Reviewerly/exHarmony huggingface dataset:
Exploring ML testing in practice

There is a growing interest in industry and academia in machine learning (ML) testing. We believe that industry and academia need to learn together to produce rigorous and relevant knowledge. In this study, we initiate a collaboration between stakeholders from one case company, one research institute, and one university. To establish a common view of the problem domain, we applied an interactive rapid review of the state of the art. Four researchers from Lund University and RISE Research Institutes and four practitioners from Axis Communications reviewed a set of 180 primary studies on ML testing. We developed a taxonomy for the communication around ML testing challenges and results and identified a list of 12 review questions relevant for Axis Communications. The three most important questions (data testing, metrics for assessment, and test generation) were mapped to the literature, and an in-depth analysis of the 35 primary studies matching the most important question (data testing) was made. A final set of the five best matches were analysed and we reflect on the criteria for applicability and relevance for the industry. The taxonomies are helpful for communication but not final. Furthermore, there was no perfect match to the case company's investigated review question (data testing). However, we extracted relevant approaches from the five studies on a conceptual level to support later context-specific improvements. We found the interactive rapid review approach useful for triggering and aligning communication between the different stakeholders.
Improved Masked Image Generation with Token-Critic

Non-autoregressive generative transformers recently demonstrated impressive image generation performance, and orders of magnitude faster sampling than their autoregressive counterparts. However, optimal parallel sampling from the true joint distribution of visual tokens remains an open challenge. In this paper we introduce Token-Critic, an auxiliary model to guide the sampling of a non-autoregressive generative transformer. Given a masked-and-reconstructed real image, the Token-Critic model is trained to distinguish which visual tokens belong to the original image and which were sampled by the generative transformer. During non-autoregressive iterative sampling, Token-Critic is used to select which tokens to accept and which to reject and resample. Coupled with Token-Critic, a state-of-the-art generative transformer significantly improves its performance, and outperforms recent diffusion models and GANs in terms of the trade-off between generated image quality and diversity, in the challenging class-conditional ImageNet generation.
Information and Communication Technology (ICT) and youth unemployment in Africa

Africa is lagging in infrastructural development including Information and Communication Technology (ICT). As there are rising employment opportunities in the ICT-intensive industries globally including Africa, enhanced knowledge and use of ICT may tend to reduce youth unemployment in Africa. Thus, this study investigates the effect of ICT on youth unemployment in Africa using a sample of 41 African countries between 2003 and 2018. The study employs a dynamic Generalized Method of Moment (GMM) approach and constructs a composite ICT index to combine key indicators of ICT, using the principal component approach. We also account for the interactive role of education in the ICT-youth unemployment nexus. Relying on the Phillip curve theoretical model, the effect of inflation, physical capital accumulation, level of corruption, and economic growth are also examined. The results show that youth unemployment in Africa can be reduced by higher ICT deployment and usage, which confirms our hypothesis in this study. There is also evidence that education enhances the potential of ICT usage to reduce youth unemployment. Furthermore, we find that the Phillip curve hypothesis holds, as the inflation rate has a negative effect on youth unemployment. More so, there is evidence that youth unemployment in Africa can be reduced by higher physical capital accumulation, lower level of corruption, and higher economic growth. With falling economic growth and looming economic recession in many African countries, governments would need to revise educational curricula to include ICT-based training to reduce the level of youth unemployment in the medium to long term period.
A deep learning method to detect opioid prescription and opioid use disorder from electronic health records

As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD. We developed an informatics algorithm that trains two deep learning models over patient Electronic Health Records (EHRs) using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both challenging outcomes. Our deep learning models incorporate elements from EHRs to predict opioid prescription with an F1-score of 0.88 ± 0.003 and an AUC-ROC of 0.93 ± 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 ± 0.05 and AUC-ROC of 0.94 ± 0.008. Our model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a) deep learning approaches in predicting OUD and b) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore our model is the first to predict opioid prescribing behavior. Algorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic.
Knowledge Fusion Distillation: Improving Distillation with Multi-scale Attention Mechanisms

