Reviewerly/exHarmony huggingface dataset:
Distance Learning Perceptions from Higher Education Students—The Case of Portugal

This research study examines the attitudes of Portuguese higher education students regarding compulsory digital and distance learning university courses during the second semester of the academic year 2019–2020 during the COVID-19 pandemic. The methodology was quantitative, being the undergraduate and postgraduate students surveyed to find their perceptions about distance and online education in Portugal. The findings of the study highlighted the relationship between distance and online learning. The key concern of the respondents is related to the formal and contextual dimensions of the online class regime. The values examined, taken as a whole, allow us to conclude that with this teaching regime, in terms of awareness, there is acceptance and benefit. The sense of ambiguity in which this transformation took place, as well as the climate surrounding this phase, are worth noting. The teaching and evaluation methodologies used have been embraced and show a very wide range of choices on the part of the teaching teams and the students’ various interests, just as in the teaching regime of the classroom. The fact that students feel the need for face-to-face classes, however, is of great importance for practical and laboratory classes. This reality, which is a challenge to face in the future, is hard to overcome.
Blockchain and Its Impacts on Agri-Food Supply Chain Network Management

Blockchain is an emerging meta-technology and considered a new institutional technology with the potential to change the governance of vertically integrated food supply chains. This paper investigates the effects on coordination mechanisms in vertically cooperating agri-food networks that result from the implementation of different blockchain technology platform types (BCTPT). The research is based on an extensive literature overview and exploratory use cases of BCTPT implementations in the agri-food industry which are presented to illustrate the applicability of the findings. Our analysis shows that BCTPT predominantly differentiate through the coordination mechanisms exerting of power, information sharing, decision-making, and collective learning benefits. We also reveal that blockchain use cases with high success rates typically operate in a vertical ecosystem where a focal firm assumes the responsibility for coordinating the activities in the supply chain network. These use cases are typically operationalized in tracking and tracing applications as well as in provenance-based information provision, which either operate in vertically coordinated private blockchain or consortium-type blockchain platforms. We conclude that the choice of a specific BCTPT with its respective coordination mechanisms is a key determinant of the economic success of the intended use case, the efficient management of the supply chain network, and eventually for the chosen digital business model. This paper will close a research gap, as the potential impacts of different blockchain technology platform types on digital agri-food business models and its supply chain management have scarcely been researched.
Sentiment Analysis of Persian Movie Reviews Using Deep Learning

Sentiment analysis aims to automatically classify the subject’s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.
Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders

Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., "the," "a,"). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval

Recent research demonstrates the effectiveness of using fine-tuned language models (LM) for dense retrieval.However, dense retrievers are hard to train, typically requiring heavily engineered fine-tuning pipelines to realize their full potential.In this paper, we identify and address two underlying problems of dense retrievers: i) fragility to training data noise and ii) requiring large batches to robustly learn the embedding space.We use the recently proposed Condenser pre-training architecture, which learns to condense information into the dense vector through LM pre-training.On top of it, we propose coCondenser, which adds an unsupervised corpus-level contrastive loss to warm up the passage embedding space.Experiments on MS-MARCO, Natural Question, and Trivia QA datasets show that coCondenser removes the need for heavy data engineering such as augmentation, synthesis, or filtering, and the need for large batch training.It shows comparable performance to RocketQA, a state-of-the-art, heavily engineered system, using simple small batch fine-tuning. 1
Meta-Learning Probabilistic Inference for Prediction

This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. VERSA substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate VERSA on benchmark datasets where the method sets new state-of-the-art results, handles arbitrary numbers of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.
Big science and big data in nephrology

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.
Secure, Lossless, and Noise-resistive Image Encryption using Chaos, Hyper-chaos, and DNA Sequence Operation

This paper proposes an image encryption scheme using the combination of chaos, hyper-chaos, and DNA sequence operation. The proposed scheme performs three stages of encryption operations. Those are selection-level hyper-chaotic sequence based DNA-shuffling operation, key-image based DNA-diffusion operation, and hyper-chaotic sequence based DNA-shuffling operation. The advantages of this scheme are higher key space, higher confusion or randomness of pixels, higher sensitivity to the keys and plaintext pixels, stronger resistivity to the noises, and lossless encryption and decryption. Moreover, selection-level hyper-chaotic sequence based DNA-shuffling operation generates more complexities in the confusion process which increases the strength of encryptions and decryptions. The computer simulation and security analyses confirm the good encryption results of the proposed scheme and strong resistivity to the commonly used attacks.
Diverging deep learning cognitive computing techniques into cyber forensics

