Reviewerly/exHarmony huggingface dataset:
Decentralized AI-Enabled Trusted Wireless Network: A New Collaborative Computing Paradigm for Internet of Things

With the innovative technology development and wide application in industrial Internet, internet of things (IoT) is increasingly showing the characteristics of regional autonomy and distributed decentralized interconnection. For its large-coverage and unique high-speed wireless multi-service differentiated access capabilities, 6G has become a promising networking technology in distributed and decentralized IoT. However, its introduction will further aggravate the problems of trusted access, trusted resource collaboration and security prevention in distributed and decentralized IoT. As a technology that can reliably store and share untrustworthy key information among multiple distributed networks, blockchain can provide a feasible solution to the above problems with the optimization capabilities of Artificial Intelligence (AI). This article combines blockchain and AI to propose a collaborative computing paradigm for IoT to support decentralized trusted wireless network. First, a decentralized trusted collaborative computing architecture is designed based on the interaction of the main-side chains to provide and manage the trusted collaborative computing services within and among the 6G-based wireless communication networks. Next, a multi-factor enhanced authentication unit that integrates the resource allocation in wireless network is constructed. Meanwhile a decentralized trusted collaborative authentication mechanism based on the cross-layer state channel is proposed. After that, this article proposes a decentralized trusted resource collaboration mechanism based on AI, which dynamically and flexibly allocates intra-do-main and inter-domain resources with the requirements of service collaboration, to improve service capabilities. Finally, a trusted collaborative security management and control mechanism based on AI for monitoring and handling abnormal events is proposed to ensure the reliability and security of terminal access and service transmission.
Analysis of heavy metals content in well water and its predictive insights for students’ wellbeing in Almajiri Schools, Sokoto East, Nigeria

Education is a right to every child, but Sokoto is suffering from many issues ravaging education, therewith, a major issue is the Almajiri school. Because the Almajiri schools are abandoned there is every tendency that their drinking water is contaminated considering the rising pattern of pollution across the globe. The aim of the study was to carry out an analysis of heavy metal content in well water and propel some predictive insights for students well-being in Almajiri schools in Sokoto, Nigeria. Samples of well water were collected from Almajiri schools in Sokoto state and metals were analyzed using atomic abortion spectroscopy. 60.10 ± 5.1 to 600.1± 0.01, 100.1 ± 0.5 to 600.0 ± 0.02, are the ranges of calcium, and magnesium macroelements determined. The ranges for essential heavy metals zinc, and iron are as follows; 2.34 ± 0.05 to 5.00 ± 0.02 (zinc), and 0.54± 0.05 to 10.43 ± 0.5 (iron) respectively. The ranges of Pb, Hg, and Cd assessed are as follows: lead (0.0 to 1.06 ± 0.005), mercury (0.10 ± 0.002 to 2.501 ± 0.01), and cadmium (0.00122 ±0.0004 to 0.0761 ± 0.0001). The HI for the heavy metals determined are in the order of schools B&gt;D&gt;A&gt;C. The metals determined are either excess or little (with respect to essential heavy metals) in many cases. In one school, the HI is far above 1 and therefore risk the population with effects. Thus, can elicit harm to the consumers. They can affect the academic, cognitive capacity of the Alamjiri children. There is need to take proper measures to save the health and future development of the Almajiri child against heavy metals poisoning possibility.
An Efficient Audio Encryption Scheme Based on Elliptic Curve over Finite Fields

Elliptic curve (EC) based cryptographic systems are more trustworthy than the currently used cryptographic approaches since they require less computational work while providing good security. This paper shows how to use an EC to make a good cryptosystem for encrypting digital audio. As a preliminary step, the system uses an EC of a particular type over a binary extension field to distort the digital audio pixel position. It reduces the inter-correlation between pixels in the original audio, making the system resistant to statistical attacks. In creating confusion in the data, an EC over a binary extension field is used to make a different number of substitution boxes (S-boxes). The suggested design employs a unique curve that relies on efficient EC arithmetic operations in the diffusion module. As a result, it generates high-quality pseudo-random numbers (PRNs) and achieves optimal diffusion in encrypted audio files with less processing work. Audio files of various sizes and kinds can all be encrypted using the provided algorithm. Moreover, the results show that this method effectively protects many kinds of audio recordings and is more resistant to statistical and differential attacks.
A novel grid multi-structure chaotic attractor and its application in medical image encryption

