Reviewerly/exHarmony huggingface dataset:
An efficient edge/cloud medical system for rapid detection of level of consciousness in emergency medicine based on explainable machine learning models

Emergency medicine (EM) is one of the attractive research fields in which researchers investigate their efforts to diagnose and treat unforeseen illnesses or injuries. There are many tests and observations are involved in EM. Detection of the level of consciousness is one of these observations, which can be detected using several methods. Among these methods, the automatic estimation of the Glasgow coma scale (GCS) is studied in this paper. The GCS is a medical score used to describe a patient's level of consciousness. This type of scoring system requires medical examination that may not be available with the shortage of the medical expert. Therefore, the automatic medical calculation for a patient's level of consciousness is highly needed. Artificial intelligence has been deployed in several applications and appears to have a high performance regarding providing automatic solutions. The main objective of this work is to introduce the edge/cloud system to improve the efficiency of the consciousness measurement through efficient local data processing. Moreover, an efficient machine learning (ML) model to predict the level of consciousness of a certain patient based on the patient's demographic, vital signs, and laboratory tests is proposed, as well as maintaining the explainability issue using Shapley additive explanations (SHAP) that provides natural language explanation in a form that helps the medical expert to understand the final prediction. The developed ML model is validated using vital signs and laboratory tests extracted from the MIMIC III dataset, and it achieves superior performance (mean absolute error (MAE) = 0.269, mean square error (MSE) = 0.625, R2 score = 0.964). The resulting model is accurate, medically intuitive, and trustworthy.
Hybrid embeddings for transition-based dependency parsing of free word order languages

Neural Dependency parsing relies on embeddings such as word embeddings and part of speech (POS) embeddings. We propose embeddings which convey more meanings in case of Arabic scripted, morphologically rich, free word order languages. In such languages, part of speech (POS) and morphological features (feats) of a particular word in a sentence govern the suffixes of another word in the same sentence. Keeping this in view, we augment the famous quote "a word is known by the company it keeps" and propose that "a POS is known by the company of suffixes it keeps" and "a morphological feat is known by the company of suffixes it keeps". We propose two novel embeddings which are XPOSngram and FEATSngram embeddings. These embeddings are trained on heterogeneous items i.e. the pair of language specific POS (XPOS) and n-grams, referred to as 'XPOSngram'; and morphological feats and n-grams, called 'FEATSngram'. We call these new type of embeddings hybrid embeddings. We perform experiments on five treebanks, taken from universal dependencies (UD), which belong to four Arabic-scripted, morphologically rich, free word order, and low-resource languages (i.e. Urdu, Arabic, Persian and Uyghur). These treebanks consist of 42985 sentences in total. The experimental results show that on the average, the proposed approach has ≈1.24%, ≈0.84% and ≈3.31% gain in unlabelled attachment score (UAS) over the state of the art language specific POS embeddings, universal POS embeddings and n-gram embeddings based approaches respectively. We have compared the results of hybrid embeddings for Arabic language with the state of the art ArWordVec embeddings. The proposed solution achieves UAS which is ≈10.27% higher than the UAS achieved by ArWordVec. We have further compared the results of hybrid embeddings of Urdu with two state of the art Urdu word embeddings. The results show that the best hybrid embedding has a UAS ≈3.32% and ≈5.015% higher than the two embeddings. We have also tested the proposed methodology for five treebanks of non-Arabic scripted languages from the UD, which are Belarusian, Dutch, German, Greek, and Hungarian languages. The experimental results demonstrate that the proposed approach not only outperform for Arabic scripted languages, but generalizes well for non-Arabic scripted, free word order languages with an average gain of ≈2.5%, ≈2.8% and ≈7.5% in UAS over the state of the art XPOS, UPOS and n-gram based approaches.
Uncertainty Quantification for Text Classification

This half-day tutorial introduces modern techniques for practical uncertainty quantification specifically in the context of multi-class and multi-label text classification. First, we explain the usefulness of estimating aleatoric uncertainty and epistemic uncertainty for text classification models. Then, we describe several state-of-the-art approaches to uncertainty quantification and analyze their scalability to big text data: Virtual Ensemble in GBDT, Bayesian Deep Learning (including Deep Ensemble, Monte-Carlo Dropout, Bayes by Backprop, and their generalization Epistemic Neural Networks), as well as Evidential Deep Learning (including Prior Networks and Posterior Networks). Next, we discuss typical application scenarios of uncertainty quantification in text classification (including in-domain calibration, cross-domain robustness, and novel class detection). Finally, we list popular performance metrics for the evaluation of uncertainty quantification effectiveness in text classification. Practical hands-on examples/exercises are provided to the attendees for them to experiment with different uncertainty quantification methods on a few real-world text classification datasets such as CLINC150.
DocILE 2023 Teaser: Document Information Localization and Extraction

