Reviewerly/exHarmony huggingface dataset:
Building a fuzzy sentiment dimension for multidimensional analysis in social networks

The continuous growth of social networks has made them one of the main information sources for researchers and companies, but at the same time, their pre-processing and analysis represent a great challenge. In this work, we create a Fuzzy Sentiment Dimension from textual data from social networks to allow sentiment analysis jointly with standard dimensions in a multidimensional model, and make easier and more flexible sentiment analysis in social networks. In particular, we use technologies such as fuzzy logic and multidimensional analysis, together with unsupervised tools for sentiment analysis. The use of unsupervised tools for sentiment analysis also allows both previously labeled and unlabeled documents to be analyzed. The performance results of the experimentation demonstrate the feasibility of the proposal. • Automatic creation of a new Fuzzy Sentiment Dimension from texts of social networks. • Fuzzy sentiment analysis of texts from social networks. • Multidimensional sentiment analysis of the texts integrated with conventional dimensions.
SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment

Purpose: The Breast Pathology Quantitative Biomarkers (BreastPathQ) Challenge was a Grand Challenge organized jointly by the international society for optics and photonics (SPIE), the American Association of Physicists in Medicine (AAPM), the U.S. National Cancer Institute (NCI), and the U.S. Food and Drug Administration (FDA). The task of the BreastPathQ Challenge was computerized estimation of tumor cellularity (TC) in breast cancer histology images following neoadjuvant treatment. Approach: A total of 39 teams developed, validated, and tested their TC estimation algorithms during the challenge. The training, validation, and testing sets consisted of 2394, 185, and 1119 image patches originating from 63, 6, and 27 scanned pathology slides from 33, 4, and 18 patients, respectively. The summary performance metric used for comparing and ranking algorithms was the average prediction probability concordance (PK) using scores from two pathologists as the TC reference standard. Results: Test PK performance ranged from 0.497 to 0.941 across the 100 submitted algorithms. The submitted algorithms generally performed well in estimating TC, with high-performing algorithms obtaining comparable results to the average interrater PK of 0.927 from the two pathologists providing the reference TC scores. Conclusions: The SPIE-AAPM-NCI BreastPathQ Challenge was a success, indicating that artificial intelligence/machine learning algorithms may be able to approach human performance for cellularity assessment and may have some utility in clinical practice for improving efficiency and reducing reader variability. The BreastPathQ Challenge can be accessed on the Grand Challenge website.
A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles

During the procedure, a location-based service (LBS) query, the real location provided by the vehicle user may results in the disclosure of vehicle location privacy. Moreover, the point of interest retrieval service requires high accuracy of location information. However, some privacy preservation methods based on anonymity or obfuscation will affect the service quality. Hence, we study the location privacy-preserving method based on dummy locations in this paper. We propose a vehicle location privacy-preservation method based on dummy locations under road restriction in Internet of vehicles (IoV). In order to improve the validity of selected dummy locations under road restriction, entropy is used to represent the degree of anonymity, and the effective distance is introduced to represent the characteristics of location distribution. We present a dummy location selection algorithm to maximize the anonymous entropy and the effective distance of candidate location set consisting of vehicle user’s location and dummy locations, which ensures the uncertainty and dispersion of selected dummy locations. The proposed location privacy-preservation method does not need a trustable third-party server, and it protects the location privacy of vehicles as well as guaranteeing the LBS quality. The performance analysis and simulation results show that the proposed location privacy-preservation method can improve the validity of dummy locations and enhance the preservation of location privacy compared with other methods based on dummy locations.
Digital Scanning of Welds and Influence of Sampling Resolution on the Predicted Fatigue Performance: Modelling, Experiment and Simulation