Grid multi-scroll/wing chaotic systems are complex non-linear dynamic systems, which are widely used in secure communication. The grid multi-scroll/wing chaotic systems are usually realized by using the function control method, which has a complex realization method, many control parameters, and a simple unit attractor structure. In this paper, based on the Hopfield neural network, a memristive Hopfield neural network model is proposed by using the memristor synapse control method. The model can generate novel grid multi-structure chaotic attractors, which have the characteristics of a simple implementation method, few control parameters, and complex unit attractor structure. Firstly, the generation mechanism of the grid multi-structure chaotic attractors is analyzed by the equilibrium points and stability. Secondly, its basic dynamical characteristics including the Lyapunov exponent spectrum, fractal dimension, time series, power spectrum, bifurcation diagram, and Poincaré section are analyzed. Thirdly, an analog circuit of the neural network model is designed and realized by Multisim. Finally, combined with the chaos encryption principle, an image encryption scheme is designed based on the generated grid multi-structure attractors. Experimental results show that compared with the existing schemes, the proposed scheme has larger information entropy, higher key sensitivity, and a good application prospect.
Introducing UberText 2.0: A Corpus of Modern Ukrainian at Scale

This paper addresses the need for massive corpora for a low-resource language and presents the publicly available UberText 2.0 corpus for the Ukrainian language and discusses the methodology of its construction. While the collection and maintenance of such a corpus is more of a data extraction and data engineering task, the corpus itself provides a solid foundation for natural language processing tasks. It can enable the creation of contemporary language models and word embeddings, resulting in a better performance of numerous downstream tasks for the Ukrainian language. In addition, the paper and software developed can be used as a guidance and model solution for other low-resource languages. The resulting corpus is available for download on the project page. It has 3.274 billion tokens, consists of 8.59 million texts and takes up 32 gigabytes of space.
Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare. To assess the current status of various state-of-the-art language models in healthcare, this paper studies the problem by initially proposing two different Healthcare datasets, Indian Healthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real world Indian hospital query data in English and multiple Indic languages (Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with the query intents as well as entities. Our aim is to detect query intents and corresponding entities. We perform extensive experiments on a set of models which in various realistic settings and explore two scenarios based on the access to English data only (less costly) and access to target language data (more expensive). We analyze context specific practical relevancy through empirical analysis. The results, expressed in terms of overall F-score show that our approach is practically useful to identify intents and entities.
Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts

Emotion-Cause Pair Extraction (ECPE) task aims to pair all emotions and corresponding causes in documents.ECPE is an important task for developing human-like responses.However, previous ECPE research is conducted based on news articles, which has different characteristics compared to dialogues.To address this issue, we propose a Pair-Relationship Guided Mixture-of-Experts (PRG-MoE) model, which considers dialogue features (e.g., speaker information).PRG-MoE automatically learns relationship between utterances and advises a gating network to incorporate dialogue features in the evaluation, yielding substantial performance improvement.We employ a new ECPE dataset, which is an English dialogue dataset, with more emotion-cause pairs in documents than news articles.We also propose Cause Type Classification that classifies emotion-cause pairs according to the types of the cause of a detected emotion.For reproducing the results, we make available all our code and data.
Prompt-based Learning for Text Readability Assessment

We propose the novel adaptation of a pre-trained seq2seq model for readability assessment. We prove that a seq2seq model - T5 or BART - can be adapted to discern which text is more difficult from two given texts (pairwise). As an exploratory study to prompt-learn a neural network for text readability in a text-to-text manner, we report useful tips for future work in seq2seq training and ranking-based approach to readability assessment. Specifically, we test nine input-output formats/prefixes and show that they can significantly influence the final model performance.Also, we argue that the combination of text-to-text training and pairwise ranking setup 1) enables leveraging multiple parallel text simplification data for teaching readability and 2) trains a neural model for the general concept of readability (therefore, better cross-domain generalization). At last, we report a 99.6% pairwise classification accuracy on Newsela and a 98.7% for OneStopEnglish, through a joint training approach. Our code is available at github.com/brucewlee/prompt-learning-readability.
Developing Synthetic Applications Benchmarks on Composable Cyberinfrastructure: A Study of Scaling Molecular Dynamics Applications on GPUs

