Reviewerly/exHarmony huggingface dataset:
Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning

Accommodating long-running deep learning (DL) training and inference jobs is challenging on GPU clusters that use traditional batch schedulers, such as Slurm. Given fixed wall clock time limits, DL researchers usually need to run a sequence of batch jobs and experience long interruptions on overloaded machines. Such interruptions significantly lower the research productivity and QoS for services that are deployed in production. To mitigate the issues from interruption, we propose the design of a proactive provisioner and investigate a set of statistical learning and reinforcement learning (RL) techniques, including random forest, xgboost, Deep Q-Network, and policy gradient. Using production job traces from three GPU clusters, we train each model using a subset of the trace and then evaluate their generality using the remaining validation subset. We introduce Mirage, a Slurm-compatible resource provisioner that integrates the candidate ML methods. Our experiments show that the Mirage can reduce interruption by 17--100% and safeguard 23%-76% of jobs with zero interruption across varying load levels on the three clusters.
Controlling the false discovery rate in transformational sparsity: Split Knockoffs

Abstract Controlling the False Discovery Rate (FDR) in a variable selection procedure is critical for reproducible discoveries, and it has been extensively studied in sparse linear models. However, it remains largely open in scenarios where the sparsity constraint is not directly imposed on the parameters but on a linear transformation of the parameters to be estimated. Examples of such scenarios include total variations, wavelet transforms, fused LASSO, and trend filtering. In this paper, we propose a data-adaptive FDR control method, called the Split Knockoff method, for this transformational sparsity setting. The proposed method exploits both variable and data splitting. The linear transformation constraint is relaxed to its Euclidean proximity in a lifted parameter space, which yields an orthogonal design that enables the orthogonal Split Knockoff construction. To overcome the challenge that exchangeability fails due to the heterogeneous noise brought by the transformation, new inverse supermartingale structures are developed via data splitting for provable FDR control without sacrificing power. Simulation experiments demonstrate that the proposed methodology achieves the desired FDR and power. We also provide an application to Alzheimer’s Disease study, where atrophy brain regions and their abnormal connections can be discovered based on a structural Magnetic Resonance Imaging dataset.
Classifying Schizophrenia Cases by Artificial Neural Network Using Japanese Web-Based Survey Data: Case-Control Study

Background In Japan, challenges were reported in accurately estimating the prevalence of schizophrenia among the general population. Retrieving previous studies, we investigated that patients with schizophrenia were more likely to experience poor subjective well-being and various physical, psychiatric, and social comorbidities. These factors might have great potential for precisely classifying schizophrenia cases in order to estimate the prevalence. Machine learning has shown a positive impact on many fields, including epidemiology, due to its high-precision modeling capability. It has been applied in research on mental disorders. However, few studies have applied machine learning technology to the precise classification of schizophrenia cases by variables of demographic and health-related backgrounds, especially using large-scale web-based surveys. Objective The aim of the study is to construct an artificial neural network (ANN) model that can accurately classify schizophrenia cases from large-scale Japanese web-based survey data and to verify the generalizability of the model. Methods Data were obtained from a large Japanese internet research pooled panel (Rakuten Insight, Inc) in 2021. A total of 223 individuals, aged 20-75 years, having schizophrenia, and 1776 healthy controls were included. Answers to the questions in a web-based survey were formatted as 1 response variable (self-report diagnosed with schizophrenia) and multiple feature variables (demographic, health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities). An ANN was applied to construct a model for classifying schizophrenia cases. Logistic regression (LR) was used as a reference. The performances of the models and algorithms were then compared. Results The model trained by the ANN performed better than LR in terms of area under the receiver operating characteristic curve (0.86 vs 0.78), accuracy (0.93 vs 0.91), and specificity (0.96 vs 0.94), while the model trained by LR showed better sensitivity (0.63 vs 0.56). Comparing the performances of the ANN and LR, the ANN was better in terms of area under the receiver operating characteristic curve (bootstrapping: 0.847 vs 0.773 and cross-validation: 0.81 vs 0.72), while LR performed better in terms of accuracy (0.894 vs 0.856). Sleep medication use, age, household income, and employment type were the top 4 variables in terms of importance. Conclusions This study constructed an ANN model to classify schizophrenia cases using web-based survey data. Our model showed a high internal validity. The findings are expected to provide evidence for estimating the prevalence of schizophrenia in the Japanese population and informing future epidemiological studies.
What to tell when? – Information Provision as a Game