The lack of data for information extraction (IE) from semi-structured business documents is a real problem for the IE community. Publications relying on large-scale datasets use only proprietary, unpublished data due to the sensitive nature of such documents. Publicly available datasets are mostly small and domain-specific. The absence of a large-scale public dataset or benchmark hinders the reproducibility and cross-evaluation of published methods. The DocILE 2023 competition, hosted as a lab at the CLEF 2023 conference and as an ICDAR 2023 competition, will run the first major benchmark for the tasks of Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR) from business documents. With thousands of annotated real documents from open sources, a hundred thousand of generated synthetic documents, and nearly a million unlabeled documents, the DocILE lab comes with the largest publicly available dataset for KILE and LIR. We are looking forward to contributions from the Computer Vision, Natural Language Processing, Information Retrieval, and other communities. The data, baselines, code and up-to-date information about the lab and competition are available at https://docile.rossum.ai/ .
Ablesbarkeitsmesser: A System for Assessing the Readability of German Text

While several approaches have been proposed for estimating the readability of English texts, there is much less work for other languages. In this paper, we present an online service, available at https://readability-check.org/ , that provides five well-established statistical methods and two machine learning models for measuring the readability of texts in German. For the machine learning methods, we train two BERT models. To bring all the measures together, we provide an interactive website that allows users to evaluate the readability of German texts at the sentence level. Our research can be useful for anyone who wants to know whether the text content at hand is easy or difficult and therefore can be used in certain situations or rather needs to be adapted and improved. In education, for example, it can help to assess the suitability of a particular teaching material for a particular grade.
Understanding and Mitigating Gender Bias in Information Retrieval Systems

Recent studies have shown that information retrieval systems may exhibit stereotypical gender biases in outcomes which may lead to discrimination against minority groups, such as different genders, and impact users' decision making and judgements. In this tutorial, we inform the audience of studies that have systematically reported the presence of stereotypical gender biases in Information Retrieval (IR) systems and different pre-trained Natural Language Processing (NLP) models. We further classify existing work on gender biases in IR systems and NLP models as being related to (1) relevance judgement datasets, (2) structure of retrieval methods, (3) representations learnt for queries and documents, (4) and pre-trained embedding models. Based on the aforementioned categories, we present a host of methods from the literature that can be leveraged to measure, control, or mitigate the existence of stereotypical biases within IR systems and different NLP models that are used for down-stream tasks. Besides, we introduce available datasets and collections that are widely used for studying the existence of gender biases in IR systems and NLP models, the evaluation metrics that can be used for measuring the level of bias and utility of the models, and de-biasing methods that can be leveraged to mitigate gender biases within those models.
BioASQ at CLEF2023: The Eleventh Edition of the Large-Scale Biomedical Semantic Indexing and Question Answering Challenge

The large-scale biomedical semantic indexing and question-answering challenge (BioASQ) aims at the continuous advancement of methods and tools to meet the need of biomedical researchers and practitioners for efficient and precise access to the ever-increasing resources of their domain. With this purpose, during the last ten years a series of annual challenges have been organized with specific shared tasks on large-scale biomedical semantic indexing and question answering. Benchmark datasets have been concomitantly provided in alignment with the real needs of biomedical experts. BioASQ provides a unique common testbed where different teams around the world can investigate and compare new approaches for identifying and accessing biomedical knowledge. The eleventh version of the BioASQ Challenge will be held as an evaluation Lab within CLEF2023. In this version, three shared tasks will be presented: (i) the automated retrieval of relevant material for biomedical questions, and the generation of comprehensible answers. (ii) the synergistic retrieval of relevant material and generation of answers for open biomedical questions about developing topics, in collaboration with the experts posing the questions. (iii) the automated indexing of unlabelled clinical procedures-specific medical documents, primarily clinical case reports written in Spanish, with biomedical concepts and the extraction of human-interpretable evidence. As BioASQ rewards the methods that outperform the state of the art in these shared tasks, it pushes the research frontier towards approaches that accelerate access to biomedical knowledge.
YOLO Network Based Intelligent Municipal Waste Management in Internet of Things