The potential for infinite scaling, improved performance, and better sharing of computing resources motivates researchers to adapt to composable infrastructures. Measuring performance on these systems requires an assessment of how the composed configuration itself affects performance which goes beyond traditional scaling approaches. We emphasize the subtle relationship between the nature of the calculation and the configuration of the composable infrastructure. New application benchmarking strategies are explored to inform the optimal configurations and best computing practices for composable systems. Realistic molecular dynamics research workflows are employed as benchmarks for composed GPU systems to develop a benchmarking strategy that yields recognizable and informative results. We employ the practices on a realistic case for a molecular dynamics research workflow on a composed GPU system. We discuss the identification of computational bottlenecks and establish a need for new benchmark performance suites that can help researchers articulate optimum compositions for composable infrastructure.
Unsupervised Anomaly Detection Using Variational Autoencoder with Gaussian Random Field Prior

We propose a new model of Variational Autoencoder (VAE) for Anomaly Detection (AD) with improved modeling power. More precisely, we introduce a VAE model with a Gaussian Random Field (GRF) prior, namely VAE-GRF, which generalizes the classical VAE model. We show that, under some assumptions, the VAE-GRF largely outperforms the traditional VAE and some other probabilistic models developed for AD. Our experimental results suggest that the VAE-GRF could be used as a relevant VAE baseline in place of the traditional VAE with very limited additional computational cost. We provide competitive results on the public MVTec benchmark dataset for visual inspection, as well as on the public Livestock dataset dedicated to the task of unsupervised animal detection from aerial images.
Noiseless linear amplification and loss-tolerant quantum relay using coherent-state superpositions

Noiseless linear amplification (NLA) is useful for a wide variety of quantum protocols. Here we propose a fully scalable amplifier which, for asymptotically large sizes, can perform perfect fidelity NLA on any quantum state. Given finite resources, however, it is designed to perform perfect fidelity NLA on coherent states and their arbitrary superpositions. Our scheme is a generalization of the multiphoton quantum scissor teleamplifier, which we implement using a coherent-state superposition resource state. Furthermore, we prove our NLA is also a loss-tolerant relay for multiary phase-shift keyed coherent states. Finally, we demonstrate that our NLA is also useful for continuous-variable entanglement distillation, even with realistic experimental imperfections.
Time-synchronization error in twin-field quantum key distribution

For twin-field quantum key distribution (TF-QKD), it is commonly known that erroneous time synchronization leads to decoherence of the twin fields, compromising the performance of the TF-QKD. However, more quantitative information about such an effect still needs further revelation in order to understand the detailed requirement of time synchronization in TF-QKD and to further optimize the TF-QKD system for better performance. Here, we provide an analytical model describing TF-QKD with time-synchronization errors, choosing phase-matching quantum key distribution for our simulation. The model is constructed by a relation between the fringe visibility of the interferometry and the time-synchronization error described by the time difference between the arrival of twin-field signals from both users. By a numerical simulation, we demonstrate that our model can dramatically improve the tolerance of the system against the time-synchronization error, achieving a much higher secure key rate and communication distance with inaccurate time synchronization. Through this work, we intend to start a further discussion on the synchronization demand of TF-QKD.
Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.
The Effectiveness of Using Mobile Learning Application on Undergraduates’ Intrinsic Motivation and Their General Nutrition Knowledge

This research aims to investigate the effectiveness of using a mobile learning application on undergraduates’ intrinsic motivation and their general nutrition knowledge. This quasi-experimental study involved the participation of 125 students who were divided into two groups: a control group consisting of 62 students and an experimental group comprising 63 students. Pre- and post-intrinsic motivation questionnaires and a nutritional knowledge test were administered. The results showed significant differences between the control group and the experimental group. The experimental group exhibited a higher level of knowledge, and there were no statistically significant differences in intrinsic motivation between the control group and the experimental group. The application was found to be effective in improving nutrition knowledge but did not enhance intrinsic motivation. Further research may be needed to understand the factors that influence intrinsic motivation and how to effectively increase it.
FSE-Net: feature selection and enhancement network for mammogram classification