Digital weld quality assurance systems are increasingly used to capture local geometrical variations that can be detrimental for the fatigue strength of welded components. In this study, a method is proposed to determine the required scanning sampling resolution for proper fatigue assessment. Based on FE analysis of laser-scanned welded joints, fatigue failure probabilities are computed using a Weakest-link fatigue model with experimentally determined parameters. By down-sampling of the scanning data in the FE simulations, it is shown that the uncertainty and error in the fatigue failure probability prediction increases with decreased sampling resolution. The required sampling resolution is thereafter determined by setting an allowable error in the predicted failure probability. A sampling resolution of 200 to 250 μm has been shown to be adequate for the fatigue-loaded welded joints investigated in the current study. The resolution requirements can be directly incorporated in production for continuous quality assurance of welded structures. The proposed probabilistic model used to derive the resolution requirement accurately captures the experimental fatigue strength distribution, with a correlation coefficient of 0.9 between model and experimental failure probabilities. This work therefore brings novelty by deriving sampling resolution requirements based on the influence of stochastic topographical variations on the fatigue strength distribution.
Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images

We report the ability of two deep learning-based decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhibitor therapy into two distinct survival groups. Both systems analyze functional and morphological properties of epithelial regions in digital histopathology whole slide images stained with the SP263 PD-L1 antibody. The first system learns to replicate the pathologist assessment of the Tumor Cell (TC) score with a cut-point for positivity at 25% for patient stratification. The second system is free from assumptions related to TC scoring and directly learns patient stratification from the overall survival time and event information. Both systems are built on a novel unpaired domain adaptation deep learning solution for epithelial region segmentation. This approach significantly reduces the need for large pixel-precise manually annotated datasets while superseding serial sectioning or re-staining of slides to obtain ground truth by cytokeratin staining. The capacity of the first system to replicate the TC scoring by pathologists is evaluated on 703 unseen cases, with an addition of 97 cases from an independent cohort. Our results show Lin's concordance values of 0.93 and 0.96 against pathologist scoring, respectively. The ability of the first and second system to stratify anti-PD-L1 treated patients is evaluated on 151 clinical samples. Both systems show similar stratification powers (first system: HR = 0.539, p = 0.004 and second system: HR = 0.525, p = 0.003) compared to TC scoring by pathologists (HR = 0.574, p = 0.01).
The EU Digital COVID Certificate: A Preliminary Data Protection Impact Assessment

On 20 May 2021, the European Commission, Council and Parliament announced a breakthrough in the trialogue negotiations to establish the European Union (EU) Digital COVID Certificate. Originally, this standardisation effort was labelled as “Digital Green Certificate” and – “[i]n view of the urgency” – presented without a data protection impact assessment. It should allow citizens and residents of Member States to prove that they are either vaccinated against COVID-19, have recently tested negative or are currently immune against the virus. This article considers the proposal from a privacy perspective, taking into account the opinion of EU data protection authorities, ongoing negotiations in the EU institutions and relevant developments on the national and international level. While the European Parliament and others tried to improve the original Commission proposal, questions around the appropriateness and effectiveness of the framework remain. The technological and organisational implementation is essentially left to Member States, who already have started to develop their own tracing and identification systems.
Improving Zero-Shot Cross-lingual Transfer for Multilingual Question Answering over Knowledge Graph

Multilingual question answering over knowledge graph (KGQA) aims to derive answers from a knowledge graph (KG) for questions in multiple languages.To be widely applicable, we focus on its zero-shot transfer setting.That is, we can only access training data in a highresource language, while need to answer multilingual questions without any labeled data in target languages.A straightforward approach is resorting to pre-trained multilingual models (e.g., mBERT) for cross-lingual transfer, but there is a still significant gap of KGQA performance between source and target languages.In this paper, we exploit unsupervised bilingual lexicon induction (BLI) to map training questions in source language into those in target language as augmented training data, which circumvents language inconsistency between training and inference.Furthermore, we propose an adversarial learning strategy to alleviate syntax-disorder of the augmented data, making the model incline to both languageand syntax-independence.Consequently, our model narrows the gap in zero-shot crosslingual transfer.Experiments on two multilingual KGQA datasets with 11 zero-resource languages verify its effectiveness.
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model