More than ever before, the world is nowadays experiencing increased cyber-attacks in all areas of our daily lives. This situation has made combating cybercrimes a daily struggle for both individuals and organisations. Furthermore, this struggle has been aggravated by the fact that today's cybercriminals have gone a step ahead and are able to employ complicated cyber-attack techniques. Some of those techniques are minuscule and inconspicuous in nature and often camouflage in the facade of authentic requests and commands. In order to combat this menace, especially after a security incident has happened, cyber security professionals as well as digital forensic investigators are always forced to sift through large and complex pools of data also known as Big Data in an effort to unveil Potential Digital Evidence (PDE) that can be used to support litigations. Gathered PDE can then be used to help investigators arrive at particular conclusions and/or decisions. In the case of cyber forensics, what makes the process even tough for investigators is the fact that Big Data often comes from multiple sources and has different file formats. Forensic investigators often have less time and budget to handle the increased demands when it comes to the analysis of these large amounts of complex data for forensic purposes. It is for this reason that the authors in this paper have realised that Deep Learning (DL), which is a subset of Artificial Intelligence (AI), has very distinct use-cases in the domain of cyber forensics, and even if many people might argue that it's not an unrivalled solution, it can help enhance the fight against cybercrime. This paper therefore proposes a generic framework for diverging DL cognitive computing techniques into Cyber Forensics (CF) hereafter referred to as the DLCF Framework. DL uses some machine learning techniques to solve problems through the use of neural networks that simulate human decision-making. Based on these grounds, DL holds the potential to dramatically change the domain of CF in a variety of ways as well as provide solutions to forensic investigators. Such solutions can range from, reducing bias in forensic investigations to challenging what evidence is considered admissible in a court of law or any civil hearing and many more.
Assessing the Generalizability of Code2vec Token Embeddings

Many Natural Language Processing (NLP) tasks, such as sentiment analysis or syntactic parsing, have benefited from the development of word embedding models.In particular, regardless of the training algorithms, the learned embeddings have often been shown to be generalizable to different NLP tasks.In contrast, despite recent momentum on word embeddings for source code, the literature lacks evidence of their generalizability beyond the example task they have been trained for.In this experience paper, we identify 3 potential downstream tasks, namely code comments generation, code authorship identification, and code clones detection, that source code token embedding models can be applied to.We empirically assess a recently proposed code token embedding model, namely code2vec's token embeddings.Code2vec was trained on the task of predicting method names, and while there is potential for using the vectors it learns on other tasks, it has not been explored in literature.Therefore, we fill this gap by focusing on its generalizability for the tasks we have identified.Eventually, we show that source code token embeddings cannot be readily leveraged for the downstream tasks.Our experiments even show that our attempts to use them do not result in any improvements over less sophisticated methods.We call for more research into effective and general use of code embeddings.
Breast Cancer Image Classification via Multi-Network Features and Dual-Network Orthogonal Low-Rank Learning

Histopathological image analysis is an important technique for early diagnosis and detection of breast cancer in clinical practice. However, it has limited efficiency and thus the detection of breast cancer is still an open issue in medical image analysis. To improve the early diagnostic accuracy of breast cancer and reduce the workload of doctors, we devise a classification framework based on histology images by combining deep learning with machine learning methodologies in this paper. Specifically, we devise a multi-network feature extraction model by using pre-trained deep convolution neural networks (DCNNs), develop an effective feature dimension reduction method and train an ensemble support vector machine (E-SVM). First, we preprocess the histological images via scale transformation and color enhancement methods. Second, the multi-network features are extracted by using four pre-trained DCNNs (e.g., DenseNet-121, ResNet-50, multi-level InceptionV3, and multi-level VGG-16). Third, a feature selection method via dual-network orthogonal low-rank learning (DOLL) is further developed for performance boosting and overfitting alleviation. Finally, an E-SVM is trained via fused features and voting strategy to perform the classification task, which classifies the images into four classes (i.e., benign, in situ carcinomas, invasive carcinomas, and normal). We evaluate the proposed method on the public ICIAR 2018 Challenge dataset of histology images of breast cancer and achieve a high classification accuracy of 97.70%. Experimental results show that our method can achieve quite promising performance and outperform state-of-the-art methods.
Mapping Human–Computer Interaction Research Themes and Trends from Its Existence to Today: A Topic Modeling-Based Review of past 60 Years

As it covers a wide spectrum, the research literature of human-computer interaction (HCI) studies has a rich and multi-disciplinary content where there are limited studies demonstrating the big picture of the field. Such an analysis provides researchers with a better understanding of the field, revealing current issues, challenges, and potential research gaps. This study aims to explore the research trends in the developmental stages of the HCI studies over the past 60 years. Automated text mining with probabilistic topic modeling has been used to analyze 41,720 journal articles that are indexed by the SCOPUS database between 1957 and 2018. The results of this study reveal 21 major topics mapping the research landscape of HCI. By extending the discovered topics beyond a snapshot, the topics were analyzed considering their developmental stages, volume, and accelerations to provide a panoramic view that shows the increase and decrease of trends over time. In this context, the transition of HCI studies from machine-oriented systems to human-oriented systems indicates its future direction toward context-aware adaptive systems.
Multi-Fact Correction in Abstractive Text Summarization