The success of deep learning has brought breakthroughs in many fields. However, the increased performance of deep learning models is often accompanied by an increase in their depth and width, which conflicts with the storage, energy consumption, and computational power of edge devices. Knowledge distillation, as an effective model compression method, can transfer knowledge from complex teacher models to student models. Self-distillation is a special type of knowledge distillation, which does not to require a pre-trained teacher model. However, existing self-distillation methods rarely consider how to effectively use the early features of the model. Furthermore, most self-distillation methods use features from the deepest layers of the network to guide the training of the branches of the network, which we find is not the optimal choice. In this paper, we found that the feature maps obtained by early feature fusion do not serve as a good teacher to guide their own training. Based on this, we propose a selective feature fusion module and further obtain a new self-distillation method, knowledge fusion distillation. Extensive experiments on three datasets have demonstrated that our method has comparable performance to state-of-the-art distillation methods. In addition, the performance of the network can be further enhanced when fused features are integrated into the network.
Makerspaces in Libraries at U.S. Public Colleges and Universities: A Census

Makerspaces continue to grow in popularity in library ecosystems, yet no one has done a census on important data points, such as total active makerspaces, planned makerspaces, and types of makerspaces in public colleges and universities in the United States. The objective of this report is to gather baseline data on makerspaces, the majority of which are in academic libraries. Using an audit of websites, the researchers conducted a census of makerspaces at colleges and universities, collecting such data as makerspace status, location, and departmental affiliation. The researchers identified 284 active makerspaces and 35 planned makerspaces across 214 institutions; of these, 110 were affiliated with their institution's library. This census offers a helpful baseline, but more data collection will provide a fuller representation of makerspaces in the United States.
High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection

Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by more than 20% in dense breasts. Additionally, extremely dense cases reported an increased risk of cancer compared to low-density breasts. This study aims to improve the mass detection performance in high-density breasts using synthetic high-density full-field digital mammograms (FFDM) as data augmentation during breast mass detection model training. To this end, a total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets were trained for low-to-high-density image translation in high-resolution mammograms. The training images were split by breast density BI-RADS categories, being BI-RADS A almost entirely fatty and BI-RADS D extremely dense breasts. Our results showed that the proposed data augmentation technique improved the sensitivity and precision of mass detection in models trained with small datasets and improved the domain generalization of the models trained with large databases. In addition, the clinical realism of the synthetic images was evaluated in a reader study involving two expert radiologists and one surgical oncologist.
Improving the Polarity of Text through word2vec Embedding for Primary Classical Arabic Sentiment Analysis

Over the past decade, Sentiment analysis has attracted significant researcher attention. Despite a huge number of studies in this field, Sentiment analysis of authors' books (classical Arabic) with extracting the embedding features has not yet been done. The recent feature extraction of Arabic text depends on the frequency of the words within the corpus without extracting the relation between these words. This paper aims to create a new classical Arabic dataset CASAD from many art books by collecting sentences from several stories with human-expert labeling. Additionally, the feature extraction of those datasets is created by word embedding techniques equivalent to Word2vec that are able to extract the deep relation which means features of the formal Arabic language. These features are evaluated by several types of machine learning for classical Arabic, for example, support vector machines (SVM), Logistic Regression (LR), Naive Bayes (NB) K-Nearest Neighbors (KNN), Latent Dirichlet Allocation (LDA) and Classification And Regression Trees (CART). Moreover, statistical methods such as validation and reliability are applied to evaluate this dataset's label. Finally, our experiments evaluated the classification rate of the feature-extraction matrices in two and three classes using six machine-learning algorithms for tenfold cross-validation that showed that the Logistic Regression with Word2Vec approach is the most accurate in predicting topic-polarity occurrence.
Revisiting Related-Key Boomerang Attacks on AES Using Computer-Aided Tool