Multi-label emotion classification is an important task in NLP and is essential to many applications.In this work, we propose a sequence-to-emotion (Seq2Emo) approach, which implicitly models emotion correlations in a bi-directional decoder.Experiments on SemEval'18 and GoEmotions datasets show that our approach outperforms state-of-the-art methods (without using external data).In particular, Seq2Emo outperforms the binary relevance (BR) and classifier chain (CC) approaches in a fair setting. 1
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer

Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant.Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e.g.positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence.In this paper, we introduce a large-scale benchmark, STYLEPTB, with (1) paired sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical, syntactic, semantic, and thematic transfers of text, as well as (2) compositions of multiple transfers which allow modeling of fine-grained stylistic changes as building blocks for more complex, high-level transfers.By benchmarking existing methods on STYLEPTB, we find that they struggle to model fine-grained changes and have an even more difficult time composing multiple styles.As a result, STYLEPTB brings novel challenges that we hope will encourage future research in controllable text style transfer, compositional models, and learning disentangled representations.Solving these challenges would present important steps towards controllable text generation.
Predicting Design Impactful Changes in Modern Code Review: A Large-Scale Empirical Study

Companies have adopted modern code review as a key technique for continuously monitoring and improving the quality of software changes. One of the main motivations for this is the early detection of design impactful changes, to prevent that design-degrading ones prevail after each code review. Even though design degradation symptoms often lead to changes' rejections, practices of modern code review alone are actually not sufficient to avoid or mitigate design decay. Software design degrades whenever one or more symptoms of poor structural decisions, usually represented by smells, end up being introduced by a change. Design degradation may be related to both technical and social aspects in collaborative code reviews. Unfortunately, there is no study that investigates if code review stakeholders, e.g, reviewers, could benefit from approaches to distinguish and predict design impactful changes with technical and/or social aspects. By analyzing 57,498 reviewed code changes from seven open-source systems, we report an investigation on prediction of design impactful changes in modern code review. We evaluated the use of six ML algorithms to predict design impactful changes. We also extracted and assessed 41 different features based on both social and technical aspects. Our results show that Random Forest and Gradient Boosting are the best algorithms. We also observed that the use of technical features results in more precise predictions. However, the use of social features alone, which are available even before the code review starts (e.g., for team managers or change assigners), also leads to highly-accurate prediction. Therefore social and/or technical prediction models can be used to support further design inspection of suspicious changes early in a code review process. Finally, we provide an enriched dataset that allows researchers to investigate the context behind design impactful changes during the code review process.
AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding

Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms.This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction.One line of previous work constructs attribute-specific models, through separate decoders or entirely separate models.However, this approach constrains knowledge sharing across different attributes.Other contributions use a single multiattribute model, with different techniques to embed attribute information.But sharing the entire network parameters across all attributes can limit the model's capacity to capture attribute-specific characteristics.In this paper we present AdaTag, which uses adaptive decoding to handle extraction.We parameterize the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module.This allows for separate, but semantically correlated, decoders to be generated on the fly for different attributes.This approach facilitates knowledge sharing, while maintaining the specificity of each attribute.Our experiments on a realworld e-Commerce dataset show marked improvements over previous methods.
Positional Artefacts Propagate Through Masked Language Model Embeddings

In this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers.Namely, we find cases of persistent outlier neurons within BERT and RoBERTa's hidden state vectors that consistently bear the smallest or largest values in said vectors.In an attempt to investigate the source of this information, we introduce a neuron-level analysis method, which reveals that the outliers are closely related to information captured by positional embeddings.We also pre-train the RoBERTa-base models from scratch and find that the outliers disappear without using positional embeddings.These outliers, we find, are the major cause of anisotropy of encoders' raw vector spaces, and clipping them leads to increased similarity across vectors.We demonstrate this in practice by showing that clipped vectors can more accurately distinguish word senses, as well as lead to better sentence embeddings when mean pooling.In three supervised tasks, we find that clipping does not affect the performance.
Targeting the Benchmark: On Methodology in Current Natural Language Processing Research