Constantly informing systems (CIS), which provide us with information over a long period of time, face a particular challenge in providing useful information: Not only does a person's information base change but also their mood or abilities may change.An information provision strategy should hence take these aspects into account.In this paper, we describe our vision for supporting the design of CIS.We envisage using psychological models of the human mind and emotions in a game of information provision.The analysis of these games will give comparative insights into design variants.
Actors' strike ends – but AI battle may not be over

An agreement on the use of digital replicas has ended the Hollywood actors' strike, but the industry could still drastically change, says Jeremy Hsu
When morphology meets regular polysemy

This study investigates the existence of complex morphological patterns that include a regular polysemy extension. Such complex patterns derive ambiguous words whose semantic types are both related and specified. To address this issue, nouns derived from verbs in French are investigated. A sample of 2,953 deverbal nouns ending with 46 suffixes are collected and systematically analyzed with respect to semantic properties and lexical ambiguity. Thirty-eight complex patterns are identified. The formation of ambiguous nouns whose meanings fit into a regular polysemy pattern appears as a discriminative property of some suffixes. Different kinds of patterns are further distinguished, depending on whether secondary meanings in complex patterns can be directly related to a base verb or not. In accordance with the literature on the polysemy of nominalizations, the semantic categories of EVENT and RESULT are prominent in morphological patterns, but other semantic types such as AGENT, INSTITUTION and COGNITIVE OBJECT are importantly represented as well. In addition, metonymy is the main semantic figure observed in complex patterns. Overall, the article sheds new light on the relationship between word formation and lexical polysemy by describing the possibility of direct association between them.
Optimizing large real‐world data analysis with parquet files in R: A step‐by‐step tutorial

The use of open-source programming languages can facilitate open science practices in real-world evidence (RWE) studies. Real-world studies often rely on using big data, which makes using such languages complicated. We demonstrate an efficient approach that enables RWE researchers to use R to undertake RWE analysis tasks from cohort building to reporting.Using the Merative Marketscan data (2017-2019), we developed an R function to transform the data into parquet format to be used in R. Then, we compared the differences in data size before and after transformation. We compared the performance of the transformed data in R to the original data in terms of numerical consistency and running times required to complete simple exploratory tasks. To show how the transformed databases can be used in practice, we conducted a simplified replication of an active comparator new user study from the literature. All codes are available on GitHub.Our approach exhibited high efficiency in data storage, as evidenced by the converted data size, which ranged from 10% to 43% of that of the original data files. The runtime of the exploratory tasks in R generally outperformed that of the original data with SAS. We showed, through example, how the converted data can be efficiently used to implement an RWE study.We demonstrate a free and efficient solution to facilitate the use of open-source programming languages with big real-world databases, which can facilitate the adoption of open science practices.
Closely arranged inshore ship detection using a bi-directional attention feature pyramid network

The detection of inshore ships in Synthetic Aperture Radar (SAR) images is seriously disturbed by shore buildings, especially for closely arranged inshore ships whose appearance is similar when compared with detection of deep-sea ships. There are many interference factors such as speckle noise, cross sidelobes, and defocusing in SAR images. These factors can seriously interfere with feature extraction, and the traditional Fully Convolutional One-Stage (FCOS) network often cannot effectively distinguish small-scale ships from backgrounds. Additionally, for closely arranged inshore ships, missed detections and inaccurate positioning often occur. In this paper, a method of inshore ship detection based on Bi-directional Attention Feature Pyramid Network (BAFPN) is proposed. In order to improve the detection ability of small-scale ships, the BAFPN is based on the FCOS network, which connects a Convolutional Block Attention Module (CBAM) to each feature map of the pyramid and can extract rich semantic features. Then, the idea from Path-Aggregation Network (PANet) is adopted to splice a bottom-up pyramid structure behind the original pyramid structure, further highlighting the features of different scales and improving the ability of the network to accurately locate ships under complex backgrounds, thereby avoiding missed detections in closely arranged inshore ship detection. Finally, a weighted feature fusion method is proposed, which makes the feature information extracted from the feature map have different focuses and can improve the accuracy of ship detection. Experiments on SAR image ship datasets show that the mAP for the SSDD and HRSID reached 0.902 and 0.839 respectively. The proposed method can effectively improve the ship positioning accuracy while maintaining a fast detection speed, and achieves better results for ship detection under complex background.
Multi-Channel 2D-CNN Attention Based BiLSTM Method for Low-Resource Ewe Sentiment Analysis