In recent years, several MILP models were introduced to search automatically for boomerang distinguishers and boomerang attacks on block ciphers. However, they can only be used when the key schedule is linear. Here, a new model is introduced to deal with nonlinear key schedules as it is the case for AES. This model is more complex and actually it is too slow for exhaustive search. However, when some hints are added to the solver, it found the current best related-key boomerang attack on AES-192 with $$2^{124}$$ time, $$2^{124}$$ data, and $$2^{79.8}$$ memory complexities, which is better than the one presented by Biryukov and Khovratovich at ASIACRYPT 2009 with complexities $$2^{176}/2^{123}/2^{152}$$ respectively. This represents a huge improvement for the time and memory complexity, illustrating the power of MILP in cryptanalysis.
Automatic Cyberstalking Detection on Twitter in Real-Time using Hybrid Approach

Many people are using Twitter for thought expression and information sharing in real-time.Twitter is one of the trendiest social media applications that cybercriminals also widely use to harass the victim in the form of cyberstalking.Cyberstalkers target the victim through sexism, racism, offensive language, hate language, trolling, and fake accounts on Twitter.This paper proposed a framework for automatic cyberstalking detection on Twitter in realtime using the hybrid approach.Initially, experimental works were performed on recent unlabeled tweets collected through Twitter API using three different methods: lexicon-based, machine learning, and hybrid approach.The TF-IDF feature extraction method was used with all the applied methods to obtain the feature vectors from the tweets.The lexicon-based process produced maximum accuracy of 91.1%, and the machine learning approach achieved maximum accuracy of 92.4%.In comparison, the hybrid approach achieved the highest accuracy of 95.8% for classifying unlabeled tweets fetched through Twitter API.The machine learning approach performed better than the lexicon-based, while the performance of the proposed hybrid approach was outstanding.The hybrid method with a different approach was again applied to classify and label the live tweets collected by Twitter Streaming in real-time.Once again, the hybrid approach provided the outstanding result as expected, with an accuracy of 94.2%, recall of 94.1%, the precision of 94.6%, f-score of 94.1%, and the best AUC of 98%.The performance of machine learning classifiers was measured in each dataset labeled by all three methods.Experimental results in this study show that the proposed hybrid approach performed better than other implemented approaches in both recent and live tweets classification.The performance of SVM was better than other machine learning algorithms with all applied approaches.
Modulation format recognition using CNN-based transfer learning models

Transfer learning (TL) appears to be a potential method for transferring information from general to specialized activities. Unfortunately, experimenting using various TL models does not yield good results. In this paper, we propose a model built from scratch with the Hough transform (HT) of constellation diagrams to improve modulation format recognition. The HT is utilized to project points on the constellation diagrams on the Hough space. The HT translates constellation diagram points into lines. Features can then be extracted from the HT domain. Constellation diagrams for eight different modulation formats (2/4/8/16—PSK and 8/16/32/64—QAM) are obtained at optical signal-to-noise ratios (OSNRs) ranging from 5 to 30 dB. The proposed system is based on classification and TL. The obtained results indicate that even at low OSNR values, the proposed system can blindly recognize the wireless optical modulation format with a classification accuracy of up to 99%.
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning

The end-to-end speech translation (E2E-ST) model has gradually become a mainstream paradigm due to its low latency and less error propagation. However, it is non-trivial to train such a model well due to the task complexity and data scarcity. The speech-and-text modality differences result in the E2E-ST model performance usually inferior to the corresponding machine translation (MT) model. Based on the above observation, existing methods often use sharing mechanisms to carry out <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>implicit knowledge transfer</b></i> by imposing various constraints. However, the final model often performs worse on the MT task than the MT model trained alone, which means that the knowledge transfer ability of this method is also limited. To deal with these problems, we propose the FCCL ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> ine- and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> oarse- Granularity <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> ontrastive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning) approach for E2E-ST, which makes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicit knowledge transfer</i> through cross-modal multi-grained contrastive learning. A key ingredient of our approach is applying contrastive learning at both sentence- and frame-level to give the comprehensive guide for extracting speech representations containing rich semantic information. In addition, we adopt a simple whitening method to alleviate the representation degeneration in the MT model, which adversely affects contrast learning. Experiments on the MuST-C benchmark show that our proposed approach significantly outperforms the state-of-the-art E2E-ST baselines on all eight language pairs. Further analysis indicates that FCCL can free up its capacity from learning grammatical structure information and force more layers to learn semantic information.
The Metaverse and Web 3.0

