Reviewerly/exHarmony huggingface dataset:
One2Set: Generating Diverse Keyphrases as a Set

Recently, the sequence-to-sequence models have made remarkable progress on the task of keyphrase generation (KG) by concatenating multiple keyphrases in a predefined order as a target sequence during training.However, the keyphrases are inherently an unordered set rather than an ordered sequence.Imposing a predefined order will introduce wrong bias during training, which can highly penalize shifts in the order between keyphrases.In this work, we propose a new training paradigm ONE2SET without predefining an order to concatenate the keyphrases.To fit this paradigm, we propose a novel model that utilizes a fixed set of learned control codes as conditions to generate a set of keyphrases in parallel.To solve the problem that there is no correspondence between each prediction and target during training, we propose a K-step target assignment mechanism via bipartite matching, which greatly increases the diversity and reduces the duplication ratio of generated keyphrases.The experimental results on multiple benchmarks demonstrate that our approach significantly outperforms the state-of-the-art methods.
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

Hybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world.However, Question Answering (QA) over such hybrid data is largely neglected in existing research.In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and their compositions.We further propose a novel QA model termed TAGOP, which is capable of reasoning over both tables and text.It adopts sequence tagging to extract relevant cells from the table along with relevant spans from the text to infer their semantics, and then applies symbolic reasoning over them with a set of aggregation operators to arrive at the final answer.TAGOP achieves 58.0% in F 1 , which is an 11.1% absolute increase over the previous best baseline model, according to our experiments on TAT-QA.But this result still lags far behind the performance of human expert, i.e. 90.8% in F 1 .It demonstrates that our TAT-QA is very challenging and can serve as a benchmark for training and testing powerful QA models that address hybrid data.
Self-Supervised Learning for Few-Shot Medical Image Segmentation

Fully-supervised deep learning segmentation models are inflexible when encountering new unseen semantic classes and their fine-tuning often requires significant amounts of annotated data. Few-shot semantic segmentation (FSS) aims to solve this inflexibility by learning to segment an arbitrary unseen semantically meaningful class by referring to only a few labeled examples, without involving fine-tuning. State-of-the-art FSS methods are typically designed for segmenting natural images and rely on abundant annotated data of training classes to learn image representations that generalize well to unseen testing classes. However, such a training mechanism is impractical in annotation-scarce medical imaging scenarios. To address this challenge, in this work, we propose a novel self-supervised FSS framework for medical images, named SSL-ALPNet, in order to bypass the requirement for annotations during training. The proposed method exploits superpixel-based pseudo-labels to provide supervision signals. In addition, we propose a simple yet effective adaptive local prototype pooling module which is plugged into the prototype networks to further boost segmentation accuracy. We demonstrate the general applicability of the proposed approach using three different tasks: organ segmentation of abdominal CT and MRI images respectively, and cardiac segmentation of MRI images. The proposed method yields higher Dice scores than conventional FSS methods which require manual annotations for training in our experiments.
The effect of HRM practices and employees’ job satisfaction on employee performance

Saudi Ports, is one of the busiest seaports in the world and the biggest ports in the Middle East, which is facing challenges from different ports such as Salalah and Dubai ports especially in the cargo section, which can release their cargoes and goods in a short time without any congestion of stock.The current challenges of Saudi Ports Authority are the overstock of cargo which is the result of low performance of workers at cargo field.This paper investigated the root of this problem based on the role of the human resource practices such as training and development, reward, job analysis, social support, recruitment and selection, employee relationship and empowerment, employee satisfaction into employee performance.In other words, the current study explored to know whether HRM practices offer direct impact on the employee performance or through employees' job satisfaction on employee performance to solve the said problem.The study analysed the 367 Saudi port authority employees' data and found a positive significant relationship between HRM practices and employee performance.Furthermore, the current study revealed a positive relationship between employee job satisfaction and employee performance.Moreover, the present study found the insignificant relationship between HRM practices and employee job satisfaction but reported no significant mediating role of employee job satisfaction between HRM practices and employee performance.
Vault: Fast Bootstrapping for the Algorand Cryptocurrency