The unavailability of an annotated dataset for a low-resource Ewe language makes it difficult to develop an automated system to appropriately evaluate public opinion on events, news, policies, and regulations. In this study, we collected and preprocessed a low-resourced document-level Ewe sentiment dataset based on social media comments. We used three (3) features learned by word embeddings (Global vectors, word-to-vector, and FastText) rather than hand-crafted features. We further proposed a novel method termed MC2D-CNN+BiLSTM-Attn to detect the exact sentiment feature from the Ewe dataset. Extensive experiments indicate that the proposed method efficiently classifies various sentiments and is superior to benchmark deep learning methods. Results show that in detecting the precise sentiments from raw Ewe textual context, the BiLSTM incorporating Glove outperforms word2vec and FastText embedding with an accuracy of 0.727. Furthermore, Attn+BiLSTM and Multi-channel CNN methods incorporating the word2vec embedding layer perform better than Glove and FastText embedding with an accuracy of 0.848 and 0.896. In contrast, our proposed method with the same word2vec embedding recorded 0.949.
Ways of increasing students' self-organisation for self-study

The study aims to reveal the psychological and pedagogical indicators of self-management in a student's personality and systematize contemporary synergetic approaches for distance education. The research hypothesis suggests the possibility of optimizing students' motivation for self-management and self-learning for efficient education in a distance mode. The study's methodology is comprehensive and uses a qualitative approach. 463 students from higher institutions in Kazakhstan participated in the study. The research used a survey method in which students assessed their self-study skills. Qualitative analysis of the questionnaires made it possible to divide the respondents into three subgroups according to the results of their ability to self-study. Training and additional classes were planned and conducted in these subgroups, considering the results of the power to self-organize and self-learn, which had a positive effect. Students have different indicators of self-learning abilities, and dividing them into subgroups and differentiating the approach to organizing training has significantly increased self-learning abilities. The synergetic approach allows students to develop their self-management skills and use innovative methods to improve their subject knowledge quality. Using differentiated and synergistic approaches in online learning helps to improve the quality of learning in general.
Cryptography from Planted Graphs: Security with Logarithmic-Size Messages

We study the following broad question about cryptographic primitives: is it possible to achieve security against arbitrary $$\textsf{poly}(n)$$ -time adversary with $$O(\log n)$$ -size messages? It is common knowledge that the answer is “no” unless information-theoretic security is possible. In this work, we revisit this question by considering the setting of cryptography with public information and computational security. We obtain the following main results, assuming variants of well-studied intractability assumptions: The above positive results guarantee that any adversary of size $$n^{o(\log n)}$$ achieves an $$n^{-\varOmega (1)}$$ distinguishing advantage. We show how to make the advantage negligible by slightly increasing the asymptotic message size, still improving over all known constructions. The security of our constructions is based on the conjectured hardness of variants of the planted clique problem, which was extensively studied in the algorithms, statistical inference, and complexity theory communities. Our work provides the first applications of such assumptions to improving the efficiency of mainstream cryptographic primitives, gives evidence for the necessity of such assumptions, and suggests new questions in this domain that may be of independent interest.
High-speed Parallel Processing with Photonic Feedforward Reservoir Computing

High-speed photonic reservoir computing (RC) has garnered significant interest in neuromorphic computing. However, existing reservoir layer (RL) architectures mostly rely on time-delayed feedback loops and use analog-to-digital converters for offline digital processing in the implementation of the readout layer, posing inherent limitations on their speed and capabilities. In this paper, we propose a non-feedback method that utilizes the pulse broadening effect induced by optical dispersion to implement a RL. By combining the multiplication of the modulator with the summation of the pulse temporal integration of the distributed feedback-laser diode, we successfully achieve the linear regression operation of the optoelectronic analog readout layer. Our proposed fully-analog feed-forward photonic RC (FF-PhRC) system is experimentally demonstrated to be effective in chaotic signal prediction, spoken digit recognition, and MNIST classification. Additionally, using wavelength-division multiplexing, our system manages to complete parallel tasks and improve processing capability up to 10 GHz per wavelength. The present work highlights the potential of FF-PhRC as a high-performance, high-speed computing tool for real-time neuromorphic computing.
Financial fraud victimization: an examination of distress and financial complications