It has become a common pattern in our field: One group introduces a language task, exemplified by a dataset, which they argue is challenging enough to serve as a benchmark.They also provide a baseline model for it, which then soon is improved upon by other groups.Often, research efforts then move on, and the pattern repeats itself.What is typically left implicit is the argumentation for why this constitutes progress, and progress towards what.In this paper, I try to step back for a moment from this pattern and work out possible argumentations and their parts.
Development of Augmented Reality Application for Learning Computer Network Device

&lt;span lang="EN-US"&gt;Applied augmented reality works by detecting imageries or pictures, normally called markers, by using smartphone camera that detects these preplaced markers. Augmented reality has seen wide application in various fields, one of them is education. In the field of education, augmented reality is utilized to make learning process more engaging and attractive. Starting from the problem that learning computer networks on introduce to network device which is currently still delivered conventionally. So, this research makes a solution to this problem by developing learning media using augmented reality (AR) technology, which is a technology that combines two-dimensional or three-dimensional virtual objects into a real environment and then projects these virtual objects in real time. The purpose of this research is to build AR-based applications in learning computer network devices in order to increase understanding, generate motivation and student interest. The methodology used in this research consist of Envisioning Phase(Problem Identification, Planning Phase(Planning), Developing Phase(Design), Stabilizing Phase(Testing), Deploying Phase(Implementation). This study uses 31 students as sample and the data was analyzed using the SUS(System Usability Scale). The result show &lt;/span&gt;&lt;span lang="EN-US"&gt;evaluates the usability using SUS of 31 respondents and it can be concluded that this AR application can be accepted by these students in its use with &lt;/span&gt;&lt;span lang="EN-US"&gt;SUS Score obtained was 78.5&lt;/span&gt;
Sparsity-aware channel estimation for mmWave massive MIMO: A deep CNN-based approach

The deep convolutional neural network (CNN) is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output (MIMO) systems. The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training. Then accurate channel inference can be efficiently implemented using the trained network. The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas. It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.
CCSA: Hybrid Cuckoo Crow Search Algorithm for Task Scheduling in Cloud Computing

Services are delivered promptly and efficiently managed over the Internet using cloud computing.Data and storage services are offered to users through cloud computing without regard to the actual physical location of the users.The cloud environment includes huge number of tasks and computing resources.Identification of appropriate virtual machine (VM) for allocation of resources to complete any submitted task is done through the task scheduling algorithms, which play a significant role in the cloud computing process.Task scheduling techniques improvise the makespan of the schedule and account for considerable reduction of cost expended.In this paper, an efficient hybridized scheduling algorithm that replicates the parasitic behaviour of the cuckoo and food gathering habit of the crow bird, named Cuckoo Crow Search Algorithm (CCSA) had been presented for improvising the task scheduling process.The crow bird always stares at its neighbours looking for a better food source than the one it currently possesses.At some circumstances the crow even goes a step further and steals its neighbour's food.The CCSA had been inspired from such characteristics of these birds and had been designed to be applied in the cloud environment for identifying a suitable VM for carrying out the task scheduling process.The proposed CCSA accounts for reduction in makespan and cost and its performance had been experimentally verified by comparing it with other existing algorithms like Multi objective -Ant Colony Optimization (MO-ACO), ACO, Min-Min and the results had been presented.The proposed CCSA technique had produced an improvement of 3.14%, 10.70%, and 21.60% for makespan and had reduced the overall cost to the tune of 4.56%, 11.19% and 19.35% when compared with MO-ACO, ACO, Min-Min algorithms respectively when used with 10 VMs.The detailed description of results obtained had been described in result section.
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis

While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data.Such issues come to be manifest in performance problems when faced with out-ofdistribution data in the field.One recent solution has been to use counterfactually augmented datasets in order to reduce any reliance on spurious patterns that may exist in the original data.Producing high-quality augmented data can be costly and time-consuming as it usually needs to involve human feedback and crowdsourcing efforts.In this work, we propose an alternative by describing and evaluating an approach to automatically generating counterfactual data for the purpose of data augmentation and explanation.A comprehensive evaluation on several different datasets and using a variety of state-of-the-art benchmarks demonstrate how our approach can achieve significant improvements in model performance when compared to models training on the original data and even when compared to models trained with the benefit of human-generated augmented data.