Decentralized cryptocurrencies rely on participants to keep track of the state of the system in order to verify new transactions.As the number of users and transactions grows, this requirement becomes a significant burden, requiring users to download, verify, and store a large amount of data to participate.Vault is a new cryptocurrency design based on Algorand that minimizes these storage and bootstrapping costs for participants.Vault's design is based on Algorand's proof-of-stake consensus protocol and uses several techniques to achieve its goals.First, Vault decouples the storage of recent transactions from the storage of account balances, which enables Vault to delete old account state.Second, Vault allows sharding state across participants in a way that preserves strong security guarantees.Finally, Vault introduces the notion of stamping certificates, which allow a new client to catch up securely and efficiently in a proofof-stake system without having to verify every single block.Experiments with a prototype implementation of Vault's data structures show that Vault's design reduces the bandwidth cost of joining the network as a full client by 99.7% compared to Bitcoin and 90.5% compared to Ethereum when downloading a ledger containing 500 million transactions.
An Imperceptible Medical Image Watermarking Framework for Automated Diagnosis of Retinal Pathologies in an eHealth Arrangement

The newly driven concept of electronic health (eHealth) has made distance irrelevant in healthcare.eHealth, which is commonly known as an electronic form of healthcare, refers broadly to the use of advanced information and communication technology within healthcare environments.However, its practical implementation poses many sets of challenges.One of the major challenges is authentication and integrity verification of vital medical data.Proper verification of patients with associated medical images or data is crucial to conduct accurate medical diagnostics.This paper presents an imperceptible watermarkingbased security framework to address the issues of authentication and integrity verification of medical images for eHealth applications.The electronic patient record (EPR), as a watermark image, is embedded in optical coherence tomography (OCT)/fundus scan (consisting of healthy and diseased scans), using a hybrid watermarking technique based on fast curvelet transform (FCT) and singular value decomposition (SVD).The proposed work showed a high level of robustness, imperceptibility, and security for medical images when compared with the state-of-the-art existing watermarking techniques.In addition, we conducted a comparative analysis of the watermarked OCT/fundus scans with non-watermarked scans, and our results validate that the insertion of the watermark in the retinal images does not affect the automated medical image diagnosis of various retinal pathologies.Moreover, the correct recovery of the EPR from the watermarked scans makes the proposed framework applicable to the authentication of medical images for a computerbased automated diagnostic system in an eHealth arrangement.
MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework

As large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources. Recently, a deep learning approach has been proposed with promising results in a variety of research areas. However, applying such deep learning approaches to heterogeneous dataset involves the strenuous design of deep architectures. Thus, in this paper, we present a deep learning-based python package that can efficiently perform data integration. The python package we propose is called MildInt (Deep learning-based Multimodal longitudinal data integration framework), which provides a pre-trained deep learning architecture for classification tasks. MildInt contains two learning phases: 1) learning feature representation from each modality of the data and 2) training a classifier for the final decision. Adopting a deep architecture in the first phase leads to learning more task-relevant feature representation than a linear model. In the second phase, a linear classifier is used for detecting and investigating biomarkers from multimodal data. By combining the linear model and the deep learning model, higher accuracy and better interpretability can be achieved at the same time. We validate the performance of our package using simulation data and real data. For the real data, as a pilot study, we used clinical and multimodal neuroimaging datasets in Alzheimer’s Disease to predict the disease progression. In such experiments, we show that MildInt is capable of integrating multiple forms of numerical data, including time series data, for extracting complementary features from the multimodal dataset.
Interview Choice Reveals Your Preference on the Market

Online recruitment services are now rapidly changing the landscape of hiring traditions on the job market. There are hundreds of millions of registered users with resumes, and tens of millions of job postings available on the Web. Learning good job-resume matching for recruitment services is important. Existing studies on job-resume matching generally focus on learning good representations of job descriptions and resume texts with comprehensive matching structures. We assume that it would bring benefits to learn the preference of both recruiters and job-seekers from previous interview histories and expect such preference is helpful to improve job-resume matching. To this end, in this paper, we propose a novel matching network with preference modeled. The key idea is to explore the latent preference given the history of all interviewed candidates for a job posting and the history of all job applications for a particular talent. To be more specific, we propose a profiling memory module to learn the latent preference representation by interacting with both the job and resume sides. We then incorporate the preference into the matching framework as an end-to-end learnable neural network. Based on the real-world data from an online recruitment platform namely "Boss Zhipin", the experimental results show that the proposed model could improve the job-resume matching performance against a series of state-of-the-art methods. In this way, we demonstrate that recruiters and talents indeed have preference and such preference can improve job-resume matching on the job market.
Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education