It's becoming more worrying that unchecked urban garbage buildup might lead to environmental contamination and potential health risks for city dwellers. People don't use their recycling bins correctly, thus they aren't very effective. IoT and AI advancements have made it possible to replace the outdated trash management infrastructure with one that incorporates smart sensors for real-time monitoring and more efficient waste management. Having a sophisticated, computer-based system to handle garbage is crucial. Hand-picking, or the human separation of garbage into its many components, is an essential part of the waste management process. An intelligent waste material classification system is proposed in this study to streamline the process; it is created utilising a novel methodology based on image processing methods, the diagnosis of photos from waste management. The YOLOv5 public dataset of trash management was used for training, fine-tuning, and testing the suggested technique. The plan also includes the introduction of a smart garbage can's architectural design, which incorporates a microprocessor and many sensors. In order to keep an eye on things, the suggested system uses the Internet of Things and Bluetooth connection. An integral aspect of any effective municipal waste management system is a central monitoring facility that use that data to plan for things like the deployment and maintenance of adaptive equipment, as well as the garbage collection and vehicle routing strategies that bring it all home.
An N-gram-Based BERT model for Sentiment Classification Using Movie Reviews

An abundance of product reviews and opinions is being produced every day across the internet and other media. Sentiment analysis analyzes those data and classifies them as positive or negative. In this paper, a classification model is proposed for n-gram sentiment analysis using BERT. Specifically, the large IMDB movie review dataset is used that contains 50K instances. This dataset is tokenized and encoded into unigrams, bigrams, and trigrams and their combinations such as unigram and bigram, bigram and trigram, and unigram, bigram, and trigram. The proposed BERT model employs on these extracted features. Then, this model is evaluated using the F1 score and its micro, macro, and weighted-average scores. The model shows comparable results to state-of-the-art methods for all n-gram features. In particular, the model achieves 94.64% highest accuracy for the combination of bigram and trigram features, and 94.68% unigram, bigram, and trigram features than other n-gram features.
A Composition of Web Services Using the Markov Decision Process and Long-Short Term Memory

The uncertainty of Quality of Service and the stochastic nature of web services make selecting an optimal web service composition a difficult task. The term “composition” refers to the process of combining the atomic web services into a complex task that satisfies all of the functionality of the requested service. The term “Quality of Service” refers to intangible features that are rarely the focus of academic inquiry. To standardize the skyline services, a relevance function is computed. For each service type, the top k web services are selected based on their relevance function, and then the Markov decision method is used to further refine the list of available services. Efficient web service selection and composition is maximized via the combination of a skyline, a Relevancy function, and a Markov decision process. The experimental findings support the improved performance of the proposed method.
Distributed Cyber-infrastructures and Artificial Intelligence in Hybrid Post-Quantum Era

Distributed cyber-infrastructures and Artificial Intelligence (AI) are transformative technologies that will play a pivotal role in the future of society and the scientific community. Internet of Things (IoT) applications harbor vast quantities of connected devices that collect a massive amount of sensitive information (e.g., medical, financial), which is usually analyzed either at the edge or federated cloud systems via AI/Machine Learning (ML) algorithms to make critical decisions (e.g., diagnosis). It is of paramount importance to ensure the security, privacy, and trustworthiness of data collection, analysis, and decision-making processes. However, system complexity and increased attack surfaces make these applications vulnerable to system breaches, single-point of failures, and various cyber-attacks. Moreover, the advances in quantum computing exacerbate the security and privacy challenges. That is, emerging quantum computers can break conventional cryptographic systems that offer cyber-security services, public key infrastructures, and privacy-enhancing technologies. Therefore, there is a vital need for new cyber-security paradigms that can address the resiliency, long-term security, and efficiency requirements of distributed cyber infrastructures.In this work, we propose a vision of distributed architecture and cyber-security framework that uniquely synergizes secure computation, Physical Quantum Key Distribution (PQKD), NIST Post- Quantum Cryptography (PQC) efforts, and AI/ML algorithms to achieve breach-resilient, functional and efficient cyber-security services. At the heart of our proposal lies a new Multi-Party Computation Quantum Network Core (MPC-QNC) that enables fast and yet quantum-safe execution of distributed computation protocols via integration of PQKD infrastructure and hardware- acceleration elements. We showcase the capabilities of MPC- QNC by instantiating it for Public Key Infrastructures (PKI) and federated ML in our HDQPKI and TPQ-ML, frameworks, respectively. HDQPKI (to the best of our knowledge) is the first hybrid and distributed post-quantum PKI that harnesses PQKD and NIST PQC standards to offer the highest level of quantum safety with a breach-resiliency against active adversaries. TPQ-ML presents a post-quantum secure and privacy-preserving federated ML infrastructure.
Secure and Efficient Privacy-preserving Authentication Scheme using Cuckoo Filter in Remote Patient Monitoring Network