Purpose This study expands the empirical understanding of financial fraud victims and the consequences that emerge as a result of financial fraud victimization. In addition, this study aims to assess the impact of the unique role victims play in financial fraud and the impact self-identifying as a victim has on the negative consequences they experience. Design/methodology/approach Data from the Supplemental Fraud Survey to the National Crime Victimization Survey are used to assess the negative consequences of financial fraud victimization. Findings Results suggest that victims of financial fraud experience increased distress and financial complications following their victimization experience. In addition, self-reported victim status is found to significantly increase a respondent’s likelihood of reporting emotional distress and financial complications. Implications for research, theory and policy are discussed. Originality/value While empirical studies on the consequences of identity theft victimization have been increasing in recent years, financial fraud victimization remains understudied. Given the victim involvement in financial fraud, the consideration of financial fraud independent of identity theft fraud is vital.
An Interpretable Machine Learning Model with Deep Learning-Based Imaging Biomarkers for Diagnosis of Alzheimer’s Disease

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer’s Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.
IMMERSIVE TECHNOLOGIES IN PRIMARY EDUCATION

The article is devoted to the problem of using immersive technologies in primary education. It was found that immersive technologies have rapidly developed in recent years from doubtfully promising to trendy and used everywhere. It has been proven that their introduction into the educational process of the New Ukrainian School is a need today and a factor in improving the quality of education. The purpose of the article is determined: to study the peculiarities of the use of immersive technologies in primary education. The essence of immersive technologies has been clarified. The components of immersive technologies are highlighted: real (objective), augmented (added) and virtual reality. The qualitative characteristics of the use of immersive technologies in primary education have been determined. Examples of the use of immersive technologies in the process of studying various educational fields of primary education are given, in particular: language and literature, mathematics, natural sciences, informatics, technology, social and health care, civics and history, art and physical education. It was determined that the use of immersive technologies in primary classes may include: virtual tours, virtual laboratories, AR-books, AR-games, 3D modeling. Some applications of immersive technologies for use in primary education are proposed. In the process of studying the features of immersive technologies in primary education, the advantages and disadvantages of their use were determined. It was found that immersive technologies contribute to the improvement of the quality of the educational process, making it more interactive, adaptable and exciting for schoolchildren; expand learning opportunities; contribute to a better understanding of complex concepts; stimulate motivation and interest in learning, memory and concentration, creativity and activity of younger schoolchildren. It was found that the use of immersive technologies in education significantly expands the toolkit of a modern specialist. Key words: immersive technologies, virtual reality, augmented reality, application, younger students, primary education.
A Lightweight Image Encryption Scheme Using DNA Coding and Chaos

Protecting transmitted multimedia data such as images is a significant concern. This work proposes an encryption algorithm for greyscale images using a Pseudo-Random Number Generator (PRNG), DNA coding, and pixel sum. The proposed approach is implemented on a Genesys 2 FPGA using minimal hardware resources and can operate at a maximum frequency of 110.8 MHz. In addition, several performance evaluation tests are conducted for multiple images, including statistical analysis of the encrypted image, keyspace analysis, and differential attack analysis. The system is compared to recent works with respect to encryption quality and used hardware resources. The proposed scheme outperformed recent chaos-based image encryption schemes.
Integration of federated learning with IoT for smart cities applications, challenges, and solutions

In the past few years, privacy concerns have grown, making the financial models of businesses more vulnerable to attack. In many cases, it is hard to emphasize the importance of monitoring things in real-time with data from Internet of Things (IoT) devices. The people who make the IoT devices and those who use them face big problems when they try to use Artificial Intelligence (AI) techniques in real-world applications, where data must be collected and processed at a central location. Federated learning (FL) has made a decentralized, cooperative AI system that can be used by many IoT apps that use AI. It is possible because it can train AI on IoT devices that are spread out and do not need to share data. FL allows local models to be trained on local data and share their knowledge to improve a global model. Also, shared learning allows models from all over the world to be trained using data from all over the world. This article looks at the IoT in all of its forms, including “smart” businesses, “smart” cities, “smart” transportation, and “smart” healthcare. This study looks at the safety problems that the federated learning with IoT (FL-IoT) area has brought to market. This research is needed to explore because federated learning is a new technique, and a small amount of work is done on challenges faced during integration with IoT. This research also helps in the real world in such applications where encrypted data must be sent from one place to another. Researchers and graduate students are the audience of our article.