In science, technology, engineering, arts, and mathematics (STEAM) education, artificial intelligence (AI) analytics are useful as educational scaffolds to educe (draw out) the students’ AI-Thinking skills in the form of AI-assisted human-centric reasoning for the development of knowledge and competencies. This paper demonstrates how STEAM learners, rather than computer scientists, can use AI to predictively simulate how concrete mixture inputs might affect the output of compressive strength under different conditions (e.g., lack of water and/or cement, or different concrete compressive strengths required for art creations). To help STEAM learners envision how AI can assist them in human-centric reasoning, two AI-based approaches will be illustrated: first, a Naïve Bayes approach for supervised machine-learning of the dataset, which assumes no direct relations between the mixture components; and second, a semi-supervised Bayesian approach to machine-learn the same dataset for possible relations between the mixture components. These AI-based approaches enable controlled experiments to be conducted in-silico, where selected parameters could be held constant, while others could be changed to simulate hypothetical “what-if” scenarios. In applying AI to think discursively, AI-Thinking can be educed from the STEAM learners, thereby improving their AI literacy, which in turn enables them to ask better questions to solve problems.
Source patterns of potentially toxic elements (PTEs) and mining activity contamination level in soils of Taltal city (northern Chile)

Mining activities are among the main sources of potentially toxic elements (PTEs) in the environment which constitute a real concern worldwide, especially in developing countries. These activities have been carried out for more than a century in Chile, South America, where, as evidence of incorrect waste disposal practices, several abandoned mining waste deposits were left behind. This study aimed to understand multi-elements geochemistry, source patterns and mobility of PTEs in soils of the Taltal urban area (northern Chile). Topsoil samples (n = 125) were collected in the urban area of Taltal city (6 km2) where physicochemical properties (redox potential, electric conductivity and pH) as well as chemical concentrations for 35 elements were determined by inductively coupled plasma optical emission spectrometer. Data were treated following a robust workflow, which included factor analysis (based on ilr-transformed data), a new robust compositional contamination index (RCCI), and fractal/multi-fractal interpolation in GIS environment. This approach allowed to generate significant elemental associations, identifying pool of elements related either to the geological background, pedogenic processes accompanying soil formation or to anthropogenic activities. In particular, the study eventually focused on a pool of 6 PTEs (As, Cd, Cr, Cu, Pb, and Zn), their spatial distribution in the Taltal city, and the potential sources and mechanisms controlling their concentrations. Results showed generally low baseline values of PTEs in most sites of the surveyed area. On a smaller number of sites, however, higher values concentrations of As, Cd, Cu, Zn and Pb were found. These corresponded to very high RCCI contamination level and were correlated to potential anthropogenic sources, such as the abandoned mining waste deposits in the north-eastern part of the Taltal city. This study highlighted new and significant insight on the contamination levels of Taltal city, and its links with anthropogenic activities. Further research is considered to be crucial to extend this assessment to the entire region. This would provide a comprehensive overview and vital information for the development of intervention limits and guide environmental legislation for these pollutants in Chilean soils.
Functional Adversarial Attacks

We propose functional adversarial attacks, a novel class of threat models for crafting adversarial examples to fool machine learning models. Unlike a standard lp-ball threat model, a functional adversarial threat model allows only a single function to be used to perturb input features to produce an adversarial example. For example, a functional adversarial attack applied on colors of an image can change all red pixels simultaneously to light red. Such global uniform changes in images can be less perceptible than perturbing pixels of the image individually. For simplicity, we refer to functional adversarial attacks on image colors as ReColorAdv, which is the main focus of our experiments. We show that functional threat models can be combined with existing additive (lp) threat models to generate stronger threat models that allow both small, individual perturbations and large, uniform changes to an input. Moreover, we prove that such combinations encompass perturbations that would not be allowed in either constituent threat model. In practice, ReColorAdv can significantly reduce the accuracy of a ResNet-32 trained on CIFAR-10. Furthermore, to the best of our knowledge, combining ReColorAdv with other attacks leads to the strongest existing attack even after adversarial training.
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks

Reducing the numerical precision of data and computation is extremely effective in accelerating deep learning training workloads. Towards this end, 8-bit floating point representations (FP8) were recently proposed for DNN training. However, its applicability was demonstrated on a few selected models only and significant degradation is observed when popular networks such as MobileNet and Transformer are trained using FP8. This degradation is due to the inherent precision requirement difference in the forward and backward passes of DNN training. Using theoretical insights, we propose a hybrid FP8 (HFP8) format and DNN end-to-end distributed training procedure. We demonstrate, using HFP8, the successful training of deep learning models across a whole spectrum of applications including Image Classification, Object Detection, Language and Speech without accuracy degradation. Finally, we demonstrate that, by using the new 8 bit format, we can directly quantize a pre-trained model down to 8-bits without losing accuracy by simply fine-tuning batch normalization statistics. These novel techniques enable a new generations of 8-bit hardware that are robust for building and deploying neural network models.
Wasserstein Weisfeiler-Lehman Graph Kernels

Most graph kernels are an instance of the class of R-Convolution kernels, which measure the similarity of objects by comparing their substructures. Despite their empirical success, most graph kernels use a naive aggregation of the final set of substructures, usually a sum or average, thereby potentially discarding valuable information about the distribution of individual components. Furthermore, only a limited instance of these approaches can be extended to continuously attributed graphs. We propose a novel method that relies on the Wasserstein distance between the node feature vector distributions of two graphs, which allows to find subtler differences in data sets by considering graphs as high-dimensional objects, rather than simple means. We further propose a Weisfeiler--Lehman inspired embedding scheme for graphs with continuous node attributes and weighted edges, enhance it with the computed Wasserstein distance, and thus improve the state-of-the-art prediction performance on several graph classification tasks.
Gender and Digital Teaching Competence in Dual Vocational Education and Training

In recent decades, technological advances have been revolutionizing all areas of society, including the teaching resources and methodologies used in the world of education. Teachers are in the process of adapting to develop the digital skills they need for the use of Information and Communication Technologies (ICTs), a process that must be permanent and in which there are still knowledge gaps undermining its application. This study aims to determine whether this lack of digital skills is influenced by the gender of teachers, for example, whether there is a gender gap in ICT application in teaching, specifically Dual Vocational Education and Training, which is a teaching area that has been growing exponentially in recent years. A descriptive quantitative method has been used for this study with a sample of 1568 teachers of Dual Vocational Education and Training from the Autonomous Community of Andalusia, with data collected through a questionnaire. The results show that while the level of knowledge of ICT resources is medium among this group and is therefore improvable, there are no significant gender differences between teachers with respect to the application of e-skills by teaching professionals, despite the existence in other contexts of a large digital gender gap in new technology professionals.
A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms

Immunohistochemistry (IHC) is a diagnostic technique used throughout pathology. A machine learning algorithm that could predict individual cell immunophenotype based on hematoxylin and eosin (H&E) staining would save money, time, and reduce tissue consumed. Prior approaches have lacked the spatial accuracy needed for cell-specific analytical tasks. Here IHC performed on destained H&E slides is used to create a neural network that is potentially capable of predicting individual cell immunophenotype. Twelve slides were stained with H&E and scanned to create digital whole slide images. The H&E slides were then destained, and stained with SOX10 IHC. The SOX10 IHC slides were scanned, and corresponding H&E and IHC digital images were registered. Color-thresholding and machine learning techniques were applied to the registered H&E and IHC images to segment 3,396,668 SOX10-negative cells and 306,166 SOX10-positive cells. The resulting segmentation was used to annotate the original H&E images, and a convolutional neural network was trained to predict SOX10 nuclear staining. Sixteen thousand three hundred and nine image patches were used to train the virtual IHC (vIHC) neural network, and 1,813 image patches were used to quantitatively evaluate it. The resulting vIHC neural network achieved an area under the curve of 0.9422 in a receiver operator characteristics analysis when sorting individual nuclei. The vIHC network was applied to additional images from clinical practice, and was evaluated qualitatively by a board-certified dermatopathologist. Further work is needed to make the process more efficient and accurate for clinical use. This proof-of-concept demonstrates the feasibility of creating neural network-driven vIHC assays.