scalable variational inference techniques. Thanks to this formulation, it is possible to obtain a flexible calibration approach, which is easy to implement in development environments featuring automatic differentiation and exploiting GPU-type hardware. We show how our proposal yields a powerful alternative to the state-of-the-art by means of experimental validations on various calibration problems. We conclude the paper by showing how we can carry out adaptive experimental design, and by discussing the identifiability properties of the proposed calibration model.
Graph-Based Siamese Network for Authorship Verification

In this work, we propose a novel approach to solve the authorship identification task on a cross-topic and open-set scenario. Authorship verification is the task of determining whether or not two texts were written by the same author. We model the documents in a graph representation and then a graph neural network extracts relevant features from these graph representations. We present three strategies to represent the texts as graphs based on the co-occurrence of the POS labels of words. We propose a Siamese Network architecture composed of graph convolutional networks along with pooling and classification layers. We present different variants of the architecture and discuss the performance of each one. To evaluate our approach we used a collection of fanfiction texts provided by the PAN@CLEF 2021 shared task in two settings: a “small” corpus and a “large” corpus. Our graph-based approach achieved average scores (AUC ROC, F1, Brier score, F0.5u, and C@1) between 90% and 92.83% when training on the “small” and “large” corpus, respectively. Our model obtain results comparable to those of the state of the art in this task and greater than traditional baselines.