With the ubiquitous advancement in smart medical devices and systems, the potential of Remote Patient Monitoring (RPM) network is evolving in modern healthcare systems. The medical professionals (doctors, nurses, or medical experts) can access vitals and sensitive physiological information about the patients and provide proper treatment to improve the quality of life through the RPM network. However, the wireless nature of communication in the RPM network makes it challenging to design an efficient mechanism for secure communication. Many authentication schemes have been proposed in recent years to ensure the security of the RPM network. Pseudonym, digital signature, and Authenticated Key Exchange (AKE) protocols are used for the Internet of Medical Things (IoMT) to develop secure authorization and privacy-preserving communication. However, traditional authentication protocols face overhead challenges due to maintaining a large set of key-pairs or pseudonyms results on the hospital cloud server. In this research work, we identify this research gap and propose a novel secure and efficient privacy-preserving authentication scheme using cuckoo filters for the RPM network. The use of cuckoo filters in our proposed scheme provides an efficient way for mutual anonymous authentication and a secret shared key establishment process between medical professionals and patients. Moreover, we identify the misbehaving sensor nodes using a correlation-based anomaly detection model to establish secure communication. The security analysis and formal security validation using SPAN and AVISPA tools show the robustness of our proposed scheme against message modification attacks, replay attacks, and man-in-the-middle attacks.
Small Object Detection Method based on Improved YOLOv5

An improved small object detection method based on YOLOv5 algorithm is presented to solve the problems of dense and uneven small target samples, less extractable feature information and susceptible to background interference in aerial images taken by unmanned aerial vehicles. Firstly, CBAM attention mechanism is introduced into the network. Secondly, a new small object detection layer is added to realize four detection structures to recognize different size objects, to increase the ability to detect small and weak objects. Finally, in the post-processing section, EIoU_Loss is used to instead GIoU_Loss as a loss function of bounding box regression in order to increase the speed of bounding box regression and increase positioning precision. Experiments on the public dataset VisDrone, demonstrate that this method's average accuracy is 36.7%, which is 7.6% more effective than the standard procedure. The results show that the method proposed in this paper has good performance for UAV small object detection tasks.
Natural image matting based on image inpainting

Deep image matting is a hot problem with applications in computer vision and image processing. It has been widely used in image composition, film production and video editing etc. The current matting method based on image inpainting uses a deep neural network to inpaint the foreground target and background region to further improve the accuracy of alpha matte. However, when the trimap contains large unknown regions, the excessive inpainting of foreground and background produce a lot of redundant information, which leads to a degradation of alpha matte quality. Therefore, to address this issue, a natural image matting method based on image inpainting is designed. This method involves the refinement process for trimap, which improves the quality of the trimap, enlarges the foreground and background regions providing additional information for image matting. Extensive experimental results on the composition-1k dataset demonstrate that the presented method provide high-quality alpha mattes not only in the case that the trimap contains small unknown regions, but also in the case that the trimap contains large unknown regions.
On Deep Holes of Elliptic Curve Codes

We give a method to construct deep holes for elliptic curve codes. For long elliptic curve codes, we conjecture that our construction is complete in the sense that it gives all deep holes. Some evidence and heuristics on the completeness are provided by means of connections with problems and results in finite geometry.
Accelerating Graph Convolutional Networks Through a PIM-Accelerated Approach

Graph convolutional networks (GCNs) are promising to enable machine learning on graph data.GCNs show potential vertex-level and intra-vertex parallelism for GPU acceleration, but their irregular memory accesses arising in aggregation operations and the inherent sparsity for vertex features of graphs cause inefficiencies on the GPU.In this paper, we present gPIM, which aims to accelerate GCNs inference through a processing-in-memory (PIM) enabled architecture.gPIM is expected to perform compute-intensive combination on the GPU while aggregation and memory-bound combination are offloaded to the PIM-featured hybrid memory cubes (HMCs).To maximize the efficiency of such GPU-HMC architecture, gPIM is novel with two key designs: 1) A GCN-induced graph partitioning that minimizes communication overheads between cubes, 2) A programmer-transparent performance estimation mechanism that predicts the performance bound of operations accurately for workload offloading.Experimental results show that gPIM significantly outperforms Intel Xeon E5-2680v3 CPU (8,979.52×),NVIDIA Tesla V100 GPU (96.01×), and a state-of-the-art GCN accelerator AWB-GCN (4.18×).