Pre-trained neural abstractive summarization systems have dominated extractive strategies on news summarization performance, at least in terms of ROUGE.However, systemgenerated abstractive summaries often face the pitfall of factual inconsistency: generating incorrect facts with respect to the source text.To address this challenge, we propose Span-Fact, a suite of two factual correction models that leverages knowledge learned from question answering models to make corrections in system-generated summaries via span selection.Our models employ single or multimasking strategies to either iteratively or autoregressively replace entities in order to ensure semantic consistency w.r.t. the source text, while retaining the syntactic structure of summaries generated by abstractive summarization models.Experiments show that our models significantly boost the factual consistency of system-generated summaries without sacrificing summary quality in terms of both automatic metrics and human evaluation.
PyMT5: multi-mode translation of natural language and Python code with transformers

Simultaneously modeling source code and natural language has many exciting applications in automated software development and understanding.Pursuant to achieving such technology, we introduce PYMT5, the PYTHON method text-to-text transfer transformer, which is trained to translate between all pairs of PYTHON method feature combinations: a single model that can both predict whole methods from natural language documentation strings (docstrings) and summarize code into docstrings of any common style.We present an analysis and modeling effort of a large-scale parallel corpus of 26 million PYTHON methods and 7.7 million method-docstring pairs, demonstrating that for docstring and method generation, PYMT5 outperforms similarlysized auto-regressive language models (GPT2) which were English pre-trained or randomly initialized.On the CODE-SEARCHNET test set, our best model predicts 92.1% syntactically correct method bodies, achieved a BLEU score of 8.59 for method generation and 16.3 for docstring
TAP: Text-Aware Pre-training for Text-VQA and Text-Caption

In this paper, we propose Text-Aware Pre-training (TAP) for Text-VQA and Text-Caption tasks. These two tasks aim at reading and understanding scene text in images for question answering and image caption generation, respectively. In contrast to conventional vision-language pretraining that fails to capture scene text and its relationship with the visual and text modalities, TAP explicitly incorporates scene text (generated from OCR engines) during pretraining. With three pre-training tasks, including masked language modeling (MLM), image-text (contrastive) matching (ITM), and relative (spatial) position prediction (RPP), pre-training with scene text effectively helps the model learn a better aligned representation among the three modalities: text word, visual object, and scene text. Due to this aligned representation learning, even pre-trained on the same downstream task dataset, TAP already boosts the absolute accuracy on the TextVQA dataset by +5:4%, compared with a non-TAP baseline. To further improve the performance, we build a large-scale scene text-related imagetext dataset based on the Conceptual Caption dataset, named OCR-CC, which contains 1:4 million images with scene text. Pre-trained on this OCR-CC dataset, our approach outperforms the state of the art by large margins on multiple tasks, i.e., +8:3% accuracy on TextVQA, +8:6% accuracy on ST-VQA, and +10:2 CIDEr score on TextCaps.
Traffic Data Collection and Analysis based on Wireless Sensor Network

The development of urban environments lead to many issues, one among them is the traffic blockage. For this speedy running world, adequate traffic measures should be taken with the help of modern technology. In the process of building smart cities, we should plan for the proper management of traffic. To control this traffic issue, we need effective computation and prediction of traffic flow. To achieve this, the traffic flow data should be collected and analyzed properly. Wireless sensor network (WSN) is an auto-configured group of sensors that monitor and record physical parameters of the environment like temperature, pressure, sound, motion, etc. and organize the data at a central location. These WSNs find their application in various fields these days like in the military for battlefield surveillance, in health monitoring systems, and many more. One such application can be used in the traffic management system. The traffic flow data can be collected using the wireless sensor network, it constitutes wireless modules and magnetometer sensors which gives the vehicle flow data along with their speed and location accurately. This system is cost-efficient, consumes less power, and requires less maintenance. The data is collected and transmitted to the cloud server using the mobile internet. The traffic flow data is then computed using a method based on incremental noise addition for chaotic identification. This method involves the addition of noise signals with different intensities incrementally by the Pseudo-periodic surrogate (PPS) method and the mean value of delayed mutual information (MD) is used to measure the complexity of the signal. Depending upon the trends of the changes in the noise mixed-signal, the characteristics can be determined. Hence, we conclude that this method helps in bringing out an organized traffic management system.