The metaverse is a new frontier in consumption. It is a digital place where people can buy and consume anything they want, whenever they want. It is an oasis of freedom and choice, and it has the potential to change the way we live and work. The future of the metaverse is placed where data and technology merge to create an experience that's both unique and engaging. With information overload becoming a weekly reality, it is crucial for businesses to understand how their consumers are engaging with their offerings. This chapter synthesised the current research and practice to answer the following questions: How is the metaverse changing the way we consume and communicate? How is Web 3.0 empowering and transforming the metaverse? What are the threats Web 3.0 is bringing to our privacy on the internet?
Generation of Limit Cycles in Nonlinear Systems: Machine Leaning Based Type-3 Fuzzy Control

A limit cycle is a cyclic path of oscillation on which the states of a nonlinear system settle. A considerable number of practical systems such as robots, converters, and heartbeat require generating the sustainable oscillatory behaviours. The objective is to design a controller to generate limit cycle with specific behaviours. In this paper, a novel fuzzy control (FC) strategy is introduced to create the limit-cycle for nonlinear complex dynamics with unknown uncertainties. The suggested controller benefits from new interval type-3 fuzzy logic, allowing the control synthesis to improve the quality of closed-loop response and robust performance. The adaptively learned backstepping controller based on FC is employed to analyze the convergence and robustness. Various simulations are proposed to ensure the efficiency of the fuzzy-based control law and adaption rules.
Contextualized medication information extraction using Transformer-based deep learning architectures

To develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. This project is part of the 2022 n2c2 challenge. We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes. We explored 6 state-of-the-art pretrained transformer models for the three subtasks, including GatorTron, a large language model pretrained using > 90 billion words of text (including > 80 billion words from > 290 million clinical notes identified at the University of Florida Health). We evaluated our NLP systems using annotated data and evaluation scripts provided by the 2022 n2c2 organizers. Our GatorTron models achieved the best F1-scores of 0.9828 for medication extraction (ranked 3rd), 0.9379 for event classification (ranked 2nd), and the best micro-average accuracy of 0.9126 for context classification. GatorTron outperformed existing transformer models pretrained using smaller general English text and clinical text corpora, indicating the advantage of large language models. This study demonstrated the advantage of using large transformer models for contextual medication information extraction from clinical narratives.
Extracting social determinants of health from clinical note text with classification and sequence-to-sequence approaches

Abstract Objective Social determinants of health (SDOH) are nonmedical factors that can influence health outcomes. This paper seeks to extract SDOH from clinical texts in the context of the National NLP Clinical Challenges (n2c2) 2022 Track 2 Task. Materials and Methods Annotated and unannotated data from the Medical Information Mart for Intensive Care III (MIMIC-III) corpus, the Social History Annotation Corpus, and an in-house corpus were used to develop 2 deep learning models that used classification and sequence-to-sequence (seq2seq) approaches. Results The seq2seq approach had the highest overall F1 scores in the challenge’s 3 subtasks: 0.901 on the extraction subtask, 0.774 on the generalizability subtask, and 0.889 on the learning transfer subtask. Discussion Both approaches rely on SDOH event representations that were designed to be compatible with transformer-based pretrained models, with the seq2seq representation supporting an arbitrary number of overlapping and sentence-spanning events. Models with adequate performance could be produced quickly, and the remaining mismatch between representation and task requirements was then addressed in postprocessing. The classification approach used rules to generate entity relationships from its sequence of token labels, while the seq2seq approach used constrained decoding and a constraint solver to recover entity text spans from its sequence of potentially ambiguous tokens. Conclusion We proposed 2 different approaches to extract SDOH from clinical texts with high accuracy. However, accuracy suffers on text from new healthcare institutions not present in the training data, and thus generalization remains an important topic for future study.