Abstract Objective . Early detection and diagnosis allow for intervention and treatment at an early stage of breast cancer. Despite recent advances in computer aided diagnosis systems based on convolutional neural networks for breast cancer diagnosis, improving the classification performance of mammograms remains a challenge due to the various sizes of breast lesions and difficult extraction of small lesion features. To obtain more accurate classification results, many studies choose to directly classify region of interest (ROI) annotations, but labeling ROIs is labor intensive. The purpose of this research is to design a novel network to automatically classify mammogram image as cancer and no cancer, aiming to mitigate or address the above challenges and help radiologists perform mammogram diagnosis more accurately. Approach . We propose a novel feature selection and enhancement network (FSE-Net) to fully exploit the features of mammogram images, which requires only mammogram images and image-level labels without any bounding boxes or masks. Specifically, to obtain more contextual information, an effective feature selection module is proposed to adaptively select the receptive fields and fuse features from receptive fields of different scales. Moreover, a feature enhancement module is designed to explore the correlation between feature maps of different resolutions and to enhance the representation capacity of low-resolution feature maps with high-resolution feature maps. Main results . The performance of the proposed network has been evaluated on the CBIS-DDSM dataset and INbreast dataset. It achieves an accuracy of 0.806 with an AUC of 0.866 on the CBIS-DDSM dataset and an accuracy of 0.956 with an AUC of 0.974 on the INbreast dataset. Significance . Through extensive experiments and saliency map visualization analysis, the proposed network achieves the satisfactory performance in the mammogram classification task, and can roughly locate suspicious regions to assist in the final prediction of the entire images.
RestoreFormer++: Towards Real-World Blind Face Restoration From Undegraded Key-Value Pairs

Blind face restoration aims at recovering high-quality face images from those with unknown degradations. Current algorithms mainly introduce priors to complement high-quality details and achieve impressive progress. However, most of these algorithms ignore abundant contextual information in the face and its interplay with the priors, leading to sub-optimal performance. Moreover, they pay less attention to the gap between the synthetic and real-world scenarios, limiting the robustness and generalization to real-world applications. In this work, we propose RestoreFormer++, which on the one hand introduces fully-spatial attention mechanisms to model the contextual information and the interplay with the priors, and on the other hand, explores an extending degrading model to help generate more realistic degraded face images to alleviate the synthetic-to-real-world gap. Compared with current algorithms, RestoreFormer++ has several crucial benefits. First, instead of using a multi-head self-attention mechanism like the traditional visual transformer, we introduce multi-head cross-attention over multi-scale features to fully explore spatial interactions between corrupted information and high-quality priors. In this way, it can facilitate RestoreFormer++ to restore face images with higher realness and fidelity. Second, in contrast to the recognition-oriented dictionary, we learn a reconstruction-oriented dictionary as priors, which contains more diverse high-quality facial details and better accords with the restoration target. Third, we introduce an extending degrading model that contains more realistic degraded scenarios for training data synthesizing, and thus helps to enhance the robustness and generalization of our RestoreFormer++ model. Extensive experiments show that RestoreFormer++ outperforms state-of-the-art algorithms on both synthetic and real-world datasets.
DoveDB: A Declarative and Low-Latency Video Database

Concerning the usability and efficiency to manage video data generated from large-scale cameras, we demonstrate DoveDB, a declarative and low-latency video database. We devise a more comprehensive video query language called VMQL to improve the expressiveness of previous SQL-like languages, which are augmented with functionalities for model-oriented management and deployment. We also propose a light-weight ingestion scheme to extract tracklets of all the moving objects and build semantic indexes to facilitate efficient query processing. For user interaction, we construct a simulation environment with 120 cameras deployed in a road network and demonstrate three interesting scenarios. Using VMQL, users are allowed to 1) train a visual model using SQL-like statement and deploy it on dozens of target cameras simultaneously for online inference; 2) submit multi-object tracking (MOT) requests on target cameras, store the ingested results and build semantic indexes; and 3) issue an aggregation or top- k query on the ingested cameras and obtain the response within milliseconds. A preliminary video introduction of DoveDB is available at https://www.youtube.com/watch?v=N139dEyvAJk